Genetic Epidemiology最新文献

Two-step tests for gene–environment ($��G�\; �\times �\; �E�$) interactions exploit marginal single-nucleotide polymorphism (SNP) effects to improve the power of a genome-wide interaction scan. They combine a screening step based on marginal effects used to “bin” SNPs for weighted hypothesis testing in the second step to deliver greater power over single-step tests while preserving the genome-wide Type I error. However, the presence of many SNPs with detectable marginal effects on the trait of interest can reduce power by “displacing” true interactions with weaker marginal effects and by adding to the number of tests that need to be corrected for multiple testing. We introduce a new significance-based allocation into bins for Step-2 $��G�\; �\times �\; �E�$ testing that overcomes the displacement issue and propose a computationally efficient approach to account for multiple testing within bins. Simulation results demonstrate that these simple improvements can provide substantially greater power than current methods under several scenarios. An application to a multistudy collaboration for understanding colorectal cancer reveals a G × Sex interaction located near the SMAD7 gene.

The large-scale open access whole-exome sequencing (WES) data of the UK Biobank ~200,000 participants is accelerating a new wave of genetic association studies aiming to identify rare and functional loss-of-function (LoF) variants associated with complex traits and diseases. We proposed to merge the WES genotypes and the genome-wide genotyping (GWAS) genotypes of 167,000 UKB homogeneous European participants into a combined reference panel, and then to impute 241,911 UKB homogeneous European participants who had the GWAS genotypes only. We then used the imputed data to replicate association identified in the discovery WES sample. The average imputation accuracy measure r² is modest to high for LoF variants at all minor allele frequency intervals: 0.942 at MAF interval (0.01, 0.5), 0.807 at (1.0 × 10⁻³, 0.01), 0.805 at (1.0 × 10⁻⁴, 1.0 × 10⁻³), 0.664 at (1.0 × 10⁻⁵, 1.0 × 10⁻⁴) and 0.410 at (0, 1.0 × 10⁻⁵). As applications, we studied associations of LoF variants with estimated heel BMD and four lipid traits. In addition to replicating dozens of previously reported genes, we also identified three novel associations, two genes PLIN1 and ANGPTL3 for high-density-lipoprotein cholesterol and one gene PDE3B for triglycerides. Our results highlighted the strength of WES based genotype imputation as well as provided useful imputed data within the UKB cohort.

The clustering of proteins is of interest in cancer cell biology. This article proposes a hierarchical Bayesian model for protein (variable) clustering hinging on correlation structure. Starting from a multivariate normal likelihood, we enforce the clustering through prior modeling using angle-based unconstrained reparameterization of correlations and assume a truncated Poisson distribution (to penalize a large number of clusters) as prior on the number of clusters. The posterior distributions of the parameters are not in explicit form and we use a reversible jump Markov chain Monte Carlo based technique is used to simulate the parameters from the posteriors. The end products of the proposed method are estimated cluster configuration of the proteins (variables) along with the number of clusters. The Bayesian method is flexible enough to cluster the proteins as well as estimate the number of clusters. The performance of the proposed method has been substantiated with extensive simulation studies and one protein expression data with a hereditary disposition in breast cancer where the proteins are coming from different pathways.

The minor allele of rs373863828, a missense variant in CREB3 Regulatory Factor, is associated with several cardiometabolic phenotypes in Polynesian peoples. To better understand the variant, we tested the association of rs373863828 with a panel of correlated phenotypes (body mass index [BMI], weight, height, HDL cholesterol, triglycerides, and total cholesterol) using multivariate Bayesian association and network analyses in a Samoa cohort (n = 1632), Aotearoa New Zealand cohort (n = 1419), and combined cohort (n = 2976). An expanded set of phenotypes (adding estimated fat and fat-free mass, abdominal circumference, hip circumference, and abdominal-hip ratio) was tested in the Samoa cohort (n = 1496). In the Samoa cohort, we observed significant associations (log₁₀ Bayes Factor [BF] ≥ 5.0) between rs373863828 and the overall phenotype panel (8.81), weight (8.30), and BMI (6.42). In the Aotearoa New Zealand cohort, we observed suggestive associations (1.5 < log₁₀BF < 5) between rs373863828 and the overall phenotype panel (4.60), weight (3.27), and BMI (1.80). In the combined cohort, we observed concordant signals with larger log₁₀BFs. In the Samoa-specific expanded phenotype analyses, we also observed significant associations between rs373863828 and fat mass (5.65), abdominal circumference (5.34), and hip circumference (5.09). Bayesian networks provided evidence for a direct association of rs373863828 with weight and indirect associations with height and BMI.

Using high-dimensional genetic variants such as single nucleotide polymorphisms (SNP) to predict complex diseases and traits has important applications in basic research and other clinical settings. For example, predicting gene expression is a necessary first step to identify (putative) causal genes in transcriptome-wide association studies. Due to weak signals, high-dimensionality, and linkage disequilibrium (correlation) among SNPs, building such a prediction model is challenging. However, functional annotations at the SNP level (e.g., as epigenomic data across multiple cell- or tissue-types) are available and could be used to inform predictor importance and aid in outcome prediction. Existing approaches to incorporate annotations have been based mainly on (generalized) linear models. Bayesian additive regression trees (BART), in contrast, is a reliable method to obtain high-quality nonlinear out of sample predictions without overfitting. Unfortunately, the default prior from BART may be too inflexible to handle sparse situations where the number of predictors approaches or surpasses the number of observations. Motivated by our real data application, this article proposes an alternative prior based on the logit normal distribution because it provides a framework that is adaptive to sparsity and can model informative functional annotations. It also provides a framework to incorporate prior information about the between SNP correlations. Computational details for carrying out inference are presented along with the results from a simulation study and a genome-wide prediction analysis of the Alzheimer's Disease Neuroimaging Initiative data.

Mendelian randomization (MR) is the use of genetic variants to assess the existence of a causal relationship between a risk factor and an outcome of interest. Here, we focus on two-sample summary-data MR analyses with many correlated variants from a single gene region, particularly on cis-MR studies which use protein expression as a risk factor. Such studies must rely on a small, curated set of variants from the studied region; using all variants in the region requires inverting an ill-conditioned genetic correlation matrix and results in numerically unstable causal effect estimates. We review methods for variable selection and estimation in cis-MR with summary-level data, ranging from stepwise pruning and conditional analysis to principal components analysis, factor analysis, and Bayesian variable selection. In a simulation study, we show that the various methods have comparable performance in analyses with large sample sizes and strong genetic instruments. However, when weak instrument bias is suspected, factor analysis and Bayesian variable selection produce more reliable inferences than simple pruning approaches, which are often used in practice. We conclude by examining two case studies, assessing the effects of low-density lipoprotein-cholesterol and serum testosterone on coronary heart disease risk using variants in the HMGCR and SHBG gene regions, respectively.

There is an increasing interest in using multiple types of omics features (e.g., DNA sequences, RNA expressions, methylation, protein expressions, and metabolic profiles) to study how the relationships between phenotypes and genotypes may be mediated by other omics markers. Genotypes and phenotypes are typically available for all subjects in genetic studies, but typically, some omics data will be missing for some subjects, due to limitations such as cost and sample quality. In this article, we propose a powerful approach for mediation analysis that accommodates missing data among multiple mediators and allows for various interaction effects. We formulate the relationships among genetic variants, other omics measurements, and phenotypes through linear regression models. We derive the joint likelihood for models with two mediators, accounting for arbitrary patterns of missing values. Utilizing computationally efficient and stable algorithms, we conduct maximum likelihood estimation. Our methods produce unbiased and statistically efficient estimators. We demonstrate the usefulness of our methods through simulation studies and an application to the Metabolic Syndrome in Men study.

Populations of non-European ancestry are substantially underrepresented in genome-wide association studies (GWAS). As genetic effects can differ between ancestries due to possibly different causal variants or linkage disequilibrium patterns, a meta-analysis that includes GWAS of all populations yields biased estimation in each of the populations and the bias disproportionately impacts non-European ancestry populations. This is because meta-analysis combines study-specific estimates with inverse variance as the weights, which causes biases towards studies with the largest sample size, typical of the European ancestry population. In this paper, we propose two empirical Bayes (EB) estimators to borrow the strength of information across populations although accounting for between-population heterogeneity. Extensive simulation studies show that the proposed EB estimators are largely unbiased and improve efficiency compared to the population-specific estimator. In contrast, even though the meta-analysis estimator has a much smaller variance, it yields significant bias when the genetic effect is heterogeneous across populations. We apply the proposed EB estimators to a large-scale trans-ancestry GWAS of stroke and demonstrate that the EB estimators reduce the variance of the population-specific estimator substantially, with the effect estimates close to the population-specific estimates.