Impact of repeated measures and sample selection on genome‐wide association studies of fasting glucose

Although genome‐wide association studies (GWAS) have been performed in longitudinal studies, most used only a single trait measure. GWAS of fasting glucose have generally included only normoglycemic individuals. We examined the impact of both repeated measures and sample selection on GWAS in Atherosclerosis Risk In Communities (ARIC), a study which obtained four longitudinal measures of fasting glucose and included both individuals with and without prevalent diabetes. The sample included Caucasians and the Affymetrix 6.0 chip was used for genotyping. Sample sizes for GWAS analyses ranged from 8,372 (first study visit) to 5,782 (average fasting glucose). Candidate SNP analyses with SNPs identified through fasting glucose or diabetes GWAS were conducted in 9,133 individuals, including 761 with prevalent diabetes. For a constant sample size, smaller P‐values were obtained for the average measure of fasting glucose compared to values at any single visit, and two additional significant GWAS signals were detected. For four candidate SNPs (rs780094, rs10830963, rs7903146, and rs4607517), the strength of association between genotype and glucose was significantly (P‐interaction<0.05) different in those with and without prevalent diabetes, and for all five fasting glucose candidate SNPs (rs780094, rs10830963, rs560887, rs4607517, and rs13266634) the association with measured fasting glucose was more significant in the smaller sample without prevalent diabetes than in the larger combined sample of those with and without diabetes. This analysis demonstrates the potential utility of averaging trait values in GWAS studies and explores the advantage of using only individuals without prevalent diabetes in GWAS of fasting glucose. Genet. Epidemiol. 34: 665‐673, 2010. © 2010 Wiley‐Liss, Inc.     

Two‐Phase Designs to Follow‐Up Genome‐Wide Association Signals With DNA Resequencing Studies

Genome‐wide association studies (GWAS) of complex traits have generated many association signals for single nucleotide polymorphisms (SNPs). To understand the underlying causal genetic variant(s), focused DNA resequencing of targeted genomic regions is commonly used, yet the current cost of resequencing limits sample sizes for resequencing studies. Information from the large GWAS can be used to guide choice of samples for resequencing, such as the SNP genotypes in the targeted genomic region. Viewing the GWAS tag‐SNPs as imperfect surrogates for the underlying causal variants, yet expecting that the tag‐SNPs are correlated with the causal variants, a reasonable approach is a two‐phase case‐control design, with the GWAS serving as the first‐phase and the resequencing study serving as the second‐phase. Using stratified sampling based on both tag‐SNP genotypes and case‐control status, we explore the gains in power of a two‐phase design relative to randomly sampling cases and controls for resequencing (i.e., ignoring tag‐SNP genotypes). Simulation results show that stratified sampling based on both tag‐SNP genotypes and case‐control status is not likely to have lower power than stratified sampling based only on case‐control status, and can sometimes have substantially greater power. The gain in power depends on the amount of linkage disequilibrium between the tag‐SNP and causal variant alleles, as well as the effect size of the causal variant. Hence, the two‐phase design provides an efficient approach to follow‐up GWAS signals with DNA resequencing.     

Polymorphisms in sodium taurocholate cotransporting polypeptide are not associated with hepatitis B virus clearance in Chinese Tibetans and Uygurs

An association between polymorphisms in the sodium taurocholate cotransporting polypeptide (NTCP) and the natural course of hepatitis B virus (HBV) infection in the Chinese Han population has been noted. However, it is not known whether these polymorphisms are associated with the risk of developing chronic HBV infection in other racial or ethnic populations. Accordingly, we conducted a candidate single nucleotide polymorphism (SNP) association study in Tibetan and Uygur HBV-infected patients. A total of 1302 subjects including 871 Tibetans and 431 Uygurs were recruited. According to their serological and clinical characteristics, each ethnic group was divided into two groups comprising spontaneous clearance individuals and persistently infected patients. Three SNPs were genotyped by a high resolution melting curve methodology. Among the SNPs, rs2296651 exhibited a minor allele frequency of < 0.01. The frequency of allele A at rs4646287 was much higher in Tibetans (9.4% for Tibetans and 4.6% for Uygurs, p < 0.001) than in Uygurs, but the frequency of allele A at rs7154439 was the opposite (15.7% for Tibetans and 20.5% for Uygurs, p = 0.002). Irrespective of race, no significant differences in the frequency distributions of the three SNP alleles or genotypes were observed between the case and clearance groups. Moreover, none of the NTCP haplotypes were statistically different between the two groups. Data from the Tibetan patients could be grouped by HBeAg status, viral load and HBV genotype; however, no significant differences were found in the SNP genotype distribution for each characteristic. In conclusion, the NTCP polymorphisms we identified tended to be ethnicity-dependent. For Tibetans and Uygurs, no association of the three SNPs (rs7154439, rs4646287 and rs2296651) and their haplotypes with HBV chronicity was observed. Examination of SNPs in NTCP for their specific associations with the course of HBV infection in other ethnic minority groups is now required.     

Single Nucleotide Polymorphisms in Close Proximity to the Fibroblast Growth Factor 21 (FGF21) Gene Found to Be Associated with Sugar Intake in a Swedish Population

Hereditary mechanisms are partially responsible for individual differences in sensitivity to and the preference for sweet taste. The primary aim of this study was to examine the associations between 10 genetic variants and the intake of total sugar, added sugar, and sugars with sweet taste (i.e., monosaccharides and sucrose) in a middle-aged Swedish population. Two single nucleotide polymorphisms (SNPs) within the Fibroblast grow factor 21 (FGF21) gene, seven top hits from a genome-wide association study (GWAS) on total sugar intake, and one SNP within the fat mass and obesity associated (FTO) gene (the only SNP reaching GWAS significance in a previous study), were explored in relation to various forms of sugar intake in 22,794 individuals from the Malmö Diet and Cancer Study, a population-based cohort for which data were collected between 1991–1996. Significant associations (p = 6.82 × 10⁻⁷ − 1.53 × 10⁻³) were observed between three SNPs (rs838145, rs838133, and rs8103840) in close relation to the FGF21 gene with high Linkage Disequilibrium, and all the studied sugar intakes. For the rs11642841 within the FTO gene, associations were found exclusively among participants with a body mass index ≥ 25 (p < 5 × 10⁻³). None of the remaining SNPs studied were associated with sugar intake in our cohort. A further GWAS should be conducted to identify novel genetic variants associated with the intake of sugar.     

Consistency between cross-sectional and longitudinal SNP: blood lipid associations

Various studies have linked different genetic single nucleotide polymorphisms (SNPs) to different blood lipids (BL), but whether these “connections” were identified using cross-sectional or longitudinal (i.e., changes over time) designs has received little attention. Cross-sectional and longitudinal assessments of BL [total, high-, low-density lipoprotein cholesterol (TC, HDL, LDL), triglycerides (TG)] and non-genetic factors (body mass index, smoking, alcohol intake) were measured for 2,002 Geneva, Switzerland, adults during 1999–2008 (two measurements, median 6 years apart), and 20 SNPs in 13 BL metabolism-related genes. Fixed and mixed effects repeated measures linear regression models, respectively, were employed to identify cross-sectional and longitudinal SNP:BL associations among the 1,516 (76%) study participants who reported not being treated for hypercholesterolemia at either measurement time. One-third more (12 vs. 9) longitudinal than cross-sectional associations were found [Bonferroni-adjusted two-tailed p < 0.00125 (=0.05/2)/20) for each of the four ensembles of 20 SNP:individual BL associations tested under the two study designs]. There was moderate consistency between the cross-sectional and longitudinal findings, with eight SNP:BL associations consistently identified across both study designs: [APOE.2 and APOE.4 (rs7412 and rs429358)]:TC; HL/LIPC (rs2070895):HDL; [APOB (rs1367117), APOE.2 and APOE.4 (rs7412 and rs429358)]:LDL; [APOA5 (rs2072560) and APOC III (rs5128)]:TG. The results suggest that cross-sectional studies, which include most genome-wide association studies (GWAS), can assess the large majority of SNP:BL associations. In the present analysis, which was much less powered than a GWAS, the cross-sectional study was around 2/3 (67%) as efficient as the longitudinal study.     

The Impact of Improved Microarray Coverage and Larger Sample Sizes on Future Genome‐Wide Association Studies

Genome‐wide association studies (GWAS) have identified many single nucleotide polymorphisms (SNPs) associated with complex traits. However, the genetic heritability of most of these traits remains unexplained. To help guide future studies, we address the crucial question of whether future GWAS can detect new SNP associations and explain additional heritability given the new availability of larger GWAS SNP arrays, imputation, and reduced genotyping costs. We first describe the pairwise and imputation coverage of all SNPs in the human genome by commercially available GWAS SNP arrays, using the 1000 Genomes Project as a reference. Next, we describe the findings from 6 years of GWAS of 172 chronic diseases, calculating the power to detect each of them while taking array coverage and sample size into account. We then calculate the power to detect these SNP associations under different conditions using improved coverage and/or sample sizes. Finally, we estimate the percentages of SNP associations and heritability previously detected and detectable by future GWAS under each condition. Overall, we estimated that previous GWAS have detected less than one‐fifth of all GWAS‐detectable SNPs underlying chronic disease. Furthermore, increasing sample size has a much larger impact than increasing coverage on the potential of future GWAS to detect additional SNP‐disease associations and heritability.     

Genome-wide association study identifies three common variants associated with serologic response to vitamin E supplementation in men

Vitamin E inhibits lipid peroxidation in cell membranes, prevents oxidative damage to DNA by scavenging free radicals, and reduces carcinogen production. No study to our knowledge, however, has examined the association between genetic variants and response to long-term vitamin E supplementation. We conducted a genome-wide association study (GWAS) of common variants associated with circulating α-tocopherol concentrations following 3 y of controlled supplementation. The study population included 2112 middle-aged, male smokers in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study cohort who received a trial supplementation of α-tocopherol (50 mg/d) and had fasting serum α-tocopherol concentrations measured after 3 y. Serum concentrations were log-transformed for statistical analysis and general linear models adjusted for age, BMI, serum total cholesterol, and cancer case status. Associations with serum response to α-tocopherol supplementation achieved genome-wide significance for 2 single nucleotide polymorphisms (SNP): rs964184 on 11q23.3 (P = 2.6 × 10(-12)) and rs2108622 on 19pter-p13.11 (P = 2.2 × 10(-7)), and approached genome-wide significance for one SNP, rs7834588 on 8q12.3 (P = 6.2 × 10(-7)). Combined, these SNP explain 3.4% of the residual variance in serum α-tocopherol concentrations during controlled vitamin E supplementation. A GWAS has identified 3 genetic variants at different loci that appear associated with serum concentrations after vitamin E supplementation in men. Identifying genetic variants that influence serum nutrient biochemical status (e.g., α-tocopherol) under supplementation conditions improves our understanding of the biological determinants of these nutritional exposures and their associations with cancer etiology.     

Using GWAS top hits to inform priors in Bayesian fine-mapping association studies

The default causal single-nucleotide polymorphism (SNP) effect size prior in Bayesian fine-mapping studies is usually the Normal distribution. This choice is often based on computational convenience, rather than evidence that it is the most suitable prior distribution. The choice of prior is important because previous studies have shown considerable sensitivity of causal SNP Bayes factors to the form of the prior. In some well-studied diseases there are now considerable numbers of genome-wide association study (GWAS) top hits along with estimates of the number of yet-to-be-discovered causal SNPs. We show how the effect sizes of the top hits and estimates of the number of yet-to-be-discovered causal SNPs can be used to choose between the Laplace and Normal priors, to estimate the prior parameters and to quantify the uncertainty in this estimation. The methodology can readily be applied to other priors. We show that the top hits available from breast cancer GWAS provide overwhelming support for the Laplace over the Normal prior, which has important consequences for variant prioritisation. This work in this paper enables practitioners to derive more objective priors than are currently being used and could lead to prioritisation of different variants.     

Genome-wide association study of antibody response to smallpox vaccine

We performed a genome-wide association study (GWAS) of antibody levels in a multi-ethnic group of 1071 healthy smallpox vaccine recipients. In Caucasians, the most prominent association was found with promoter SNP rs10489759 in the LOC647132 pseudogene on chromosome 1 (p=7.77×10(-8)). In African-Americans, we identified eight genetic loci at p<5×10(-7). The SNP association with the lowest p-value (rs10508727, p=1.05×10(-10)) was in the Mohawk homeobox (MKX) gene on chromosome 10. Other candidate genes included LOC388460, GPR158, ZHX2, SPIRE1, GREM2, CSMD1, and RUNX1. In Hispanics, the top six associations between genetic variants and antibody levels had p-values less than 5×10(-7), with p=1.78×10(-10) for the strongest statistical association (promoter SNP rs12256830 in the PCDH15 gene). In addition, SNP rs4748153 in the immune response gene PRKCQ (protein kinase C, theta) was significantly associated with neutralizing antibody levels (p=2.51×10(-8)). Additional SNP associations in Hispanics (p≤3.40×10(-7)) were mapped to the KIF6/LOC100131899, CYP2C9, and ANKLE2/GOLGA3 genes. This study has identified candidate SNPs that may be important in regulating humoral immunity to smallpox vaccination. Replication studies, as well as studies elucidating the functional consequences of contributing genes and polymorphisms, are underway.     

Analysis of 60 Reported Glioma Risk SNPs Replicates Published GWAS Findings but Fails to Replicate Associations From Published Candidate‐Gene Studies

Genomewide association studies (GWAS) and candidate‐gene studies have implicated single‐nucleotide polymorphisms (SNPs) in at least 45 different genes as putative glioma risk factors. Attempts to validate these associations have yielded variable results and few genetic risk factors have been consistently replicated. We conducted a case‐control study of Caucasian glioma cases and controls from the University of California San Francisco (810 cases, 512 controls) and the Mayo Clinic (852 cases, 789 controls) in an attempt to replicate previously reported genetic risk factors for glioma. Sixty SNPs selected from the literature (eight from GWAS and 52 from candidate‐gene studies) were successfully genotyped on an Illumina custom genotyping panel. Eight SNPs in/near seven different genes (TERT, EGFR, CCDC26, CDKN2A, PHLDB1, RTEL1, TP53) were significantly associated with glioma risk in the combined dataset (P < 0.05), with all associations in the same direction as in previous reports. Several SNP associations showed considerable differences across histologic subtype. All eight successfully replicated associations were first identified by GWAS, although none of the putative risk SNPs from candidate‐gene studies was associated in the full case‐control sample (all P values > 0.05). Although several confirmed associations are located near genes long known to be involved in gliomagenesis (e.g., EGFR, CDKN2A, TP53), these associations were first discovered by the GWAS approach and are in noncoding regions. These results highlight that the deficiencies of the candidate‐gene approach lay in selecting both appropriate genes and relevant SNPs within these genes.     

Disproportionate Vitamin A Deficiency in Women of Specific Ethnicities Linked to Differences in Allele Frequencies of Vitamin A-Related Polymorphisms

Background: While the current national prevalence rate of vitamin A deficiency (VAD) is estimated to be less than 1%, it is suggested that it varies between different ethnic groups and races within the U.S. We assessed the prevalence of VAD in pregnant women of different ethnic groups and tested these prevalence rates for associations with the vitamin A-related single nucleotide polymorphism (SNP) allele frequencies in each ethnic group. Methods: We analyzed two independent datasets of serum retinol levels with self-reported ethnicities and the differences of allele frequencies of the SNPs associated with vitamin A metabolism between groups in publicly available datasets. Results: Non-Hispanic Black and Hispanic pregnant women showed high VAD prevalence in both datasets. Interestingly, the VAD prevalence for Hispanic pregnant women significantly differed between datasets (p = 1.973 × 10⁻¹⁰, 95%CI 0.04–0.22). Alleles known to confer the risk of low serum retinol (rs10882272 C and rs738409 G) showed higher frequencies in the race/ethnicity groups with more VAD. Moreover, minor allele frequencies of a set of 39 previously reported SNPs associated with vitamin A metabolism were significantly different between the populations of different ancestries than those of randomly selected SNPs (p = 0.030). Conclusions: Our analysis confirmed that VAD prevalence varies between different ethnic groups/races and may be causally associated with genetic variants conferring risk for low retinol levels. Assessing genetic variant information prior to performing an effective nutrient supplementation program will help us plan more effective food-based interventions.     

iFunMed: Integrative functional mediation analysis of GWAS and eQTL studies

Genome-wide association studies (GWAS) have successfully identified thousands of genetic variants contributing to disease and other phenotypes. However, significant obstacles hamper our ability to elucidate causal variants, identify genes affected by causal variants, and characterize the mechanisms by which genotypes influence phenotypes. The increasing availability of genome-wide functional annotation data is providing unique opportunities to incorporate prior information into the analysis of GWAS to better understand the impact of variants on disease etiology. Although there have been many advances in incorporating prior information into prioritization of trait-associated variants in GWAS, functional annotation data have played a secondary role in the joint analysis of GWAS and molecular (i.e., expression) quantitative trait loci (eQTL) data in assessing evidence for association. To address this, we develop a novel mediation framework, iFunMed, to integrate GWAS and eQTL data with the utilization of publicly available functional annotation data. iFunMed extends the scope of standard mediation analysis by incorporating information from multiple genetic variants at a time and leveraging variant-level summary statistics. Data-driven computational experiments convey how informative annotations improve single-nucleotide polymorphism (SNP) selection performance while emphasizing robustness of iFunMed to noninformative annotations. Application to Framingham Heart Study data indicates that iFunMed is able to boost detection of SNPs with mediation effects that can be attributed to regulatory mechanisms.     

Genetic susceptibility to Tuberculosis: Interaction between HLA-DQA1 and age of onset

Several genome-wide association studies (GWAS) identified new single nucleotide polymorphisms (SNPs) with susceptibility to Tuberculosis (TB). However, many of them were not replicated across ethnic groups. The cause of this phenomenon of genetic heterogeneity is uncertain. Here, we attempted to replicate and evaluate the mechanism that causes genetic heterogeneity in several putative TB predisposition loci found by previous GWAS, including chromosome 18q, ASAP1, DUSP14, and HLA-DQA1. A Chinese cohort of 1200 TB patients and 1280 population controls were genotyped. The results showed that genetic predisposition to TB might operate in an age-specific manner. While no significant association was found in the whole samples, a SNP of HLA-DQA1, rs9272785, showed suggestive association within the young-onset TB subgroup (onset at 20–40 years of age, N = 396). The results provide support for the hypothesis that there are different pathogenesis mechanisms causing clinical TB disease in different age groups, and that genetics probably play a substantial role only in young-onset TB.     

Fine‐Mapping Additive and Dominant SNP Effects Using Group‐LASSO and Fractional Resample Model Averaging

Genomewide association studies (GWAS) sometimes identify loci at which both the number and identities of the underlying causal variants are ambiguous. In such cases, statistical methods that model effects of multiple single‐nucleotide polymorphisms (SNPs) simultaneously can help disentangle the observed patterns of association and provide information about how those SNPs could be prioritized for follow‐up studies. Current multi‐SNP methods, however, tend to assume that SNP effects are well captured by additive genetics; yet when genetic dominance is present, this assumption translates to reduced power and faulty prioritizations. We describe a statistical procedure for prioritizing SNPs at GWAS loci that efficiently models both additive and dominance effects. Our method, LLARRMA‐dawg, combines a group LASSO procedure for sparse modeling of multiple SNP effects with a resampling procedure based on fractional observation weights. It estimates for each SNP the robustness of association with the phenotype both to sampling variation and to competing explanations from other SNPs. In producing an SNP prioritization that best identifies underlying true signals, we show the following: our method easily outperforms a single‐marker analysis; when additive‐only signals are present, our joint model for additive and dominance is equivalent to or only slightly less powerful than modeling additive‐only effects; and when dominance signals are present, even in combination with substantial additive effects, our joint model is unequivocally more powerful than a model assuming additivity. We also describe how performance can be improved through calibrated randomized penalization, and discuss how dominance in ungenotyped SNPs can be incorporated through either heterozygote dosage or multiple imputation.     

The Associations of Selenoprotein Genetic Variants with the Risks of Colorectal Adenoma and Colorectal Cancer: Case–Control Studies in Irish and Czech Populations

Background: Selenium manifests its biological effects through its incorporation into selenoproteins, which play several roles in countering oxidative and inflammatory responses implicated in colorectal carcinogenesis. Selenoprotein genetic variants may contribute to colorectal cancer (CRC) development, as we previously observed for SNP variants in a large European prospective study and a Czech case–control cohort. Methods: We tested if significantly associated selenoprotein gene SNPs from these studies were also associated with CRC risk in case–control studies from Ireland (colorectal neoplasia, i.e., cancer and adenoma cases: 450, controls: 461) and the Czech Republic (CRC cases: 718, controls: 646). Genotyping of 23 SNPs (20 in the Irish and 13 in the Czechs) was performed by competitive specific allele-specific PCR (KASPar). Multivariable adjusted logistic regression was used to assess the associations with CRC development. Results: We found significant associations with an increased CRC risk for rs5859 (SELENOF) and rs2972994 (SELENOP) in the Irish cohort but only with rs4802034 (SELENOV) in the Czechs. Significant associations were observed for rs5859 (SELENOF), rs4659382 (SELENON), rs2972994 (SELENOP), rs34713741 (SELENOS), and the related Se metabolism gene variant rs2275129 (SEPHS1) with advanced colorectal neoplasia development. However, none of these findings retained significance after multiple testing corrections. Conclusions: Several SNPs previously associated with CRC risk were also associated with CRC or colorectal neoplasia development in either the Irish or Czech cohorts. Selenoprotein gene variation may modify CRC risk across diverse European populations, although the specific variants may differ.     

Multiple SNP Set Analysis for Genome‐Wide Association Studies Through Bayesian Latent Variable Selection

The power of genome‐wide association studies (GWAS) for mapping complex traits with single‐SNP analysis (where SNP is single‐nucleotide polymorphism) may be undermined by modest SNP effect sizes, unobserved causal SNPs, correlation among adjacent SNPs, and SNP‐SNP interactions. Alternative approaches for testing the association between a single SNP set and individual phenotypes have been shown to be promising for improving the power of GWAS. We propose a Bayesian latent variable selection (BLVS) method to simultaneously model the joint association mapping between a large number of SNP sets and complex traits. Compared with single SNP set analysis, such joint association mapping not only accounts for the correlation among SNP sets but also is capable of detecting causal SNP sets that are marginally uncorrelated with traits. The spike‐and‐slab prior assigned to the effects of SNP sets can greatly reduce the dimension of effective SNP sets, while speeding up computation. An efficient Markov chain Monte Carlo algorithm is developed. Simulations demonstrate that BLVS outperforms several competing variable selection methods in some important scenarios.     

Two-phase stratified sampling designs for regional sequencing

By systematic examination of common tag single-nucleotide polymorphisms (SNPs) across the genome, the genome-wide association study (GWAS) has proven to be a successful approach to identify genetic variants that are associated with complex diseases and traits. Although the per base pair cost of sequencing has dropped dramatically with the advent of the next-generation technologies, it may still only be feasible to obtain DNA sequence data for a portion of available study subjects due to financial constraints. Two-phase sampling designs have been used frequently in large-scale surveys and epidemiological studies where certain variables are too costly to be measured on all subjects. We consider two-phase stratified sampling designs for genetic association, in which tag SNPs for candidate genes or regions are genotyped on all subjects in phase 1, and a proportion of subjects are selected into phase 2 based on genotypes at one or more tag SNPs. Deep sequencing in the region is then applied to genotype phase 2 subjects at sequence SNPs. We investigate alternative sampling designs for selection of phase 2 subjects within strata defined by tag SNP genotypes and develop methods of inference for sequence SNP variant associations using data from both phases. In comparison to methods that use data from phase 2 alone, the combined analysis improves efficiency.     

Interpreting Joint SNP Analysis Results: When Are Two Distinct Signals Really Two Distinct Signals?

In genetic association studies, much effort has focused on moving beyond the initial single‐nucleotide polymorphism (SNP)‐by‐SNP analysis. One approach is to reanalyze a chromosomal region where an association has been detected, jointly analyzing the SNP thought to best represent that association with each additional SNP in the region. Such joint analyses may help identify additional, statistically independent association signals. However, it is possible for a single genetic effect to produce joint SNP results that would typically be interpreted as two distinct effects (e.g., both SNPs are significant in the joint model). We present a general approach that can (1) identify conditions under which a single variant could produce a given joint SNP result, and (2) use these conditions to identify variants from a list of known SNPs (e.g., 1000 Genomes) as candidates that could produce the observed signal. We apply this method to our previously reported joint result for smoking involving rs16969968 and rs588765 in CHRNA5. We demonstrate that it is theoretically possible for a joint SNP result suggestive of two independent signals to be produced by a single causal variant. Furthermore, this variant need not be highly correlated with the two tested SNPs or have a large odds ratio. Our method aids in interpretation of joint SNP results by identifying new candidate variants for biological causation that would be missed by traditional approaches. Also, it can connect association findings that may seem disparate due to lack of high correlations among the associated SNPs.     

Association of obesity-related genetic variants with endometrial cancer risk: a report from the Shanghai Endometrial Cancer Genetics Study

Obesity is a well-established risk factor for endometrial cancer, the most common gynecologic malignancy. Recent genome-wide association studies (GWAS) have identified multiple genetic markers for obesity. The authors evaluated the association of obesity-related single nucleotide polymorphisms (SNPs) with endometrial cancer using GWAS data from their recently completed study, the Shanghai Endometrial Cancer Genetics Study, which comprised 832 endometrial cancer cases and 2,049 controls (1996-2005). Thirty-five SNPs previously associated with obesity or body mass index (BMI; weight (kg)/height (m)(2)) at a minimum significance level of ≤5 × 10(-7) in the US National Human Genome Research Institute's GWAS catalog (http://genome.gov/gwastudies) and representing 26 unique loci were evaluated by either direct genotyping or imputation. The authors found that for 22 of the 26 unique loci tested (84.6%), the BMI-associated risk variants were present at a higher frequency in cases than in population controls (P = 0.0003). Multiple regression analysis showed that 9 of 35 BMI-associated variants, representing 7 loci, were significantly associated (P ≤ 0.05) with the risk of endometrial cancer; for all but 1 SNP, the direction of association was consistent with that found for BMI. For consistent SNPs, the allelic odds ratios ranged from 1.15 to 1.29. These 7 loci are in the SEC16B/RASAL, TMEM18, MSRA, SOX6, MTCH2, FTO, and MC4R genes. The associations persisted after adjustment for BMI, suggesting that genetic markers of obesity provide value in addition to BMI in predicting endometrial cancer risk.     

乳腺癌全基因组关联研究的现状及其应用价值的分析方法

目的 从全基因组关联研究（GWAS）发现的与2型糖尿病（T2DM）发病风险相关的单核苷酸基因多态性（SNP）位点中,筛选出可用于预测妊娠期糖尿病（GDM）女性产后出现T2DM的SNP。 方法 利用2009 — 2010年天津市妊娠期糖尿病随机干预试验研究基线收集的1 240名GDM女性（80名T2DM和1 160名非T2DM）血样,检测并分析40个GWAS发现的与T2DM发病相关的SNP位点。基于单个SNP的遗传风险解释比例,结合产后T2DM的发生情况,联合采用ROC曲线下面积（AUC）、整合区分指数（IDI）及再分类净优化指数（NRI）进行候选SNP筛选。 结果 以遗传风险解释比例最大的SNP（rs10906115）为基础,依次纳入其他的SNP,共发现1个SNP可显著改善AUC（P = 0.019）,8个SNP可显著改善IDI（P < 0.05）,8个SNP可显著改善NRI（P < 0.05）。综合所有40个GWAS发现的SNP位点对AUC、IDI及NRI的改善情况,共有11个SNP（rs10906115,rs2779116,rs7034200,rs7041847,rs780094,rs5015480,rs11212617,rs831571,rs7944584,rs6815464,rs35767）可用于预测GDM女性产后发生T2DM的风险,同时11个SNP可累计解释5.5 %的GDM女性产后发生T2DM的遗传风险。 结论 为避免资源浪费,建议在众多GWAS发现的与T2DM发病相关的SNP位点中,选择能够显著改善风险预测能力或风险再分类能力的11个SNP用于预测GDM女性产后发生T2DM的风险。