Charles Kooperberg

Common genetic variants buffered by Hsp90 are candidates for human diseases of signaling such as cancer. Like cancer, morphological abnormalities buffered by Hsp90 are discrete threshold traits with a continuous underlying basis of liability determining their probability of occurrence. QTL and deletion maps for one of the most frequent Hsp90-dependent abnormalities in Drosophila, deformed eye ( dfe ), were replicated across three genetically related artificial selection lines using strategies dependent on proximity to the dfe threshold and the direction of genetic and environmental effects. Up to 17 dfe loci (QTL) linked by 7 interactions were detected based on the ability of small recombinant regions of an unaffected and completely homozygous control genotype to dominantly suppress or enhance dfe penetrance at its threshold in groups of isogenic recombinant flies, and over 20 deletions increased dfe penetrance from a low expected value in one or more line, identifying a complex network of genes responsible for the dfe phenotype. Replicated comparisons of these whole-genome mapping approaches identified several QTL regions narrowly defined by deletions and 4 candidate genes, with additional uncorrelated QTL and deletions highlighting differences between the approaches and the need for caution in attributing the effect of deletions directly to QTL genes.

African-American (AA) women have earlier menarche on average than women of European ancestry (EA), and earlier menarche is a risk factor for obesity and type 2 diabetes among other chronic diseases. Identification of common genetic variants associated with age at menarche has a potential value in pointing to the genetic pathways underlying chronic disease risk, yet comprehensive genome-wide studies of age at menarche are lacking for AA women. In this study, we tested the genome-wide association of self-reported age at menarche with common single-nucleotide polymorphisms (SNPs) in a total of 18 089 AA women in 15 studies using an additive genetic linear regression model, adjusting for year of birth and population stratification, followed by inverse-variance weighted meta-analysis (Stage 1). Top meta-analysis results were then tested in an independent sample of 2850 women (Stage 2). First, while no SNP passed the pre-specified P < 5 × 10⁻⁸ threshold for significance in Stage 1, suggestive associations were found for variants near FLRT2 and PIK3R1, and conditional analysis identified two independent SNPs (rs339978 and rs980000) in or near RORA, strengthening the support for this suggestive locus identified in EA women. Secondly, an investigation of SNPs in 42 previously identified menarche loci in EA women demonstrated that 25 (60%) of them contained variants significantly associated with menarche in AA women. The findings provide the first evidence of cross-ethnic generalization of menarche loci identified to date, and suggest a number of novel biological links to menarche timing in AA women.

High-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglyceride (TG) levels are influenced by both genes and the environment. Genome-wide association studies (GWAS) have identified ~100 common genetic variants associated with HDL-C, LDL-C, and/or TG levels, mostly in populations of European descent, but little is known about the modifiers of these associations. Here, we investigated whether GWAS-identified SNPs for lipid traits exhibited heterogeneity by sex in the Population Architecture using Genomics and Epidemiology (PAGE) study. A sex-stratified meta-analysis was performed for 49 GWAS-identified SNPs for fasting HDL-C, LDL-C, and ln(TG) levels among adults self-identified as European American (25,013). Heterogeneity by sex was established when phet < 0.001. There was evidence for heterogeneity by sex for two SNPs for ln(TG) in the APOA1/C3/A4/A5/BUD13 gene cluster: rs28927680 (phet = 7.4x10-7) and rs3135506 (phet = 4.3x10-4), one SNP in PLTP for HDL levels (rs7679; phet = 9.9x10-4), and one in HMGCR for LDL levels (rs12654264; phet = 3.1x10-5). We replicated heterogeneity by sex in five of seventeen loci previously reported by genome-wide studies (binomial p = 0.0009). We also present results for other racial/ethnic groups in the supplementary materials, to provide a resource for future meta-analyses. We provide further evidence for sex-specific effects of SNPs in the APOA1/C3/A4/A5/BUD13 gene cluster, PLTP, and HMGCR on fasting triglyceride levels in European Americans from the PAGE study. Our findings emphasize the need for considering context-specific effects when interpreting genetic associations emerging from GWAS, and also highlight the difficulties in replicating interaction effects across studies and across racial/ethnic groups.

Several two-stage multiple testing procedures have been proposed to detect gene-environment interaction in genome-wide association studies. In this article, we elucidate general conditions that are required for validity and power of these procedures, and we propose extensions of two-stage procedures using the case-only estimator of gene-treatment interaction in randomized clinical trials. We develop a unified estimating equation approach to proving asymptotic independence between a filtering statistic and an interaction test statistic in a range of situations, including marginal association and interaction in a generalized linear model with a canonical link. We assess the performance of various two-stage procedures in simulations and in genetic studies from Women’s Health Initiative clinical trials.

Unraveling the nature of genetic interactions is crucial to obtaining a more complete picture of complex diseases. It is thought that gene‐gene interactions play an important role in the etiology of cancer, cardiovascular, and immune‐mediated disease. Interactions among genes are defined as phenotypic effects that differ from those observed for independent contributions of each gene, usually detected by univariate logistic regression methods. Using a multivariate extension of linkage disequilibrium (LD), we have developed a new method, based on distances between sample covariance matrices for groups of single nucleotide polymorphisms (SNPs), to test for interaction effects of two groups of genes associated with a disease phenotype. Since a disease‐associated interacting locus will often be in LD with more than one marker in the region, a method that examines a set of markers in a region collectively can offer greater power than traditional methods. Our method effectively identifies interaction effects in simulated data, as well as in data on the genetic contributions to the risk for graft‐versus‐host disease following hematopoietic stem cell transplantation.

We examined the association between HNF1B variants identified in a recent genome-wide association study and endometrial cancer in two large case-control studies nested in prospective cohorts: the Multiethnic Cohort Study (MEC) and the Women's Health Initiative (WHI) as part of the Population Architecture using Genomics and Epidemiology (PAGE) study. A total of 1,357 incident cases of invasive endometrial cancer and 7,609 controls were included in the analysis (MEC: 426 cases/3,854 controls; WHI: 931cases/3,755 controls). The majority of women in the WHI were European American, while the MEC included sizable numbers of African Americans, Japanese and Latinos. We estimated the odds ratios (ORs) per allele and 95% confidence intervals (CIs) of each SNP using unconditional logistic regression adjusting for age, body mass index, and four principal components of ancestry informative markers. The combined ORs were estimated using fixed effect models. Rs4430796 and rs7501939 were associated with endometrial cancer risk in MEC and WHI with no heterogeneity observed across racial/ethnic groups (P≥0.21) or between studies (P≥0.70). The OR_{per allele} was 0.82 (95% CI: 0.75, 0.89; P = 5.63×10⁻⁶) for rs4430796 (G allele) and 0.79 (95% CI: 0.73, 0.87; P = 3.77×10⁻⁷) for rs7501939 (A allele). The associations with the risk of Type I and Type II tumors were similar (P≥0.19). Adjustment for additional endometrial cancer risk factors such as parity, oral contraceptive use, menopausal hormone use, and smoking status had little effect on the results. In conclusion, HNF1B SNPs are associated with risk of endometrial cancer and that the associated relative risks are similar for Type I and Type II tumors.

Objective—: To test whether estrogen receptor polymorphisms modify the effects of postmenopausal hormone therapy on biomarkers and on risk of coronary heart disease events, stroke, or venous thromboembolism. Methods and Results—: The design was a nested case-control study in the Women's Health Initiative trials of postmenopausal hormone therapy. The study included all cases in the first 4 years: 359 cases of coronary heart disease, 248 of stroke, and 217 of venous thromboembolism. Six estrogen receptor-α polymorphisms and 1 estrogen receptor-β polymorphism were genotyped; 8 biomarkers known to be affected by hormone therapy were measured at baseline and 1 year after randomization. The polymorphisms were not associated with risk of vascular events and did not modify the increased risks of coronary heart disease, stroke, or venous thromboembolism due to hormone therapy. However, a reduced response of plasmin-antiplasmin to hormone therapy was noted for estrogen receptor-1 IVS1 (intron number 1)-354 (interaction P P =0.014) and estrogen receptor-1 IVS1-1415 (interaction P P =0.014). Conclusion—: Estrogen receptor polymorphisms reduce the effect of postmenopausal hormone therapy on plasmin-antiplasmin, a marker of coagulation and fibrinolysis. However, screening for estrogen receptor polymorphisms to identify women at less risk of adverse cardiovascular outcomes is not likely to be useful in making decisions about hormone therapy treatment.

Over the last few years, many new genetic associations have been identified by genome‐wide association studies (GWAS). There are potentially many uses of these identified variants: a better understanding of disease etiology, personalized medicine, new leads for studying underlying biology, and risk prediction. Recently, there has been some skepticism regarding the prospects of risk prediction using GWAS, primarily motivated by the fact that individual effect sizes of variants associated with the phenotype are mostly small. However, there have also been arguments that many disease‐associated variants have not yet been identified; hence, prospects for risk prediction may improve if more variants are included. From a risk prediction perspective, it is reasonable to average a larger number of predictors, of which some may have (limited) predictive power, and some actually may be noise. The idea being that when added together, the combined small signals results in a signal that is stronger than the noise from the unrelated predictors. We examine various aspects of the construction of models for the estimation of disease probability. We compare different methods to construct such models, to examine how implementation of cross‐validation may influence results, and to examine which single nucleotide polymorphisms (SNPs) are most useful for prediction. We carry out our investigation on GWAS of the Welcome Trust Case Control Consortium. For Crohn's disease, we confirm our results on another GWAS. Our results suggest that utilizing a larger number of SNPs than those which reach genome‐wide significance, for example using the lasso, improves the construction of risk prediction models.Genet. Epidemiol. 34: 643‐652, 2010.