Peter Kraft

A genome-wide association (GWA) study of more than 180,000 individuals has identified hundreds of genetic variants in at least 180 loci associated with adult human height. The loci are not clustered randomly but are enriched for genes involved in growth-related processes that influence adult height. This demonstrates that GWA studies of common human traits, and therefore of many diseases, can identify large numbers of loci that implicate potential causal genes. This very large genome-wide association study identifies hundreds of new genetic variants influencing adult height in at least 180 loci enriched for genes involved in skeletal growth defects. The results show that the likely causal gene is often located near the most strongly associated variant, that many loci have multiple independently associated variants and that associated variants are enriched for likely functional effects on genes. Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits1, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait2,3. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P < 0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.

By combining genome-wide association data from 8,130 individuals with type 2 diabetes (T2D) and 38,987 controls of European descent and following up previously unidentified meta-analysis signals in a further 34,412 cases and 59,925 controls, we identified 12 new T2D association signals with combined P<5x10(-8). These include a second independent signal at the KCNQ1 locus; the first report, to our knowledge, of an X-chromosomal association (near DUSP9); and a further instance of overlap between loci implicated in monogenic and multifactorial forms of diabetes (at HNF1A). The identified loci affect both beta-cell function and insulin action, and, overall, T2D association signals show evidence of enrichment for genes involved in cell cycle regulation. We also show that a high proportion of T2D susceptibility loci harbor independent association signals influencing apparently unrelated complex traits.

Joel Hirschhorn and colleagues report results of a large-scale genome-wide association and replication study for obesity-related traits. The newly discovered loci are enriched for genes expressed in the central nervous system, and may thus contribute to weight gain by modulating food intake. Similar results are reported in a related study by Gudmar Thorleifsson and colleagues. Common variants at only two loci, FTO and MC4R, have been reproducibly associated with body mass index (BMI) in humans. To identify additional loci, we conducted meta-analysis of 15 genome-wide association studies for BMI (n > 32,000) and followed up top signals in 14 additional cohorts (n > 59,000). We strongly confirm FTO and MC4R and identify six additional loci (P < 5 × 10−8): TMEM18, KCTD15, GNPDA2, SH2B1, MTCH2 and NEGR1 (where a 45-kb deletion polymorphism is a candidate causal variant). Several of the likely causal genes are highly expressed or known to act in the central nervous system (CNS), emphasizing, as in rare monogenic forms of obesity, the role of the CNS in predisposition to obesity.

We conducted a genome-wide association study (GWAS) of breast cancer by genotyping 528,173 SNPs in 1,145 postmenopausal women of European ancestry with invasive breast cancer and 1,142 controls. We identified four SNPs in intron 2 of FGFR2 (which encodes a receptor tyrosine kinase and is amplified or overexpressed in some breast cancers) that were highly associated with breast cancer and confirmed this association in 1,776 affected individuals and 2,072 controls from three additional studies. Across the four studies, the association with all four SNPs was highly statistically significant (Ptrend for the most strongly associated SNP (rs1219648) = 1.1 × 10−10; population attributable risk = 16%). Four SNPs at other loci most strongly associated with breast cancer in the initial GWAS were not associated in the replication studies. Our summary results from the GWAS are available online in a form that should speed the identification of additional risk loci.

Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R(2) increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase.

Waist-hip ratio (WHR) is a measure of body fat distribution and a predictor of metabolic consequences independent of overall adiposity. WHR is heritable, but few genetic variants influencing this trait have been identified. We conducted a meta-analysis of 32 genome-wide association studies for WHR adjusted for body mass index (comprising up to 77,167 participants), following up 16 loci in an additional 29 studies (comprising up to 113,636 subjects). We identified 13 new loci in or near RSPO3, VEGFA, TBX15-WARS2, NFE2L3, GRB14, DNM3-PIGC, ITPR2-SSPN, LY86, HOXC13, ADAMTS9, ZNRF3-KREMEN1, NISCH-STAB1 and CPEB4 (P = 1.9 × 10⁻⁹ to P = 1.8 × 10⁻⁴⁰) and the known signal at LYPLAL1. Seven of these loci exhibited marked sexual dimorphism, all with a stronger effect on WHR in women than men (P for sex difference = 1.9 × 10⁻³ to P = 1.2 × 10⁻¹³). These findings provide evidence for multiple loci that modulate body fat distribution independent of overall adiposity and reveal strong gene-by-sex interactions.

We followed our initial genome-wide association study (GWAS) of 527,869 SNPs on 1,172 individuals with prostate cancer and 1,157 controls of European origin-nested in the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial prospective study-by testing 26,958 SNPs in four independent studies (total of 3,941 cases and 3,964 controls). In the combined joint analysis, we confirmed three previously reported loci (two independent SNPs at 8q24 and one in HNF1B (formerly known as TCF2 on 17q); P < 10(-10)). In addition, loci on chromosomes 7, 10 (two loci) and 11 were highly significant (between P < 7.31 x 10(-13) and P < 2.14 x 10(-6)). Loci on chromosome 10 include MSMB, which encodes beta-microseminoprotein, a primary constituent of semen and a proposed prostate cancer biomarker, and CTBP2, a gene with antiapoptotic activity; the locus on chromosome 7 is at JAZF1, a transcriptional repressor that is fused by chromosome translocation to SUZ12 in endometrial cancer. Of the nine loci that showed highly suggestive associations (P < 2.5 x 10(-5)), four best fit a recessive model and included candidate susceptibility genes: CPNE3, IL16 and CDH13. Our findings point to multiple loci with moderate effects associated with susceptibility to prostate cancer that, taken together, in the future may predict high risk in select individuals.

Obesity is associated with vitamin D deficiency, and both are areas of active public health concern. We explored the causality and direction of the relationship between body mass index (BMI) and 25-hydroxyvitamin D [25(OH)D] using genetic markers as instrumental variables (IVs) in bi-directional Mendelian randomization (MR) analysis.We used information from 21 adult cohorts (up to 42,024 participants) with 12 BMI-related SNPs (combined in an allelic score) to produce an instrument for BMI and four SNPs associated with 25(OH)D (combined in two allelic scores, separately for genes encoding its synthesis or metabolism) as an instrument for vitamin D. Regression estimates for the IVs (allele scores) were generated within-study and pooled by meta-analysis to generate summary effects. Associations between vitamin D scores and BMI were confirmed in the Genetic Investigation of Anthropometric Traits (GIANT) consortium (n = 123,864). Each 1 kg/m(2) higher BMI was associated with 1.15% lower 25(OH)D (p = 6.52×10⁻²⁷). The BMI allele score was associated both with BMI (p = 6.30×10⁻⁶²) and 25(OH)D (-0.06% [95% CI -0.10 to -0.02], p = 0.004) in the cohorts that underwent meta-analysis. The two vitamin D allele scores were strongly associated with 25(OH)D (p≤8.07×10⁻⁵⁷ for both scores) but not with BMI (synthesis score, p = 0.88; metabolism score, p = 0.08) in the meta-analysis. A 10% higher genetically instrumented BMI was associated with 4.2% lower 25(OH)D concentrations (IV ratio: -4.2 [95% CI -7.1 to -1.3], p = 0.005). No association was seen for genetically instrumented 25(OH)D with BMI, a finding that was confirmed using data from the GIANT consortium (p≥0.57 for both vitamin D scores).On the basis of a bi-directional genetic approach that limits confounding, our study suggests that a higher BMI leads to lower 25(OH)D, while any effects of lower 25(OH)D increasing BMI are likely to be small. Population level interventions to reduce BMI are expected to decrease the prevalence of vitamin D deficiency.

Anna Köttgen and colleagues report genome-wide association studies for serum urate in over 140,000 individuals from the Global Urate Genetics Consortium (GUGC). They identify 18 loci newly associated with serum urate concentrations and confirm 10 known loci, characterize their associations with gout and include a network analysis suggesting a role for inhibins-activins pathways in regulating urate homeostasis. Elevated serum urate concentrations can cause gout, a prevalent and painful inflammatory arthritis. By combining data from >140,000 individuals of European ancestry within the Global Urate Genetics Consortium (GUGC), we identified and replicated 28 genome-wide significant loci in association with serum urate concentrations (18 new regions in or near TRIM46, INHBB, SFMBT1, TMEM171, VEGFA, BAZ1B, PRKAG2, STC1, HNF4G, A1CF, ATXN2, UBE2Q2, IGF1R, NFAT5, MAF, HLF, ACVR1B-ACVRL1 and B3GNT4). Associations for many of the loci were of similar magnitude in individuals of non-European ancestry. We further characterized these loci for associations with gout, transcript expression and the fractional excretion of urate. Network analyses implicate the inhibins-activins signaling pathways and glucose metabolism in systemic urate control. New candidate genes for serum urate concentration highlight the importance of metabolic control of urate production and excretion, which may have implications for the treatment and prevention of gout.

The potential for genome-wide association studies to relate phenotypes to specific genetic variation is greatly increased when data can be combined or compared across multiple studies.To facilitate replication and validation across studies, RTI International (Research Triangle Park, North Carolina) and the National Human Genome Research Institute (Bethesda, Maryland) are collaborating on the consensus measures for Phenotypes and eXposures (PhenX) project.The goal of PhenX is to identify 15 high-priority, well-established, and broadly applicable measures for each of 21 research domains.PhenX measures are selected by working groups of domain experts using a consensus process that includes input from the scientific community.The selected measures are then made freely available to the scientific community via the PhenX Toolkit.Thus, the PhenX Toolkit provides the research community with a core set of high-quality, well-established, low-burden measures intended for use in large-scale genomic studies.PhenX measures will have the most impact when included at the experimental design stage.The PhenX Toolkit also includes links to standards and resources in an effort to facilitate data harmonization to legacy data.Broad acceptance and use of PhenX measures will promote cross-study comparisons to increase statistical power for identifying and replicating variants associated with complex diseases and with gene-gene and gene-environment interactions.

We conducted a two-stage genome-wide association study of pancreatic cancer, a cancer with one of the lowest survival rates worldwide. We genotyped 558,542 SNPs in 1,896 individuals with pancreatic cancer and 1,939 controls drawn from 12 prospective cohorts plus one hospital-based case-control study. We conducted a combined analysis of these groups plus an additional 2,457 affected individuals and 2,654 controls from eight case-control studies, adjusting for study, sex, ancestry and five principal components. We identified an association between a locus on 9q34 and pancreatic cancer marked by the SNP rs505922 (combined P = 5.37 x 10(-8); multiplicative per-allele odds ratio 1.20; 95% confidence interval 1.12-1.28). This SNP maps to the first intron of the ABO blood group gene. Our results are consistent with earlier epidemiologic evidence suggesting that people with blood group O may have a lower risk of pancreatic cancer than those with groups A or B.

GWAS have emerged as popular tools for identifying genetic variants that are associated with disease risk. Standard analysis of a case-control GWAS involves assessing the association between each individual genotyped SNP and disease risk. However, this approach suffers from limited reproducibility and difficulties in detecting multi-SNP and epistatic effects. As an alternative analytical strategy, we propose grouping SNPs together into SNP sets on the basis of proximity to genomic features such as genes or haplotype blocks, then testing the joint effect of each SNP set. Testing of each SNP set proceeds via the logistic kernel-machine-based test, which is based on a statistical framework that allows for flexible modeling of epistatic and nonlinear SNP effects. This flexibility and the ability to naturally adjust for covariate effects are important features of our test that make it appealing in comparison to individual SNP tests and existing multimarker tests. Using simulated data based on the International HapMap Project, we show that SNP-set testing can have improved power over standard individual-SNP analysis under a wide range of settings. In particular, we find that our approach has higher power than individual-SNP analysis when the median correlation between the disease-susceptibility variant and the genotyped SNPs is moderate to high. When the correlation is low, both individual-SNP analysis and the SNP-set analysis tend to have low power. We apply SNP-set analysis to analyze the Cancer Genetic Markers of Susceptibility (CGEMS) breast cancer GWAS discovery-phase data.

Height is a classic polygenic trait, reflecting the combined influence of multiple as-yet-undiscovered genetic factors. We carried out a meta-analysis of genome-wide association study data of height from 15,821 individuals at 2.2 million SNPs, and followed up the strongest findings in >10,000 subjects. Ten newly identified and two previously reported loci were strongly associated with variation in height (P values from 4 x 10(-7) to 8 x 10(-22)). Together, these 12 loci account for approximately 2% of the population variation in height. Individuals with < or =8 height-increasing alleles and > or =16 height-increasing alleles differ in height by approximately 3.5 cm. The newly identified loci, along with several additional loci with strongly suggestive associations, encompass both strong biological candidates and unexpected genes, and highlight several pathways (let-7 targets, chromatin remodeling proteins and Hedgehog signaling) as important regulators of human stature. These results expand the picture of the biological regulation of human height and of the genetic architecture of this classical complex trait.

We conducted a genome-wide association study of pancreatic cancer in 3,851 affected individuals (cases) and 3,934 unaffected controls drawn from 12 prospective cohort studies and 8 case-control studies. Based on a logistic regression model for genotype trend effect that was adjusted for study, age, sex, self-described ancestry and five principal components, we identified eight SNPs that map to three loci on chromosomes 13q22.1, 1q32.1 and 5p15.33. Two correlated SNPs, rs9543325 (P = 3.27 x 10(-11), per-allele odds ratio (OR) 1.26, 95% CI 1.18-1.35) and rs9564966 (P = 5.86 x 10(-8), per-allele OR 1.21, 95% CI 1.13-1.30), map to a nongenic region on chromosome 13q22.1. Five SNPs on 1q32.1 map to NR5A2, and the strongest signal was at rs3790844 (P = 2.45 x 10(-10), per-allele OR 0.77, 95% CI 0.71-0.84). A single SNP, rs401681 (P = 3.66 x 10(-7), per-allele OR 1.19, 95% CI 1.11-1.27), maps to the CLPTM1L-TERT locus on 5p15.33, which is associated with multiple cancers. Our study has identified common susceptibility loci for pancreatic cancer that warrant follow-up studies.

We conducted a three-stage genome-wide association study (GWAS) of breast cancer in 9,770 cases and 10,799 controls in the Cancer Genetic Markers of Susceptibility (CGEMS) initiative. In stage 1, we genotyped 528,173 SNPs in 1,145 cases of invasive breast cancer and 1,142 controls. In stage 2, we analyzed 24,909 top SNPs in 4,547 cases and 4,434 controls. In stage 3, we investigated 21 loci in 4,078 cases and 5,223 controls. Two new loci achieved genome-wide significance. A pericentromeric SNP on chromosome 1p11.2 (rs11249433; P = 6.74 x 10(-10) adjusted genotype test, 2 degrees of freedom) resides in a large linkage disequilibrium block neighboring NOTCH2 and FCGR1B; this signal was stronger for estrogen-receptor-positive tumors. A second SNP on chromosome 14q24.1 (rs999737; P = 1.74 x 10(-7)) localizes to RAD51L1, a gene in the homologous recombination DNA repair pathway. We also confirmed associations with loci on chromosomes 2q35, 5p12, 5q11.2, 8q24, 10q26 and 16q12.1.

Elevated plasma levels of branched chain amino acids detected prior to pancreatic cancer diagnosis may result from whole body tissue breakdown occurring during the early stages of this disease. Most patients with pancreatic ductal adenocarcinoma (PDAC) are diagnosed with advanced disease and survive less than 12 months1. PDAC has been linked with obesity and glucose intolerance2,3,4, but whether changes in circulating metabolites are associated with early cancer progression is unknown. To better understand metabolic derangements associated with early disease, we profiled metabolites in prediagnostic plasma from individuals with pancreatic cancer (cases) and matched controls from four prospective cohort studies. We find that elevated plasma levels of branched-chain amino acids (BCAAs) are associated with a greater than twofold increased risk of future pancreatic cancer diagnosis. This elevated risk was independent of known predisposing factors, with the strongest association observed among subjects with samples collected 2 to 5 years before diagnosis, when occult disease is probably present. We show that plasma BCAAs are also elevated in mice with early-stage pancreatic cancers driven by mutant Kras expression but not in mice with Kras-driven tumors in other tissues, and that breakdown of tissue protein accounts for the increase in plasma BCAAs that accompanies early-stage disease. Together, these findings suggest that increased whole-body protein breakdown is an early event in development of PDAC.

Rosalind Eeles and colleagues report meta-analysis of genome-wide association studies for prostate cancer and genotyping on the custom iCOGS array in 25,074 cases and 24,272 controls from 32 studies available in the PRACTICAL Consortium. They identify 23 new prostate cancer susceptibility loci, 20 of which are associated with both aggressive and non-aggressive disease. Prostate cancer is the most frequently diagnosed cancer in males in developed countries. To identify common prostate cancer susceptibility alleles, we genotyped 211,155 SNPs on a custom Illumina array (iCOGS) in blood DNA from 25,074 prostate cancer cases and 24,272 controls from the international PRACTICAL Consortium. Twenty-three new prostate cancer susceptibility loci were identified at genome-wide significance (P < 5 × 10−8). More than 70 prostate cancer susceptibility loci, explaining ∼30% of the familial risk for this disease, have now been identified. On the basis of combined risks conferred by the new and previously known risk loci, the top 1% of the risk distribution has a 4.7-fold higher risk than the average of the population being profiled. These results will facilitate population risk stratification for clinical studies.

Standard statistical approaches for prioritization of variants for functional testing in fine-mapping studies either use marginal association statistics or estimate posterior probabilities for variants to be causal under simplifying assumptions. Here, we present a probabilistic framework that integrates association strength with functional genomic annotation data to improve accuracy in selecting plausible causal variants for functional validation. A key feature of our approach is that it empirically estimates the contribution of each functional annotation to the trait of interest directly from summary association statistics while allowing for multiple causal variants at any risk locus. We devise efficient algorithms that estimate the parameters of our model across all risk loci to further increase performance. Using simulations starting from the 1000 Genomes data, we find that our framework consistently outperforms the current state-of-the-art fine-mapping methods, reducing the number of variants that need to be selected to capture 90% of the causal variants from an average of 13.3 to 10.4 SNPs per locus (as compared to the next-best performing strategy). Furthermore, we introduce a cost-to-benefit optimization framework for determining the number of variants to be followed up in functional assays and assess its performance using real and simulation data. We validate our findings using a large scale meta-analysis of four blood lipids traits and find that the relative probability for causality is increased for variants in exons and transcription start sites and decreased in repressed genomic regions at the risk loci of these traits. Using these highly predictive, trait-specific functional annotations, we estimate causality probabilities across all traits and variants, reducing the size of the 90% confidence set from an average of 17.5 to 13.5 variants per locus in this data.

Douglas Easton and colleagues report results of a large multistage genome-wide association study of breast cancer. The study identifies two new breast cancer risk loci on chromosomes 3p24 and 17q23.2. Genome-wide association studies (GWAS) have identified seven breast cancer susceptibility loci, but these explain only a small fraction of the familial risk of the disease. Five of these loci were identified through a two-stage GWAS involving 390 familial cases and 364 controls in the first stage, and 3,990 cases and 3,916 controls in the second stage1. To identify additional loci, we tested over 800 promising associations from this GWAS in a further two stages involving 37,012 cases and 40,069 controls from 33 studies in the CGEMS collaboration and Breast Cancer Association Consortium. We found strong evidence for additional susceptibility loci on 3p (rs4973768: per-allele OR = 1.11, 95% CI = 1.08–1.13, P = 4.1 × 10−23) and 17q (rs6504950: per-allele OR = 0.95, 95% CI = 0.92–0.97, P = 1.4 × 10−8). Potential causative genes include SLC4A7 and NEK10 on 3p and COX11 on 17q.

Data for multiple common susceptibility alleles for breast cancer may be combined to identify women at different levels of breast cancer risk. Such stratification could guide preventive and screening strategies. However, empirical evidence for genetic risk stratification is lacking.We investigated the value of using 77 breast cancer-associated single nucleotide polymorphisms (SNPs) for risk stratification, in a study of 33 673 breast cancer cases and 33 381 control women of European origin. We tested all possible pair-wise multiplicative interactions and constructed a 77-SNP polygenic risk score (PRS) for breast cancer overall and by estrogen receptor (ER) status. Absolute risks of breast cancer by PRS were derived from relative risk estimates and UK incidence and mortality rates.There was no strong evidence for departure from a multiplicative model for any SNP pair. Women in the highest 1% of the PRS had a three-fold increased risk of developing breast cancer compared with women in the middle quintile (odds ratio [OR] = 3.36, 95% confidence interval [CI] = 2.95 to 3.83). The ORs for ER-positive and ER-negative disease were 3.73 (95% CI = 3.24 to 4.30) and 2.80 (95% CI = 2.26 to 3.46), respectively. Lifetime risk of breast cancer for women in the lowest and highest quintiles of the PRS were 5.2% and 16.6% for a woman without family history, and 8.6% and 24.4% for a woman with a first-degree family history of breast cancer.The PRS stratifies breast cancer risk in women both with and without a family history of breast cancer. The observed level of risk discrimination could inform targeted screening and prevention strategies. Further discrimination may be achievable through combining the PRS with lifestyle/environmental factors, although these were not considered in this report.

We conducted a multi-stage genome-wide association study of natural hair color in more than 10,000 men and women of European ancestry from the United States and Australia. An initial analysis of 528,173 single nucleotide polymorphisms (SNPs) genotyped on 2,287 women identified IRF4 and SLC24A4 as loci highly associated with hair color, along with three other regions encompassing known pigmentation genes. We confirmed these associations in 7,028 individuals from three additional studies. Across these four studies, SLC24A4 rs12896399 and IRF4 rs12203592 showed strong associations with hair color, with p = 6.0×10−62 and p = 7.46×10−127, respectively. The IRF4 SNP was also associated with skin color (p = 6.2×10−14), eye color (p = 6.1×10−13), and skin tanning response to sunlight (p = 3.9×10−89). A multivariable analysis pooling data from the initial GWAS and an additional 1,440 individuals suggested that the association between rs12203592 and hair color was independent of rs1540771, a SNP between the IRF4 and EXOC2 genes previously found to be associated with hair color. After adjustment for rs12203592, the association between rs1540771 and hair color was not significant (p = 0.52). One variant in the MATP gene was associated with hair color. A variant in the HERC2 gene upstream of the OCA2 gene showed the strongest and independent association with hair color compared with other SNPs in this region, including three previously reported SNPs. The signals detected in a region around the MC1R gene were explained by MC1R red hair color alleles. Our results suggest that the IRF4 and SLC24A4 loci are associated with human hair color and skin pigmentation.

Although use of oral anticoagulants (OACs) is increasing, there is a substantial lack of data on how to treat OAC-associated intracerebral hemorrhage (ICH).To assess the association of anticoagulation reversal and blood pressure (BP) with hematoma enlargement and the effects of OAC resumption.Retrospective cohort study at 19 German tertiary care centers (2006-2012) including 1176 individuals for analysis of long-term functional outcome, 853 for analysis of hematoma enlargement, and 719 for analysis of OAC resumption.Reversal of anticoagulation during acute phase, systolic BP at 4 hours, and reinitiation of OAC for long-term treatment.Frequency of hematoma enlargement in relation to international normalized ratio (INR) and BP. Incidence analysis of ischemic and hemorrhagic events with or without OAC resumption. Factors associated with favorable (modified Rankin Scale score, 0-3) vs unfavorable functional outcome.Hemorrhage enlargement occurred in 307 of 853 patients (36.0%). Reduced rates of hematoma enlargement were associated with reversal of INR levels <1.3 within 4 hours after admission (43/217 [19.8%]) vs INR of ≥1.3 (264/636 [41.5%]; P < .001) and systolic BP <160 mm Hg at 4 hours (167/504 [33.1%]) vs ≥160 mm Hg (98/187 [52.4%]; P < .001). The combination of INR reversal <1.3 within 4 hours and systolic BP of <160 mm Hg at 4 hours was associated with lower rates of hematoma enlargement (35/193 [18.1%] vs 220/498 [44.2%] not achieving these values; OR, 0.28; 95% CI, 0.19-0.42; P < .001) and lower rates of in-hospital mortality (26/193 [13.5%] vs 103/498 [20.7%]; OR, 0.60; 95% CI, 0.37-0.95; P = .03). OAC was resumed in 172 of 719 survivors (23.9%). OAC resumption showed fewer ischemic complications (OAC: 9/172 [5.2%] vs no OAC: 82/547 [15.0%]; P < .001) and not significantly different hemorrhagic complications (OAC: 14/172 [8.1%] vs no OAC: 36/547 [6.6%]; P = .48). Propensity-matched survival analysis in patients with atrial fibrillation who restarted OAC showed a decreased HR of 0.258 (95% CI, 0.125-0.534; P < .001) for long-term mortality. Functional long-term outcome was unfavorable in 786 of 1083 patients (72.6%).Among patients with OAC-associated ICH, reversal of INR <1.3 within 4 hours and systolic BP <160 mm Hg at 4 hours were associated with lower rates of hematoma enlargement, and resumption of OAC therapy was associated with lower risk of ischemic events. These findings require replication and assessment in prospective studies.clinicaltrials.gov Identifier: NCT01829581.

To identify loci for age at menarche, we performed a meta-analysis of 32 genome-wide association studies in 87,802 women of European descent, with replication in up to 14,731 women. In addition to the known loci at LIN28B (P = 5.4 × 10⁻⁶⁰) and 9q31.2 (P = 2.2 × 10⁻³³), we identified 30 new menarche loci (all P < 5 × 10⁻⁸) and found suggestive evidence for a further 10 loci (P < 1.9 × 10⁻⁶). The new loci included four previously associated with body mass index (in or near FTO, SEC16B, TRA2B and TMEM18), three in or near other genes implicated in energy homeostasis (BSX, CRTC1 and MCHR2) and three in or near genes implicated in hormonal regulation (INHBA, PCSK2 and RXRG). Ingenuity and gene-set enrichment pathway analyses identified coenzyme A and fatty acid biosynthesis as biological processes related to menarche timing.

Population-based estimates of the risk of breast cancer associated with germline pathogenic variants in cancer-predisposition genes are critically needed for risk assessment and management in women with inherited pathogenic variants.

Abstract Genome‐wide scans of nucleotide variation in human subjects are providing an increasing number of replicated associations with complex disease traits. Most of the variants detected have small effects and, collectively, they account for a small fraction of the total genetic variance. Very large sample sizes are required to identify and validate findings. In this situation, even small sources of systematic or random error can cause spurious results or obscure real effects. The need for careful attention to data quality has been appreciated for some time in this field, and a number of strategies for quality control and quality assurance (QC/QA) have been developed. Here we extend these methods and describe a system of QC/QA for genotypic data in genome‐wide association studies (GWAS). This system includes some new approaches that (1) combine analysis of allelic probe intensities and called genotypes to distinguish gender misidentification from sex chromosome aberrations, (2) detect autosomal chromosome aberrations that may affect genotype calling accuracy, (3) infer DNA sample quality from relatedness and allelic intensities, (4) use duplicate concordance to infer SNP quality, (5) detect genotyping artifacts from dependence of Hardy‐Weinberg equilibrium test P ‐values on allelic frequency, and (6) demonstrate sensitivity of principal components analysis to SNP selection. The methods are illustrated with examples from the “Gene Environment Association Studies” (GENEVA) program. The results suggest several recommendations for QC/QA in the design and execution of GWAS. Genet. Epidemiol . 34: 591–602, 2010. © 2010 Wiley‐Liss, Inc.

We give critical attention to the assumptions underlying Mendelian randomization analysis and their biological plausibility. Several scenarios violating the Mendelian randomization assumptions are described, including settings with inadequate phenotype definition, the setting of time-varying exposures, the presence of gene–environment interaction, the existence of measurement error, the possibility of reverse causation, and the presence of linkage disequilibrium. Data analysis examples are given, illustrating that the inappropriate use of instrumental variable techniques when the Mendelian randomization assumptions are violated can lead to biases of enormous magnitude. To help address some of the strong assumptions being made, three possible approaches are suggested. First, the original proposal of Katan (Lancet. 1986;1:507–508) for Mendelian randomization was not to use instrumental variable techniques to obtain estimates but merely to examine genotype–outcome associations to test for the presence of an effect of the exposure on the outcome. We show that this more modest goal and approach can circumvent many, though not all, the potential biases described. Second, we discuss the use of sensitivity analysis in evaluating the consequences of violations in the assumptions and in attempting to correct for those violations. Third, we suggest that a focus on negative, rather than positive, Mendelian randomization results may turn out to be more reliable.

A major goal of the Human Genome Project was to facilitate the identification of inherited genetic variants that increase or decrease the risk of complex diseases. The completion of the International HapMap Project and the development of new methods for genotyping individual DNA samples at 500,000 or more loci have led to a wave of discoveries through genomewide association studies. These analyses have identified common genetic variants that are associated with the risk of more than 40 diseases and human phenotypes. Several companies have begun offering direct-to-consumer testing that uses the same single-nucleotide polymorphism chips that are used in genomewide . . .

Complex disease by definition results from the interplay of genetic and environmental factors. However, it is currently unclear how gene-environment interaction can best be used to locate complex disease susceptibility loci, particularly in the context of studies where between 1,000 and 1,000,000 markers are scanned for association with disease. We present a joint test of marginal association and gene-environment interaction for case-control data. We compare the power and sample size requirements of this joint test to other analyses: the marginal test of genetic association, the standard test for gene-environment interaction based on logistic regression, and the case-only test for interaction that exploits gene-environment independence. Although for many penetrance models the joint test of genetic marginal effect and interaction is not the most powerful, it is nearly optimal across all penetrance models we considered. In particular, it generally has better power than the marginal test when the genetic effect is restricted to exposed subjects and much better power than the tests of gene-environment interaction when the genetic effect is not restricted to a particular exposure level. This makes the joint test an attractive tool for large-scale association scans where the true gene-environment interaction model is unknown.

The genetic architectures of common, complex diseases are largely uncharacterized. We modeled the genetic architecture underlying genome-wide association study (GWAS) data for rheumatoid arthritis and developed a new method using polygenic risk-score analyses to infer the total liability-scale variance explained by associated GWAS SNPs. Using this method, we estimated that, together, thousands of SNPs from rheumatoid arthritis GWAS explain an additional 20% of disease risk (excluding known associated loci). We further tested this method on datasets for three additional diseases and obtained comparable estimates for celiac disease (43% excluding the major histocompatibility complex), myocardial infarction and coronary artery disease (48%) and type 2 diabetes (49%). Our results are consistent with simulated genetic models in which hundreds of associated loci harbor common causal variants and a smaller number of loci harbor multiple rare causal variants. These analyses suggest that GWAS will continue to be highly productive for the discovery of additional susceptibility loci for common diseases.

Genomewide association studies have identified multiple genetic variants associated with breast cancer. The extent to which these variants add to existing risk-assessment models is unknown.We used information on traditional risk factors and 10 common genetic variants associated with breast cancer in 5590 case subjects and 5998 control subjects, 50 to 79 years of age, from four U.S. cohort studies and one case-control study from Poland to fit models of the absolute risk of breast cancer. With the use of receiver-operating-characteristic curve analysis, we calculated the area under the curve (AUC) as a measure of discrimination. By definition, random classification of case and control subjects provides an AUC of 50%; perfect classification provides an AUC of 100%. We calculated the fraction of case subjects in quintiles of estimated absolute risk after the addition of genetic variants to the traditional risk model.The AUC for a risk model with age, study and entry year, and four traditional risk factors was 58.0%; with the addition of 10 genetic variants, the AUC was 61.8%. About half the case subjects (47.2%) were in the same quintile of risk as in a model without genetic variants; 32.5% were in a higher quintile, and 20.4% were in a lower quintile.The inclusion of newly discovered genetic factors modestly improved the performance of risk models for breast cancer. The level of predicted breast-cancer risk among most women changed little after the addition of currently available genetic information.

Ischemic stroke is the second leading cause of death worldwide. Only one moderately effective therapy exists, albeit with contraindications that exclude 90% of the patients. This medical need contrasts with a high failure rate of more than 1,000 pre-clinical drug candidates for stroke therapies. Thus, there is a need for translatable mechanisms of neuroprotection and more rigid thresholds of relevance in pre-clinical stroke models. One such candidate mechanism is oxidative stress. However, antioxidant approaches have failed in clinical trials, and the significant sources of oxidative stress in stroke are unknown. We here identify NADPH oxidase type 4 (NOX4) as a major source of oxidative stress and an effective therapeutic target in acute stroke. Upon ischemia, NOX4 was induced in human and mouse brain. Mice deficient in NOX4 (Nox4(-/-)) of either sex, but not those deficient for NOX1 or NOX2, were largely protected from oxidative stress, blood-brain-barrier leakage, and neuronal apoptosis, after both transient and permanent cerebral ischemia. This effect was independent of age, as elderly mice were equally protected. Restoration of oxidative stress reversed the stroke-protective phenotype in Nox4(-/-) mice. Application of the only validated low-molecular-weight pharmacological NADPH oxidase inhibitor, VAS2870, several hours after ischemia was as protective as deleting NOX4. The extent of neuroprotection was exceptional, resulting in significantly improved long-term neurological functions and reduced mortality. NOX4 therefore represents a major source of oxidative stress and novel class of drug target for stroke therapy.

Age at menarche and age at natural menopause are associated with causes of substantial morbidity and mortality such as breast cancer and cardiovascular disease. We conducted a joint analysis of two genome-wide association studies of these two traits in a total of 17,438 women from the Nurses' Health Study (NHS, N = 2,287) and the Women's Genome Health Study (WGHS, N = 15,151). For age at menarche, we identified ten associated SNPs (P = 1 × 10(-7)-3 × 10(-13)) clustered at 6q21 (in or near the gene LIN28B) and 9q31.2 (in an intergenic region). For age at natural menopause, we identified 13 associated SNPs (P = 1 × 10(-7)-1 × 10(-21)) clustered at 20p12.3 (in the gene MCM8), 19q13.42 (in or near the gene BRSK1), 5q35.2 (in or near genes UIMC1 and HK3) and 6p24.2 (in the gene SYCP2L). These newly identified loci might expand understanding of the biological pathways regulating these two traits.

Recently, genetic association findings for nicotine dependence, smoking behavior, and smoking-related diseases converged to implicate the chromosome 15q25.1 region, which includes the CHRNA5-CHRNA3-CHRNB4 cholinergic nicotinic receptor subunit genes. In particular, association with the nonsynonymous CHRNA5 SNP rs16969968 and correlates has been replicated in several independent studies. Extensive genotyping of this region has suggested additional statistically distinct signals for nicotine dependence, tagged by rs578776 and rs588765. One goal of the Consortium for the Genetic Analysis of Smoking Phenotypes (CGASP) is to elucidate the associations among these markers and dichotomous smoking quantity (heavy versus light smoking), lung cancer, and chronic obstructive pulmonary disease (COPD). We performed a meta-analysis across 34 datasets of European-ancestry subjects, including 38,617 smokers who were assessed for cigarettes-per-day, 7,700 lung cancer cases and 5,914 lung-cancer-free controls (all smokers), and 2,614 COPD cases and 3,568 COPD-free controls (all smokers). We demonstrate statistically independent associations of rs16969968 and rs588765 with smoking (mutually adjusted p-values<10(-35) and <10(-8) respectively). Because the risk alleles at these loci are negatively correlated, their association with smoking is stronger in the joint model than when each SNP is analyzed alone. Rs578776 also demonstrates association with smoking after adjustment for rs16969968 (p<10(-6)). In models adjusting for cigarettes-per-day, we confirm the association between rs16969968 and lung cancer (p<10(-20)) and observe a nominally significant association with COPD (p = 0.01); the other loci are not significantly associated with either lung cancer or COPD after adjusting for rs16969968. This study provides strong evidence that multiple statistically distinct loci in this region affect smoking behavior. This study is also the first report of association between rs588765 (and correlates) and smoking that achieves genome-wide significance; these SNPs have previously been associated with mRNA levels of CHRNA5 in brain and lung tissue.

A three-stage genome-wide association study recently identified single nucleotide polymorphisms (SNPs) in five loci (fibroblast growth receptor 2 (FGFR2), trinucleotide repeat containing 9 (TNRC9), mitogen-activated protein kinase 3 K1 (MAP3K1), 8q24, and lymphocyte-specific protein 1 (LSP1)) associated with breast cancer risk. We investigated whether the associations between these SNPs and breast cancer risk varied by clinically important tumor characteristics in up to 23,039 invasive breast cancer cases and 26,273 controls from 20 studies. We also evaluated their influence on overall survival in 13,527 cases from 13 studies. All participants were of European or Asian origin. rs2981582 in FGFR2 was more strongly related to ER-positive (per-allele OR (95%CI) = 1.31 (1.27-1.36)) than ER-negative (1.08 (1.03-1.14)) disease (P for heterogeneity = 10(-13)). This SNP was also more strongly related to PR-positive, low grade and node positive tumors (P = 10(-5), 10(-8), 0.013, respectively). The association for rs13281615 in 8q24 was stronger for ER-positive, PR-positive, and low grade tumors (P = 0.001, 0.011 and 10(-4), respectively). The differences in the associations between SNPs in FGFR2 and 8q24 and risk by ER and grade remained significant after permutation adjustment for multiple comparisons and after adjustment for other tumor characteristics. Three SNPs (rs2981582, rs3803662, and rs889312) showed weak but significant associations with ER-negative disease, the strongest association being for rs3803662 in TNRC9 (1.14 (1.09-1.21)). rs13281615 in 8q24 was associated with an improvement in survival after diagnosis (per-allele HR = 0.90 (0.83-0.97). The association was attenuated and non-significant after adjusting for known prognostic factors. Our findings show that common genetic variants influence the pathological subtype of breast cancer and provide further support for the hypothesis that ER-positive and ER-negative disease are biologically distinct. Understanding the etiologic heterogeneity of breast cancer may ultimately result in improvements in prevention, early detection, and treatment.

BackgroundOther than several rare, highly penetrant familial syndromes, genetic risk factors for sporadic pancreatic cancer are largely unknown. ABO blood type is an inherited characteristic that in previous small studies has been associated with the risk of gastrointestinal malignancies.

Obesity has been proposed as a risk factor for pancreatic cancer.Pooled data were analyzed from the National Cancer Institute Pancreatic Cancer Cohort Consortium (PanScan) to study the association between prediagnostic anthropometric measures and risk of pancreatic cancer. PanScan applied a nested case-control study design and included 2170 cases and 2209 control subjects. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using unconditional logistic regression for cohort-specific quartiles of body mass index (BMI [calculated as weight in kilograms divided by height in meters squared]), weight, height, waist circumference, and waist to hip ratio as well as conventional BMI categories (underweight, <18.5; normal weight, 18.5-24.9; overweight, 25.0-29.9; obese, 30.0-34.9; and severely obese, > or = 35.0). Models were adjusted for potential confounders.In all of the participants, a positive association between increasing BMI and risk of pancreatic cancer was observed (adjusted OR for the highest vs lowest BMI quartile, 1.33; 95% CI, 1.12-1.58; P(trend) < .001). In men, the adjusted OR for pancreatic cancer for the highest vs lowest quartile of BMI was 1.33 (95% CI, 1.04-1.69; P(trend) < .03), and in women it was 1.34 (95% CI, 1.05-1.70; P(trend) = .01). Increased waist to hip ratio was associated with increased risk of pancreatic cancer in women (adjusted OR for the highest vs lowest quartile, 1.87; 95% CI, 1.31-2.69; P(trend) = .003) but less so in men.These findings provide strong support for a positive association between BMI and pancreatic cancer risk. In addition, centralized fat distribution may increase pancreatic cancer risk, especially in women.

The CpG island methylator phenotype (CIMP or CIMP-high) with extensive promoter methylation is a distinct phenotype in colorectal cancer. However, a choice of markers for CIMP has been controversial. A recent extensive investigation has selected five methylation markers (<i>CACNA1G</i>, <i>IGF2</i>, <i>NEUROG1</i>, <i>RUNX3</i>, and <i>SOCS1</i>) as surrogate markers for epigenomic aberrations in tumor. The use of these markers as a CIMP-specific panel needs to be validated by an independent, large dataset. Using MethyLight assays on 920 colorectal cancers from two large prospective cohort studies, we quantified DNA methylation in eight CIMP-specific markers [the above five plus <i>CDKN2A</i> (p16), <i>CRABP1</i>, and <i>MLH1</i>]. A CIMP-high cutoff was set at ≥6/8 or ≥5/8 methylated promoters, based on tumor distribution and <i>BRAF</i>/<i>KRAS</i> mutation frequencies. All but two very specific markers [<i>MLH1</i> (98% specific) and <i>SOCS1</i> (93% specific)] demonstrated ≥85% sensitivity and ≥80% specificity, indicating overall good concordance in methylation patterns and good performance of these markers. Based on sensitivity, specificity, and false positives and negatives, the eight markers were ranked in order as: <i>RUNX3</i>, <i>CACNA1G</i>, <i>IGF2</i>, <i>MLH1</i>, <i>NEUROG1</i>, <i>CRABP1</i>, <i>SOCS1</i>, and <i>CDKN2A</i>. In conclusion, a panel of markers including at least <i>RUNX3</i>, <i>CACNA1G</i>, <i>IGF2</i>, and <i>MLH1</i> can serve as a sensitive and specific marker panel for CIMP-high.

To newly identify loci for age at natural menopause, we carried out a meta-analysis of 22 genome-wide association studies (GWAS) in 38,968 women of European descent, with replication in up to 14,435 women. In addition to four known loci, we identified 13 loci newly associated with age at natural menopause (at P < 5 × 10(-8)). Candidate genes located at these newly associated loci include genes implicated in DNA repair (EXO1, HELQ, UIMC1, FAM175A, FANCI, TLK1, POLG and PRIM1) and immune function (IL11, NLRP11 and PRRC2A (also known as BAT2)). Gene-set enrichment pathway analyses using the full GWAS data set identified exoDNase, NF-κB signaling and mitochondrial dysfunction as biological processes related to timing of menopause.

Abstract T cells contribute to the pathophysiology of ischemic stroke by yet unknown mechanisms. Mice with transgenic T-cell receptors (TCRs) and mutations in costimulatory molecules were used to define the minimal immunologic requirements for T cell–mediated ischemic brain damage. Stroke was induced in recombination activating gene 1–deficient (RAG1−/−) mice devoid of T and B cells, RAG1−/− mice reconstituted with B cells or T cells, TCR-transgenic mice bearing 1 single CD8+ (2C/RAG2, OTI/RAG1 mice) or CD4+ (OTII/RAG1, 2D2/RAG1 mice) TCR, mice lacking accessory molecules of TCR stimulation (CD28−/−, PD1−/−, B7-H1−/− mice), or mice deficient in nonclassical T cells (natural killer T [NKT] and γδ T cells) by transient middle cerebral artery occlusion (tMCAO). Stroke outcome was assessed at day 1. RAG1−/− mice and RAG1−/− mice reconstituted with B cells developed significantly smaller brain infarctions compared with controls, but thrombus formation after FeCl3-induced vessel injury was unimpaired. In contrast, TCR-transgenic mice and mice lacking costimulatory TCR signals were fully susceptible to tMCAO similar to mice lacking NKT and γδ T cells. These findings were corroborated by adoptive transfer experiments. Our data demonstrate that T cells critically contribute to cerebral ischemia, but their detrimental effect neither depends on antigen recognition nor TCR costimulation or thrombus formation.

Smoking is an established risk factor for pancreatic cancer; however, detailed examination of the association of smoking intensity, smoking duration, and cumulative smoking dose with pancreatic cancer is limited. The authors analyzed pooled data from the international Pancreatic Cancer Cohort Consortium nested case-control study (1,481 cases, 1,539 controls). Odds ratios and 95% confidence intervals were calculated by using unconditional logistic regression. Smoking intensity effects were examined with an excess odds ratio model that was linear in pack-years and exponential in cigarettes smoked per day and its square. When compared with never smokers, current smokers had a significantly elevated risk (odds ratio (OR) = 1.77, 95% confidence interval (CI): 1.38, 2.26). Risk increased significantly with greater intensity (> or =30 cigarettes/day: OR = 1.75, 95% CI: 1.27, 2.42), duration (> or =50 years: OR = 2.13, 95% CI: 1.25, 3.62), and cumulative smoking dose (> or =40 pack-years: OR = 1.78, 95% CI: 1.35, 2.34). Risk more than 15 years after smoking cessation was similar to that for never smokers. Estimates of excess odds ratio per pack-year declined with increasing intensity, suggesting greater risk for total exposure delivered at lower intensity for longer duration than for higher intensity for shorter duration. This finding and the decline in risk after smoking cessation suggest that smoking has a late-stage effect on pancreatic carcinogenesis.

Abstract We have recently identified T cells as important mediators of ischemic brain damage, but the contribution of the different T-cell subsets is unclear. Forkhead box P3 (FoxP3)–positive regulatory T cells (Tregs) are generally regarded as prototypic anti-inflammatory cells that maintain immune tolerance and counteract tissue damage in a variety of immune-mediated disorders. In the present study, we examined the role of Tregs after experimental brain ischemia/reperfusion injury. Selective depletion of Tregs in the DEREG mouse model dramatically reduced infarct size and improved neurologic function 24 hours after stroke and this protective effect was preserved at later stages of infarct development. The specificity of this detrimental Treg effect was confirmed by adoptive transfer experiments in wild-type mice and in Rag1−/− mice lacking lymphocytes. Mechanistically, Tregs induced microvascular dysfunction in vivo by increased interaction with the ischemic brain endothelium via the LFA-1/ICAM-1 pathway and platelets and these findings were confirmed in vitro. Ablation of Tregs reduced microvascular thrombus formation and improved cerebral reperfusion on stroke, as revealed by ultra-high-field magnetic resonance imaging at 17.6 Tesla. In contrast, established immunoregulatory characteristics of Tregs had no functional relevance. We define herein a novel and unexpected role of Tregs in a primary nonimmunologic disease state.

Rachael Stolzenberg-Solomon, Laufey Amundadottir and colleagues report a genome-wide association study of pancreatic cancer. They identify four new susceptibility loci. We performed a multistage genome-wide association study including 7,683 individuals with pancreatic cancer and 14,397 controls of European descent. Four new loci reached genome-wide significance: rs6971499 at 7q32.3 (LINC-PINT, per-allele odds ratio (OR) = 0.79, 95% confidence interval (CI) 0.74–0.84, P = 3.0 × 10−12), rs7190458 at 16q23.1 (BCAR1/CTRB1/CTRB2, OR = 1.46, 95% CI 1.30–1.65, P = 1.1 × 10−10), rs9581943 at 13q12.2 (PDX1, OR = 1.15, 95% CI 1.10–1.20, P = 2.4 × 10−9) and rs16986825 at 22q12.1 (ZNRF3, OR = 1.18, 95% CI 1.12–1.25, P = 1.2 × 10−8). We identified an independent signal in exon 2 of TERT at the established region 5p15.33 (rs2736098, OR = 0.80, 95% CI 0.76–0.85, P = 9.8 × 10−14). We also identified a locus at 8q24.21 (rs1561927, P = 1.3 × 10−7) that approached genome-wide significance located 455 kb telomeric of PVT1. Our study identified multiple new susceptibility alleles for pancreatic cancer that are worthy of follow-up studies.

Prostate cancer is a highly heritable disease with large disparities in incidence rates across ancestry populations. We conducted a multiancestry meta-analysis of prostate cancer genome-wide association studies (107,247 cases and 127,006 controls) and identified 86 new genetic risk variants independently associated with prostate cancer risk, bringing the total to 269 known risk variants. The top genetic risk score (GRS) decile was associated with odds ratios that ranged from 5.06 (95% confidence interval (CI), 4.84-5.29) for men of European ancestry to 3.74 (95% CI, 3.36-4.17) for men of African ancestry. Men of African ancestry were estimated to have a mean GRS that was 2.18-times higher (95% CI, 2.14-2.22), and men of East Asian ancestry 0.73-times lower (95% CI, 0.71-0.76), than men of European ancestry. These findings support the role of germline variation contributing to population differences in prostate cancer risk, with the GRS offering an approach for personalized risk prediction.

Polygenic risk scores (PRSs), which often aggregate results from genome-wide association studies, can bridge the gap between initial discovery efforts and clinical applications for the estimation of disease risk using genetics. However, there is notable heterogeneity in the application and reporting of these risk scores, which hinders the translation of PRSs into clinical care. Here, in a collaboration between the Clinical Genome Resource (ClinGen) Complex Disease Working Group and the Polygenic Score (PGS) Catalog, we present the Polygenic Risk Score Reporting Standards (PRS-RS), in which we update the Genetic Risk Prediction Studies (GRIPS) Statement to reflect the present state of the field. Drawing on the input of experts in epidemiology, statistics, disease-specific applications, implementation and policy, this comprehensive reporting framework defines the minimal information that is needed to interpret and evaluate PRSs, especially with respect to downstream clinical applications. Items span detailed descriptions of study populations, statistical methods for the development and validation of PRSs and considerations for the potential limitations of these scores. In addition, we emphasize the need for data availability and transparency, and we encourage researchers to deposit and share PRSs through the PGS Catalog to facilitate reproducibility and comparative benchmarking. By providing these criteria in a structured format that builds on existing standards and ontologies, the use of this framework in publishing PRSs will facilitate translation into clinical care and progress towards defining best practice.

Prostate cancer (PrCa) is the most frequently diagnosed male cancer in developed countries. We conducted a multi-stage genome-wide association study for PrCa and previously reported the results of the first two stages, which identified 16 PrCa susceptibility loci. We report here the results of stage 3, in which we evaluated 1,536 SNPs in 4,574 individuals with prostate cancer (cases) and 4,164 controls. We followed up ten new association signals through genotyping in 51,311 samples in 30 studies from the Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium. In addition to replicating previously reported loci, we identified seven new prostate cancer susceptibility loci on chromosomes 2p11, 3q23, 3q26, 5p12, 6p21, 12q13 and Xq12 (P = 4.0 × 10(-8) to P = 2.7 × 10(-24)). We also identified a SNP in TERT more strongly associated with PrCa than that previously reported. More than 40 PrCa susceptibility loci, explaining ∼25% of the familial risk in this disease, have now been identified.

Krina Zondervan and colleagues report a genome-wide association study for endometriosis. The authors identify a susceptibility locus on chromosome 7p15. Endometriosis is a common gynecological disease associated with pelvic pain and subfertility. We conducted a genome-wide association study (GWAS) in 3,194 individuals with surgically confirmed endometriosis (cases) and 7,060 controls from Australia and the UK. Polygenic predictive modeling showed significantly increased genetic loading among 1,364 cases with moderate to severe endometriosis. The strongest association signal was on 7p15.2 (rs12700667) for 'all' endometriosis (P = 2.6 × 10−7, odds ratio (OR) = 1.22, 95% CI 1.13–1.32) and for moderate to severe disease (P = 1.5 × 10−9, OR = 1.38, 95% CI 1.24–1.53). We replicated rs12700667 in an independent cohort from the United States of 2,392 self-reported, surgically confirmed endometriosis cases and 2,271 controls (P = 1.2 × 10−3, OR = 1.17, 95% CI 1.06–1.28), resulting in a genome-wide significant P value of 1.4 × 10−9 (OR = 1.20, 95% CI 1.13–1.27) for 'all' endometriosis in our combined datasets of 5,586 cases and 9,331 controls. rs12700667 is located in an intergenic region upstream of the plausible candidate genes NFE2L3 and HOXA10.

Abstract It is increasingly recognized that pathway analyses—a joint test of association between the outcome and a group of single nucleotide polymorphisms (SNPs) within a biological pathway—could potentially complement single‐SNP analysis and provide additional insights for the genetic architecture of complex diseases. Building upon existing P ‐value combining methods, we propose a class of highly flexible pathway analysis approaches based on an adaptive rank truncated product statistic that can effectively combine evidence of associations over different SNPs and genes within a pathway. The statistical significance of the pathway‐level test statistics is evaluated using a highly efficient permutation algorithm that remains computationally feasible irrespective of the size of the pathway and complexity of the underlying test statistics for summarizing SNP‐ and gene‐level associations. We demonstrate through simulation studies that a gene‐based analysis that treats the underlying genes, as opposed to the underlying SNPs, as the basic units for hypothesis testing, is a very robust and powerful approach to pathway‐based association testing. We also illustrate the advantage of the proposed methods using a study of the association between the nicotinic receptor pathway and cigarette smoking behaviors. Genet. Epidemiol. 33:700–709, 2009. Published 2009 Wiley‐Liss, Inc.

Replication helps ensure that a genotype-phenotype association observed in a genome-wide association (GWA) study represents a credible association and is not a chance finding or an artifact due to uncontrolled biases. We discuss prerequisites for exact replication; issues of heterogeneity; advantages and disadvantages of different methods of data synthesis across multiple studies; frequentist vs. Bayesian inferences for replication; and challenges that arise from multi-team collaborations. While consistent replication can greatly improve the credibility of a genotype-phenotype association, it may not eliminate spurious associations due to biases shared by many studies. Conversely, lack of replication in well-powered follow-up studies usually invalidates the initially proposed association, although occasionally it may point to differences in linkage disequilibrium or effect modifiers across studies.

Intracerebral hemorrhage (ICH) is the stroke subtype with the worst prognosis and has no established acute treatment. ICH is classified as lobar or nonlobar based on the location of ruptured blood vessels within the brain. These different locations also signal different underlying vascular pathologies. Heritability estimates indicate a substantial genetic contribution to risk of ICH in both locations. We report a genome-wide association study of this condition that meta-analyzed data from six studies that enrolled individuals of European ancestry. Case subjects were ascertained by neurologists blinded to genotype data and classified as lobar or nonlobar based on brain computed tomography. ICH-free control subjects were sampled from ambulatory clinics or random digit dialing. Replication of signals identified in the discovery cohort with p < 1 × 10−6 was pursued in an independent multiethnic sample utilizing both direct and genome-wide genotyping. The discovery phase included a case cohort of 1,545 individuals (664 lobar and 881 nonlobar cases) and a control cohort of 1,481 individuals and identified two susceptibility loci: for lobar ICH, chromosomal region 12q21.1 (rs11179580, odds ratio [OR] = 1.56, p = 7.0 × 10−8); and for nonlobar ICH, chromosomal region 1q22 (rs2984613, OR = 1.44, p = 1.6 × 10−8). The replication included a case cohort of 1,681 individuals (484 lobar and 1,194 nonlobar cases) and a control cohort of 2,261 individuals and corroborated the association for 1q22 (p = 6.5 × 10−4; meta-analysis p = 2.2 × 10−10) but not for 12q21.1 (p = 0.55; meta-analysis p = 2.6 × 10−5). These results demonstrate biological heterogeneity across ICH subtypes and highlight the importance of ascertaining ICH cases accordingly. Intracerebral hemorrhage (ICH) is the stroke subtype with the worst prognosis and has no established acute treatment. ICH is classified as lobar or nonlobar based on the location of ruptured blood vessels within the brain. These different locations also signal different underlying vascular pathologies. Heritability estimates indicate a substantial genetic contribution to risk of ICH in both locations. We report a genome-wide association study of this condition that meta-analyzed data from six studies that enrolled individuals of European ancestry. Case subjects were ascertained by neurologists blinded to genotype data and classified as lobar or nonlobar based on brain computed tomography. ICH-free control subjects were sampled from ambulatory clinics or random digit dialing. Replication of signals identified in the discovery cohort with p < 1 × 10−6 was pursued in an independent multiethnic sample utilizing both direct and genome-wide genotyping. The discovery phase included a case cohort of 1,545 individuals (664 lobar and 881 nonlobar cases) and a control cohort of 1,481 individuals and identified two susceptibility loci: for lobar ICH, chromosomal region 12q21.1 (rs11179580, odds ratio [OR] = 1.56, p = 7.0 × 10−8); and for nonlobar ICH, chromosomal region 1q22 (rs2984613, OR = 1.44, p = 1.6 × 10−8). The replication included a case cohort of 1,681 individuals (484 lobar and 1,194 nonlobar cases) and a control cohort of 2,261 individuals and corroborated the association for 1q22 (p = 6.5 × 10−4; meta-analysis p = 2.2 × 10−10) but not for 12q21.1 (p = 0.55; meta-analysis p = 2.6 × 10−5). These results demonstrate biological heterogeneity across ICH subtypes and highlight the importance of ascertaining ICH cases accordingly.

In recent years, a number of large-scale genome-wide association studies have been published for human traits adjusted for other correlated traits with a genetic basis. In most studies, the motivation for such an adjustment is to discover genetic variants associated with the primary outcome independently of the correlated trait. In this report, we contend that this objective is fulfilled when the tested variants have no effect on the covariate or when the correlation between the covariate and the outcome is fully explained by a direct effect of the covariate on the outcome. For all other scenarios, an unintended bias is introduced with respect to the primary outcome as a result of the adjustment, and this bias might lead to false positives. Here, we illustrate this point by providing examples from published genome-wide association studies, including large meta-analysis of waist-to-hip ratio and waist circumference adjusted for body mass index (BMI), where genetic effects might be biased as a result of adjustment for body mass index. Using both theory and simulations, we explore this phenomenon in detail and discuss the ramifications for future genome-wide association studies of correlated traits and diseases.

Venous thromboembolism (VTE), the third leading cause of cardiovascular mortality, is a complex thrombotic disorder with environmental and genetic determinants. Although several genetic variants have been found associated with VTE, they explain a minor proportion of VTE risk in cases. We undertook a meta-analysis of genome-wide association studies (GWASs) to identify additional VTE susceptibility genes. Twelve GWASs totaling 7,507 VTE case subjects and 52,632 control subjects formed our discovery stage where 6,751,884 SNPs were tested for association with VTE. Nine loci reached the genome-wide significance level of 5 × 10(-8) including six already known to associate with VTE (ABO, F2, F5, F11, FGG, and PROCR) and three unsuspected loci. SNPs mapping to these latter were selected for replication in three independent case-control studies totaling 3,009 VTE-affected individuals and 2,586 control subjects. This strategy led to the identification and replication of two VTE-associated loci, TSPAN15 and SLC44A2, with lead risk alleles associated with odds ratio for disease of 1.31 (p = 1.67 × 10(-16)) and 1.21 (p = 2.75 × 10(-15)), respectively. The lead SNP at the TSPAN15 locus is the intronic rs78707713 and the lead SLC44A2 SNP is the non-synonymous rs2288904 previously shown to associate with transfusion-related acute lung injury. We further showed that these two variants did not associate with known hemostatic plasma markers. TSPAN15 and SLC44A2 do not belong to conventional pathways for thrombosis and have not been associated to other cardiovascular diseases nor related quantitative biomarkers. Our findings uncovered unexpected actors of VTE etiology and pave the way for novel mechanistic concepts of VTE pathophysiology.

Osteoarthritis affects over 300 million people worldwide. Here, we conduct a genome-wide association study meta-analysis across 826,690 individuals (177,517 with osteoarthritis) and identify 100 independently associated risk variants across 11 osteoarthritis phenotypes, 52 of which have not been associated with the disease before. We report thumb and spine osteoarthritis risk variants and identify differences in genetic effects between weight-bearing and non-weight-bearing joints. We identify sex-specific and early age-at-onset osteoarthritis risk loci. We integrate functional genomics data from primary patient tissues (including articular cartilage, subchondral bone, and osteophytic cartilage) and identify high-confidence effector genes. We provide evidence for genetic correlation with phenotypes related to pain, the main disease symptom, and identify likely causal genes linked to neuronal processes. Our results provide insights into key molecular players in disease processes and highlight attractive drug targets to accelerate translation.

Common diseases such as type 2 diabetes are phenotypically heterogeneous. Obesity is a major risk factor for type 2 diabetes, but patients vary appreciably in body mass index. We hypothesized that the genetic predisposition to the disease may be different in lean (BMI<25 Kg/m²) compared to obese cases (BMI≥30 Kg/m²). We performed two case-control genome-wide studies using two accepted cut-offs for defining individuals as overweight or obese. We used 2,112 lean type 2 diabetes cases (BMI<25 kg/m²) or 4,123 obese cases (BMI≥30 kg/m²), and 54,412 un-stratified controls. Replication was performed in 2,881 lean cases or 8,702 obese cases, and 18,957 un-stratified controls. To assess the effects of known signals, we tested the individual and combined effects of SNPs representing 36 type 2 diabetes loci. After combining data from discovery and replication datasets, we identified two signals not previously reported in Europeans. A variant (rs8090011) in the LAMA1 gene was associated with type 2 diabetes in lean cases (P = 8.4×10⁻⁹, OR = 1.13 [95% CI 1.09-1.18]), and this association was stronger than that in obese cases (P = 0.04, OR = 1.03 [95% CI 1.00-1.06]). A variant in HMG20A--previously identified in South Asians but not Europeans--was associated with type 2 diabetes in obese cases (P = 1.3×10⁻⁸, OR = 1.11 [95% CI 1.07-1.15]), although this association was not significantly stronger than that in lean cases (P = 0.02, OR = 1.09 [95% CI 1.02-1.17]). For 36 known type 2 diabetes loci, 29 had a larger odds ratio in the lean compared to obese (binomial P = 0.0002). In the lean analysis, we observed a weighted per-risk allele OR = 1.13 [95% CI 1.10-1.17], P = 3.2×10⁻¹⁴. This was larger than the same model fitted in the obese analysis where the OR = 1.06 [95% CI 1.05-1.08], P = 2.2×10⁻¹⁶. This study provides evidence that stratification of type 2 diabetes cases by BMI may help identify additional risk variants and that lean cases may have a stronger genetic predisposition to type 2 diabetes.

We report the first genome-wide association study of habitual caffeine intake. We included 47,341 individuals of European descent based on five population-based studies within the United States. In a meta-analysis adjusted for age, sex, smoking, and eigenvectors of population variation, two loci achieved genome-wide significance: 7p21 (P = 2.4×10−19), near AHR, and 15q24 (P = 5.2×10−14), between CYP1A1 and CYP1A2. Both the AHR and CYP1A2 genes are biologically plausible candidates as CYP1A2 metabolizes caffeine and AHR regulates CYP1A2.

Analysis of 4,405 variants in 89,050 European subjects from 41 case-control studies identified three independent association signals for estrogen-receptor-positive tumors at 11q13. The strongest signal maps to a transcriptional enhancer element in which the G allele of the best candidate causative variant rs554219 increases risk of breast cancer, reduces both binding of ELK4 transcription factor and luciferase activity in reporter assays, and may be associated with low cyclin D1 protein levels in tumors. Another candidate variant, rs78540526, lies in the same enhancer element. Risk association signal 2, rs75915166, creates a GATA3 binding site within a silencer element. Chromatin conformation studies demonstrate that these enhancer and silencer elements interact with each other and with their likely target gene, CCND1.

BACKGROUND Rigorous studies are necessary to demonstrate suitability of metabolomics platforms to profile metabolites in archived plasma within epidemiologic studies of human disease, for which attenuation of effect estimates due to measurement error is a key concern. METHODS Using a liquid chromatography–tandem mass spectrometry platform, we quantified 257 metabolites from archived plasma to evaluate metabolite interassay reproducibility, reproducibility with delayed processing, and within-person reproducibility over time. Interassay reproducibility was assessed with CVs from 60 duplicate plasma samples donated by participants in the Nurses' Health Study and Health Professionals Follow-up Study, and 20 QC pool plasma replicates. Metabolite reproducibility over a 24- to 48-h processing delay (n = 48 samples) and within-person reproducibility over 1–2 years (n = 80 samples) were assessed using Spearman and intraclass correlation coefficients (ICCs). RESULTS CVs were &amp;lt;20% for 92% of metabolites and generally were similar by plasma anticoagulant type (heparin or EDTA) and fasting time. Approximately 75% of metabolites were reproducible over delays in processing of blood samples (Spearman correlation or ICC ≥0.75, comparing immediate and 24-h delayed processing). Carbohydrates and purine/pyrimidine derivatives were most adversely affected by the processing delay. Ninety percent of metabolites were reproducible over 1–2 years within individuals (Spearman correlation or ICC ≥0.4). CONCLUSIONS For potential use in epidemiologic studies, the majority of plasma metabolites had low CVs and were reproducible over a 24-h processing delay and within individuals over 1–2 years. Certain metabolites, such as carbohydrates and purine/pyrimidine derivatives, may be challenging to evaluate if samples have delayed processing.

Cerebral ischemia induces a profound neuro-inflammatory response, but the underlying molecular mechanisms are poorly understood. Inflammasomes (NLRP1, NLRP3, NLRC4, AIM2) are intracellular multi-protein complexes which can induce sets of pro-inflammatory cyto- and chemokines, and thereby guide inflammation. We, here, assessed the functional role of NLRP3 in ischemia/reperfusion (I/R) injury in a mouse model of transient cerebral ischemia.Ischemic stroke was induced in C57Bl/6 mice by 60 min transient middle cerebral artery occlusion (tMCAO) and 3, 7 or 23 h of reperfusion, a paradigm of I/R injury. The expression patterns of inflammasomes in the ischemic hemispheres were evaluated by semiquantitative real-time PCR and Western Blot analysis accompanied by protein localization using immunocytochemistry. Finally, animals were treated with the inflammasome inhibitors Sulforaphane, Genipin, MCC950 or vehicle, directly before or upon recanalization after tMCAO. Stroke outcome was assessed, including infarct size and functional deficits, local inflammatory response, neuronal survival as well as blood-brain barrier function on day 1 after tMCAO.After tMCAO the relative gene expression levels of NLRP3 increased 20-30x within 1 day in the ischemic hemisphere which translated into an increased expression of NLRP3 in neurons. Accordingly, the gene expression levels of the NLRP3-modulator, Bruton's Tyrosine Kinase (BTK), and the NLRP3-inducible cytokine IL-1β significantly rose. Lesser or non-significant changes were seen for the other inflammasomes. Application of inflammasome inhibitors covering all inflammasomes or specifically NLRP3 significantly reduced infarct volumes when given before or after tMCAO and was accompanied by clear evidence for reduced activation of caspase 1. This stroke attenuating effect coincided with less immune cell infiltration in the ischemic hemisphere and preservation of the blood-brain barrier integrity.Our data show that induction of the NLRP3 inflammasome in neurons drives neuroinflammation in acute ischemic stroke. Early blockade of NLRP3 protects from I/R injury by mitigating inflammation and stabilizing the blood-brain barrier.

Osteosarcoma is the most common primary bone malignancy of adolescents and young adults. To better understand the genetic etiology of osteosarcoma, we performed a multistage genome-wide association study consisting of 941 individuals with osteosarcoma (cases) and 3,291 cancer-free adult controls of European ancestry. Two loci achieved genome-wide significance: a locus in the GRM4 gene at 6p21.3 (encoding glutamate receptor metabotropic 4; rs1906953; P = 8.1 × 10⁻⁹) and a locus in the gene desert at 2p25.2 (rs7591996 and rs10208273; P = 1.0 × 10⁻⁸ and 2.9 × 10⁻⁷, respectively). These two loci warrant further exploration to uncover the biological mechanisms underlying susceptibility to osteosarcoma.

Many human traits are highly correlated. This correlation can be leveraged to improve the power of genetic association tests to identify markers associated with one or more of the traits. Principal component analysis (PCA) is a useful tool that has been widely used for the multivariate analysis of correlated variables. PCA is usually applied as a dimension reduction method: the few top principal components (PCs) explaining most of total trait variance are tested for association with a predictor of interest, and the remaining components are not analyzed. In this study we review the theoretical basis of PCA and describe the behavior of PCA when testing for association between a SNP and correlated traits. We then use simulation to compare the power of various PCA-based strategies when analyzing up to 100 correlated traits. We show that contrary to widespread practice, testing only the top PCs often has low power, whereas combining signal across all PCs can have greater power. This power gain is primarily due to increased power to detect genetic variants with opposite effects on positively correlated traits and variants that are exclusively associated with a single trait. Relative to other methods, the combined-PC approach has close to optimal power in all scenarios considered while offering more flexibility and more robustness to potential confounders. Finally, we apply the proposed PCA strategy to the genome-wide association study of five correlated coagulation traits where we identify two candidate SNPs that were not found by the standard approach.

Venous thromboembolism (VTE) is a significant contributor to morbidity and mortality. To advance our understanding of the biology contributing to VTE, we conducted a genome-wide association study (GWAS) of VTE and a transcriptome-wide association study (TWAS) based on imputed gene expression from whole blood and liver. We meta-analyzed GWAS data from 18 studies for 30 234 VTE cases and 172 122 controls and assessed the association between 12 923 718 genetic variants and VTE. We generated variant prediction scores of gene expression from whole blood and liver tissue and assessed them for association with VTE. Mendelian randomization analyses were conducted for traits genetically associated with novel VTE loci. We identified 34 independent genetic signals for VTE risk from GWAS meta-analysis, of which 14 are newly reported associations. This included 11 newly associated genetic loci (C1orf198, PLEK, OSMR-AS1, NUGGC/SCARA5, GRK5, MPHOSPH9, ARID4A, PLCG2, SMG6, EIF5A, and STX10) of which 6 replicated, and 3 new independent signals in 3 known genes. Further, TWAS identified 5 additional genetic loci with imputed gene expression levels differing between cases and controls in whole blood (SH2B3, SPSB1, RP11-747H7.3, RP4-737E23.2) and in liver (ERAP1). At some GWAS loci, we found suggestive evidence that the VTE association signal for novel and previously known regions colocalized with expression quantitative trait locus signals. Mendelian randomization analyses suggested that blood traits may contribute to the underlying risk of VTE. To conclude, we identified 16 novel susceptibility loci for VTE; for some loci, the association signals are likely mediated through gene expression of nearby genes.

We must embrace a multidimensional, continuous view of ancestry and move away from continental ancestry categories.

Meredith Yeager and colleagues with the Cancer Genetics Markers of Susceptibility (CGEMS) initiative report a new association to prostate cancer at chromosome 8q24. This defines a new locus, region 4, which shows association to prostate cancer susceptibility independent of previously reported associations at 8q24. We report a genome-wide association study in 10,286 cases and 9,135 controls of European ancestry in the Cancer Genetic Markers of Susceptibility (CGEMS) initiative. We identify a new association with prostate cancer risk on chromosome 8q24 (rs620861, P = 1.3 × 10−10, heterozygote OR = 1.17, 95% CI 1.10–1.24; homozygote OR = 1.33, 95% CI 1.21–1.45). This defines a new locus associated with prostate cancer susceptibility on 8q24.

Abstract A recent genome-wide association study (PanScan) identified significant associations at the ABO gene locus with risk of pancreatic cancer, but the influence of specific ABO genotypes remains unknown. We determined ABO genotypes (OO, AO, AA, AB, BO, and BB) in 1,534 cases and 1,583 controls from 12 prospective cohorts in PanScan, grouping participants by genotype-derived serologic blood type (O, A, AB, and B). Adjusted odds ratios (ORs) for pancreatic cancer by ABO alleles were calculated using logistic regression. Compared with blood type O, the ORs for pancreatic cancer in subjects with types A, AB, and B were 1.38 [95% confidence interval (95% CI), 1.18–1.62], 1.47 (95% CI, 1.07–2.02), and 1.53 (95% CI, 1.21–1.92), respectively. The incidence rates for blood types O, A, AB, and B were 28.9, 39.9, 41.8, and 44.5 cases per 100,000 subjects per year. An increase in risk was noted with the addition of each non-O allele. Compared with OO genotype, subjects with AO and AA genotype had ORs of 1.33 (95% CI, 1.13–1.58) and 1.61 (95% CI, 1.22–2.18), whereas subjects with BO and BB genotypes had ORs of 1.45 (95% CI, 1.14–1.85) and 2.42 (1.28–4.57). The population attributable fraction for non-O blood type was 19.5%. In a joint model with smoking, current smokers with non-O blood type had an adjusted OR of 2.68 (95% CI, 2.03–3.54) compared with nonsmokers of blood type O. We concluded that ABO genotypes were significantly associated with pancreatic cancer risk. Cancer Res; 70(3); 1015–23

The estimated effect of a marker allele from the initial study reporting the marker-allele association is often exaggerated relative to the estimated effect in follow-up studies (the "winner's curse" phenomenon). This is a particular concern for genome-wide association studies, where markers typically must pass very stringent significance thresholds to be selected for replication. A related problem is the overestimation of the predictive accuracy that occurs when the same data set is used to select a multilocus risk model from a wide range of possible models and then estimate the accuracy of the final model ("over-fitting"). Even in the absence of these quantitative biases, researchers can over-state the qualitative importance of their findings--for example, by focusing on relative risks in a context where sensitivity and specificity may be more appropriate measures. Epidemiologists need to be aware of these potential problems: as authors, to avoid or minimize them, and as readers, to detect them.

Melanocortin-4 receptor (MC4R) plays critical roles in regulating food intake and energy balance. Recent genome wide scans found common variants near MC4R were related to obesity and insulin resistance. We examined the associations of the reported variants rs17782313 (T>C) and rs17700633 (G>A) with dietary intakes, weight change and diabetes risk in 5724 women (1533 with type 2 diabetes) from a prospective cohort. Under an additive inheritance model, SNP rs17782313 was significantly associated with high intakes of total energy (P = 0.028), total fat (P = 0.008) and protein (P = 0.003). Adjustment for age, BMI, diabetes status and other covariates did not appreciably change the associations. The SNP was also associated with significantly increasing trend of percentage of energy from total fat (P for trend = 0.037). The associations between SNP rs17782313 and higher BMI (P = 0.002) were independent of dietary intakes. In addition, carriers of allele-C had 0.2 kg/m(2) greater 10-year increase in BMI from cohort baseline 1976 to 1986 (P = 0.028) compared with the non-carriers. Moreover, per allele-C of rs17782313 was associated with 14% (2-32%) increased risk of type 2 diabetes, adjusting for BMI and other covariates. SNP rs1770833 was not significantly associated with either dietary intakes or obesity traits. In conclusion, the common SNP rs17782313 near MC4R gene was significantly associated with higher intakes of total energy and dietary fat. In addition, the SNP was related to greater long-term weight change and increased risk of diabetes in women.

Colorectal cancer is the second leading cause of cancer death in developed countries. Genome-wide association studies (GWAS) have successfully identified novel susceptibility loci for colorectal cancer. To follow up on these findings, and try to identify novel colorectal cancer susceptibility loci, we present results for GWAS of colorectal cancer (2,906 cases, 3,416 controls) that have not previously published main associations. Specifically, we calculated odds ratios and 95% confidence intervals using log-additive models for each study. In order to improve our power to detect novel colorectal cancer susceptibility loci, we performed a meta-analysis combining the results across studies. We selected the most statistically significant single nucleotide polymorphisms (SNPs) for replication using ten independent studies (8,161 cases and 9,101 controls). We again used a meta-analysis to summarize results for the replication studies alone, and for a combined analysis of GWAS and replication studies. We measured ten SNPs previously identified in colorectal cancer susceptibility loci and found eight to be associated with colorectal cancer (p value range 0.02 to 1.8 × 10(-8)). When we excluded studies that have previously published on these SNPs, five SNPs remained significant at p < 0.05 in the combined analysis. No novel susceptibility loci were significant in the replication study after adjustment for multiple testing, and none reached genome-wide significance from a combined analysis of GWAS and replication. We observed marginally significant evidence for a second independent SNP in the BMP2 region at chromosomal location 20p12 (rs4813802; replication p value 0.03; combined p value 7.3 × 10(-5)). In a region on 5p33.15, which includes the coding regions of the TERT-CLPTM1L genes and has been identified in GWAS to be associated with susceptibility to at least seven other cancers, we observed a marginally significant association with rs2853668 (replication p value 0.03; combined p value 1.9 × 10(-4)). Our study suggests a complex nature of the contribution of common genetic variants to risk for colorectal cancer.

Human pigmentation is a polygenic quantitative trait with high heritability. Although a large number of single nucleotide polymorphisms (SNPs) have been identified in pigmentation genes, very few SNPs have been examined in relation to human pigmentary phenotypes and skin cancer risk. We evaluated the associations between 15 SNPs in 8 candidate pigmentation genes (TYR, TYRP1, OCA2, SLC24A5, SLC45A2, POMC, ASIP and ATRN) and both pigmentary phenotypes (hair color, skin color and tanning ability) and skin cancer risk in a nested case-control study of Caucasians within the Nurses' Health Study (NHS) among 218 melanoma cases, 285 squamous cell carcinoma (SCC) cases, 300 basal cell carcinoma (BCC) cases and 870 common controls. We found that the TYR Arg402Gln variant was significantly associated with skin color (p-value = 7.7 x 10(-4)) and tanning ability (p-value = 7.3 x 10(-4)); the SLC45A2 Phe374Leu variant was significantly associated with hair color (black to blonde) (p-value = 2.4 x 10(-7)), skin color (p-value = 1.1 x 10(-7)) and tanning ability (p-value = 2.5 x 10(-4)). These associations remained significant after controlling for MC1R variants. No significant associations were found between these polymorphisms and the risk of skin cancer. We observed that the TYRP1 rs1408799 and SLC45A2 1721 C>G were associated with melanoma risk (OR, 0.77; 95% CI, 0.60-0.98 and OR, 0.75; 95% CI, 0.60-0.95, respectively). The TYR Ser192Tyr was associated with SCC risk (OR, 1.23; 95% CI, 1.00-1.50). The TYR haplotype carrying only the Arg402Gln variant allele was significantly associated with SCC risk (OR, 1.35; 95% CI, 1.04-1.74). The OCA2 Arg419Gln and ASIP g.8818 A>G were associated with BCC risk (OR, 1.50; 95% CI, 1.06-2.13 and OR, 0.73; 95% CI, 0.53-1.00, respectively). The haplotype near ASIP (rs4911414[T] and rs1015362[G]) was significantly associated with fair skin color (OR, 2.28; 95% CI, 1.46-3.57) as well as the risks of melanoma (OR, 1.68; 95% CI, 1.18-2.39) and SCC (OR, 1.54; 95% CI, 1.08-2.19). These associations remained similar after adjusting for pigmentary phenotypes and MC1R variants. The statistical power of our study was modest and additional studies are warranted to confirm the associations observed in the present study. Our study provides evidence for the contribution of pigmentation genetic variants, in addition to the MC1R variants, to variation in human pigmentary phenotypes and possibly the development of skin cancer.

To identify type 2 diabetes (T2D) susceptibility loci, we conducted genome-wide association (GWA) scans in nested case–control samples from two prospective cohort studies, including 2591 patients and 3052 controls of European ancestry. Validation was performed in 11 independent GWA studies of 10 870 cases and 73 735 controls. We identified significantly associated variants near RBMS1 and ITGB6 genes at 2q24, best-represented by SNP rs7593730 (combined OR = 0.90, 95% CI = 0.86–0.93; P = 3.7 × 10−8). The frequency of the risk-lowering allele T is 0.23. Variants in this region were nominally related to lower fasting glucose and HOMA-IR in the MAGIC consortium (P < 0.05). These data suggest that the 2q24 locus may influence the T2D risk by affecting glucose metabolism and insulin resistance.

Low plasma B-vitamin levels and elevated homocysteine have been associated with cancer, cardiovascular disease and neurodegenerative disorders. Common variants in FUT2 on chromosome 19q13 were associated with plasma vitamin B12 levels among women in a genome-wide association study in the Nurses' Health Study (NHS) NCI-Cancer Genetic Markers of Susceptibility (CGEMS) project. To identify additional loci associated with plasma vitamin B12, homocysteine, folate and vitamin B6 (active form pyridoxal 5'-phosphate, PLP), we conducted a meta-analysis of three GWA scans (total n = 4763, consisting of 1658 women in NHS-CGEMS, 1647 women in Framingham-SNP-Health Association Resource (SHARe) and 1458 men in SHARe). On chromosome 19q13, we confirm the association of plasma vitamin B12 with rs602662 and rs492602 (P-value = 1.83 x 10(-15) and 1.30 x 10(-14), respectively) in strong linkage disequilibrium (LD) with rs601338 (P = 6.92 x 10(-15)), the FUT2 W143X nonsense mutation. We identified additional genome-wide significant loci for plasma vitamin B12 on chromosomes 6p21 (P = 4.05 x 10(-08)), 10p12 (P-value=2.87 x 10(-9)) and 11q11 (P-value=2.25 x 10(-10)) in genes with biological relevance. We confirm the association of the well-studied functional candidate SNP 5,10-methylene tetrahydrofolate reductase (MTHFR) Ala222Val (dbSNP ID: rs1801133; P-value=1.27 x 10(-8)), on chromosome 1p36 with plasma homocysteine and identify an additional genome-wide significant locus on chromosome 9q22 (P-value=2.06 x 10(-8)) associated with plasma homocysteine. We also identified genome-wide associations with variants on chromosome 1p36 with plasma PLP (P-value=1.40 x 10(-15)). Genome-wide significant loci were not identified for plasma folate. These data reveal new biological candidates and confirm prior candidate genes for plasma homocysteine, plasma vitamin B12 and plasma PLP.

The past 3 months have seen the publication of a series of studies examining the inherited genetic underpinnings of common diseases such as prostate cancer, breast cancer, diabetes, and in this issue of the Journal, coronary artery disease (reported by Samani et al., pages 443–453). These genomewide association studies have been able to examine interpatient differences in inherited genetic variability at an unprecedented level of resolution, thanks to the development of microarrays, or chips, capable of assessing more than 500,000 single-nucleotide polymorphisms (SNPs) in a single sample. This “SNP-chip” technology capitalizes on a catalogue of common human genetic variations that . . .

The strong observational association between total homocysteine (tHcy) concentrations and risk of coronary artery disease (CAD) and the null associations in the homocysteine-lowering trials have prompted the need to identify genetic variants associated with homocysteine concentrations and risk of CAD.We tested whether common genetic polymorphisms associated with variation in tHcy are also associated with CAD.We conducted a meta-analysis of genome-wide association studies (GWAS) on tHcy concentrations in 44,147 individuals of European descent. Polymorphisms associated with tHcy (P < 10(⁻⁸) were tested for association with CAD in 31,400 cases and 92,927 controls.Common variants at 13 loci, explaining 5.9% of the variation in tHcy, were associated with tHcy concentrations, including 6 novel loci in or near MMACHC (2.1 × 10⁻⁹), SLC17A3 (1.0 × 10⁻⁸), GTPB10 (1.7 × 10⁻⁸), CUBN (7.5 × 10⁻¹⁰), HNF1A (1.2 × 10⁻¹²)), and FUT2 (6.6 × 10⁻⁹), and variants previously reported at or near the MTHFR, MTR, CPS1, MUT, NOX4, DPEP1, and CBS genes. Individuals within the highest 10% of the genotype risk score (GRS) had 3-μmol/L higher mean tHcy concentrations than did those within the lowest 10% of the GRS (P = 1 × 10⁻³⁶). The GRS was not associated with risk of CAD (OR: 1.01; 95% CI: 0.98, 1.04; P = 0.49).We identified several novel loci that influence plasma tHcy concentrations. Overall, common genetic variants that influence plasma tHcy concentrations are not associated with risk of CAD in white populations, which further refutes the causal relevance of moderately elevated tHcy concentrations and tHcy-related pathways for CAD.

Abstract Motivation: Inference of ancestry using genetic data is motivated by applications in genetic association studies, population genetics and personal genomics. Here, we provide methods and software for improved ancestry inference using genome-wide single nucleotide polymorphism (SNP) weights from external reference panels. This approach makes it possible to leverage the rich ancestry information that is available from large external reference panels, without the administrative and computational complexities of re-analyzing the raw genotype data from the reference panel in subsequent studies. Results: We extensively validate our approach in multiple African American, Latino American and European American datasets, making use of genome-wide SNP weights derived from large reference panels, including HapMap 3 populations and 6546 European Americans from the Framingham Heart Study. We show empirically that our approach provides much greater accuracy than either the prevailing ancestry-informative marker (AIM) approach or the analysis of genome-wide target genotypes without a reference panel. For example, in an independent set of 1636 European American genome-wide association study samples, we attained prediction accuracy (R2) of 1.000 and 0.994 for the first two principal components using our method, compared with 0.418 and 0.407 using 150 published AIMs or 0.955 and 0.003 by applying principal component analysis directly to the target samples. We finally show that the higher accuracy in inferring ancestry using our method leads to more effective correction for population stratification in association studies. Availability: The SNPweights software is available online at http://www.hsph.harvard.edu/faculty/alkes-price/software/. Contact: aprice@hsph.harvard.edu or cychen@mail.harvard.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Prostate cancer (PrCa) is the most common non-skin cancer diagnosed among males in developed countries and the second leading cause of cancer mortality, yet little is known regarding its etiology and factors that influence clinical outcome. Genome-wide association studies (GWAS) of PrCa have identified at least 30 distinct loci associated with small differences in risk. We conducted a GWAS in 2782 advanced PrCa cases (Gleason grade ≥ 8 or tumor stage C/D) and 4458 controls with 571 243 single nucleotide polymorphisms (SNPs). Based on in silico replication of 4679 SNPs (Stage 1, P < 0.02) in two published GWAS with 7358 PrCa cases and 6732 controls, we identified a new susceptibility locus associated with overall PrCa risk at 2q37.3 (rs2292884, P= 4.3 × 10(-8)). We also confirmed a locus suggested by an earlier GWAS at 12q13 (rs902774, P= 8.6 × 10(-9)). The estimated per-allele odds ratios for these loci (1.14 for rs2292884 and 1.17 for rs902774) did not differ between advanced and non-advanced PrCa (case-only test for heterogeneity P= 0.72 and P= 0.61, respectively). Further studies will be needed to assess whether these or other loci are differentially associated with PrCa subtypes.

Lymphocytes are important players in the pathophysiology of acute ischemic stroke. The interaction of lymphocytes with endothelial cells and platelets, termed thrombo-inflammation, fosters microvascular dysfunction and secondary infarct growth. FTY720, a sphingosine-1-phosphate receptor modulator, blocks the egress of lymphocytes from lymphoid organs and has been shown to reduce ischemic neurodegeneration; however, the underlying mechanisms are unclear. We investigated the mode of FTY720 action in models of cerebral ischemia.Transient middle cerebral artery occlusion (tMCAO) was induced in wild-type and lymphocyte-deficient Rag1(-/-) mice treated with FTY720 (1 mg/kg) or vehicle immediately before reperfusion. Stroke outcome was assessed 24 hours later. Immune cells in the blood and brain were counted by flow cytometry. The integrity of the blood-brain barrier was analyzed using Evans Blue dye. Thrombus formation was determined by immunohistochemistry and Western blot, and was correlated with cerebral perfusion.FTY720 significantly reduced stroke size and improved functional outcome in wild-type mice on day 1 and day 3 after transient middle cerebral artery occlusion. This protective effect was lost in lymphocyte-deficient Rag1(-/-) mice and in cultured neurons subjected to hypoxia. Less lymphocytes were present in the cerebral vasculature of FTY720-treated wild-type mice, which in turn reduced thrombosis and increased cerebral perfusion. In contrast, FTY720 was unable to prevent blood-brain barrier breakdown and transendothelial immune cell trafficking after transient middle cerebral artery occlusion.Induction of lymphocytopenia and concomitant reduction of microvascular thrombosis are key modes of FTY720 action in stroke. In contrast, our findings in Rag1(-/-) mice and cultured neurons argue against direct neuroprotective effects of FTY720.

Leukocyte telomere length (LTL) is a potential indicator of cellular aging; however, its relation to physical activity and sedentary behavior is unclear. The authors examined cross-sectionally associations among activity, sedentary behavior, and LTL among 7,813 women aged 43-70 years in the Nurses' Health Study. Participants self-reported activity by questionnaire in 1988 and 1992 and sedentary behavior in 1992. Telomere length in peripheral blood leukocytes, collected in 1989-1990, was measured by quantitative polymerase chain reaction. The least-squares mean telomere length (z-score) was calculated after adjustment for age and other potential confounders. For total activity, moderately or highly active women had a 0.07-standard deviation (SD) increase in LTL (2-sided P(trend) = 0.02) compared with those least active. Greater moderate- or vigorous-intensity activity was also associated with increased LTL (SD = 0.11 for 2-4 vs. <1 hour/week and 0.04 for ≥7 vs. <1 hour/week; 2-sided P(trend) = 0.02). Specifically, calisthenics or aerobics was associated with increased LTL (SD = 0.10 for ≥2.5 vs. 0 hours/week; 2-sided P(trend) = 0.04). Associations remained after adjustment for body mass index. Other specific activities and sitting were unassociated with LTL. Although associations were modest, these findings suggest that even moderate amounts of activity may be associated with longer telomeres, warranting further investigation in large prospective studies.

Platelets are anuclear organelle-rich cell fragments derived from bone marrow megakaryocytes (MKs) that safeguard vascular integrity.The major platelet organelles, α-granules, release proteins that participate in thrombus formation and hemostasis.Proteins stored in α-granules are also thought to play a role in inflammation and wound healing, but their functional significance in vivo is unknown.Mutations in NBEAL2 have been linked to gray platelet syndrome (GPS), a rare bleeding disorder characterized by macrothrombocytopenia, with platelets lacking α-granules.Here we show that Nbeal2-knockout mice display the characteristics of human GPS, with defective α-granule biogenesis in MKs and their absence from platelets.Nbeal2 deficiency did not affect MK differentiation and proplatelet formation in vitro or platelet life span in vivo.Nbeal2-deficient platelets displayed impaired adhesion, aggregation, and coagulant activity ex vivo that translated into defective arterial thrombus formation and protection from thrombo-inflammatory brain infarction following focal cerebral ischemia.In a model of excisional skin wound repair, Nbeal2-deficient mice exhibited impaired development of functional granulation tissue due to severely reduced differentiation of myofibroblasts in the absence of α-granule secretion.This study demonstrates that platelet α-granule constituents are critically required not only for hemostasis but also thrombosis, acute thrombo-inflammatory disease states, and tissue reconstitution after injury.

Blood soluble E-selectin (sE-selectin) levels have been related to various conditions such as type 2 diabetes. We performed a genome-wide association study among women of European ancestry from the Nurses' Health Study, and identified genome-wide significant associations between a cluster of markers at the ABO locus (9q34) and plasma sE-selectin concentration. The strongest association was with rs651007, which explained ∼9.71% of the variation in sE-selectin concentrations. SNP rs651007 was also nominally associated with soluble intracellular cell adhesion molecule-1 (sICAM-1) (P = 0.026) and TNF-R2 levels (P = 0.018), independent of sE-selectin. In addition, the genetic-inferred ABO blood group genotypes were associated with sE-selectin concentrations (P = 3.55 × 10−47). Moreover, we found that the genetic-inferred blood group B was associated with a decreased risk (OR = 0.44, 0.27–0.70) of type 2 diabetes compared with blood group O, adjusting for sE-selectin, sICAM-1, TNF-R2 and other covariates. Our findings indicate that the genetic variants at ABO locus affect plasma sE-selectin levels and diabetes risk. The genetic associations with diabetes risk were independent of sE-selectin levels.

BackgroundThe association of vitamin D status with prostate cancer is controversial; no association has been observed for overall incidence, but there is a potential link with lethal disease.

<h3>Background</h3> Depression is a heritable trait that exists on a continuum of varying severity and duration. Yet, the search for genetic variants associated with depression has had few successes. We exploit the entire continuum of depression to find common variants for depressive symptoms. <h3>Methods</h3> In this genome-wide association study, we combined the results of 17 population-based studies assessing depressive symptoms with the Center for Epidemiological Studies Depression Scale. Replication of the independent top hits (<i>p</i><1×10⁻⁵) was performed in five studies assessing depressive symptoms with other instruments. In addition, we performed a combined meta-analysis of all 22 discovery and replication studies. <h3>Results</h3> The discovery sample comprised 34,549 individuals (mean age of 66.5) and no loci reached genome-wide significance (lowest <i>p</i> = 1.05×10⁻⁷). Seven independent single nucleotide polymorphisms were considered for replication. In the replication set (<i>n</i> = 16,709), we found suggestive association of one single nucleotide polymorphism with depressive symptoms (rs161645, 5q21, <i>p</i> = 9.19×10⁻³). This 5q21 region reached genome-wide significance (<i>p</i> = 4.78×10⁻⁸) in the overall meta-analysis combining discovery and replication studies (<i>n</i> = 51,258). <h3>Conclusions</h3> The results suggest that only a large sample comprising more than 50,000 subjects may be sufficiently powered to detect genes for depressive symptoms.

Abstract Chronic inflammation has been hypothesized to be a risk factor for prostate cancer. The Toll-like receptor 4 (TLR4) presents the bacterial lipopolysaccharide (LPS), which interacts with ligand-binding protein and CD14 (LPS receptor) and activates expression of inflammatory genes through nuclear factor-κB and mitogen-activated protein kinase signaling. A previous case-control study found a modest association of a polymorphism in the TLR4 gene [11381G/C, GG versus GC/CC: odds ratio (OR), 1.26] with risk of prostate cancer. We assessed if sequence variants of TLR4 were associated with the risk of prostate cancer. In a nested case-control design within the Health Professionals Follow-up Study, we identified 700 participants with prostate cancer diagnosed after they had provided a blood specimen in 1993 and before January 2000. Controls were 700 age-matched men without prostate cancer who had had a prostate-specific antigen test after providing a blood specimen. We genotyped 16 common (&amp;gt;5%) single nucleotide polymorphisms (SNP) discovered in a resequencing study spanning TLR4 to test for association between sequence variation in TLR4 and prostate cancer. Homozygosity for the variant alleles of eight SNPs was associated with a statistically significantly lower risk of prostate cancer (TLR4_1893, TLR4_2032, TLR4_2437, TLR4_7764, TLR4_11912, TLR4_16649, TLR4_17050, and TLR4_17923), but the TLR4_15844 polymorphism corresponding to 11381G/C was not associated with prostate cancer (GG versus CG/CC: OR, 1.01; 95% confidence interval, 0.79-1.29). Six common haplotypes (cumulative frequency, 81%) were observed; the global test for association between haplotypes and prostate cancer was statistically significant (χ2 = 14.8 on 6 degrees of freedom; P = 0.02). Two common haplotypes were statistically significantly associated with altered risk of prostate cancer. Inherited polymorphisms of the innate immune gene TLR4 are associated with risk of prostate cancer. (Cancer Res 2005; 65(24): 11771-8)

Heritability, the fraction of phenotypic variation explained by genetic variation, has been estimated for many phenotypes in a range of populations, organisms, and time points. The recent development of efficient genotyping and sequencing technology has led researchers to attempt to identify the genetic variants responsible for the genetic component of phenotype directly via GWAS. The gap between the phenotypic variance explained by GWAS results and those estimated from classical heritability methods has been termed the "missing heritability problem". In this work, we examine modern methods for estimating heritability, which use the genotype and sequence data directly. We discuss them in the context of classical heritability methods, the missing heritability problem, and describe their implications for understanding the genetic architecture of complex phenotypes.

Recently, several genome-wide association studies have identified various genetic susceptibility loci for breast cancer. Relatively little is known about the possible interactions between these loci and the established risk factors for breast cancer.To assess interactions between single-nucleotide polymorphisms (SNPs) and established risk factors, we prospectively collected DNA samples and questionnaire data from 8576 breast cancer case subjects and 11 892 control subjects nested within the National Cancer Institute's Breast and Prostate Cancer Cohort Consortium (BPC3). We genotyped 17 germline SNPs (FGFR2-rs2981582, FGFR2-rs3750817, TNRC9-rs3803662, 2q35-rs13387042, MAP3K1-rs889312, 8q24-rs13281615, CASP8-rs1045485, LSP1-rs3817198, COL1A1-rs2075555, COX11-rs6504950, RNF146-rs2180341, 6q25-rs2046210, SLC4A7-rs4973768, NOTCH2-rs11249433, 5p12-rs4415084, 5p12-rs10941679, RAD51L1-rs999737), and odds ratios were estimated by logistic regression to confirm previously reported associations with breast cancer risk. We performed likelihood ratio test to assess interactions between 17 SNPs and nine established risk factors (age at menarche, parity, age at menopause, use of hormone replacement therapy, family history, height, body mass index, smoking status, and alcohol consumption), and a correction for multiple testing of 153 tests (adjusted P value threshold = .05/153 = 3 × 10(-4)) was done. Case-case comparisons were performed for possible differential associations of polymorphisms by subgroups of tumor stage, estrogen and progesterone receptor status, and age at diagnosis. All statistical tests were two-sided.We confirmed the association of 14 SNPs with breast cancer risk (P(trend) = 2.57 × 10(-3) -3.96 × 10(-19)). Three SNPs (LSP1-rs3817198, COL1A1-rs2075555, and RNF146-rs2180341) did not show association with breast cancer risk. After accounting for multiple testing, no statistically significant interactions were detected between the 17 SNPs and the nine risk factors. We also confirmed that SNPs in FGFR2 and TNRC9 were associated with greater risk of estrogen receptor-positive than estrogen receptor-negative breast cancer (P(heterogeneity) = .0016 for FGFR2-rs2981582 and P(heterogeneity) = .0053 for TNRC9-rs3803662). SNP 5p12-rs10941679 was statistically significantly associated with greater risk of progesterone receptor-positive than progesterone receptor-negative breast cancer (P(heterogeneity) = .0028).This study does not support the hypothesis that known common breast cancer susceptibility loci strongly modify the associations between established risk factors and breast cancer.

We identified a strong association (P = 5.36 x 10(-17)) between rs492602 in FUT2 and plasma vitamin B(12) levels in a genome-wide scan (n = 1,658) and an independent replication sample (n = 1,059) from the Nurses' Health Study. Women homozygous for the rs492602[G] allele had higher B(12) levels. This allele is in strong linkage disequilibrium with the FUT2 nonsecretor variant encoding W143X, suggesting a plausible mechanism for altered B(12) absorption and plasma levels.

BackgroundObesity and diabetes mellitus are associated with an increased risk of pancreatic cancer. These associations may be secondary to consequences of peripheral insulin resistance, pancreatic β-cell dysfunction, or hyperglycemia itself. Hemoglobin A1c (HbA1c) is a measure of hyperglycemia, whereas plasma insulin and proinsulin are markers of peripheral insulin resistance, and the proinsulin to insulin ratio marks pancreatic β-cell dysfunction.

Genome-wide association studies (GWAS) have identified more than a dozen loci associated with colorectal cancer (CRC) risk. Here, we examined potential effect-modification between single-nucleotide polymorphisms (SNP) at 10 of these loci and probable or established environmental risk factors for CRC in 7,016 CRC cases and 9,723 controls from nine cohort and case-control studies. We used meta-analysis of an efficient empirical-Bayes estimator to detect potential multiplicative interactions between each of the SNPs [rs16892766 at 8q23.3 (EIF3H/UTP23), rs6983267 at 8q24 (MYC), rs10795668 at 10p14 (FLJ3802842), rs3802842 at 11q23 (LOC120376), rs4444235 at 14q22.2 (BMP4), rs4779584 at 15q13 (GREM1), rs9929218 at 16q22.1 (CDH1), rs4939827 at 18q21 (SMAD7), rs10411210 at 19q13.1 (RHPN2), and rs961253 at 20p12.3 (BMP2)] and select major CRC risk factors (sex, body mass index, height, smoking status, aspirin/nonsteroidal anti-inflammatory drug use, alcohol use, and dietary intake of calcium, folate, red meat, processed meat, vegetables, fruit, and fiber). The strongest statistical evidence for a gene-environment interaction across studies was for vegetable consumption and rs16892766, located on chromosome 8q23.3, near the EIF3H and UTP23 genes (nominal P(interaction) = 1.3 × 10(-4); adjusted P = 0.02). The magnitude of the main effect of the SNP increased with increasing levels of vegetable consumption. No other interactions were statistically significant after adjusting for multiple comparisons. Overall, the association of most CRC susceptibility loci identified in initial GWAS seems to be invariant to the other risk factors considered; however, our results suggest potential modification of the rs16892766 effect by vegetable consumption.

Two recent studies independently identified polymorphisms in the 8q24 region, including a single nucleotide polymorphism (rs1447295), strongly associated with prostate cancer risk. Here, we replicate the overall association in a large nested case-control study from the National Cancer Institute Breast and Prostate Cancer Cohort Consortium using 6,637 prostate cancer cases and 7,361 matched controls. We also examine whether this polymorphism is associated with breast cancer among 2,604 Caucasian breast cancer cases and 3,118 matched controls. The rs1447295 marker was strongly associated with prostate cancer among Caucasians (P = 1.23 x 10(-13)). When we exclude the Multiethnic Cohort samples, previously reported by Freedman et al., the association remains highly significant (P = 8.64 x 10(-13)). Compared with wild-type homozygotes, carriers with one copy of the minor allele had an OR(AC) = 1.34 (99% confidence intervals, 1.19-1.50) and carriers with two copies of the minor allele had an OR(AA) = 1.86 (99% confidence intervals, 1.30-2.67). Among African Americans, the genotype association was statistically significant in men diagnosed with prostate cancer at an early age (P = 0.011) and nonsignificant for those diagnosed at a later age (P = 0.924). This difference in risk by age at diagnosis was not present among Caucasians. We found no statistically significant difference in risk when tumors were classified by Gleason score, stage, or mortality. We found no association between rs1447295 and breast cancer risk (P = 0.590). Although the gene responsible has yet to be identified, the validation of this marker in this large sample of prostate cancer cases leaves little room for the possibility of a false-positive result.

Prostate-specific antigen screening has led to enormous overtreatment of prostate cancer because of the inability to distinguish potentially lethal disease at diagnosis. We reasoned that by identifying an mRNA signature of Gleason grade, the best predictor of prognosis, we could improve prediction of lethal disease among men with moderate Gleason 7 tumors, the most common grade, and the most indeterminate in terms of prognosis.Using the complementary DNA-mediated annealing, selection, extension, and ligation assay, we measured the mRNA expression of 6,100 genes in prostate tumor tissue in the Swedish Watchful Waiting cohort (n = 358) and Physicians' Health Study (PHS; n = 109). We developed an mRNA signature of Gleason grade comparing individuals with Gleason ≤ 6 to those with Gleason ≥ 8 tumors and applied the model among patients with Gleason 7 to discriminate lethal cases.We built a 157-gene signature using the Swedish data that predicted Gleason with low misclassification (area under the curve [AUC] = 0.91); when this signature was tested in the PHS, the discriminatory ability remained high (AUC = 0.94). In men with Gleason 7 tumors, who were excluded from the model building, the signature significantly improved the prediction of lethal disease beyond knowing whether the Gleason score was 4 + 3 or 3 + 4 (P = .006).Our expression signature and the genes identified may improve our understanding of the de-differentiation process of prostate tumors. Additionally, the signature may have clinical applications among men with Gleason 7, by further estimating their risk of lethal prostate cancer and thereby guiding therapy decisions to improve outcomes and reduce overtreatment.

OBJECTIVE—To examine the longitudinal association of fat mass–and obesity-associated (FTO) variant with obesity, circulating adipokine levels, and FTO expression in various materials from human and mouse. RESEARCH DESIGN AND METHODS—We genotyped rs9939609 in 2,287 men and 3,520 women from two prospective cohorts. Plasma adiponectin and leptin were measured in a subset of diabetic men (n = 854) and women (n = 987). Expression of FTO was tested in adipocytes from db/db mice and mouse macrophages. RESULTS—We observed a trend toward decreasing associations between rs9939609 and BMI at older age (≥65 years) in men, whereas the associations were constant across different age groups in women. In addition, the single nucleotide polymorphism (SNP) rs9939609 was associated with lower plasma adiponectin (log[e]− means, 1.82 ± 0.04, 1.73 ± 0.03, and 1.68 ± 0.05 for TT, TA, and AA genotypes, respectively; P for trend = 0.02) and leptin (log[e]− means, 3.56 ± 0.04, 3.63 ± 0.04, and 3.70 ± 0.06; P for trend = 0.06) in diabetic women. Adjustment for BMI attenuated the associations. FTO gene was universally expressed in human and mice tissues, including adipocytes. In an ancillary study of adipocytes from db/db mice, FTO expression was ∼50% lower than in those from wild-type mice. CONCLUSIONS—The association between FTO SNP rs9939609 and obesity risk may decline at older age. The variant affects circulating adiponectin and leptin levels through the changes in BMI. In addition, the expression of FTO gene was reduced in adipocytes from db/db mice.

Genome-wide association studies have identified numerous genetic loci for spirometic measures of pulmonary function, forced expiratory volume in one second (FEV(1)), and its ratio to forced vital capacity (FEV(1)/FVC). Given that cigarette smoking adversely affects pulmonary function, we conducted genome-wide joint meta-analyses (JMA) of single nucleotide polymorphism (SNP) and SNP-by-smoking (ever-smoking or pack-years) associations on FEV(1) and FEV(1)/FVC across 19 studies (total N = 50,047). We identified three novel loci not previously associated with pulmonary function. SNPs in or near DNER (smallest P(JMA = )5.00×10(-11)), HLA-DQB1 and HLA-DQA2 (smallest P(JMA = )4.35×10(-9)), and KCNJ2 and SOX9 (smallest P(JMA = )1.28×10(-8)) were associated with FEV(1)/FVC or FEV(1) in meta-analysis models including SNP main effects, smoking main effects, and SNP-by-smoking (ever-smoking or pack-years) interaction. The HLA region has been widely implicated for autoimmune and lung phenotypes, unlike the other novel loci, which have not been widely implicated. We evaluated DNER, KCNJ2, and SOX9 and found them to be expressed in human lung tissue. DNER and SOX9 further showed evidence of differential expression in human airway epithelium in smokers compared to non-smokers. Our findings demonstrated that joint testing of SNP and SNP-by-environment interaction identified novel loci associated with complex traits that are missed when considering only the genetic main effects.

Candidate gene and genome-wide association studies (GWAS) have identified 11 independent susceptibility loci associated with bladder cancer risk. To discover additional risk variants, we conducted a new GWAS of 2422 bladder cancer cases and 5751 controls, followed by a meta-analysis with two independently published bladder cancer GWAS, resulting in a combined analysis of 6911 cases and 11 814 controls of European descent. TaqMan genotyping of 13 promising single nucleotide polymorphisms with P < 1 × 10(-5) was pursued in a follow-up set of 801 cases and 1307 controls. Two new loci achieved genome-wide statistical significance: rs10936599 on 3q26.2 (P = 4.53 × 10(-9)) and rs907611 on 11p15.5 (P = 4.11 × 10(-8)). Two notable loci were also identified that approached genome-wide statistical significance: rs6104690 on 20p12.2 (P = 7.13 × 10(-7)) and rs4510656 on 6p22.3 (P = 6.98 × 10(-7)); these require further studies for confirmation. In conclusion, our study has identified new susceptibility alleles for bladder cancer risk that require fine-mapping and laboratory investigation, which could further understanding into the biological underpinnings of bladder carcinogenesis.

The interest in performing gene-environment interaction studies has seen a significant increase with the increase of advanced molecular genetics techniques. Practically, it became possible to investigate the role of environmental factors in disease risk and hence to investigate their role as genetic effect modifiers. The understanding that genetics is important in the uptake and metabolism of toxic substances is an example of how genetic profiles can modify important environmental risk factors to disease. Several rationales exist to set up gene-environment interaction studies and the technical challenges related to these studies-when the number of environmental or genetic risk factors is relatively small-has been described before. In the post-genomic era, it is now possible to study thousands of genes and their interaction with the environment. This brings along a whole range of new challenges and opportunities. Despite a continuing effort in developing efficient methods and optimal bioinformatics infrastructures to deal with the available wealth of data, the challenge remains how to best present and analyze genome-wide environmental interaction (GWEI) studies involving multiple genetic and environmental factors. Since GWEIs are performed at the intersection of statistical genetics, bioinformatics and epidemiology, usually similar problems need to be dealt with as for genome-wide association gene-gene interaction studies. However, additional complexities need to be considered which are typical for large-scale epidemiological studies, but are also related to "joining" two heterogeneous types of data in explaining complex disease trait variation or for prediction purposes.

A family history of pancreatic cancer has consistently been associated with increased risk of pancreatic cancer. However, uncertainty remains about the strength of this association. Results from previous studies suggest a family history of select cancers (i.e., ovarian, breast and colorectal) could also be associated, although not as strongly, with increased risk of pancreatic cancer. We examined the association between a family history of 5 types of cancer (pancreas, prostate, ovarian, breast and colorectal) and risk of pancreatic cancer using data from a collaborative nested case-control study conducted by the Pancreatic Cancer Cohort Consortium. Cases and controls were from cohort studies from the United States, Europe and China, and a case-control study from the Mayo Clinic. Analyses of family history of pancreatic cancer included 1,183 cases and 1,205 controls. A family history of pancreatic cancer in a parent, sibling or child was associated with increased risk of pancreatic cancer [multivariate-adjusted odds ratios (ORs) = 1.76, 95% confidence interval (CI) = 1.19-2.61]. A family history of prostate cancer was also associated with increased risk (OR = 1.45, 95% CI = 1.12-1.89). There were no statistically significant associations with a family history of ovarian cancer (OR = 0.82, 95% CI = 0.52-1.31), breast cancer (OR = 1.21, 95% CI = 0.97-1.51) or colorectal cancer (OR = 1.17, 95% CI = 0.93-1.47). Our results confirm a moderate sized association between a family history of pancreatic cancer and risk of pancreatic cancer and also provide evidence for an association with a family history of prostate cancer worth further study.

Epidemiological studies have reported inconsistent associations between telomere length (TL) and risk for various cancers.These inconsistencies are likely attributable, in part, to biases that arise due to post-diagnostic and post-treatment TL measurement.To avoid such biases, we used a Mendelian randomization approach and estimated associations between nine TL-associated SNPs and risk for five common cancer types (breast, lung, colorectal, ovarian and prostate cancer, including subtypes) using data on 51 725 cases and 62 035 controls.We then used an inverse-variance weighted average of the SNPspecific associations to estimate the association between a genetic score representing long TL and cancer risk.The long TL genetic score was significantly associated with increased risk of lung adenocarcinoma (P = 6.3 × 10 -15 ), even after exclusion of a SNP residing in a known lung cancer susceptibility region (TERT-CLPTM1L) P = 6.6 × 10 -6 ).Under Mendelian randomization assumptions, the association estimate [odds ratio (OR) = 2.78] is interpreted as the OR for lung adenocarcinoma corresponding to a 1000 bp increase in TL.The weighted TL SNP score was not associated with other cancer types or subtypes.Our finding that genetic determinants of long TL increase lung adenocarcinoma risk avoids issues with reverse causality and residual confounding that arise in observational studies of TL and disease risk.Under Mendelian randomization assumptions, our finding suggests that longer TL increases lung adenocarcinoma risk.However, caution regarding this causal interpretation is warranted in light of the potential issue of pleiotropy, and a more general interpretation is that SNPs influencing telomere biology are also implicated in lung adenocarcinoma risk.

We conducted a multistage genome-wide association study (GWAS) of tanning response after exposure to sunlight in over 9,000 men and women of European ancestry who live in the United States. An initial analysis of 528,173 single-nucleotide polymorphisms (SNPs) genotyped on 2,287 women identified LOC401937 (rs966321) on chromosome 1 as a novel locus highly associated with tanning ability, and we confirmed this association in 870 women controls from a skin cancer case-control study with joint P-value=1.6 x 10(-9). We further genotyped this SNP in two subsequent replication studies (one with 3,750 women and the other with 2,405 men). This association was not replicated in either of these two studies. We found that several SNPs reaching the genome-wide significance level are located in or adjacent to the loci previously known as pigmentation genes: MATP, IRF4, TYR, OCA2, and MC1R. Overall, these tanning ability-related loci are similar to the hair color-related loci previously reported in the GWAS of hair color.

Although genome-wide association studies (GWAS) for prostate cancer (PrCa) have identified more than 100 risk regions, most of the risk genes at these regions remain largely unknown. Here we integrate the largest PrCa GWAS (N = 142,392) with gene expression measured in 45 tissues (N = 4458), including normal and tumor prostate, to perform a multi-tissue transcriptome-wide association study (TWAS) for PrCa. We identify 217 genes at 84 independent 1 Mb regions associated with PrCa risk, 9 of which are regions with no genome-wide significant SNP within 2 Mb. 23 genes are significant in TWAS only for alternative splicing models in prostate tumor thus supporting the hypothesis of splicing driving risk for continued oncogenesis. Finally, we use a Bayesian probabilistic approach to estimate credible sets of genes containing the causal gene at a pre-defined level; this reduced the list of 217 associations to 109 genes in the 90% credible set. Overall, our findings highlight the power of integrating expression with PrCa GWAS to identify novel risk loci and prioritize putative causal genes at known risk loci.

We developed an absolute risk model to identify individuals in the general population at elevated risk of pancreatic cancer.Using data on 3,349 cases and 3,654 controls from the PanScan Consortium, we developed a relative risk model for men and women of European ancestry based on non-genetic and genetic risk factors for pancreatic cancer. We estimated absolute risks based on these relative risks and population incidence rates.Our risk model included current smoking (multivariable adjusted odds ratio (OR) and 95% confidence interval: 2.20 [1.84-2.62]), heavy alcohol use (>3 drinks/day) (OR: 1.45 [1.19-1.76]), obesity (body mass index >30 kg/m(2)) (OR: 1.26 [1.09-1.45]), diabetes >3 years (nested case-control OR: 1.57 [1.13-2.18], case-control OR: 1.80 [1.40-2.32]), family history of pancreatic cancer (OR: 1.60 [1.20-2.12]), non-O ABO genotype (AO vs. OO genotype) (OR: 1.23 [1.10-1.37]) to (BB vs. OO genotype) (OR 1.58 [0.97-2.59]), rs3790844(chr1q32.1) (OR: 1.29 [1.19-1.40]), rs401681(5p15.33) (OR: 1.18 [1.10-1.26]) and rs9543325(13q22.1) (OR: 1.27 [1.18-1.36]). The areas under the ROC curve for risk models including only non-genetic factors, only genetic factors, and both non-genetic and genetic factors were 58%, 57% and 61%, respectively. We estimate that fewer than 3/1,000 U.S. non-Hispanic whites have more than a 5% predicted lifetime absolute risk.Although absolute risk modeling using established risk factors may help to identify a group of individuals at higher than average risk of pancreatic cancer, the immediate clinical utility of our model is limited. However, a risk model can increase awareness of the various risk factors for pancreatic cancer, including modifiable behaviors.

Abstract Endometrial cancer is a disease primarily driven by cumulative exposure to estrogen unopposed by progesterone. Reproductive factors associated with changes in endogenous hormone levels and use of exogenous hormones such as postmenopausal hormones influence the risk of disease. The authors used the Nurses' Health Study, comprised of 121,700 nurses, to assess the above associations. Over 28 years of follow‐up, 778 adenocarcinoma cases were diagnosed and 1,850,078 person‐years were accumulated. Cox proportional hazards models were used to estimate relative risks (RR) and 95% confidence intervals (CI). A late age at menarche decreased the risk independent of body mass index (BMI) ( P ‐trend = 0.02). A late age at menopause increased cancer risk ( P ‐trend = 0.0003). An advanced age at last birth reduced the risk ( P ‐trend &lt; 0.0001), however, an inverse association with age at first birth and parity diminished after adjustment for age at last birth. Compared with never users, an increased risk was observed among long‐term (≥5 years) users of both estrogen (E) (RR = 7.67, 95% CI: 5.57, 10.57) and combined estrogen plus progesterone (E+P) (RR = 1.52, 95% CI: 1.03, 2.23). Normal‐weight (BMI &lt; 25) women had the highest risk following E or E+P use ( P ‐interaction‐E = 0.0008, P ‐interaction‐E+P = 0.02). The findings from this study underscore the importance of hormonal mechanisms in endometrial carcinogenesis.