Polymorphisms in genes coding for enzymes that activate tobacco lung carcinogens may generate inter-individual differences in lung cancer risk. Previous studies had limited sample sizes, poor exposure characterization, and a few single nucleotide polymorphisms (SNPs) tested in candidate genes. We analyzed 25 SNPs (some previously untested) in 2101 primary lung cancer cases and 2120 population controls from the Environment And Genetics in Lung cancer Etiology (EAGLE) study from six phase I metabolic genes, including cytochrome P450s, microsomal epoxide hydrolase, and myeloperoxidase. We evaluated the main genotype effects and genotype-smoking interactions in lung cancer risk overall and in the major histology subtypes. We tested the combined effect of multiple SNPs on lung cancer risk and on gene expression. Findings were prioritized based on significance thresholds and consistency across different analyses, and accounted for multiple testing and prior knowledge. Two haplotypes in EPHX1 were significantly associated with lung cancer risk in the overall population. In addition, CYP1B1 and CYP2A6 polymorphisms were inversely associated with adenocarcinoma and squamous cell carcinoma risk, respectively. Moreover, the association between CYP1A1 rs2606345 genotype and lung cancer was significantly modified by intensity of cigarette smoking, suggesting an underlying dose-response mechanism. Finally, increasing number of variants at CYP1A1/A2 genes revealed significant protection in never smokers and risk in ever smokers. Results were supported by differential gene expression in non-tumor lung tissue samples with down-regulation of CYP1A1 in never smokers and up-regulation in smokers from CYP1A1/A2 SNPs. The significant haplotype associations emphasize that the effect of multiple SNPs may be important despite null single SNP-associations, and warrants consideration in genome-wide association studies (GWAS). Our findings emphasize the necessity of post-GWAS fine mapping and SNP functional assessment to further elucidate cancer risk associations.

Genome-wide association studies (GWAS) have led to a rapid increase in available data on common genetic variants and phenotypes and numerous discoveries of new loci associated with susceptibility to common complex diseases. Integrating the evidence from GWAS and candidate gene studies depends on concerted efforts in data production, online publication, database development, and continuously updated data synthesis. Here the authors summarize current experience and challenges on these fronts, which were discussed at a 2008 multidisciplinary workshop sponsored by the Human Genome Epidemiology Network. Comprehensive field synopses that integrate many reported gene-disease associations have been systematically developed for several fields, including Alzheimer's disease, schizophrenia, bladder cancer, coronary heart disease, preterm birth, and DNA repair genes in various cancers. The authors summarize insights from these field synopses and discuss remaining unresolved issues -- especially in the light of evidence from GWAS, for which they summarize empirical P-value and effect-size data on 223 discovered associations for binary outcomes (142 with P < 10(-7)). They also present a vision of collaboration that builds reliable cumulative evidence for genetic associations with common complex diseases and a transparent, distributed, authoritative knowledge base on genetic variation and human health. As a next step in the evolution of Human Genome Epidemiology reviews, the authors invite investigators to submit field synopses for possible publication in the American Journal of Epidemiology.

To identify genetic loci influencing central obesity and fat distribution, we performed a meta-analysis of 16 genome-wide association studies (GWAS, N = 38,580) informative for adult waist circumference (WC) and waist-hip ratio (WHR). We selected 26 SNPs for follow-up, for which the evidence of association with measures of central adiposity (WC and/or WHR) was strong and disproportionate to that for overall adiposity or height. Follow-up studies in a maximum of 70,689 individuals identified two loci strongly associated with measures of central adiposity; these map near TFAP2B (WC, P = 1.9x10(-11)) and MSRA (WC, P = 8.9x10(-9)). A third locus, near LYPLAL1, was associated with WHR in women only (P = 2.6x10(-8)). The variants near TFAP2B appear to influence central adiposity through an effect on overall obesity/fat-mass, whereas LYPLAL1 displays a strong female-only association with fat distribution. By focusing on anthropometric measures of central obesity and fat distribution, we have identified three loci implicated in the regulation of human adiposity.

Recently, genome-wide association studies have identified loci across a segment of chromosome 8q24 (128,100,000-128,700,000) associated with the risk of breast, colon and prostate cancers. At least three regions of 8q24 have been independently associated with prostate cancer risk; the most centromeric of which appears to be population specific. Haplotypes in two contiguous but independent loci, marked by rs6983267 and rs1447295, have been identified in the Cancer Genetic Markers of Susceptibility project ( http://cgems.cancer.gov ), which genotyped more than 5,000 prostate cancer cases and 5,000 controls of European origin. The rs6983267 locus is also strongly associated with colorectal cancer. To ascertain a comprehensive catalog of common single-nucleotide polymorphisms (SNPs) across the two regions, we conducted a resequence analysis of 136 kb (chr8: 128,473,000-128,609,802) using the Roche/454 next-generation sequencing technology in 39 prostate cancer cases and 40 controls of European origin. We have characterized a comprehensive catalog of common (MAF > 1%) SNPs within this region, including 442 novel SNPs and have determined the pattern of linkage disequilibrium across the region. Our study has generated a detailed map of genetic variation across the region, which should be useful for choosing SNPs for fine mapping of association signals in 8q24 and investigations of the functional consequences of select common variants.

Genetic variations, such as single nucleotide polymorphisms (SNPs) in microRNAs (miRNA) or in the miRNA binding sites may affect the miRNA dependent gene expression regulation, which has been implicated in various cancers, including breast cancer, and may alter individual susceptibility to cancer. We investigated associations between miRNA related SNPs and breast cancer risk. First we evaluated 2,196 SNPs in a case-control study combining nine genome wide association studies (GWAS). Second, we further investigated 42 SNPs with suggestive evidence for association using 41,785 cases and 41,880 controls from 41 studies included in the Breast Cancer Association Consortium (BCAC). Combining the GWAS and BCAC data within a meta-analysis, we estimated main effects on breast cancer risk as well as risks for estrogen receptor (ER) and age defined subgroups. Five miRNA binding site SNPs associated significantly with breast cancer risk: rs1045494 (odds ratio (OR) 0.92; 95% confidence interval (CI): 0.88-0.96), rs1052532 (OR 0.97; 95% CI: 0.95-0.99), rs10719 (OR 0.97; 95% CI: 0.94-0.99), rs4687554 (OR 0.97; 95% CI: 0.95-0.99, and rs3134615 (OR 1.03; 95% CI: 1.01-1.05) located in the 3' UTR of CASP8, HDDC3, DROSHA, MUSTN1, and MYCL1, respectively. DROSHA belongs to miRNA machinery genes and has a central role in initial miRNA processing. The remaining genes are involved in different molecular functions, including apoptosis and gene expression regulation. Further studies are warranted to elucidate whether the miRNA binding site SNPs are the causative variants for the observed risk effects.

Height has an extremely polygenic pattern of inheritance. Genome-wide association studies (GWAS) have revealed hundreds of common variants that are associated with human height at genome-wide levels of significance. However, only a small fraction of phenotypic variation can be explained by the aggregate of these common variants. In a large study of African-American men and women (n = 14,419), we genotyped and analyzed 966,578 autosomal SNPs across the entire genome using a linear mixed model variance components approach implemented in the program GCTA (Yang et al Nat Genet 2010), and estimated an additive heritability of 44.7% (se: 3.7%) for this phenotype in a sample of evidently unrelated individuals. While this estimated value is similar to that given by Yang et al in their analyses, we remain concerned about two related issues: (1) whether in the complete absence of hidden relatedness, variance components methods have adequate power to estimate heritability when a very large number of SNPs are used in the analysis; and (2) whether estimation of heritability may be biased, in real studies, by low levels of residual hidden relatedness. We addressed the first question in a semi-analytic fashion by directly simulating the distribution of the score statistic for a test of zero heritability with and without low levels of relatedness. The second question was addressed by a very careful comparison of the behavior of estimated heritability for both observed (self-reported) height and simulated phenotypes compared to imputation R2 as a function of the number of SNPs used in the analysis. These simulations help to address the important question about whether today's GWAS SNPs will remain useful for imputing causal variants that are discovered using very large sample sizes in future studies of height, or whether the causal variants themselves will need to be genotyped de novo in order to build a prediction model that ultimately captures a large fraction of the variability of height, and by implication other complex phenotypes. Our overall conclusions are that when study sizes are quite large (5,000 or so) the additive heritability estimate for height is not apparently biased upwards using the linear mixed model; however there is evidence in our simulation that a very large number of causal variants (many thousands) each with very small effect on phenotypic variance will need to be discovered to fill the gap between the heritability explained by known versus unknown causal variants. We conclude that today's GWAS data will remain useful in the future for causal variant prediction, but that finding the causal variants that need to be predicted may be extremely laborious.

Most men diagnosed with prostate cancer will experience indolent disease; hence, discovering genetic variants that distinguish aggressive from nonaggressive prostate cancer is of critical clinical importance for disease prevention and treatment. In a multistage, case-only genome-wide association study of 12,518 prostate cancer cases, we identify two loci associated with Gleason score, a pathological measure of disease aggressiveness: rs35148638 at 5q14.3 (RASA1, P=6.49 × 10(-9)) and rs78943174 at 3q26.31 (NAALADL2, P=4.18 × 10(-8)). In a stratified case-control analysis, the SNP at 5q14.3 appears specific for aggressive prostate cancer (P=8.85 × 10(-5)) with no association for nonaggressive prostate cancer compared with controls (P=0.57). The proximity of these loci to genes involved in vascular disease suggests potential biological mechanisms worthy of further investigation.

Clinical genetic testing is commercially available for rs61764370, an inherited variant residing in a KRAS 3' UTR microRNA binding site, based on suggested associations with increased ovarian and breast cancer risk as well as with survival time. However, prior studies, emphasizing particular subgroups, were relatively small. Therefore, we comprehensively evaluated ovarian and breast cancer risks as well as clinical outcome associated with rs61764370.

Survival after a diagnosis of breast cancer varies considerably between patients, and some of this variation may be because of germline genetic variation. We aimed to identify genetic markers associated with breast cancer-specific survival.

Esophageal adenocarcinoma is a cancer with rising incidence and poor survival. Most such cancers arise in a specialized intestinal metaplastic epithelium, which is diagnostic of Barrett's esophagus. In a genome-wide association study, we compared esophageal adenocarcinoma cases (n = 2,390) and individuals with precancerous Barrett's esophagus (n = 3,175) with 10,120 controls in 2 phases. For the combined case group, we identified three new associations. The first is at 19p13 (rs10419226: P = 3.6 × 10(-10)) in CRTC1 (encoding CREB-regulated transcription coactivator), whose aberrant activation has been associated with oncogenic activity. A second is at 9q22 (rs11789015: P = 1.0 × 10(-9)) in BARX1, which encodes a transcription factor important in esophageal specification. A third is at 3p14 (rs2687201: P = 5.5 × 10(-9)) near the transcription factor FOXP1, which regulates esophageal development. We also refine a previously reported association with Barrett's esophagus near the putative tumor suppressor gene FOXF1 at 16q24 and extend our findings to now include esophageal adenocarcinoma.

Invasive lobular breast cancer (ILC) accounts for 10-15% of all invasive breast carcinomas. It is generally ER positive (ER+) and often associated with lobular carcinoma in situ (LCIS). Genome-wide association studies have identified more than 70 common polymorphisms that predispose to breast cancer, but these studies included predominantly ductal (IDC) carcinomas. To identify novel common polymorphisms that predispose to ILC and LCIS, we pooled data from 6,023 cases (5,622 ILC, 401 pure LCIS) and 34,271 controls from 36 studies genotyped using the iCOGS chip. Six novel SNPs most strongly associated with ILC/LCIS in the pooled analysis were genotyped in a further 516 lobular cases (482 ILC, 36 LCIS) and 1,467 controls. These analyses identified a lobular-specific SNP at 7q34 (rs11977670, OR (95%CI) for ILC = 1.13 (1.09-1.18), P = 6.0 × 10(-10); P-het for ILC vs IDC ER+ tumors = 1.8 × 10(-4)). Of the 75 known breast cancer polymorphisms that were genotyped, 56 were associated with ILC and 15 with LCIS at P<0.05. Two SNPs showed significantly stronger associations for ILC than LCIS (rs2981579/10q26/FGFR2, P-het = 0.04 and rs889312/5q11/MAP3K1, P-het = 0.03); and two showed stronger associations for LCIS than ILC (rs6678914/1q32/LGR6, P-het = 0.001 and rs1752911/6q14, P-het = 0.04). In addition, seven of the 75 known loci showed significant differences between ER+ tumors with IDC and ILC histology, three of these showing stronger associations for ILC (rs11249433/1p11, rs2981579/10q26/FGFR2 and rs10995190/10q21/ZNF365) and four associated only with IDC (5p12/rs10941679; rs2588809/14q24/RAD51L1, rs6472903/8q21 and rs1550623/2q31/CDCA7). In conclusion, we have identified one novel lobular breast cancer specific predisposition polymorphism at 7q34, and shown for the first time that common breast cancer polymorphisms predispose to LCIS. We have shown that many of the ER+ breast cancer predisposition loci also predispose to ILC, although there is some heterogeneity between ER+ lobular and ER+ IDC tumors. These data provide evidence for overlapping, but distinct etiological pathways within ER+ breast cancer between morphological subtypes.

Germline variants in TP63 have been consistently associated with several tumors, including bladder cancer, indicating the importance of TP53 pathway in cancer genetic susceptibility. However, variants in other related genes, including TP53 rs1042522 (Arg72Pro), still present controversial results. We carried out an in depth assessment of associations between common germline variants in the TP53 pathway and bladder cancer risk.

Recent heritability analyses have indicated that genome-wide association studies (GWAS) have the potential to improve genetic risk prediction for complex diseases based on polygenic risk score (PRS), a simple modelling technique that can be implemented using summary-level data from the discovery samples. We herein propose modifications to improve the performance of PRS. We introduce threshold-dependent winner's-curse adjustments for marginal association coefficients that are used to weight the single-nucleotide polymorphisms (SNPs) in PRS. Further, as a way to incorporate external functional/annotation knowledge that could identify subsets of SNPs highly enriched for associations, we propose variable thresholds for SNPs selection. We applied our methods to GWAS summary-level data of 14 complex diseases. Across all diseases, a simple winner's curse correction uniformly led to enhancement of performance of the models, whereas incorporation of functional SNPs was beneficial only for selected diseases. Compared to the standard PRS algorithm, the proposed methods in combination led to notable gain in efficiency (25-50% increase in the prediction R2) for 5 of 14 diseases. As an example, for GWAS of type 2 diabetes, winner's curse correction improved prediction R2 from 2.29% based on the standard PRS to 3.10% (P = 0.0017) and incorporating functional annotation data further improved R2 to 3.53% (P = 2×10-5). Our simulation studies illustrate why differential treatment of certain categories of functional SNPs, even when shown to be highly enriched for GWAS-heritability, does not lead to proportionate improvement in genetic risk-prediction because of non-uniform linkage disequilibrium structure.

Genetic variations in human leukocyte antigens (HLA) are critical in host responses to infections, transplantation, and immunological diseases. We previously identified associations with non-Hodgkin lymphoma (NHL) and the HLA-DRB1*01:01 allele and extended ancestral haplotype (AH) 8.1 (HLA-A*01-B*08-DR*03-TNF-308A). To illuminate how HLA alleles and haplotypes may influence NHL etiology, we examined potential interactions between HLA-DRB1*01:01 and AH 8.1, and a wide range of NHL risk factors among 685 NHL cases and 646 controls from a United States population-based case-control study. We calculated odds ratios and 95% confidence intervals by HLA allele or haplotype status, adjusted for sex, age, race and study center for NHL and two major subtypes using polychotomous unconditional logistic regression models. The previously reported elevation in NHL risk associated with exposures to termite treatment and polychlorinated biphenyls were restricted to individuals who did not possess HLA-DRB1*01:01. Previous associations for NHL and DLBCL with decreased sun exposure, higher BMI, and autoimmune conditions were statistically significant only among those with AH 8.1, and null among those without AH 8.1. Our results suggest that NHL risk factors vary in their association based on HLA-DRB1*01:01 and AH 8.1 status. Our results further suggest that certain NHL risk factors may act through a common mechanism to alter NHL risk. Finally, control participants with either HLA-DRB1*01:01 or AH 8.1 reported having a family history of NHL twice as likely as those who did not have either allele or haplotype, providing the first empirical evidence that HLA associations may explain some of the well-established relationship between family history and NHL risk.

Copy number variants (CNV) can be called from SNP-arrays; however, few studies have attempted to combine both CNV and SNP calls to test for association with complex diseases. Even when SNPs are located within CNVs, two separate association analyses are necessary, to compare the distribution of bi-allelic genotypes in cases and controls (referred to as SNP-only strategy) and the number of copies of a region (referred to as CNV-only strategy). However, when disease susceptibility is actually associated with allele specific copy-number states, the two strategies may not yield comparable results, raising a series of questions about the optimal analytical approach. We performed simulations of the performance of association testing under different scenarios that varied genotype frequencies and inheritance models. We show that the SNP-only strategy lacks power under most scenarios when the SNP is located within a CNV; frequently it is excluded from analysis as it does not pass quality control metrics either because of an increased rate of missing calls or a departure from fitness for Hardy-Weinberg proportion. The CNV-only strategy also lacks power because the association testing depends on the allele which copy number varies. The combined strategy performs well in most of the scenarios. Hence, we advocate the use of this combined strategy when testing for association with SNPs located within CNVs.

We conducted a meta-analysis to identify new susceptibility loci for testicular germ cell tumor (TGCT). In the discovery phase, we analyzed 931 affected individuals and 1,975 controls from 3 genome-wide association studies (GWAS). We conducted replication in 6 independent sample sets comprising 3,211 affected individuals and 7,591 controls. In the combined analysis, risk of TGCT was significantly associated with markers at four previously unreported loci: 4q22.2 in HPGDS (per-allele odds ratio (OR) = 1.19, 95% confidence interval (CI) = 1.12-1.26; P = 1.11 × 10(-8)), 7p22.3 in MAD1L1 (OR = 1.21, 95% CI = 1.14-1.29; P = 5.59 × 10(-9)), 16q22.3 in RFWD3 (OR = 1.26, 95% CI = 1.18-1.34; P = 5.15 × 10(-12)) and 17q22 (rs9905704: OR = 1.27, 95% CI = 1.18-1.33; P = 4.32 × 10(-13) and rs7221274: OR = 1.20, 95% CI = 1.12-1.28; P = 4.04 × 10(-9)), a locus that includes TEX14, RAD51C and PPM1E. These new TGCT susceptibility loci contain biologically plausible genes encoding proteins important for male germ cell development, chromosomal segregation and the DNA damage response.

Menopausal hormone therapy (MHT) use has been consistently associated with a decreased risk of colorectal cancer (CRC) in women. Our aim was to use a genome-wide gene-environment interaction analysis to identify genetic modifiers of CRC risk associated with use of MHT.

Genome-wide association studies (GWAS) have identified common variants that predispose individuals to a higher body mass index (BMI), an independent risk factor for endometrial cancer. Composite genotype risk scores (GRS) based on the joint effect of published BMI risk loci were used to explore whether endometrial cancer shares a genetic background with obesity. Genotype and risk factor data were available on 3,376 endometrial cancer case and 3,867 control participants of European ancestry from the Epidemiology of Endometrial Cancer Consortium GWAS. A BMI GRS was calculated by summing the number of BMI risk alleles at 97 independent loci. For exploratory analyses, additional GRSs were based on subsets of risk loci within putative etiologic BMI pathways. The BMI GRS was statistically significantly associated with endometrial cancer risk (P = 0.002). For every 10 BMI risk alleles a woman had a 13% increased endometrial cancer risk (95% CI: 4%, 22%). However, after adjusting for BMI, the BMI GRS was no longer associated with risk (per 10 BMI risk alleles OR = 0.99, 95% CI: 0.91, 1.07; P = 0.78). Heterogeneity by BMI did not reach statistical significance (P = 0.06), and no effect modification was noted by age, GWAS Stage, study design or between studies (P≥0.58). In exploratory analyses, the GRS defined by variants at loci containing monogenic obesity syndrome genes was associated with reduced endometrial cancer risk independent of BMI (per BMI risk allele OR = 0.92, 95% CI: 0.88, 0.96; P = 2.1 x 10-5). Possessing a large number of BMI risk alleles does not increase endometrial cancer risk above that conferred by excess body weight among women of European descent. Thus, the GRS based on all current established BMI loci does not provide added value independent of BMI. Future studies are required to validate the unexpected observed relation between monogenic obesity syndrome genetic variants and endometrial cancer risk.

Menopause timing has a substantial impact on infertility and risk of disease, including breast cancer, but the underlying mechanisms are poorly understood. We report a dual strategy in ∼70,000 women to identify common and low-frequency protein-coding variation associated with age at natural menopause (ANM). We identified 44 regions with common variants, including two regions harboring additional rare missense alleles of large effect. We found enrichment of signals in or near genes involved in delayed puberty, highlighting the first molecular links between the onset and end of reproductive lifespan. Pathway analyses identified major association with DNA damage response (DDR) genes, including the first common coding variant in BRCA1 associated with any complex trait. Mendelian randomization analyses supported a causal effect of later ANM on breast cancer risk (∼6% increase in risk per year; P = 3 × 10(-14)), likely mediated by prolonged sex hormone exposure rather than DDR mechanisms.

Evidence from a small number of studies suggests that longer telomere length measured in peripheral leukocytes is associated with an increased risk of non-Hodgkin lymphoma (NHL). However, these studies may be biased by reverse causation, confounded by unmeasured environmental exposures and might miss time points for which prospective telomere measurement would best reveal a relationship between telomere length and NHL risk. We performed an analysis of genetically inferred telomere length and NHL risk in a study of 10 102 NHL cases of the four most common B-cell histologic types and 9562 controls using a genetic risk score (GRS) comprising nine telomere length-associated single-nucleotide polymorphisms. This approach uses existing genotype data and estimates telomere length by weighing the number of telomere length-associated variant alleles an individual carries with the published change in kb of telomere length. The analysis of the telomere length GRS resulted in an association between longer telomere length and increased NHL risk [four B-cell histologic types combined; odds ratio (OR) = 1.49, 95% CI 1.22-1.82,P-value = 8.5 × 10(-5)]. Subtype-specific analyses indicated that chronic lymphocytic leukemia or small lymphocytic lymphoma (CLL/SLL) was the principal NHL subtype contributing to this association (OR = 2.60, 95% CI 1.93-3.51,P-value = 4.0 × 10(-10)). Significant interactions were observed across strata of sex for CLL/SLL and marginal zone lymphoma subtypes as well as age for the follicular lymphoma subtype. Our results indicate that a genetic background that favors longer telomere length may increase NHL risk, particularly risk of CLL/SLL, and are consistent with earlier studies relating longer telomere length with increased NHL risk.