Chun Chieh Fan

Bipolar disorder is a highly heritable psychiatric disorder. We performed a genome-wide association study (GWAS) including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 822 variants with P < 1 × 10-4 in an additional 9,412 cases and 137,760 controls. Eight of the 19 variants that were genome-wide significant (P < 5 × 10-8) in the discovery GWAS were not genome-wide significant in the combined analysis, consistent with small effect sizes and limited power but also with genetic heterogeneity. In the combined analysis, 30 loci were genome-wide significant, including 20 newly identified loci. The significant loci contain genes encoding ion channels, neurotransmitter transporters and synaptic components. Pathway analysis revealed nine significantly enriched gene sets, including regulation of insulin secretion and endocannabinoid signaling. Bipolar I disorder is strongly genetically correlated with schizophrenia, driven by psychosis, whereas bipolar II disorder is more strongly correlated with major depressive disorder. These findings address key clinical questions and provide potential biological mechanisms for bipolar disorder.

Background Identifying individuals at risk for developing Alzheimer disease (AD) is of utmost importance. Although genetic studies have identified AD-associated SNPs in APOE and other genes, genetic information has not been integrated into an epidemiological framework for risk prediction. Methods and findings Using genotype data from 17,008 AD cases and 37,154 controls from the International Genomics of Alzheimer’s Project (IGAP Stage 1), we identified AD-associated SNPs (at p < 10−5). We then integrated these AD-associated SNPs into a Cox proportional hazard model using genotype data from a subset of 6,409 AD patients and 9,386 older controls from Phase 1 of the Alzheimer’s Disease Genetics Consortium (ADGC), providing a polygenic hazard score (PHS) for each participant. By combining population-based incidence rates and the genotype-derived PHS for each individual, we derived estimates of instantaneous risk for developing AD, based on genotype and age, and tested replication in multiple independent cohorts (ADGC Phase 2, National Institute on Aging Alzheimer’s Disease Center [NIA ADC], and Alzheimer’s Disease Neuroimaging Initiative [ADNI], total n = 20,680). Within the ADGC Phase 1 cohort, individuals in the highest PHS quartile developed AD at a considerably lower age and had the highest yearly AD incidence rate. Among APOE ε3/3 individuals, the PHS modified expected age of AD onset by more than 10 y between the lowest and highest deciles (hazard ratio 3.34, 95% CI 2.62–4.24, p = 1.0 × 10−22). In independent cohorts, the PHS strongly predicted empirical age of AD onset (ADGC Phase 2, r = 0.90, p = 1.1 × 10−26) and longitudinal progression from normal aging to AD (NIA ADC, Cochran–Armitage trend test, p = 1.5 × 10−10), and was associated with neuropathology (NIA ADC, Braak stage of neurofibrillary tangles, p = 3.9 × 10−6, and Consortium to Establish a Registry for Alzheimer’s Disease score for neuritic plaques, p = 6.8 × 10−6) and in vivo markers of AD neurodegeneration (ADNI, volume loss within the entorhinal cortex, p = 6.3 × 10−6, and hippocampus, p = 7.9 × 10−5). Additional prospective validation of these results in non-US, non-white, and prospective community-based cohorts is necessary before clinical use. Conclusions We have developed a PHS for quantifying individual differences in age-specific genetic risk for AD. Within the cohorts studied here, polygenic architecture plays an important role in modifying AD risk beyond APOE. With thorough validation, quantification of inherited genetic variation may prove useful for stratifying AD risk and as an enrichment strategy in therapeutic trials.

Abstract Accumulating evidence from genome wide association studies (GWAS) suggests an abundance of shared genetic influences among complex human traits and disorders, such as mental disorders. Here we introduce a statistical tool, MiXeR, which quantifies polygenic overlap irrespective of genetic correlation, using GWAS summary statistics. MiXeR results are presented as a Venn diagram of unique and shared polygenic components across traits. At 90% of SNP-heritability explained for each phenotype, MiXeR estimates that 8.3 K variants causally influence schizophrenia and 6.4 K influence bipolar disorder. Among these variants, 6.2 K are shared between the disorders, which have a high genetic correlation. Further, MiXeR uncovers polygenic overlap between schizophrenia and educational attainment. Despite a genetic correlation close to zero, the phenotypes share 8.3 K causal variants, while 2.5 K additional variants influence only educational attainment. By considering the polygenicity, discoverability and heritability of complex phenotypes, MiXeR analysis may improve our understanding of cross-trait genetic architectures.

<h3>Abstract</h3> <h3>Objectives</h3> To develop and validate a genetic tool to predict age of onset of aggressive prostate cancer (PCa) and to guide decisions of who to screen and at what age. <h3>Design</h3> Analysis of genotype, PCa status, and age to select single nucleotide polymorphisms (SNPs) associated with diagnosis. These polymorphisms were incorporated into a survival analysis to estimate their effects on age at diagnosis of aggressive PCa (that is, not eligible for surveillance according to National Comprehensive Cancer Network guidelines; any of Gleason score ≥7, stage T3-T4, PSA (prostate specific antigen) concentration ≥10 ng/L, nodal metastasis, distant metastasis). The resulting polygenic hazard score is an assessment of individual genetic risk. The final model was applied to an independent dataset containing genotype and PSA screening data. The hazard score was calculated for these men to test prediction of survival free from PCa. <h3>Setting</h3> Multiple institutions that were members of international PRACTICAL consortium. <h3>Participants</h3> All consortium participants of European ancestry with known age, PCa status, and quality assured custom (iCOGS) array genotype data. The development dataset comprised 31 747 men; the validation dataset comprised 6411 men. <h3>Main outcome measures</h3> Prediction with hazard score of age of onset of aggressive cancer in validation set. <h3>Results</h3> In the independent validation set, the hazard score calculated from 54 single nucleotide polymorphisms was a highly significant predictor of age at diagnosis of aggressive cancer (z=11.2, P&lt;10⁻¹⁶). When men in the validation set with high scores (&gt;98th centile) were compared with those with average scores (30th-70th centile), the hazard ratio for aggressive cancer was 2.9 (95% confidence interval 2.4 to 3.4). Inclusion of family history in a combined model did not improve prediction of onset of aggressive PCa (P=0.59), and polygenic hazard score performance remained high when family history was accounted for. Additionally, the positive predictive value of PSA screening for aggressive PCa was increased with increasing polygenic hazard score. <h3>Conclusions</h3> Polygenic hazard scores can be used for personalised genetic risk estimates that can predict for age at onset of aggressive PCa.

Mood disorders (including major depressive disorder and bipolar disorder) affect 10% to 20% of the population. They range from brief, mild episodes to severe, incapacitating conditions that markedly impact lives. Multiple approaches have shown considerable sharing of risk factors across mood disorders despite their diagnostic distinction.To clarify the shared molecular genetic basis of major depressive disorder and bipolar disorder and to highlight disorder-specific associations, we meta-analyzed data from the latest Psychiatric Genomics Consortium genome-wide association studies of major depression (including data from 23andMe) and bipolar disorder, and an additional major depressive disorder cohort from UK Biobank (total: 185,285 cases, 439,741 controls; nonoverlapping N = 609,424).Seventy-three loci reached genome-wide significance in the meta-analysis, including 15 that are novel for mood disorders. More loci from the Psychiatric Genomics Consortium analysis of major depression than from that for bipolar disorder reached genome-wide significance. Genetic correlations revealed that type 2 bipolar disorder correlates strongly with recurrent and single-episode major depressive disorder. Systems biology analyses highlight both similarities and differences between the mood disorders, particularly in the mouse brain cell types implicated by the expression patterns of associated genes. The mood disorders also differ in their genetic correlation with educational attainment-the relationship is positive in bipolar disorder but negative in major depressive disorder.The mood disorders share several genetic associations, and genetic studies of major depressive disorder and bipolar disorder can be combined effectively to enable the discovery of variants not identified by studying either disorder alone. However, we demonstrate several differences between these disorders. Analyzing subtypes of major depressive disorder and bipolar disorder provides evidence for a genetic mood disorders spectrum.

Estimating the polygenicity (proportion of causally associated single nucleotide polymorphisms (SNPs)) and discoverability (effect size variance) of causal SNPs for human traits is currently of considerable interest. SNP-heritability is proportional to the product of these quantities. We present a basic model, using detailed linkage disequilibrium structure from a reference panel of 11 million SNPs, to estimate these quantities from genome-wide association studies (GWAS) summary statistics. We apply the model to diverse phenotypes and validate the implementation with simulations. We find model polygenicities (as a fraction of the reference panel) ranging from ≃ 2 × 10−5 to ≃ 4 × 10−3, with discoverabilities similarly ranging over two orders of magnitude. A power analysis allows us to estimate the proportions of phenotypic variance explained additively by causal SNPs reaching genome-wide significance at current sample sizes, and map out sample sizes required to explain larger portions of additive SNP heritability. The model also allows for estimating residual inflation (or deflation from over-correcting of z-scores), and assessing compatibility of replication and discovery GWAS summary statistics.

Regional brain morphology has a complex genetic architecture, consisting of many common polymorphisms with small individual effects. This has proven challenging for genome-wide association studies (GWAS). Due to the distributed nature of genetic signal across brain regions, multivariate analysis of regional measures may enhance discovery of genetic variants. Current multivariate approaches to GWAS are ill-suited for complex, large-scale data of this kind. Here, we introduce the Multivariate Omnibus Statistical Test (MOSTest), with an efficient computational design enabling rapid and reliable inference, and apply it to 171 regional brain morphology measures from 26,502 UK Biobank participants. At the conventional genome-wide significance threshold of α = 5 × 10-8, MOSTest identifies 347 genomic loci associated with regional brain morphology, more than any previous study, improving upon the discovery of established GWAS approaches more than threefold. Our findings implicate more than 5% of all protein-coding genes and provide evidence for gene sets involved in neuron development and differentiation.

Schizophrenia is associated with widespread cognitive impairments. Although cognitive deficits are one of the factors most strongly associated with functional outcome in schizophrenia, current treatment strategies largely fail to ameliorate these impairments. To develop more efficient treatment strategies in patients with schizophrenia, a better understanding of the pathogenesis of these cognitive deficits is needed. Accumulating evidence indicates that genetic risk of schizophrenia may contribute to cognitive dysfunction.To identify genomic regions jointly influencing schizophrenia and the cognitive domains of reaction time and verbal-numerical reasoning, as well as general cognitive function, a phenotype that captures the shared variation in performance across cognitive domains.Combining data from genome-wide association studies from multiple phenotypes using conditional false discovery rate analysis provides increased power to discover genetic variants and could elucidate shared molecular genetic mechanisms. Data from the following genome-wide association studies, published from July 24, 2014, to January 17, 2017, were combined: schizophrenia in the Psychiatric Genomics Consortium cohort (n = 79 757 [cases, 34 486; controls, 45 271]); verbal-numerical reasoning (n = 36 035) and reaction time (n = 111 483) in the UK Biobank cohort; and general cognitive function in CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) (n = 53 949) and COGENT (Cognitive Genomics Consortium) (n = 27 888).Genetic loci identified by conditional false discovery rate analysis. Brain messenger RNA expression and brain expression quantitative trait locus functionality were determined.Among the participants in the genome-wide association studies, 21 loci jointly influencing schizophrenia and cognitive traits were identified: 2 loci shared between schizophrenia and verbal-numerical reasoning, 6 loci shared between schizophrenia and reaction time, and 14 loci shared between schizophrenia and general cognitive function. One locus was shared between schizophrenia and 2 cognitive traits and represented the strongest shared signal detected (nearest gene TCF20; chromosome 22q13.2), and was shared between schizophrenia (z score, 5.01; P = 5.53 × 10-7), general cognitive function (z score, -4.43; P = 9.42 × 10-6), and verbal-numerical reasoning (z score, -5.43; P = 5.64 × 10-8). For 18 loci, schizophrenia risk alleles were associated with poorer cognitive performance. The implicated genes are expressed in the developmental and adult human brain. Replicable expression quantitative trait locus functionality was identified for 4 loci in the adult human brain.The discovered loci improve the understanding of the common genetic basis underlying schizophrenia and cognitive function, suggesting novel molecular genetic mechanisms.

Converging evidence suggests that immune-mediated dysfunction plays an important role in the pathogenesis of frontotemporal dementia (FTD). Although genetic studies have shown that immune-associated loci are associated with increased FTD risk, a systematic investigation of genetic overlap between immune-mediated diseases and the spectrum of FTD-related disorders has not been performed.Using large genome-wide association studies (GWASs) (total n = 192,886 cases and controls) and recently developed tools to quantify genetic overlap/pleiotropy, we systematically identified single nucleotide polymorphisms (SNPs) jointly associated with FTD-related disorders-namely, FTD, corticobasal degeneration (CBD), progressive supranuclear palsy (PSP), and amyotrophic lateral sclerosis (ALS)-and 1 or more immune-mediated diseases including Crohn disease, ulcerative colitis (UC), rheumatoid arthritis (RA), type 1 diabetes (T1D), celiac disease (CeD), and psoriasis. We found up to 270-fold genetic enrichment between FTD and RA, up to 160-fold genetic enrichment between FTD and UC, up to 180-fold genetic enrichment between FTD and T1D, and up to 175-fold genetic enrichment between FTD and CeD. In contrast, for CBD and PSP, only 1 of the 6 immune-mediated diseases produced genetic enrichment comparable to that seen for FTD, with up to 150-fold genetic enrichment between CBD and CeD and up to 180-fold enrichment between PSP and RA. Further, we found minimal enrichment between ALS and the immune-mediated diseases tested, with the highest levels of enrichment between ALS and RA (up to 20-fold). For FTD, at a conjunction false discovery rate < 0.05 and after excluding SNPs in linkage disequilibrium, we found that 8 of the 15 identified loci mapped to the human leukocyte antigen (HLA) region on Chromosome (Chr) 6. We also found novel candidate FTD susceptibility loci within LRRK2 (leucine rich repeat kinase 2), TBKBP1 (TBK1 binding protein 1), and PGBD5 (piggyBac transposable element derived 5). Functionally, we found that the expression of FTD-immune pleiotropic genes (particularly within the HLA region) is altered in postmortem brain tissue from patients with FTD and is enriched in microglia/macrophages compared to other central nervous system cell types. The main study limitation is that the results represent only clinically diagnosed individuals. Also, given the complex interconnectedness of the HLA region, we were not able to define the specific gene or genes on Chr 6 responsible for our pleiotropic signal.We show immune-mediated genetic enrichment specifically in FTD, particularly within the HLA region. Our genetic results suggest that for a subset of patients, immune dysfunction may contribute to FTD risk. These findings have potential implications for clinical trials targeting immune dysfunction in patients with FTD.

In recent years, genome-wide association study (GWAS) sample sizes have become larger, the statistical power has improved and thousands of trait-associated variants have been uncovered, offering new insights into the genetic etiology of complex human traits and disorders. However, a large fraction of the polygenic architecture underlying most complex phenotypes still remains undetected. We here review the conditional false discovery rate (condFDR) method, a model-free strategy for analysis of GWAS summary data, which has improved yield of existing GWAS and provided novel findings of genetic overlap between a wide range of complex human phenotypes, including psychiatric, cardiovascular, and neurological disorders, as well as psychological and cognitive traits. The condFDR method was inspired by Empirical Bayes approaches and leverages auxiliary genetic information to improve statistical power for discovery of single-nucleotide polymorphisms (SNPs). The cross-trait condFDR strategy analyses separate GWAS data, and leverages overlapping SNP associations, i.e., cross-trait enrichment, to increase discovery of trait-associated SNPs. The extension of the condFDR approach to conjunctional FDR (conjFDR) identifies shared genomic loci between two phenotypes. The conjFDR approach allows for detection of shared genomic associations irrespective of the genetic correlation between the phenotypes, often revealing a mixture of antagonistic and agonistic directional effects among the shared loci. This review provides a methodological comparison between condFDR and other relevant cross-trait analytical tools and demonstrates how condFDR analysis may provide novel insights into the genetic relationship between complex phenotypes.

Cardiovascular (CV)- and lifestyle-associated risk factors (RFs) are increasingly recognized as important for Alzheimer's disease (AD) pathogenesis. Beyond the ε4 allele of apolipoprotein E (APOE), comparatively little is known about whether CV-associated genes also increase risk for AD. Using large genome-wide association studies and validated tools to quantify genetic overlap, we systematically identified single nucleotide polymorphisms (SNPs) jointly associated with AD and one or more CV-associated RFs, namely body mass index (BMI), type 2 diabetes (T2D), coronary artery disease (CAD), waist hip ratio (WHR), total cholesterol (TC), triglycerides (TG), low-density (LDL) and high-density lipoprotein (HDL). In fold enrichment plots, we observed robust genetic enrichment in AD as a function of plasma lipids (TG, TC, LDL, and HDL); we found minimal AD genetic enrichment conditional on BMI, T2D, CAD, and WHR. Beyond APOE, at conjunction FDR < 0.05 we identified 90 SNPs on 19 different chromosomes that were jointly associated with AD and CV-associated outcomes. In meta-analyses across three independent cohorts, we found four novel loci within MBLAC1 (chromosome 7, meta-p = 1.44 × 10-9), MINK1 (chromosome 17, meta-p = 1.98 × 10-7) and two chromosome 11 SNPs within the MTCH2/SPI1 region (closest gene = DDB2, meta-p = 7.01 × 10-7 and closest gene = MYBPC3, meta-p = 5.62 × 10-8). In a large 'AD-by-proxy' cohort from the UK Biobank, we replicated three of the four novel AD/CV pleiotropic SNPs, namely variants within MINK1, MBLAC1, and DDB2. Expression of MBLAC1, SPI1, MINK1 and DDB2 was differentially altered within postmortem AD brains. Beyond APOE, we show that the polygenic component of AD is enriched for lipid-associated RFs. We pinpoint a subset of cardiovascular-associated genes that strongly increase the risk for AD. Our collective findings support a disease model in which cardiovascular biology is integral to the development of clinical AD in a subset of individuals.

<h3>Importance</h3> People with major psychiatric disorders (MPDs) have a 10- to 20-year shorter life span than the rest of the population, and this difference is mainly due to comorbid cardiovascular diseases. Genome-wide association studies have identified common variants involved in schizophrenia (SCZ), bipolar disorder (BIP), and major depression (MD) and body mass index (BMI), a key cardiometabolic risk factor. However, genetic variants jointly influencing MPD and BMI remain largely unknown. <h3>Objective</h3> To assess the extent of the overlap between the genetic architectures of MPDs and BMI and identify genetic loci shared between them. <h3>Design, Setting, and Participants</h3> Using a conditional false discovery rate statistical framework, independent genome-wide association study data on individuals with SCZ (n = 82 315), BIP (n = 51 710), MD (n = 480 359), and BMI (n = 795 640) were analyzed. The UK Biobank cohort (n = 29 740) was excluded from the MD data set to avoid sample overlap. Data were collected from August 2017 to May 2018, and analysis began July 2018. <h3>Main Outcomes and Measures</h3> The primary outcomes were a list of genetic loci shared between BMI and MPDs and their functional pathways. <h3>Results</h3> Genome-wide association study data from 1 380 284 participants were analyzed, and the genetic correlation between BMI and MPDs varied (SCZ:<i>r </i>for genetic = −0.11,<i>P</i> = 2.1 × 10⁻¹⁰; BIP:<i>r </i>for genetic = −0.06,<i>P</i> = .0103; MD:<i>r </i>for genetic = 0.12,<i>P</i> = 6.7 × 10⁻¹⁰). Overall, 63, 17, and 32 loci shared between BMI and SCZ, BIP, and MD, respectively, were analyzed at conjunctional false discovery rate less than 0.01. Of the shared loci, 34% (73 of 213) in SCZ, 52% (36 of 69) in BIP, and 57% (56 of 99) in MD had risk alleles associated with higher BMI (conjunctional false discovery rate &lt;0.05), while the rest had opposite directions of associations. Functional analyses indicated that the overlapping loci are involved in several pathways including neurodevelopment, neurotransmitter signaling, and intracellular processes, and the loci with concordant and opposite association directions pointed mostly to different pathways. <h3>Conclusions and Relevance</h3> In this genome-wide association study, extensive polygenic overlap between BMI and SCZ, BIP, and MD were found, and 111 shared genetic loci were identified, implicating novel functional mechanisms. There was mixture of association directions in SCZ and BMI, albeit with a preponderance of discordant ones.

There is an urgent need for identifying nondemented individuals at the highest risk of progressing to Alzheimer's disease (AD) dementia. Here, we evaluated whether a recently validated polygenic hazard score (PHS) can be integrated with known in vivo cerebrospinal fluid (CSF) or positron emission tomography (PET) biomarkers of amyloid, and CSF tau pathology to prospectively predict cognitive and clinical decline in 347 cognitive normal (CN; baseline age range = 59.7-90.1, 98.85% white) and 599 mild cognitively impaired (MCI; baseline age range = 54.4-91.4, 98.83% white) individuals from the Alzheimer's Disease Neuroimaging Initiative 1, GO, and 2. We further investigated the association of PHS with post-mortem amyloid load and neurofibrillary tangles in the Religious Orders Study and Memory and Aging Project (ROSMAP) cohort (N = 485, age at death range = 71.3-108.3). In CN and MCI individuals, we found that amyloid and total tau positivity systematically varies as a function of PHS. For individuals in greater than the 50th percentile PHS, the positive predictive value for amyloid approached 100%; for individuals in less than the 25th percentile PHS, the negative predictive value for total tau approached 85%. High PHS individuals with amyloid and tau pathology showed the steepest longitudinal cognitive and clinical decline, even among APOE ε4 noncarriers. Among the CN subgroup, we similarly found that PHS was strongly associated with amyloid positivity and the combination of PHS and biomarker status significantly predicted longitudinal clinical progression. In the ROSMAP cohort, higher PHS was associated with higher post-mortem amyloid load and neurofibrillary tangles, even in APOE ε4 noncarriers. Together, our results show that even after accounting for APOE ε4 effects, PHS may be useful in MCI and preclinical AD therapeutic trials to enrich for biomarker-positive individuals at highest risk for short-term clinical progression.

Corticobasal degeneration (CBD), progressive supranuclear palsy (PSP) and a subset of frontotemporal dementia (FTD) are neurodegenerative disorders characterized by tau inclusions in neurons and glia (tauopathies). Although clinical, pathological and genetic evidence suggests overlapping pathobiology between CBD, PSP, and FTD, the relationship between these disorders is still not well understood. Using summary statistics (odds ratios and p values) from large genome-wide association studies (total n = 14,286 cases and controls) and recently established genetic methods, we investigated the genetic overlap between CBD and PSP and CBD and FTD. We found up to 800-fold enrichment of genetic risk in CBD across different levels of significance for PSP or FTD. In addition to NSF (tagging the MAPT H1 haplotype), we observed that SNPs in or near MOBP, CXCR4, EGFR, and GLDC showed significant genetic overlap between CBD and PSP, whereas only SNPs tagging the MAPT haplotype overlapped between CBD and FTD. The risk alleles of the shared SNPs were associated with expression changes in cis-genes. Evaluating transcriptome levels across adult human brains, we found a unique neuroanatomic gene expression signature for each of the five overlapping gene loci (omnibus ANOVA p < 2.0 × 10-16). Functionally, we found that these shared risk genes were associated with protein interaction and gene co-expression networks and showed enrichment for several neurodevelopmental pathways. Our findings suggest: (1) novel genetic overlap between CBD and PSP beyond the MAPT locus; (2) strong ties between CBD and FTD through the MAPT clade, and (3) unique combinations of overlapping genes that may, in part, influence selective regional or neuronal vulnerability observed in specific tauopathies.

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder characterized by loss of upper and lower motor neurons. Although novel ALS genetic variants have been identified, the shared genetic risk between ALS and other neurodegenerative disorders remains poorly understood.To examine whether there are common genetic variants that determine the risk for ALS and other neurodegenerative diseases and to identify their functional pathways.In this study conducted from December 1, 2016, to August 1, 2017, the genetic overlap between ALS, sporadic frontotemporal dementia (FTD), FTD with TDP-43 inclusions, Parkinson disease (PD), Alzheimer disease (AD), corticobasal degeneration (CBD), and progressive supranuclear palsy (PSP) were systematically investigated in 124 876 cases and controls. No participants were excluded from this study. Diagnoses were established using consensus criteria.The primary outcomes were a list of novel loci and their functional pathways in ALS, FTD, PSP, and ALS mouse models.Among 124 876 cases and controls, genome-wide conjunction analyses of ALS, FTD, PD, AD, CBD, and PSP revealed significant genetic overlap between ALS and FTD at known ALS loci: rs13302855 and rs3849942 (nearest gene, C9orf72; P = .03 for rs13302855 and P = .005 for rs3849942) and rs4239633 (nearest gene, UNC13A; P = .03). Significant genetic overlap was also found between ALS and PSP at rs7224296, which tags the MAPT H1 haplotype (nearest gene, NSF; P = .045). Shared risk genes were enriched for pathways involving neuronal function and development. At a conditional FDR P < .05, 22 novel ALS polymorphisms were found, including rs538622 (nearest gene, ERGIC1; P = .03 for ALS and FTD), which modifies BNIP1 expression in human brains (35 of 137 females; mean age, 59 years; P = .001). BNIP1 expression was significantly reduced in spinal cord motor neurons from patients with ALS (4 controls: mean age, 60.5 years, mean [SE] value, 3984 [760.8] arbitrary units [AU]; 7 patients with ALS: mean age, 56 years, mean [SE] value, 1999 [274.1] AU; P = .02), in an ALS mouse model (mean [SE] value, 13.75 [0.09] AU for 2 SOD1 WT mice and 11.45 [0.03] AU for 2 SOD1 G93A mice; P = .002) and in brains of patients with PSP (80 controls: 39 females; mean age, 82 years, mean [SE] value, 6.8 [0.2] AU; 84 patients with PSP: 33 females, mean age 74 years, mean [SE] value, 6.8 [0.1] AU; β = -0.19; P = .009) or FTD (11 controls: 4 females; mean age, 67 years; mean [SE] value, 6.74 [0.05] AU; 17 patients with FTD: 10 females; mean age, 69 years; mean [SE] value, 6.53 [0.04] AU; P = .005).This study found novel genetic overlap between ALS and diseases of the FTD spectrum, that the MAPT H1 haplotype confers risk for ALS, and identified the mitophagy-associated, proapoptotic protein BNIP1 as an ALS risk gene. Together, these findings suggest that sporadic ALS may represent a selectively pleiotropic, polygenic disorder.

Mounting evidence indicates that the polygenic basis of late-onset Alzheimer's disease can be harnessed to identify individuals at greatest risk for cognitive decline. We have previously developed and validated a polygenic hazard score comprising of 31 single nucleotide polymorphisms for predicting Alzheimer's disease dementia age of onset. In this study, we examined whether polygenic hazard scores are associated with: (i) regional tracer uptake using amyloid PET; (ii) regional volume loss using longitudinal MRI; (iii) post-mortem regional amyloid-β protein and tau associated neurofibrillary tangles; and (iv) four common non-Alzheimer's pathologies. Even after accounting for APOE, we found a strong association between polygenic hazard scores and amyloid PET standard uptake volume ratio with the largest effects within frontal cortical regions in 980 older individuals across the disease spectrum, and longitudinal MRI volume loss within the entorhinal cortex in 607 older individuals across the disease spectrum. We also found that higher polygenic hazard scores were associated with greater rates of cognitive and clinical decline in 632 non-demented older individuals, even after controlling for APOE status, frontal amyloid PET and entorhinal cortex volume. In addition, the combined model that included polygenic hazard scores, frontal amyloid PET and entorhinal cortex volume resulted in a better fit compared to a model with only imaging markers. Neuropathologically, we found that polygenic hazard scores were associated with regional post-mortem amyloid load and neuronal neurofibrillary tangles, even after accounting for APOE, validating our imaging findings. Lastly, polygenic hazard scores were associated with Lewy body and cerebrovascular pathology. Beyond APOE, we show that in living subjects, polygenic hazard scores were associated with amyloid deposition and neurodegeneration in susceptible brain regions. Polygenic hazard scores may also be useful for the identification of individuals at the highest risk for developing multi-aetiological dementia.

Brain morphology has been shown to be highly heritable, yet only a small portion of the heritability is explained by the genetic variants discovered so far. Here we extended the Multivariate Omnibus Statistical Test (MOSTest) and applied it to genome-wide association studies (GWAS) of vertex-wise structural magnetic resonance imaging (MRI) cortical measures from N=35,657 participants in the UK Biobank. We identified 695 loci for cortical surface area and 539 for cortical thickness, in total 780 unique genetic loci associated with cortical morphology robustly replicated in 8,060 children of mixed ethnicity from the Adolescent Brain Cognitive Development (ABCD) Study®. This reflects more than 8-fold increase in genetic discovery at no cost to generalizability compared to the commonly used univariate GWAS methods applied to region of interest (ROI) data. Functional follow up including gene-based analyses implicated 10% of all protein-coding genes and pointed towards pathways involved in neurogenesis and cell differentiation. Power analysis indicated that applying the MOSTest to vertex-wise structural MRI data triples the effective sample size compared to conventional univariate GWAS approaches. The large boost in power obtained with the vertex-wise MOSTest together with pronounced replication rates and highlighted biologically meaningful pathways underscores the advantage of multivariate approaches in the context of highly distributed polygenic architecture of the human brain.

The Adolescent Brain Cognitive Development (ABCD) Study® is a 10-year longitudinal study of children recruited at ages 9 and 10. A battery of neuroimaging tasks are administered biennially to track neurodevelopment and identify individual differences in brain function. This study reports activation patterns from functional MRI (fMRI) tasks completed at baseline, which were designed to measure cognitive impulse control with a stop signal task (SST; N = 5,547), reward anticipation and receipt with a monetary incentive delay (MID) task (N = 6,657) and working memory and emotion reactivity with an emotional N-back (EN-back) task (N = 6,009). Further, we report the spatial reproducibility of activation patterns by assessing between-group vertex/voxelwise correlations of blood oxygen level-dependent (BOLD) activation. Analyses reveal robust brain activations that are consistent with the published literature, vary across fMRI tasks/contrasts and slightly correlate with individual behavioral performance on the tasks. These results establish the preadolescent brain function baseline, guide interpretation of cross-sectional analyses and will enable the investigation of longitudinal changes during adolescent development.

Genetic models for cancer have been evaluated using almost exclusively European data, which could exacerbate health disparities. A polygenic hazard score (PHS1) is associated with age at prostate cancer diagnosis and improves screening accuracy in Europeans. Here, we evaluate performance of PHS2 (PHS1, adapted for OncoArray) in a multi-ethnic dataset of 80,491 men (49,916 cases, 30,575 controls). PHS2 is associated with age at diagnosis of any and aggressive (Gleason score ≥ 7, stage T3-T4, PSA ≥ 10 ng/mL, or nodal/distant metastasis) cancer and prostate-cancer-specific death. Associations with cancer are significant within European (n = 71,856), Asian (n = 2,382), and African (n = 6,253) genetic ancestries (p < 10-180). Comparing the 80th/20th PHS2 percentiles, hazard ratios for prostate cancer, aggressive cancer, and prostate-cancer-specific death are 5.32, 5.88, and 5.68, respectively. Within European, Asian, and African ancestries, hazard ratios for prostate cancer are: 5.54, 4.49, and 2.54, respectively. PHS2 risk-stratifies men for any, aggressive, and fatal prostate cancer in a multi-ethnic dataset.

To determine the impact of genetic variants on the brain, we used genetically informed brain atlases in genome-wide association studies of regional cortical surface area and thickness in 39,898 adults and 9136 children. We uncovered 440 genome-wide significant loci in the discovery cohort and 800 from a post hoc combined meta-analysis. Loci in adulthood were largely captured in childhood, showing signatures of negative selection, and were linked to early neurodevelopment and pathways associated with neuropsychiatric risk. Opposing gradations of decreased surface area and increased thickness were associated with common inversion polymorphisms. Inferior frontal regions, encompassing Broca’s area, which is important for speech, were enriched for human-specific genomic elements. Thus, a mixed genetic landscape of conserved and human-specific features is concordant with brain hierarchy and morphogenetic gradients.

Abstract With the continued promise of immunotherapy for treating cancer, understanding how host genetics contributes to the tumor immune microenvironment (TIME) is essential to tailoring cancer screening and treatment strategies. Here, we study 1084 eQTLs affecting the TIME found through analysis of The Cancer Genome Atlas and literature curation. These TIME eQTLs are enriched in areas of active transcription, and associate with gene expression in specific immune cell subsets, such as macrophages and dendritic cells. Polygenic score models built with TIME eQTLs reproducibly stratify cancer risk, survival and immune checkpoint blockade (ICB) response across independent cohorts. To assess whether an eQTL-informed approach could reveal potential cancer immunotherapy targets, we inhibit CTSS , a gene implicated by cancer risk and ICB response-associated polygenic models; CTSS inhibition results in slowed tumor growth and extended survival in vivo. These results validate the potential of integrating germline variation and TIME characteristics for uncovering potential targets for immunotherapy.

Neurodegenerative diseases likely share common underlying pathobiology. Although prior work has identified susceptibility loci associated with various dementias, few, if any, studies have systematically evaluated shared genetic risk across several neurodegenerative diseases. Using genome-wide association data from large studies (total n = 82,337 cases and controls), we utilized a previously validated approach to identify genetic overlap and reveal common pathways between progressive supranuclear palsy (PSP), frontotemporal dementia (FTD), Parkinson's disease (PD) and Alzheimer's disease (AD). In addition to the MAPT H1 haplotype, we identified a variant near the chemokine receptor CXCR4 that was jointly associated with increased risk for PSP and PD. Using bioinformatics tools, we found strong physical interactions between CXCR4 and four microglia related genes, namely CXCL12, TLR2, RALB, and CCR5. Evaluating gene expression from post-mortem brain tissue, we found that expression of CXCR4 and microglial genes functionally related to CXCR4 was dysregulated across a number of neurodegenerative diseases. Furthermore, in a mouse model of tauopathy, expression of CXCR4 and functionally associated genes was significantly altered in regions of the mouse brain that accumulate neurofibrillary tangles most robustly. Beyond MAPT, we show dysregulation of CXCR4 expression in PSP, PD, and FTD brains, and mouse models of tau pathology. Our multi-modal findings suggest that abnormal signaling across a 'network' of microglial genes may contribute to neurodegeneration and may have potential implications for clinical trials targeting immune dysfunction in patients with neurodegenerative diseases.

Sex differences in the manifestations of Alzheimer's disease are under intense investigation. Despite the emerging importance of polygenic predictions for Alzheimer's disease, sex-dependent polygenic effects have not been demonstrated. Here, using a sex crossover analysis, we show that sex-dependent autosomal genetic effects on Alzheimer's disease can be revealed by characterizing disease progress via the hazard function. We first performed sex-stratified genome-wide associations, and then applied derived sex-dependent weights to two independent cohorts. Relative to sex-mismatched scores, sex-matched polygenic hazard scores showed significantly stronger associations with age-at-disease-onset, clinical progression, amyloid deposition, neurofibrillary tangles, and composite neuropathological scores, independent of apolipoprotein E. Models without using hazard weights, i.e. polygenic risk scores, showed lower predictive power than polygenic hazard scores with no evidence for sex differences. Our results indicate that revealing sex-dependent genetic architecture requires the consideration of temporal processes of Alzheimer's disease. This has strong implications not only for the genetic underpinning of Alzheimer's disease but also for how we estimate sex-dependent polygenic effects for clinical use.

Migraine is three times more prevalent in people with bipolar disorder or depression. The relationship between schizophrenia and migraine is less certain although glutamatergic and serotonergic neurotransmission are implicated in both. A shared genetic basis to migraine and mental disorders has been suggested but previous studies have reported weak or non-significant genetic correlations and five shared risk loci. Using the largest samples to date and novel statistical tools, we aimed to determine the extent to which migraine's polygenic architecture overlaps with bipolar disorder, depression and schizophrenia beyond genetic correlation, and to identify shared genetic loci. Summary statistics from genome-wide association studies were acquired from large-scale consortia for migraine (n cases = 59 674; n controls = 316 078), bipolar disorder (n cases = 20 352; n controls = 31 358), depression (n cases = 170 756; n controls = 328 443) and schizophrenia (n cases = 40 675, n controls = 64 643). We applied the bivariate causal mixture model to estimate the number of disorder-influencing variants shared between migraine and each mental disorder, and the conditional/conjunctional false discovery rate method to identify shared loci. Loci were functionally characterized to provide biological insights. Univariate MiXeR analysis revealed that migraine was substantially less polygenic (2.8 K disorder-influencing variants) compared to mental disorders (8100-12 300 disorder-influencing variants). Bivariate analysis estimated that 800 (SD = 300), 2100 (SD = 100) and 2300 (SD = 300) variants were shared between bipolar disorder, depression and schizophrenia, respectively. There was also extensive overlap with intelligence (1800, SD = 300) and educational attainment (2100, SD = 300) but not height (1000, SD = 100). We next identified 14 loci jointly associated with migraine and depression and 36 loci jointly associated with migraine and schizophrenia, with evidence of consistent genetic effects in independent samples. No loci were associated with migraine and bipolar disorder. Functional annotation mapped 37 and 298 genes to migraine and each of depression and schizophrenia, respectively, including several novel putative migraine genes such as L3MBTL2, CACNB2 and SLC9B1. Gene-set analysis identified several putative gene sets enriched with mapped genes including transmembrane transport in migraine and schizophrenia. Most migraine-influencing variants were predicted to influence depression and schizophrenia, although a minority of mental disorder-influencing variants were shared with migraine due to the difference in polygenicity. Similar overlap with other brain-related phenotypes suggests this represents a pool of 'pleiotropic' variants that influence vulnerability to diverse brain-related disorders and traits. We also identified specific loci shared between migraine and each of depression and schizophrenia, implicating shared molecular mechanisms and highlighting candidate migraine genes for experimental validation.

Patients with schizophrenia have consistently shown brain volumetric abnormalities, implicating both etiological and pathological processes. However, the genetic relationship between schizophrenia and brain volumetric abnormalities remains poorly understood. Here, we applied novel statistical genetic approaches (MiXeR and conjunctional false discovery rate analysis) to investigate genetic overlap with mixed effect directions using independent genome-wide association studies of schizophrenia (n = 130,644) and brain volumetric phenotypes, including subcortical brain and intracranial volumes (n = 33,735). We found brain volumetric phenotypes share substantial genetic variants (74-96%) with schizophrenia, and observed 107 distinct shared loci with sign consistency in independent samples. Genes mapped by shared loci revealed (1) significant enrichment in neurodevelopmental biological processes, (2) three co-expression clusters with peak expression at the prenatal stage, and (3) genetically imputed thalamic expression of CRHR1 and ARL17A was associated with the thalamic volume as early as in childhood. Together, our findings provide evidence of shared genetic architecture between schizophrenia and brain volumetric phenotypes and suggest that altered early neurodevelopmental processes and brain development in childhood may be involved in schizophrenia development.

Personality and cognitive function are heritable mental traits whose genetic foundations may be distributed across interconnected brain functions. Previous studies have typically treated these complex mental traits as distinct constructs. We applied the 'pleiotropy-informed' multivariate omnibus statistical test to genome-wide association studies of 35 measures of neuroticism and cognitive function from the UK Biobank (n = 336,993). We identified 431 significantly associated genetic loci with evidence of abundant shared genetic associations, across personality and cognitive function domains. Functional characterization implicated genes with significant tissue-specific expression in all tested brain tissues and brain-specific gene sets. We conditioned independent genome-wide association studies of the Big 5 personality traits and cognitive function on our multivariate findings, boosting genetic discovery in other personality traits and improving polygenic prediction. These findings advance our understanding of the polygenic architecture of these complex mental traits, indicating a prominence of pleiotropic genetic effects across higher order domains of mental function such as personality and cognitive function. Hindley et al. used multivariate statistical genetics tools to examine the genetic underpinnings of cognitive and personality traits and find they are shared across higher order domains of mental functioning.

Genetic pleiotropy is abundant across spatially distributed brain characteristics derived from one neuroimaging modality (e.g. structural, functional or diffusion magnetic resonance imaging [MRI]). A better understanding of pleiotropy across modalities could inform us on the integration of brain function, micro- and macrostructure. Here we show extensive genetic overlap across neuroimaging modalities at a locus and gene level in the UK Biobank (N = 34,029) and ABCD Study (N = 8607). When jointly analysing phenotypes derived from structural, functional and diffusion MRI in a genome-wide association study (GWAS) with the Multivariate Omnibus Statistical Test (MOSTest), we boost the discovery of loci and genes beyond previously identified effects for each modality individually. Cross-modality genes are involved in fundamental biological processes and predominantly expressed during prenatal brain development. We additionally boost prediction of psychiatric disorders by conditioning independent GWAS on our multimodal multivariate GWAS. These findings shed light on the shared genetic mechanisms underlying variation in brain morphology, functional connectivity, and tissue composition.

There remains little consensus about the relationship between sex and brain structure, particularly in early adolescence. Moreover, few pediatric neuroimaging studies have analyzed both sex and gender as variables of interest-many of which included small sample sizes and relied on binary definitions of gender. The current study examined gender diversity with a continuous felt-gender score and categorized sex based on X and Y allele frequency in a large sample of children ages 9-11 years old (N = 7195). Then, a statistical model-building approach was employed to determine whether gender diversity and sex independently or jointly relate to brain morphology, including subcortical volume, cortical thickness, gyrification, and white matter microstructure. Additional sensitivity analyses found that male versus female differences in gyrification and white matter were largely accounted for by total brain volume, rather than sex per se. The model with sex, but not gender diversity, was the best-fitting model in 60.1% of gray matter regions and 61.9% of white matter regions after adjusting for brain volume. The proportion of variance accounted for by sex was negligible to small in all cases. While models including felt-gender explained a greater amount of variance in a few regions, the felt-gender score alone was not a significant predictor on its own for any white or gray matter regions examined. Overall, these findings demonstrate that at ages 9-11 years old, sex accounts for a small proportion of variance in brain structure, while gender diversity is not directly associated with neurostructural diversity.

Identifying asymptomatic older individuals at elevated risk for developing Alzheimer disease (AD) is of clinical importance. Among 1,081 asymptomatic older adults, a recently validated polygenic hazard score (PHS) significantly predicted time to AD dementia and steeper longitudinal cognitive decline, even after controlling for APOE ɛ4 carrier status. Older individuals in the highest PHS percentiles showed the highest AD incidence rates. PHS predicted longitudinal clinical decline among older individuals with moderate to high Consortium to Establish a Registry for Alzheimer's Disease (amyloid) and Braak (tau) scores at autopsy, even among APOE ɛ4 noncarriers. Beyond APOE, PHS may help identify asymptomatic individuals at highest risk for developing Alzheimer neurodegeneration. Ann Neurol 2017;82:484-488.

The ε4 allele of apolipoprotein E (APOE) is the strongest known common genetic risk factor for Alzheimer's disease (AD) and alters age of onset in retrospective studies. Here, we longitudinally test the effects of APOE ε4 genotype and age during progression from normal cognition to AD.Using data from 5381 cognitively normal older individuals and Cox proportional hazards models, we longitudinally tested the effects of APOE genotype on progression from normal cognition to mild cognitive impairment (MCI) or AD in four age strata (<60, 60-70, 70-80, 80 + ) and with a sliding window approach between ages 60 and 85.We found that APOE ε4 carrier status and dosage significantly influenced progression to MCI or AD in all four age groups and that APOE ε4-associated progression risk peaked between ages 70 and 75. We confirmed APOE ε4-associated progression risk in a subset of the cohort with pathologically proven diagnoses.Our findings indicate that in clinically normal individuals, APOE ε4 status significantly predicts progression to MCI or AD across older adulthood and that this risk varies with age. This information will be useful as therapeutic interventions become available and clinical decisions can be individually tailored based on age and genetic data.

Differential diagnosis between childhood onset attention-deficit/hyperactivity disorder (ADHD) and bipolar disorder (BD) remains a challenge, mainly due to overlapping symptoms and high rates of comorbidity. Despite this, genetic correlation reported for these disorders is low and non-significant. Here we aimed to better characterize the genetic architecture of these disorders utilizing recent large genome-wide association studies (GWAS). We analyzed independent GWAS summary statistics for ADHD (19,099 cases and 34,194 controls) and BD (20,352 cases and 31,358 controls) applying the conditional/conjunctional false discovery rate (condFDR/conjFDR) statistical framework that increases the power to detect novel phenotype-specific and shared loci by leveraging the combined power of two GWAS. We observed cross-trait polygenic enrichment for ADHD conditioned on associations with BD, and vice versa. Leveraging this enrichment, we identified 19 novel ADHD risk loci and 40 novel BD risk loci at condFDR <0.05. Further, we identified five loci jointly associated with ADHD and BD (conjFDR < 0.05). Interestingly, these five loci show concordant directions of effect for ADHD and BD. These results highlight a shared underlying genetic risk for ADHD and BD which may help to explain the high comorbidity rates and difficulties in differentiating between ADHD and BD in the clinic. Improving our understanding of the underlying genetic architecture of these disorders may aid in the development of novel stratification tools to help reduce these diagnostic difficulties.

Genome-wide association studies (GWAS) have identified several common genetic variants influencing major depression and general cognitive abilities, but little is known about whether the two share any of their genetic aetiology. Here we investigate shared genomic architectures between major depression (MD) and general intelligence (INT) with the MiXeR statistical tool and their overlapping susceptibility loci with conjunctional false discovery rate (conjFDR), which evaluate the level of overlap in genetic variants and improve the power for gene discovery between two phenotypes. We analysed GWAS data on MD (n = 480,359) and INT (n = 269,867) to characterize polygenic architecture and identify genetic loci shared between these phenotypes. Despite non-significant genetic correlation (rg = -0.0148, P = 0.50), we observed large polygenic overlap and identified 92 loci shared between MD and INT at conjFDR < 0.05. Among the shared loci, 69 and 64 are new for MD and INT, respectively. Our study demonstrates polygenic overlap between these phenotypes with a balanced mixture of effect.

Prostate cancer risk stratification using single-nucleotide polymorphisms (SNPs) demonstrates considerable promise in men of European, Asian, and African genetic ancestries, but there is still need for increased accuracy. We evaluated whether including additional SNPs in a prostate cancer polygenic hazard score (PHS) would improve associations with clinically significant prostate cancer in multi-ancestry datasets.In total, 299 SNPs previously associated with prostate cancer were evaluated for inclusion in a new PHS, using a LASSO-regularized Cox proportional hazards model in a training dataset of 72,181 men from the PRACTICAL Consortium. The PHS model was evaluated in four testing datasets: African ancestry, Asian ancestry, and two of European Ancestry-the Cohort of Swedish Men (COSM) and the ProtecT study. Hazard ratios (HRs) were estimated to compare men with high versus low PHS for association with clinically significant, with any, and with fatal prostate cancer. The impact of genetic risk stratification on the positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was also measured.The final model (PHS290) had 290 SNPs with non-zero coefficients. Comparing, for example, the highest and lowest quintiles of PHS290, the hazard ratios (HRs) for clinically significant prostate cancer were 13.73 [95% CI: 12.43-15.16] in ProtecT, 7.07 [6.58-7.60] in African ancestry, 10.31 [9.58-11.11] in Asian ancestry, and 11.18 [10.34-12.09] in COSM. Similar results were seen for association with any and fatal prostate cancer. Without PHS stratification, the PPV of PSA testing for clinically significant prostate cancer in ProtecT was 0.12 (0.11-0.14). For the top 20% and top 5% of PHS290, the PPV of PSA testing was 0.19 (0.15-0.22) and 0.26 (0.19-0.33), respectively.We demonstrate better genetic risk stratification for clinically significant prostate cancer than prior versions of PHS in multi-ancestry datasets. This is promising for implementing precision-medicine approaches to prostate cancer screening decisions in diverse populations.

Knowing how the human brain is shaped by migration and admixture is a critical step in studying human evolution [1, 2], as well as in preventing the bias of hidden population structure in brain research [3, 4]. Yet, the neuroanatomical differences engendered by population history are still poorly understood. Most of the inference relies on craniometric measurements, because morphology of the brain is presumed to be the neurocranium's main shaping force before bones are fused and ossified [5]. Although studies have shown that the shape variations of cranial bones are consistent with population history [6-8], it is unknown how much human ancestry information is retained by the human cortical surface. In our group's previous study, we found that area measures of cortical surface and total brain volumes of individuals of European descent in the United States correlate significantly with their ancestral geographic locations in Europe [9]. Here, we demonstrate that the three-dimensional geometry of cortical surface is highly predictive of individuals' genetic ancestry in West Africa, Europe, East Asia, and America, even though their genetic background has been shaped by multiple waves of migratory and admixture events. The geometry of the cortical surface contains richer information about ancestry than the areal variability of the cortical surface, independent of total brain volumes. Besides explaining more ancestry variance than other brain imaging measurements, the 3D geometry of the cortical surface further characterizes distinct regional patterns in the folding and gyrification of the human brain associated with each ancestral lineage.

Abstract Multiplex families with a high prevalence of a psychiatric disorder are often examined to identify rare genetic variants with large effect sizes. In the present study, we analysed whether the risk for bipolar disorder (BD) in BD multiplex families is influenced by common genetic variants. Furthermore, we investigated whether this risk is conferred mainly by BD-specific risk variants or by variants also associated with the susceptibility to schizophrenia or major depression. In total, 395 individuals from 33 Andalusian BD multiplex families (166 BD, 78 major depressive disorder, 151 unaffected) as well as 438 subjects from an independent, BD case/control cohort (161 unrelated BD, 277 unrelated controls) were analysed. Polygenic risk scores (PRS) for BD, schizophrenia (SCZ), and major depression were calculated and compared between the cohorts. Both the familial BD cases and unaffected family members had higher PRS for all three psychiatric disorders than the independent controls, with BD and SCZ being significant after correction for multiple testing, suggesting a high baseline risk for several psychiatric disorders in the families. Moreover, familial BD cases showed significantly higher BD PRS than unaffected family members and unrelated BD cases. A plausible hypothesis is that, in multiplex families with a general increase in risk for psychiatric disease, BD development is attributable to a high burden of common variants that confer a specific risk for BD. The present analyses demonstrated that common genetic risk variants for psychiatric disorders are likely to contribute to the high incidence of affective psychiatric disorders in the multiplex families. However, the PRS explained only part of the observed phenotypic variance, and rare variants might have also contributed to disease development.

The molecular determinants of tissue composition of the human brain remain largely unknown. Recent genome-wide association studies (GWAS) on this topic have had limited success due to methodological constraints. Here, we apply advanced whole-brain analyses on multi-shell diffusion imaging data and multivariate GWAS to two large scale imaging genetic datasets (UK Biobank and the Adolescent Brain Cognitive Development study) to identify and validate genetic association signals. We discover 503 unique genetic loci that have impact on multiple regions of human brain. Among them, more than 79% are validated in either of two large-scale independent imaging datasets. Key molecular pathways involved in axonal growth, astrocyte-mediated neuroinflammation, and synaptogenesis during development are found to significantly impact the measured variations in tissue-specific imaging features. Our results shed new light on the biological determinants of brain tissue composition and their potential overlap with the genetic basis of neuropsychiatric disorders.

The data release of Adolescent Brain Cognitive Development® (ABCD) Study represents an extensive resource for investigating factors relating to child development and mental wellbeing. The genotype data of ABCD has been used extensively in the context of genetic analysis, including genome-wide association studies and polygenic score predictions. However, there are unique opportunities provided by ABCD genetic data that have not yet been fully tapped. The diverse genomic variability, the enriched relatedness among ABCD subsets, and the longitudinal design of the ABCD challenge researchers to perform novel analyses to gain deeper insight into human brain development. Genetic instruments derived from the ABCD genetic data, such as genetic principal components, can help to better control confounds beyond the context of genetic analyses. To facilitate the use genomic information in the ABCD for inference, we here detail the processing procedures, quality controls, general characteristics, and the corresponding resources in the ABCD genotype data of release 4.0.

Williams Syndrome (WS) is a rare genetic disorder with unique behavioral features. Yet the rareness of WS has limited the number and type of studies that can be conducted in which inferences are made about how neuroanatomical abnormalities mediate behaviors. In this study, we extracted a WS-specific neuroanatomical profile from structural magnetic resonance imaging (MRI) measurements and tested its association with behavioral features of WS. Using a WS adult cohort (22 WS, 16 healthy controls), we modeled a sparse representation of a WS-specific neuroanatomical profile. The predictive performances are robust within the training cohort (10-fold cross-validation, AUC = 1.0) and accurately identify all WS individuals in an independent child WS cohort (seven WS, 59 children with diverse developmental status, AUC = 1.0). The WS-specific neuroanatomical profile includes measurements in the orbitofrontal cortex, superior parietal cortex, Sylvian fissures, and basal ganglia, and variability within these areas related to the underlying size of hemizygous deletion in patients with partial deletions. The profile intensity mediated the overall cognitive impairment as well as personality features related to hypersociability. Our results imply that the unique behaviors in WS were mediated through the constellation of abnormalities in cortical-subcortical circuitry consistent in child WS and adult WS. The robustness of the derived WS-specific neuroanatomical profile also demonstrates the potential utility of our approach in both clinical and research applications.

To determine if sex differences in verbal memory in AD are related to differences in extent or distribution of pathological tau, we studied 275 participants who were amyloid PET positive and carried clinical classifications of normal cognition, mild cognitive impairment (MCI) or dementia, and had tau (AV1451) PET. We compared tau distribution between men and women, and as a function of genetic risk. In MCI we further explored the relationship between quantity and distribution of tau in relation to verbal memory scores. Women had more tau burden overall, but this was driven by sex differences at the MCI stage. There was no significant difference in tau load by APOE e4 status. Within the MCI group the association between tau and performance in verbal memory tasks was stronger in women than men. The topography of the associations between tau and verbal memory also differed in MCI; women demonstrated stronger relationships between tau distribution and verbal memory performance, especially in the left hemisphere. These findings have implications for understanding tau distribution and spread, and in interpretation of verbal memory performance.

Genetic risk scores (GRSs) have been associated with CHD events and coronary artery calcium (CAC). We sought to evaluate the ability of a GRS to improve CAC as a screening test.Using the results of the most recent genome-wide association studies, we calculated a GRS in 6660 individuals from the Multi-Ethnic Study of Atherosclerosis and used it to determine the optimal age for an individual to undergo CAC screening.This 157-SNP GRS was predictive of non-zero CAC in individuals aged 44-54 and improved the positive yield of CAC as a screening test in this age group. The GRS was predictive of CAC in the entire multi-ethnic cohort and in each self-identified ethnic group (European American, Chinese American, African American, and Hispanic American) assessed individually. Given a specified target yield rate of non-zero CAC, an equation was derived to calculate an individual's optimal age to undergo CAC screening. In addition, a "direct-to-consumer" GRS consisting of only risk SNPs or their proxies that are directly genotyped on the 23andMe v5 chip (102-SNP GRS) was assessed in the European American population and was predictive of non-zero CAC in younger individuals.A GRS is associated with non-zero CAC in a multi-ethnic cohort and can be used to calculate the age of a person's first calcium scan, given a target threshold for CAC discovery. Furthermore, an inexpensive and widely available "direct-to-consumer" GRS was found to be a viable option to calculate the optimal age for CAC screening.

Genome-Wide Association studies have typically been limited to univariate analysis in which a single outcome measure is tested against millions of variants. Recent work demonstrates that a Multivariate Omnibus Statistic Test (MOSTest) is well powered to discover genomic effects distributed across multiple phenotypes. Applied to cortical brain MRI morphology measures, MOSTest has resulted in a drastic improvement in power to discover loci when compared to established approaches (min-P). One question that arises is how well these discovered loci replicate in independent data. Here we perform 10 times cross validation within 34,973 individuals from UK Biobank for imaging measures of cortical area, thickness and sulcal depth (>1,000 dimensionality for each). By deploying a replication method that aggregates discovered effects distributed across multiple phenotypes, termed PolyVertex Score (MOSTest-PVS), we demonstrate a higher replication yield and comparable replication rate of discovered loci for MOSTest (# replicated loci: 242-496, replication rate: 96-97%) in independent data when compared with the established min-P approach (# replicated loci: 26-55, replication rate: 91-93%). An out-of-sample replication of discovered loci was conducted with a sample of 4,069 individuals from the Adolescent Brain Cognitive Development® (ABCD) study, who are on average 50 years younger than UK Biobank individuals. We observe a higher replication yield and comparable replication rate of MOSTest-PVS compared to min-P. This finding underscores the importance of using well-powered multivariate techniques for both discovery and replication of high dimensional phenotypes in Genome-Wide Association studies.

Parkinson's disease (PD) is a highly age-related disorder, where common genetic risk variants affect both disease risk and age at onset. A statistical approach that integrates these effects across all common variants may be clinically useful for individual risk stratification. A polygenic hazard score methodology, leveraging a time-to-event framework, has recently been successfully applied in other age-related disorders.We aimed to develop and validate a polygenic hazard score model in sporadic PD.Using a Cox regression framework, we modeled the polygenic hazard score in a training data set of 11,693 PD patients and 9841 controls. The score was then validated in an independent test data set of 5112 PD patients and 5372 controls and a small single-study sample of 360 patients and 160 controls.A polygenic hazard score predicts the onset of PD with a hazard ratio of 3.78 (95% confidence interval 3.49-4.10) when comparing the highest to the lowest risk decile. Combined with epidemiological data on incidence rate, we apply the score to estimate genetically stratified instantaneous PD risk across age groups.We demonstrate the feasibility of a polygenic hazard approach in PD, integrating the genetic effects on disease risk and age at onset in a single model. In combination with other predictive biomarkers, the approach may hold promise for risk stratification in future clinical trials of disease-modifying therapies, which aim at postponing the onset of PD. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Abstract Spatial mapping is a promising strategy to investigate the mechanisms underlying the incidence of psychosis. We analyzed a case-cohort study ( n = 24,028), drawn from the 1.47 million Danish persons born between 1981 and 2005, using a novel framework for decomposing the geospatial risk for schizophrenia based on locale of upbringing and polygenic scores. Upbringing in a high environmental risk locale increases the risk for schizophrenia by 122%. Individuals living in a high gene-by-environmental risk locale have a 78% increased risk compared to those who have the same genetic liability but live in a low-risk locale. Effects of specific locales vary substantially within the most densely populated city of Denmark, with hazard ratios ranging from 0.26 to 9.26 for environment and from 0.20 to 5.95 for gene-by-environment. These findings indicate the critical synergism of gene and environment on the etiology of schizophrenia and demonstrate the potential of incorporating geolocation in genetic studies.

We determined the effect of sample size on performance of polygenic hazard score (PHS) models in prostate cancer. Age and genotypes were obtained for 40,861 men from the PRACTICAL consortium. The dataset included 201,590 SNPs per subject, and was split into training and testing sets. Established-SNP models considered 65 SNPs that had been previously associated with prostate cancer. Discovery-SNP models used stepwise selection to identify new SNPs. The performance of each PHS model was calculated for random sizes of the training set. The performance of a representative Established-SNP model was estimated for random sizes of the testing set. Mean HR98/50 (hazard ratio of top 2% to average in test set) of the Established-SNP model increased from 1.73 [95% CI: 1.69–1.77] to 2.41 [2.40–2.43] when the number of training samples was increased from 1 thousand to 30 thousand. Corresponding HR98/50 of the Discovery-SNP model increased from 1.05 [0.93–1.18] to 2.19 [2.16–2.23]. HR98/50 of a representative Established-SNP model using testing set sample sizes of 0.6 thousand and 6 thousand observations were 1.78 [1.70–1.85] and 1.73 [1.71–1.76], respectively. We estimate that a study population of 20 thousand men is required to develop Discovery-SNP PHS models while 10 thousand men should be sufficient for Established-SNP models.

Clinical variables-age, family history, genetics-are used for prostate cancer risk stratification. Recently, polygenic hazard scores (PHS46, PHS166) were validated as associated with age at prostate cancer diagnosis. While polygenic scores are associated with all prostate cancer (not specific for fatal cancers), PHS46 was also associated with age at prostate cancer death. We evaluated if adding PHS to clinical variables improves associations with prostate cancer death.Genotype/phenotype data were obtained from a nested case-control Cohort of Swedish Men (n = 3279; 2163 with prostate cancer, 278 prostate cancer deaths). PHS and clinical variables (family history, alcohol intake, smoking, heart disease, hypertension, diabetes, body mass index) were tested via univariable Cox proportional hazards models for association with age at prostate cancer death. Multivariable Cox models with/without PHS were compared with log-likelihood tests.Median age at last follow-up/prostate cancer death was 78.0 (IQR: 72.3-84.1) and 81.4 (75.4-86.3) years, respectively. On univariable analysis, PHS46 (HR 3.41 [95% CI 2.78-4.17]), family history (HR 1.72 [1.46-2.03]), alcohol (HR 1.74 [1.40-2.15]), diabetes (HR 0.53 [0.37-0.75]) were each associated with prostate cancer death. On multivariable analysis, PHS46 (HR 2.45 [1.99-2.97]), family history (HR 1.73 [1.48-2.03]), alcohol (HR 1.45 [1.19-1.76]), diabetes (HR 0.62 [0.42-0.90]) all remained associated with fatal disease. Including PHS46 or PHS166 improved multivariable models for fatal prostate cancer (p < 10-15).PHS had the most robust association with fatal prostate cancer in a multivariable model with common risk factors, including family history. Adding PHS to clinical variables may improve prostate cancer risk stratification strategies.

Schizophrenia is a heterogeneous disorder, exhibiting variability in presentation and outcomes that complicate treatment and recovery. To explore this heterogeneity, we leverage the comprehensive Danish health registries to conduct a prospective, longitudinal study from birth of 5432 individuals who would ultimately be diagnosed with schizophrenia, building individual trajectories that represent sequences of comorbid diagnoses, and describing patterns in the individual-level variability. We show that psychiatric comorbidity is prevalent among individuals with schizophrenia (82%) and multi-morbidity occur more frequently in specific, time-ordered pairs. Three latent factors capture 79% of variation in longitudinal comorbidity and broadly relate to the number of co-occurring diagnoses, the presence of child versus adult comorbidities and substance abuse. Clustering of the factor scores revealed five stable clusters of individuals, associated with specific risk factors and outcomes. The presentation and course of schizophrenia may be associated with heterogeneity in etiological factors including family history of mental disorders.

Abstract Anorexia nervosa (AN) is a heritable eating disorder (50–60%) with an array of commonly comorbid psychiatric disorders and related traits. Although significant genetic correlations between AN and psychiatric disorders and related traits have been reported, their shared genetic architecture is largely understudied. We investigated the shared genetic architecture of AN and schizophrenia (SCZ), bipolar disorder (BIP), major depression (MD), mood instability (Mood), neuroticism (NEUR), and intelligence (INT). We applied the conditional false discovery rate (FDR) method to identify novel risk loci for AN, and conjunctional FDR to identify loci shared between AN and related phenotypes, to summarize statistics from relevant genome-wide association studies (GWAS). Individual GWAS samples varied from 72,517 to 420,879 participants. Using conditional FDR we identified 58 novel AN loci. Furthermore, we identified 38 unique loci shared between AN and major psychiatric disorders (SCZ, BIP, and MD) and 45 between AN and psychological traits (Mood, NEUR, and INT). In line with genetic correlations, the majority of shared loci showed concordant effect directions. Functional analyses revealed that the shared loci are involved in 65 unique pathways, several of which overlapped across analyses, including the “signal by MST1” pathway involved in Hippo signaling. In conclusion, we demonstrated genetic overlap between AN and major psychiatric disorders and related traits, and identified novel risk loci for AN by leveraging this overlap. Our results indicate that some shared characteristics between AN and related disorders and traits may have genetic underpinnings.

Abstract Background Whether paternal age associated with offspring’s epilepsy risk is a cause of de novo mutation as men age, or just an association due to confounding factors, is still unclear. Methods We performed a population-based, multi-generation and sibling comparison study in Taiwan, which included 2 751 232 singletons born in 2001–17 who were followed until 2020. Of these, 819 371/826 087 with information on paternal/maternal grandparents were selected for multi-generation analyses and 1 748 382 with sibling(s) were selected for sibling comparison. Cox proportional hazard regression was used to estimate the hazard ratio (HR) and 95% confidence interval (CI). Results In the total cohort, there was an increased risk of epilepsy in individuals with advanced paternal age, e.g. the HR for paternal age ≥50 was1.36 (95% CI: 1.15–1.61) compared with paternal age 25–29, and fathers older than mothers, e.g. the HR for parental age difference ≥15 years was 1.29 (95% CI: 1.16–1.43). When accounting for parental age difference, the association between paternal age and epilepsy in offspring was attenuated (HR for paternal age ≥50 was 1.11, 95% CI: 0.93–1.34). Multi-generation analyses did not support the association of advanced grand-paternal age at childbirth of the parent with offspring’s risk of epilepsy. Sibling comparison analyses did not support the association of older paternal age with increased risk of epilepsy (HR was 0.96 for per year increase in paternal age, 95% CI: 0.96–0.97). Conclusions These results do not support the hypothesis that advanced paternal age is associated with epilepsy in offspring. Instead, familial factors may explain the observed paternal age association with the offspring’s risk of epilepsy.

Cognitive impairment, as a prevalent symptom of nervous system disorders, poses one of the most challenging aspects in the management of brain diseases. Lipids present in the cell membranes of all neurons within the brain and dietary lipids can regulate the cognition and memory function. In recent years, the advancements in gut microbiome research have enabled the exploration of dietary lipids targeting the gut-brain axis as a strategy for regulating cognition. This present review provides an in-depth overview of how lipids modulate cognition via the gut-brain axis depending on metabolic, immune, neural and endocrine pathways. It also comprehensively analyzes the effects of diverse lipids on the gut microbiota and intestinal barrier function, thereby affecting the central nervous system and cognitive capacity. Moreover, comparative analysis of the positive and negative effects is presented between beneficial and detrimental lipids. The former encompass monounsaturated fatty acids, short-chain fatty acids, omega-3 polyunsaturated fatty acids, phospholipids, phytosterols, fungal sterols and bioactive lipid-soluble vitamins, as well as lipid-derived gut metabolites, whereas the latter (detrimental lipids) include medium- or long-chain fatty acids, excessive proportions of n-6 polyunsaturated fatty acids, industrial trans fatty acids, and zoosterols. To sum up, the focus of this review is on how gut-brain communication mediates the impact of dietary lipids on cognitive capacity, providing a novel theoretical foundation for promoting brain cognitive health and scientific lipid consumption patterns.

Abstract The linear mixed‐effects model (LME) is a versatile approach to account for dependence among observations. Many large‐scale neuroimaging datasets with complex designs have increased the need for LME; however LME has seldom been used in whole‐brain imaging analyses due to its heavy computational requirements. In this paper, we introduce a fast and efficient mixed‐effects algorithm (FEMA) that makes whole‐brain vertex‐wise, voxel‐wise, and connectome‐wide LME analyses in large samples possible. We validate FEMA with extensive simulations, showing that the estimates of the fixed effects are equivalent to standard maximum likelihood estimates but obtained with orders of magnitude improvement in computational speed. We demonstrate the applicability of FEMA by studying the cross‐sectional and longitudinal effects of age on region‐of‐interest level and vertex‐wise cortical thickness, as well as connectome‐wide functional connectivity values derived from resting state functional MRI, using longitudinal imaging data from the Adolescent Brain Cognitive Development SM Study release 4.0. Our analyses reveal distinct spatial patterns for the annualized changes in vertex‐wise cortical thickness and connectome‐wide connectivity values in early adolescence, highlighting a critical time of brain maturation. The simulations and application to real data show that FEMA enables advanced investigation of the relationships between large numbers of neuroimaging metrics and variables of interest while considering complex study designs, including repeated measures and family structures, in a fast and efficient manner. The source code for FEMA is available via: https://github.com/cmig-research-group/cmig_tools/ .

Iron is an essential nutrient which can only be absorbed through an individual's diet. Excess iron accumulates in organs throughout the body including the brain. Iron dysregulation in the brain is commonly associated with neurodegenerative diseases like Alzheimer's disease and Parkinson's Disease (PD). Our previous research has shown that a pattern of iron accumulation in motor regions of the brain related to a genetic iron-storage disorder called hemochromatosis is associated with an increased risk of PD. To understand how diet and lifestyle factors relate to this brain endophenotype and risk of PD we analyzed the relationship between these measures, estimates of nutrient intake, and diet and lifestyle preference using data from UK Biobank.Using distinct imaging and non-imaging samples (20,477 to 28,388 and 132,023 to 150,603 participants, respectively), we performed linear and logistic regression analyses using estimated dietary nutrient intake and food preferences to predict a) brain iron accumulation score (derived from T2-Weighted Magnetic Resonance Imaging) and b) PD risk. In addition, we performed a factor analysis of diet and lifestyle preferences to investigate if latent lifestyle factors explained significant associations. Finally, we performed an instrumental variable regression of our results related to iron accumulation and PD risk to identify if there were common dietary and lifestyle factors that were jointly associated with differences in brain iron accumulation and PD risk.We found multiple highly significant associations with measures of brain iron accumulation and preferences for alcohol (factor 7: t=4.02, pFDR=0.0003), exercise (factor 11: t=-4.31, pFDR=0.0001), and high-sugar foods (factor 2: t=-3.73, pFDR=0.0007). Preference for alcohol (factor 7: t=-5.83, pFDR<1×10-8), exercise (factor 11: t=-7.66, pFDR<1×10-13), and high sugar foods (factor 2: t=6.03, pFDR<1×10-8) were also associated with PD risk. Instrumental variable regression of individual preferences revealed a significant relationship in which dietary preferences associated with higher brain iron levels also appeared to be linked to a lower risk for PD (p=0.004). A similar relationship was observed for estimates of nutrient intake (p=0.0006). Voxel-wise analysis of i) high-sugar and ii) alcohol factors confirmed T2-weighted signal differences consistent with iron accumulation patterns in motor regions of the brain including the cerebellum and basal ganglia.Dietary and lifestyle factors and preferences, especially those related to carbohydrates, alcohol, and exercise, are related to detectable differences in brain iron accumulation and alterations in risk of PD, suggesting a potential avenue for lifestyle interventions that could influence risk.

Abstract Background Studying the causes of death among deceased spouses and surviving partners may provide insights into the underlying mechanisms of the association between widowhood and mortality. This study investigated the mortality risk of widowhood in Taiwan, examined the association of the cause of death between widowed individuals and their deceased spouses and explored potential modifying effects by age, gender and duration after widowhood. Methods This matched cohort study utilized Taiwan's National Health Insurance claims database and National Death Registry. In total, 204 010 widowed men and 596 136 widowed women were identified with a mean follow-up period of 6.9 and 7.9 years, respectively, and 816 040 comparison men and 2 384 544 comparison women were selected. Results Widowhood was associated with an increased mortality risk, with widowed men having a 1.32 increased risk and widowed women having a 1.27 increased risk. Age at spousal death and duration modified the associations after widowhood. The widowed individuals are more likely to die by the same cause as the deceased spouse if they died by suicide, accident, endocrine, gastrointestinal disorders or infection. Conclusions The study suggests that healthcare policies and interventions should be developed to improve widowed individuals' health and overall welfare.

Few studies in Taiwan have looked into the burden of caregivers for the mentally ill and the influence of the burden on the quality of life among caregivers. The aim of this study is to explore the risk factors that may aggravate care burden and to assess the relationship between the caregivers' burden and their quality of life.Ninety caregivers of patients with mental illness, who were attending outpatient clinic services in Taipei City Psychiatric Centre, were assessed using a burden questionnaire and the brief questionnaire of the World Health Organization Quality of Life instrument (WHOQOL-BREF).Burden scores were significantly correlated with the number of care hours the caregivers spent daily with the patient, irrespective of their age, gender, kinship and educational level. Caregivers of patients with different psychiatric illnesses had similar levels of burden. Higher burden scores were correlated with a lower quality of life and retained unique predictive variance in multiple regressions in all four domains of the WHOQOL-BREF.These findings indicate that care burden has a significant impact on caregivers' quality of life. Daily care hours with the patient are the unique determinant of caregivers' burden in Taiwan. Measures to reduce daily care hours should be considered.

Discovering genetic variants associated with human brain structures is an on-going effort. The ENIGMA consortium conducted genome-wide association studies (GWAS) with standard multi-study analytical methodology and identified several significant single nucleotide polymorphisms (SNPs). Here we employ a novel analytical approach that incorporates functional genome annotations (e.g., exon or 5'UTR), total linkage disequilibrium (LD) scores and heterozygosity to construct enrichment scores for improved identification of relevant SNPs. The method provides increased power to detect associated SNPs by estimating stratum-specific false discovery rate (FDR), where strata are classified according to enrichment scores. Applying this approach to the GWAS summary statistics of putamen volume in the ENIGMA cohort, a total of 15 independent significant SNPs were identified (conditional FDR < 0.05). In contrast, 4 SNPs were found based on standard GWAS analysis (P < 5 × 10-8). These 11 novel loci include GATAD2B, ASCC3, DSCAML1, and HELZ, which are previously implicated in various neural related phenotypes. The current findings demonstrate the boost in power with the annotation-informed FDR method, and provide insight into the genetic architecture of the putamen.

Abstract Tuberous sclerosis complex (TSC), a heritable neurodevelopmental disorder, is caused by mutations in the TSC1 or TSC2 genes. To date, there has been little work to elucidate regional TSC1 and TSC2 gene expression within the human brain, how it changes with age, and how it may influence disease. Using a publicly available microarray dataset, we found that TSC1 and TSC2 gene expression was highest within the adult neo-cerebellum and that this pattern of increased cerebellar expression was maintained throughout postnatal development. During mid-gestational fetal development, however, TSC1 and TSC2 expression was highest in the cortical plate. Using a bioinformatics approach to explore protein and genetic interactions, we confirmed extensive connections between TSC1/TSC2 and the other genes that comprise the mammalian target of rapamycin (mTOR) pathway, and show that the mTOR pathway genes with the highest connectivity are also selectively expressed within the cerebellum. Finally, compared to age-matched controls, we found increased cerebellar volumes in pediatric TSC patients without current exposure to antiepileptic drugs. Considered together, these findings suggest that the cerebellum may play a central role in TSC pathogenesis and may contribute to the cognitive impairment, including the high incidence of autism spectrum disorder, observed in the TSC population.

Selecting individuals at high risk of Alzheimer's disease (AD) dementia and using the most sensitive outcome measures are important aspects of trial design.We divided participants from Alzheimer's Disease Neuroimaging Initiative at the 50th percentile of the predicted absolute risk of the polygenic hazard score (PHS). Outcome measures were the Alzheimer's Disease Assessment Schedule-Cognitive Subscale (ADAS-Cog), ADNI-Mem, Clinical Dementia Rating-Sum of Boxes (CDR SB), and Cognitive Function Composite 2 (CFC2). In addition to modeling, we use a power analysis compare numbers needed with each technique.Data from 188 cognitively normal and 319 mild cognitively impaired (MCI) participants were analyzed. Using the ADAS-Cog to estimate sample sizes, without stratification over 24 months, would require 930 participants with MCI, while using the CFC2 and restricting participants to those in the upper 50th percentile would require only 284 participants.Combining stratification by PHS and selection of a sensitive combined outcome measure in a cohort of patients with MCI can allow trial design that is more efficient, potentially less burdensome on participants, and more cost effective.

We previously developed an African-ancestry-specific polygenic hazard score (PHS46+African) that substantially improved prostate cancer risk stratification in men with African ancestry. The model consists of 46 SNPs identified in Europeans and 3 SNPs from 8q24 shown to improve model performance in Africans. Herein, we used principal component (PC) analysis to uncover subpopulations of men with African ancestry for whom the utility of PHS46+African may differ.Genotypic data were obtained from the PRACTICAL consortium for 6253 men with African genetic ancestry. Genetic variation in a window spanning 3 African-specific 8q24 SNPs was estimated using 93 PCs. A Cox proportional hazards framework was used to identify the pair of PCs most strongly associated with the performance of PHS46+African. A calibration factor (CF) was formulated using Cox coefficients to quantify the extent to which the performance of PHS46+African varies with PC.CF of PHS46+African was strongly associated with the first and twentieth PCs. Predicted CF ranged from 0.41 to 2.94, suggesting that PHS46+African may be up to 7 times more beneficial to some African men than others. The explained relative risk for PHS46+African varied from 3.6% to 9.9% for individuals with low and high CF values, respectively. By cross-referencing our data set with 1000 Genomes, we identified significant associations between continental and calibration groupings.We identified PCs within 8q24 that were strongly associated with the performance of PHS46+African. Further research to improve the clinical utility of polygenic risk scores (or models) is needed to improve health outcomes for men of African ancestry.

Abstract Magnetic resonance imaging (MRI) studies of the human brain are now attaining larger sample sizes with more diverse samples. However, population stratification, a key factor driving heterogeneity and confounding of associations, is seldom accounted for in neuroimaging analyses. To investigate this issue, we assessed the impact of population stratification on multimodal imaging measures using baseline data from the Adolescent Brain Cognitive Development (ABCD) Study SM (n = 10,748). Given this sociodemographically diverse sample, which broadly reflects the population composition of the United States, we performed a thorough evaluation of the impact of population stratification on derived neuroimaging metrics across five different imaging modalities: task functional MRI (task fMRI), resting state functional MRI (rsMRI), diffusion tensor images (DTI), restricted spectrum images (RSI), and structural T1 MRI (sMRI). We used parental income level as an example to highlight the impact of population stratification in confounding brain-wide associations. We show that derived metrics from structural images have up to three times more signal related to population stratification than do functional images. Controlling for population stratification in statistical models leads to a substantial reduction in the association strength between variables of interests and imaging measures, indicating the scale of potential bias. Moreover, because of unequal access to resources (such as income) across ancestral groups in United States, population stratification effects on imaging features may bias associations between parental income levels and imaging features, as we demonstrate. Our results provide a guide for researchers to critically examine the impact of population stratification and to assist in avoiding spurious brain-behavior associations. Highlights Here, we conduct a comprehensive survey of the confounding impact of population stratification in large-scale imaging studies. Morphological features from structural imaging appear to be more susceptible to the confounding effects of population stratification than do functional imaging features. The population stratification tends to inflates the association strengths between the variable of interest and imaging features. When the variable of interest is highly colinear with the population stratification, such as income levels, brain associations cannot be differentiated and may be misattributed as mediating effects. It is critical to account for population stratification in imaging analyses.

Abstract Psychiatric disorders are complex, heritable, and highly polygenic. Supported by findings of abnormalities in functional magnetic resonance imaging (fMRI) based measures of brain connectivity, current theoretical and empirical accounts have conceptualized them as disorders of brain connectivity and dysfunctional integration of brain signaling, however, the extent to which these findings reflect common genetic factors remains unclear. Here, we performed a multivariate genome-wide association analysis of fMRI-based functional brain connectivity in a sample of 30,701 individuals from the UK Biobank and investigated the shared genetic determinants with eight major psychiatric disorders. The analysis revealed significant genetic overlap between functional brain connectivity and schizophrenia, bipolar disorder, attention-deficit hyperactivity disorder, autism spectrum disorder, anxiety, and major depression, adding further genetic support for the dysconnectivity hypothesis of psychiatric disorders and identifying potential genetic and functional targets for future studies.

Polygenic risk scores (PRSs) can boost risk prediction in late-onset Alzheimer's disease (LOAD) beyond apolipoprotein E (APOE) but have not been leveraged to identify genetic resilience factors. Here, we sought to identify resilience-conferring common genetic variants in (1) unaffected individuals having high PRSs for LOAD, and (2) unaffected APOE-ε4 carriers also having high PRSs for LOAD. We used genome-wide association study (GWAS) to contrast "resilient" unaffected individuals at the highest genetic risk for LOAD with LOAD cases at comparable risk. From GWAS results, we constructed polygenic resilience scores to aggregate the addictive contributions of risk-orthogonal common variants that promote resilience to LOAD. Replication of resilience scores was undertaken in eight independent studies. We successfully replicated two polygenic resilience scores that reduce genetic risk penetrance for LOAD. We also showed that polygenic resilience scores positively correlate with polygenic risk scores in unaffected individuals, perhaps aiding in staving off disease. Our findings align with the hypothesis that a combination of risk-independent common variants mediates resilience to LOAD by moderating genetic disease risk.

Semi-continuous data present challenges in both model fitting and interpretation. Parametric distributions may be inappropriate for extreme long right tails of the data. Mean effects of covariates, susceptible to extreme values, may fail to capture relevant information for most of the sample. We propose a two-component semi-parametric Bayesian mixture model, with the discrete component captured by a probability mass (typically at zero) and the continuous component of the density modeled by a mixture of B-spline densities that can be flexibly fit to any data distribution. The model includes random effects of subjects to allow for application to longitudinal data. We specify prior distributions on parameters and perform model inference using a Markov chain Monte Carlo (MCMC) Gibbs-sampling algorithm programmed in R. Statistical inference can be made for multiple quantiles of the covariate effects simultaneously providing a comprehensive view. Various MCMC sampling techniques are used to facilitate convergence. We demonstrate the performance and the interpretability of the model via simulations and analyses on the National Consortium on Alcohol and Neurodevelopment in Adolescence study (NCANDA) data on alcohol binge drinking.

The depot differences between Subcutaneous Fat (SAF) and Visceral Fat (VAF) are critical for human well-being and disease processes in regard to energy metabolism and endocrine function. Miniature pigs (Sus scrofa) are ideal biomedical models for human energy metabolism and obesity due to the similarity of their lipid metabolism with that of humans. However, the regulation of differences in fat deposition and development remains unclear. In this study, the development of SAF and VAF was characterized and compared in Bama pig during postnatal development (infancy, puberty and adulthood), using RNA sequencing techniques (RNA-Seq). The transcriptome of SAF and VAF was profiled and isolated from 1-, 3- and 6 months-old pigs and identified 23,636 expressed genes, of which 1,165 genes were differentially expressed between the depots and/or developmental stages. Upregulated genes in SAF showed significant function and pathway enrichment in the central nervous system development, lipid metabolism, oxidation-reduction process and cell adhesion, whereas genes involved in the immune system, actin cytoskeleton organization, male gonad development and the hippo signaling pathway were preferentially expressed in VAF. Miner analysis of short time-series expression demonstrated that differentiation in gene expression patterns between the two depots corresponded to their distinct responses in sexual development, hormone signaling pathways, lipid metabolism and the hippo signaling pathway. Transcriptome analysis of SAF and VAF suggested that the depot differences in adipose tissue are not only related to lipid metabolism and endocrine function, but are closely associated with sexual development and organ size regulation.

Background: Polygenic hazard scores (PHS) estimate age-dependent genetic risk of late-onset Alzheimer’s disease (AD), but there is limited information about the performance of PHS on real-world data where the population of interest differs from the model development population and part of the model genotypes are missing or need to be imputed. Objective: The aim of this study was to estimate age-dependent risk of late-onset AD using polygenic predictors in Nordic populations. Methods: We used Desikan PHS model, based on Cox proportional hazards assumption, to obtain age-dependent hazard scores for AD from individual genotypes in the Norwegian DemGene cohort (n = 2,772). We assessed the risk discrimination and calibration of Desikan model and extended it by adding new genotype markers (the Desikan Nordic model). Finally, we evaluated both Desikan and Desikan Nordic models in two independent Danish cohorts: The Copenhagen City Heart Study (CCHS) cohort (n = 7,643) and The Copenhagen General Population Study (CGPS) cohort (n = 10,886). Results: We showed a robust prediction efficiency of Desikan model in stratifying AD risk groups in Nordic populations, even when some of the model SNPs were missing or imputed. We attempted to improve Desikan PHS model by adding new SNPs to it, but we still achieved similar risk discrimination and calibration with the extended model. Conclusion: PHS modeling has the potential to guide the timing of treatment initiation based on individual risk profiles and can help enrich clinical trials with people at high risk to AD in Nordic populations.

The existing evidence on the contextual influence of the availability of local facilities for physical activity on the cognitive health of elderly residents is sparse. This study examined the association between neighborhood physical activity facilities and cognitive health in older individuals. A cohort study of community-dwelling older adults was performed using baseline data and follow-up data from the Taiwan Biobank. Cognitive health was measured in 32,396 individuals aged 60-70 years using the Mini-Mental State Examination (MMSE) with follow-up information on 8025 participants. The district was used as the proxy for local neighborhood. To determine neighborhood physical activity facilities, school campuses, parks, activity centers, gyms, swimming pools, and stadiums were included. Multilevel linear regression models were applied to examine the associations of neighborhood physical activity facilities with baseline MMSE and MMSE decline during follow-up, with adjustment for individual factors and neighborhood socioeconomic characteristics. Multilevel analyses revealed that there was a neighborhood-level effect on cognitive health among older adults. After adjusting for compositional and neighborhood socioeconomic characteristics, baseline MMSE was higher in individuals living in the middle- (beta = 0.12, p-value = 0.140) and high-density facility (beta = 0.22, p-value = 0.025) groups than in the low-density group (p-value for trend-test = 0.031). MMSE decline during follow-up was slower in the middle- (beta = 0.15, p-value = 0.114) and high-density facility (beta = 0.27, p-value = 0.052) groups than in the low-density group (p-value for trend-test = 0.032). Greater neighborhood availability of physical activity facilities was associated with better cognitive health among older residents. These findings have implications for designing communities and developing strategies to support cognitive health of an aging population.

Abstract Genome-Wide Association studies have typically been limited to single phenotypes, given that high dimensional phenotypes incur a large multiple comparisons burden: ~1 million tests across the genome times the number of phenotypes. Recent work demonstrates that a Multivariate Omnibus Statistic Test (MOSTest) is well powered to discover genomic effects distributed across multiple phenotypes. Applied to cortical brain MRI morphology measures, MOSTest has resulted in a drastic improvement in power to discover loci when compared to established approaches (min-P). One question that arises is how well these discovered loci replicate in independent data. Here we perform 10 times cross validation within 35,644 individuals from UK Biobank for imaging measures of cortical area, thickness and sulcal depth (&gt;1,000 dimensionality for each). By deploying a replication method that aggregates discovered effects distributed across multiple phenotypes, termed PolyVertex Score (PVS), we demonstrate a higher replication yield and comparable replication rate of discovered loci for MOSTest (# replicated loci: 348-845, replication rate: 94-95%) in independent data when compared with the established min-P approach (# replicated loci: 31-68, replication rate: 65-80%). An out-of-sample replication of discovered loci was conducted with a sample of 8,336 individuals from the Adolescent Brain Cognitive Development ® (ABCD) study, who are on average 50 years younger than UK Biobank individuals. We observe a higher replication yield and comparable replication rate of MOSTest compared to min-P. This finding underscores the importance of using well-powered multivariate techniques for both discovery and replication of high dimensional phenotypes in Genome-Wide Association studies.

Drug interactions are one of the most common causes of side effects in polypharmacy. Alcoholics are a category of patients at high risk of pharmacological interactions, due to the presence of comorbidities, the concomitant intake of several medications and the pharmacokinetic and pharmacodynamic interferences of ethanol. However, the data available on this issue are limited. These reasons often frighten clinicians when prescribing appropriate pharmacological therapies for alcohol use disorder (AUD), where less than 15% of patients receive an appropriate treatment in the most severe forms. The data available in literature regarding the relevant drug–drug interactions of the medications currently approved in United States and in some European countries for the treatment of AUD (benzodiazepines, acamprosate, baclofen, disulfiram, nalmefene, naltrexone and sodium oxybate) are reviewed here. The class of benzodiazepines and disulfiram are involved in numerous pharmacological interactions, while they are not conspicuous for acamprosate. The other drugs are relatively safe for pharmacological interactions, excluding the opioid withdrawal syndrome caused by the combination of nalmefene or naltrexone with an opiate medication. The information obtained is designed to help clinicians in understanding and managing the pharmacological interactions in AUDs, especially in patients under multi-drug treatment, in order to reduce the risk of a negative interaction and to improve the treatment outcomes.

Abstract Despite great interest in using magnetic resonance imaging (MRI) for studying the effects of genes on brain structure in humans, current approaches have focused almost entirely on predefined regions of interest and had limited success. Here, we used multivariate methods to define a single neuroanatomical score of how William’s Syndrome (WS) brains deviate structurally from controls. The score is trained and validated on measures of T1 structural brain imaging in two WS cohorts (training, n = 38; validating, n = 60). We then associated this score with single nucleotide polymorphisms (SNPs) in the WS hemi-deleted region in five cohorts of neurologically and psychiatrically typical individuals (healthy European descendants, n = 1863). Among 110 SNPs within the 7q11.23 WS chromosomal region, we found one associated locus ( p = 5e–5) located at GTF2IRD1 , which has been implicated in animal models of WS. Furthermore, the genetic signals of neuroanatomical scores are highly enriched locally in the 7q11.23 compared with summary statistics based on regions of interest, such as hippocampal volumes ( n = 12,596), and also globally (SNP-heritability = 0.82, se = 0.25, p = 5e−4). The role of genetic variability in GTF2IRD1 during neurodevelopment extends to healthy subjects. Our approach of learning MRI-derived phenotypes from clinical populations with well-established brain abnormalities characterized by known genetic lesions may be a powerful alternative to traditional region of interest-based studies for identifying genetic variants regulating typical brain development.

Bayesian mixture model, B-

Abstract Personality and cognition are heritable mental traits, and their genetic determinants may be distributed across interconnected brain functions. However, previous studies have employed univariate approaches which reduce complex traits to summary measures. We applied the “pleiotropy-informed” multivariate omnibus statistical test (MOSTest) to genome-wide association studies (GWAS) of 35 item and task-level measures of neuroticism and cognition from the UK Biobank (n=336,993). We identified 431 significant genetic loci and found evidence of abundant pleiotropy across personality and cognitive domains. Functional characterisation implicated genes with significant tissue-specific expression in all tested brain tissues and enriched in brain-specific gene-sets. We conditioned independent GWAS of the Big 5 personality traits and cognition on our multivariate findings, which boosted genetic discovery in other personality traits and improved polygenic prediction. These findings advance our understanding of the polygenic architecture of complex mental traits, indicating a prominence of pleiotropic genetic effects across higher-order domains of mental function. Graphical abstract

[This corrects the article DOI: 10.1371/journal.pmed.1002487.].

Simple sequence repeats (SSRs) are commonly used as molecular markers in research on genetic diversity and discrimination among taxa or breeds because polymorphisms in these regions contribute to gene function and phenotypically important traits. In this study, we investigated genome-wide characteristics, repeat units, and polymorphisms of SSRs using sequencing data from SSR-enriched libraries created from Wuzhishan (WZS), Bama (BM), inbred Luchuan (LC) and Zangxiang (ZX) miniature pig breeds. The numbers and types of SSRs, distributions of repeat units and polymorphic SSRs varied among the four breeds. Compared to the Duroc pig reference genome, 2518 polymorphic SSRs were unique and common to all four breeds and functional annotation revealed that they may affect the coding and regulatory regions of genes. Several examples, such as FGF23, MYF6, IGF1R, and LEPROT, are associated with growth and development in pigs. Three of the polymorphic SSRs were selected to confirm the polymorphism and the corresponding alleles through fluorescence polymerase chain reaction (PCR) and capillary electrophoresis. Together, this study provides useful insights into the discovery, characteristics and distribution of SSRs in four pig breeds. The polymorphic SSRs, especially those common and unique to all four pig breeds, might affect associated genes and play important roles in growth and development.

ABSTRACT Individuals undergo protracted changes in cortical morphology during childhood and adolescence, coinciding with cognitive development. Studies quantifying the association between brain structure and cognition do not always assess regional cortical morphology relative to global brain measures and typically rely on mass univariate statistics or ROI-based analyses. After controlling for global brain measures, it is possible to detect a residual regionalisation pattern indicating the size or thickness of different regions relative to the total cortical surface area or mean thickness. Individual variability in regionalisation may be important for understanding and predicting between subject variability in cognitive performance. Here we sought to determine whether the relative configuration of cortical architecture across the whole cortex was associated with cognition using a novel multivariate omnibus statistical test (MOSTest) in 10,145 children aged 9-10 years from the Adolescent Brain and Cognitive Development (ABCD) Study. MOSTest is better powered to detect associations that are widely distributed across the cortex compared to methods that assume sparse associations. We then quantified the magnitude of the association between vertex-wise cortical morphology and cognitive performance using a linear weighted sum across vertices, based on the estimated vertex-wise effect sizes. We show that the relative pattern of cortical architecture, after removing the effects of global brain measures, predicted unique variance associated with cognition across different imaging modalities and cognitive domains. SIGNIFICANCE STATEMENT This paper demonstrates a significant advance in our understanding of the relationship between cortical morphology and individual variability in cognition. There is increasing evidence that brain-behaviour associations are distributed across the cortex. Using the unprecedented sample from the Adolescent Brain and Cognitive Development (ABCD) study and a novel application of a multivariate statistical approach (MOSTest), we have discovered specific distributed regionalization patterns across the cortex associated with cognition across multiple cognitive domains. This furthers our understanding of the relationship between brain structure and cognition, namely that these associations are not sparse and localized as assumed with traditional neuroimaging analyses. This multivariate method is extremely versatile and can be used in several different applications.

Scores on intelligence tests have been reported to correlate significantly with educational, occupational and health outcomes. Twin and genome wide association studies in adults have revealed that intelligence scores are moderately heritable. We aimed to better understand the relationship between genetic variation and intelligence in the context of the developing brain. Specifically, we questioned if a genetic predictor of intelligence derived from a large GWAS dataset a) loaded on specific factors of cognition (i.e. fluid vs. crystallized) and b) were related to differences in cortical brain morphology measured using MRI scans. To do this we calculated an intelligence polygenic score (IPS) for the Adolescent Brain Cognitive Development (ABCD) baseline data, which consists of 11,875 nine- and ten- year old children across the US. We found that the IPS was a highly significant predictor of estimates of both fluid (t=8.7, p=3.0x10−18, 0.8% variance explained) and crystallized (t=17.1, p=2.0x10−64, 3.1% variance explained) cognition. Critically we found greater predictive power for crystallized than fluid (z=5.1, p=3.1x10−7), this result replicated in ancestry stratified analysis: for Europeans (z=4.7, 3.2 x10−8) and non-Europeans (z=2.6, p=9.4x10−3). This indicates a stronger loading of IPS on crystallized cognition. IPS was significantly related to total cortical surface area (t=5.5, p=2.5x10-8, 0.4% variance explained), but not mean thickness (t=2.0, p=0.045) - after Bonferroni correction. These results replicated in the European subsample (area: t=5.4, p=6.3x10-8, mean thickness: t=2.3, p=0.021), but not in the non-European subsample (area: t=2.4, p=0.016, mean thickness: t=-0.41, p=0.68). Vertex-wise analyses within the European group showed that the surface area association is largely global across the cortex. The stronger association of IPS with crystallized compared to fluid measures is consistent with recent results that more culturally dependent measures of cognition are more heritable. These findings in children provide new evidence relevant to the developmental origins of previously observed cognitive loadings and brain morphology patterns associated with polygenic predictors of intelligence.

Abstract Objectives A polygenic hazard score (PHS 1 )—weighted sum of 54 single-nucleotide polymorphism genotypes—was previously associated with age at prostate cancer (PCa) diagnosis and improved PCa screening accuracy in Europeans. Performance in more diverse populations is unknown. We evaluated PHS association with PCa in multi-ethnic populations. Design PHS 1 was adapted for compatibility with genotype data from the OncoArray project (PHS 2 ) and tested for association with age at PCa diagnosis, at aggressive PCa diagnosis, and at PCa death. Setting Multiple international institutions. Participants Men with available OncoArray data from the PRACTICAL consortium who were not included in PHS 1 development/validation. Main Outcomes and Measures PHS 2 was tested via Cox proportional hazards models for age at PCa diagnosis, age at aggressive PCa diagnosis (any of: Gleason score ≥7, stage T3-T4, PSA≥10 ng/mL, nodal/distant metastasis), and age at PCa-specific death. Results 80,491 men of various self-reported race/ethnicities were included (30,575 controls, 49,916 PCa cases; genetic ancestry groups: 71,856 European, 6,253 African, 2,382 Asian). Median age at last follow-up was 70 years (IQR 63-76); 3,983 PCa deaths, 5,806 other deaths, 70,702 still alive. PHS 2 had 46 polymorphisms: 24 directly genotyped and 22 acceptable proxies (r 2 ≥0.94). PHS 2 was associated with age at PCa diagnosis in the multi-ethnic dataset (z=54, p&lt;10 -16 ) and in each genetic ancestry group: European (z=56, p&lt;10 -16 ), Asian (z=47, p &lt;10 -16 ), African (z=29, p &lt;10 -16 ). PHS 2 was also associated with age at aggressive PCa diagnosis in each genetic ancestry group (p&lt;10 -16 ) and with age of PCa death in the full dataset ( p &lt;10 -16 ). Comparing the 80 th and 20 th percentiles of genetic risk, men with high PHS had hazard ratios of 5.3 [95% CI: 5.0-5.7], 5.9 [5.5-6.3], and 5.7 [4.6-7.0] for PCa, aggressive PCa, and PCa-specific death, respectively. Within European, Asian, and African ancestries, analogous hazard ratios for PCa were 5.5 [5.2-5.9], 4.5 [3.2-6.3], and 2.5 [2.1-3.1], respectively. Conclusions PHS 2 is strongly associated with age at PCa diagnosis in a multi-ethnic dataset. PHS 2 stratifies men of European, Asian, and African ancestry by genetic risk for any, aggressive, and fatal PCa. Summary boxes What is already known on this topic Genetic risk stratification can identify men with greater predisposition for developing prostate cancer, but these risk models may worsen health disparities, as most have only been validated for men of European ancestry A polygenic hazard score was previously associated with age at prostate cancer diagnosis and improved PCa screening accuracy in Europeans Performance of the polygenic hazard score in multi-ethnic populations is unknown What this study adds In a dataset from 80,491 men of various self-reported race/ethnicities, the polygenic hazard score was associated with age at prostate cancer diagnosis, aggressive prostate cancer diagnosis, and prostate cancer death. PHS stratifies men of European, Asian, and African ancestry by genetic risk for any, aggressive, and fatal prostate cancer.

Abstract In the clinical diagnosis of dementia with Lewy bodies, distinction from Alzheimer’s disease is suboptimal and complicated by shared genetic risk factors and frequent co-pathology. In the present study we tested the ability of polygenic scores for Alzheimer’s disease, dementia with Lewy bodies, and Parkinson’s disease to differentiate individuals in a 2713-participant, pathologically defined sample. A dementia with Lewy bodies polygenic score that excluded apolipoprotein E due to its overlap with Alzheimer’s disease risk was specifically associated with at least limbic (transitional) Lewy-related pathology and a pathological diagnosis of dementia with Lewy bodies. An Alzheimer’s disease polygenic score was associated with neuritic plaques and neurofibrillary tangles but not Lewy-related pathology, and was most strongly associated with an Alzheimer’s pathological diagnosis. Our results indicate that an assessment of genetic risk may be useful to clinically distinguish between Alzheimer’s disease and dementia with Lewy bodies. Notably, we found no association with a Parkinson’s disease polygenic score, which aligns with evidence that dementia with Lewy bodies has a distinct genetic signature that can be exploited to improve clinical diagnoses.

Important sex differences exist in tau pathology along the Alzheimer disease (AD) continuum, with women showing enhanced tau deposition compared with men, especially during the mild cognitive impairment (MCI) phase. This study aims to identify specific genetic variants associated with sex differences in regional tau aggregation, as measured with PET.Four hundred ninety-three participants (women, n = 246; men, n = 247) who self-identified as White from the AD Neuroimaging Initiative study, with genotyping data and 18F-Flortaucipir tau PET data, were included irrespective of clinical diagnosis (cognitively normal [CN], MCI, and AD). We focused on the genetic variants within 10 genes previously shown to have sex-dependent effects on AD to reduce the burden of multiple comparisons: BIN1, MS4A6A, DNAJA2, FERMT2, APOC1, APOC1P1, FAM193B, C2orf47, TYW5, and CR1. Multivariate analysis of variance was applied to identify genetic variants associated with tau PET data in 3 regions of interests (composite regions of Braak I, Braak III/IV, and Braak V/VI stages) in women and men separately. We controlled for age, scanner manufacture, amyloid status, APOE ε4 carriership, diagnosis (CN vs MCI vs AD), and the first 10 genetic principal components to adjust for population stratification.We identified 3 genetic loci within 3 different genes associated with tau deposits specifically in women: rs79711283 within DNAJA2, rs113357081 within FERMT2, and rs74614106 within TYW5. In men, we also identified 3 loci within CR1 associated with tau deposits: rs115096248, rs113698814, and rs78150633.Our findings revealed sex-specific genetic variants associated with tau deposition independent of APOE ε4, amyloid status, and clinical diagnosis. These results provide potential molecular targets for understanding the mechanism of sex-specific tau aggregation and developing sex-specific gene-guided precision prevention or therapeutic interventions for AD.

*Department of Psychiatry, Taipei City Psychiatric Center, Taipei City Hospital and †School of Medicine, Taipei Medical University, Taipei, Taiwan [email protected]

Abstract Sex differences in the manifestations of Alzheimer’s disease (AD) are under intense investigations 1,2 . Despite the emerging importance of polygenic predictions for AD 3–8 , the sex-dependent polygenic effects have not been demonstrated. Here, using a sex crossover analysis, we show that sex-dependent autosomal genetic effects on AD can be revealed by characterizing disease progress via the hazard function. We first performed sex-stratified genome-wide associations, and then applied derived sex-dependent weights to two independent cohorts. Sex-matched polygenic hazard scores (PHS) have significantly stronger associations with age-at-disease-onset, clinical progressions, amyloid depositions, neurofibrillary tangles, and composite neuropathological scores, than sex-mismatched PHS, independent of apolipoprotein E. Models without using hazard weights, i.e. polygenic risk scores (PRS), have lower predictive power than PHS and show no evidence for sex differences. Our results indicate revealing sex-dependent genetic architecture requires the consideration of temporal processes of AD. This has strong implications not only for the genetic underpinning of AD but also for how we estimate sex-dependent polygenic effects for clinical use.

ABSTRACT Mood instability (MOOD) is a transdiagnostic phenomenon with a prominent neurobiological basis. Recent genome-wide association studies found significant positive genetic correlation between MOOD and major depression (DEP) and weak correlations with other psychiatric disorders. We investigated the polygenic overlap between MOOD and psychiatric disorders beyond genetic correlation to better characterize putative shared genetic determinants. Summary statistics for schizophrenia (SCZ, n=105,318), bipolar disorder (BIP, n=413,466), DEP (n=450,619), attention-deficit hyperactivity disorder (ADHD, n=53,293) and MOOD (n=363,705), were analysed using the bivariate causal mixture model and conjunctional false discovery rate methods to estimate the proportion of shared variants influencing MOOD and each disorder, and identify jointly associated genomic loci. MOOD correlated positively with all psychiatric disorders, but with wide variation in strength (rg=0.10-0.62). Of 10.4K genomic variants influencing MOOD, 4K-9.4K were estimated to influence psychiatric disorders. MOOD was jointly associated with DEP at 163 loci, SCZ at 110, BIP at 60 and ADHD at 25, with consistent genetic effects in independent samples. Fifty-three jointly associated loci were overlapping across two or more disorders (transdiagnostic), seven of which had discordant effect directions on psychiatric disorders. Genes mapped to loci associated with MOOD and all four disorders were enriched in a single gene-set, “synapse organization”. The extensive polygenic overlap indicates shared molecular underpinnings across MOOD and psychiatric disorders. However, distinct patterns of genetic correlation and effect directions of shared loci suggest divergent effects on corresponding neurobiological mechanisms which may relate to differences in the core clinical features of each disorder.

Abstract Background Prostate-specific-antigen (PSA) screening resulted in reduced prostate cancer (PCa) mortality in a large clinical trial, but due to a high false-positive rate, among other concerns, many guidelines do not endorse universal screening and instead recommend an individualized decision based on each patient’s risk. Genetic risk may provide key information to guide the decisions of whether and at what age to screen an individual man for PCa. Methods Genotype, PCa status, and age from 34,444 men of European ancestry from the PRACTICAL consortium database were analyzed to select single-nucleotide polymorphisms (SNPs) associated with prostate cancer diagnosis. These SNPs were then incorporated into a survival analysis to estimate their effects on age at PCa diagnosis. The resulting polygenic hazard score (PHS) is an assessment of individual genetic risk. The final model was validated in an independent dataset comprised of 6,417 men with screening PSA and genotype data. PHS was calculated for these men to test for prediction of PCa-free survival. PHS was also combined with age-specific PCa incidence data from the U.S. population to generate a PCa-Risk (PCaR) age that relates a given man’s risk to that of the population average. PHS and PCaR age were evaluated for prediction of positive predictive value (PPV) of PSA screening. Findings PHS calculated from 54 SNPs was very highly predictive of age at PCa diagnosis for men in the validation set ( p =10 −53 ). PPV of PSA screening varied from 0.18 to 0.52 for men with low and high genetic risk, respectively. PHS modulates PCa-free survival curves by an estimated 20 years between men in the 1 st or 99 th percentiles of genetic risk. Interpretation Polygenic hazard scores give personalized genetic risk estimates and can inform the decisions of whether and at what age to screen a man for PCa. Funding Department of Defense #W81XWH-13-1-0391

Identifying individuals who are most likely to accumulate tau and exhibit cognitive decline is critical for Alzheimer's disease (AD) clinical trials.Participants (N = 235) who were cognitively normal or with mild cognitive impairment from the Alzheimer's Disease Neuroimaging Initiative were stratified by a cutoff on the polygenic hazard score (PHS) at 65th percentile (above as high-risk group and below as low-risk group). We evaluated the associations between the PHS risk groups and tau positron emission tomography and cognitive decline, respectively. Power analyses estimated the sample size needed for clinical trials to detect differences in tau accumulation or cognitive change.The high-risk group showed faster tau accumulation and cognitive decline. Clinical trials using the high-risk group would require a fraction of the sample size as trials without this inclusion criterion.Incorporating a PHS inclusion criterion represents a low-cost and accessible way to identify potential participants for AD clinical trials.

While the mitigating effects of trees on shallow landslide occurrence are well recognised, the impact of landslides on tree community structure and treetree interactions have received much less research attention.The structures of tree communities before and after landslides were compared in a 25-ha subtropical forest plot.Tree-tree interactions were examined by analysing the pre-and post-landslide spatial point patterns of large (DBH ≥ 20 cm) and small (1 cm ≤ DBH < 20 cm) tree cohorts.In landslide scarps, 35 (34%) of 104 large trees and 467 (13%) of 3,072 small trees survived.Large (L) and small (S) tree cohorts were paired together for spatial analyses, including pre-landslide (PL) (LPL-SPL), surviving (S) (LS-SS), and missing (M) large-small tree paired cohorts (LM-SM).We randomly selected trees from the pre-landslide tree cohorts to create two virtual paired cohorts, the L34%-S13% and L66%-S87% paired cohorts, whose population sizes were identical to the field-observed LS-SS and LM-SM paired cohorts respectively, but with random spatial patterns.Post-landslide survival rates of trees increased monotonically with DBH.Large trees dislodged by landslides scarcely reduced small-tree survival.Evidence for this included: (i) the distance from small trees to the nearest large trees of the LM-SM paired cohort did not differ significantly from that of the virtual L66%-S87% paired cohort; (ii) survival rates of small trees near LM individuals did not differ significantly from those without large trees nearby.Surviving large trees had positive effects on the survival of small trees, indicated by: (i) the distance from small trees to the nearest large trees of the LS-SS paired cohort was significantly lower than that of the virtual L34%-S13% paired cohort; (ii) SS individuals clumped around LS individuals, whereas the virtual L34%-S13% spatial relationship was random.Large trees prevent landslide dislodgement of adjacent small trees through the nurse-plant effect.Our study suggests that landslide damage in sloping forests may be reduced simply by constantly maintaining a critical density of large trees.

Bipolar disorder (BD) is a highly heritable psychiatric disorder that associated with cortical brain abnormalities, such as a widespread pattern of thinner cortex. Such abnormality was also seen in unaffected people with a familial risk. It remains unclear whether and to what extent does BD share genetic architecture with cortical structure.

1 Abstract Here we present a development of the novel Bayesian Regularized and Anotation-Informed Integrative Analysis of Cognition (BRAINIAC) model. BRAINIAC allows for both estimation of total variance explained by all features for a given cognitive phenotype, as well as a principled assessment of the impact of annotations on relative enrichment of features compared to others in terms of variance explained, without relying on a potentially unrealistic assumption of sparsity of brain-cognition associations. We apply the BRAINIAC model to resting state fMRI data from the Adolescent Brain Cognitive Development Study.

There remains little consensus about the relationship between sex and brain structure, particularly in childhood. Moreover, few pediatric neuroimaging studies have analyzed both sex and gender as variables of interest - many of which included small sample sizes and relied on binary definitions of gender. The current study examined gender diversity with a continuous felt-gender score and categorized sex based on X and Y allele frequency in a large sample of children ages 9-11 years-old (N=7693). Then, a statistical model-building approach was employed to determine whether gender diversity and sex independently or jointly relate to brain morphology, including subcortical volume, cortical thickness, gyrification, and white matter microstructure. The model with sex, but not gender diversity, was the best-fitting model in 75% of gray matter regions and 79% of white matter regions examined. The addition of gender to the sex model explained significantly more variance than sex alone with regard to bilateral cerebellum volume, left precentral cortical thickness, as well as gyrification in the right superior frontal gyrus, right parahippocampal gyrus, and several regions in the left parietal lobe. For mean diffusivity in the left uncinate fasciculus, the model with sex, gender, and their interaction captured the most variance. Nonetheless, the magnitude of variance accounted for by sex was small in all cases and felt-gender score was not a significant predictor on its own for any white or gray matter regions examined. Overall, these findings demonstrate that at ages 9-11 years-old, sex accounts for a small proportion of variance in brain structure, while gender diversity is not directly associated with neurostructural diversity.

Post-stroke depression (PSD) may be associated with the altered brain network property. This study aimed at exploring the brain network characteristics of PSD under the classic cognitive task, i.e., the oddball task, in order to promote our understanding of the pathogenesis and the diagnosis of PSD.Nineteen stroke survivors with PSD and 18 stroke survivors with no PSD (non-PSD) were recruited. The functional near-infrared spectroscopy (fNIRS) covering the dorsolateral prefrontal cortex was recorded during the oddball task state and the resting state. The brain network characteristics were extracted using the graph theory and compared between the PSD and the non-PSD subjects. In addition, the classification performance between the PSD and non-PSD subjects was evaluated using features in the resting and the task state, respectively.Compared with the resting state, more brain network characteristics in the task state showed significant differences between the PSD and non-PSD groups, resulting in better classification performance. In the task state, the assortativity, clustering coefficient, characteristic path length, and local efficiency of the PSD subjects was larger compared with the non-PSD subjects while the global efficiency of the PSD subjects was smaller than that of the non-PSD subjects.The altered brain network properties associated with PSD in the cognitive task state were more distinct compared with the resting state, and the ability of the brain network to resist attack and transmit information was reduced in PSD patients in the task state.This study demonstrated the feasibility and superiority of investigating brain network properties in the task state for the exploration of the pathogenesis and new diagnosis methods for PSD.

Zero-inflated outcomes are very common in behavioral data, particularly for responses to psychological questionnaires. Modeling these challenging distributions is further exacerbated by the absence of established statistical models capable of characterizing total signals attributed to whole-brain imaging features, making the accurate assessment of brain-behavior relationships particularly formidable. Given this critical need, we have developed a novel variational Bayes algorithm that characterizes the total signal captured by whole-brain imaging features for zero-inflated outcomes . Our zero-inflated variance (ZIV) estimator robustly estimates the fraction of variance explained (FVE) and the proportion of non-null effects from large-scale imaging data. In simulations, ZIV outperformed other linear prediction algorithms. Applying ZIV to data from one of the largest neuroimaging studies, the Adolescent Brain Cognitive Development SM (ABCD) Study, we found that whole-brain imaging features have a larger FVE for externalizing compared to internalizing behavior. We also demonstrate that the ZIV estimator, especially applied to focal sub-scales, can localize key neurocircuitry associated with human behavior.

While genome-wide association studies (GWAS) have discovered thousands of risk loci for heritable disorders, so far even very large meta-analyses have recovered only a fraction of the heritability of most complex traits. Recent work utilizing variance components models has demonstrated that a larger fraction of the heritability of complex phenotypes is captured by the additive effects of SNPs than is evident only in loci surpassing genome-wide significance thresholds, typically set at a Bonferroni-inspired p ≤ 5 x 10 -8 . Procedures that control false discovery rate can be more powerful, yet these are still under-powered to detect the majority of non-null effects from GWAS. The current work proposes a novel Bayesian semi-parametric two-group mixture model and develops a Markov Chain Monte Carlo (MCMC) algorithm for a covariate-modulated local false discovery rate ( cmfdr ). The probability of being non-null depends on a set of covariates via a logistic function, and the non-null distribution is approximated as a linear combination of B-spline densities, where the weight of each B-spline density depends on a multinomial function of the covariates. The proposed methods were motivated by work on a large meta-analysis of schizophrenia GWAS performed by the Psychiatric Genetics Consortium (PGC). We show that the new cmfdr model fits the PGC schizophrenia GWAS test statistics well, performing better than our previously proposed parametric gamma model for estimating the non-null density and substantially improving power over usual fdr. Using loci declared significant at cmfdr ≤ 0.20, we perform follow-up pathway analyses using the Kyoto Encyclopedia of Genes and Genomes (KEGG) homo sapiens pathways database. We demonstrate that the increased yield from the cmfdr model results in an improved ability to test for pathways associated with schizophrenia compared to using those SNPs selected according to usual fdr.

ABSTRACT Cardiovascular (CV) and lifestyle associated risk factors (RFs) are increasingly recognized as important for Alzheimer’s disease (AD) pathogenesis. Beyond the ∊4 allele of apolipoprotein E ( APOE ), comparatively little is known about whether CV associated genes also increase risk for AD (genetic pleiotropy). Using large genome-wide association studies (GWASs) (total n &gt; 500,000 cases and controls) and validated tools to quantify genetic pleiotropy, we systematically identified single nucleotide polymorphisms (SNPs) jointly associated with AD and one or more CV RFs, namely body mass index (BMI), type 2 diabetes (T2D), coronary artery disease (CAD), waist hip ratio (WHR), total cholesterol (TC), low-density (LDL) and high-density lipoprotein (HDL). In fold enrichment plots, we observed robust genetic enrichment in AD as a function of plasma lipids (TC, LDL, and HDL); we found minimal AD genetic enrichment conditional on BMI, T2D, CAD, and WHR. Beyond APOE , at conjunction FDR &lt; 0.05 we identified 57 SNPs on 19 different chromosomes that were jointly associated with AD and CV outcomes including APOA4, ABCA1, ABCG5, LIPG , and MTCH2/SPI1. We found that common genetic variants influencing AD are associated with multiple CV RFs, at times with a different directionality of effect. Expression of these AD/CV pleiotropic genes was enriched for lipid metabolism processes, over-represented within astrocytes and vascular structures, highly co-expressed, and differentially altered within AD brains. Beyond APOE , we show that the polygenic component of AD is enriched for lipid associated RFs. Rather than a single causal link between genetic loci, RF and the outcome, we found that common genetic variants influencing AD are associated with multiple CV RFs. Our collective findings suggest that a network of genes involved in lipid biology also influence Alzheimer’s risk.

Abstract Introduction Polygenic hazard score (PHS) models are associated with age at diagnosis of prostate cancer. Our model developed in Europeans (PHS46), showed reduced performance in men with African genetic ancestry. We used a cross-validated search to identify SNPs that might improve performance in this population. Material and Methods Anonymized genotypic data were obtained from the PRACTICAL consortium for 6,253 men with African genetic ancestry. Ten iterations of a ten-fold cross-validation search were conducted, to select SNPs that would be included in the final PHS46+African model. The coefficients of PHS46+African were estimated in a Cox proportional hazards framework using age at diagnosis as the dependent variable and PHS46, and selected SNPs as predictors. The performance of PHS46 and PHS46+African were compared using the same cross-validated approach. Results Three SNPs (rs76229939, rs74421890, and rs5013678) were selected for inclusion in PHS46+African. All three SNPs are located on chromosome 8q24. PHS46+African showed substantial improvements in all performance metrics measured, including a 75% increase in the relative hazard of those in the upper 20% compared to the bottom 20% (2.47 to 4.34) and a 20% reduction in the relative hazard of those in the bottom 20% compared to the middle 40% (0.65 to 0.53). Conclusions We identified three SNPs that substantially improved the association of PHS46 with age at diagnosis of prostate cancer in men with African genetic ancestry to levels comparable to Europeans and Asians. A strategy of building on established statistical models might benefit ancestral groups generally under-represented in genome-wide association studies.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Abstract Introduction Prostate cancer risk stratification using single-nucleotide polymorphisms (SNPs) demonstrates considerable promise in men of European, Asian, and African genetic ancestries, but there is still need for increased accuracy. We evaluated whether including additional SNPs in a prostate cancer polygenic hazard score (PHS) would improve associations with clinically significant prostate cancer in multi-ancestry datasets. Methods In total, 299 SNPs previously associated with prostate cancer were evaluated for inclusion in a new PHS, using a LASSO-regularized Cox proportional hazards model in a training dataset of 72,181 men from the PRACTICAL Consortium. The PHS model was evaluated in four testing datasets: African ancestry, Asian ancestry, and two of European Ancestry—the Cohort of Swedish Men (COSM) and the ProtecT study. Hazard ratios (HRs) were estimated to compare men with high versus low PHS for association with clinically significant, with any, and with fatal prostate cancer. The impact of genetic risk stratification on the positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was also measured. Results The final model (PHS290) had 290 SNPs with non-zero coefficients. Comparing, for example, the highest and lowest quintiles of PHS290, the hazard ratios (HRs) for clinically significant prostate cancer were 13.73 [95% CI: 12.43-15.16] in ProtecT, 7.07 [6.58-7.60] in African ancestry, 10.31 [9.58-11.11] in Asian ancestry, and 11.18 [10.34-12.09] in COSM. Similar results were seen for association with any and fatal prostate cancer. Without PHS stratification, the PPV of PSA testing for clinically significant prostate cancer in ProtecT was 0.12 (0.11-0.14). For the top 20% and top 5% of PHS290, the PPV was 0.19 (0.15-0.22) and 0.26 (0.19-0.33), respectively. Conclusion We demonstrate better genetic risk stratification for clinically significant prostate cancer than prior versions of PHS in multi-ancestry datasets. This is promising for implementing precision-medicine approaches to prostate cancer screening decisions in diverse populations.