Efficient Software for Multi-marker, Region-Based Analysis of GWAS Data

Genetic Architecture of Gene Expression in European and African Americans: An eQTL Mapping Study in GENOA

Most existing expression quantitative trait locus (eQTL) mapping studies have been focused on individuals of European ancestry and are underrepresented in other populations including populations with African ancestry. Lack of large-scale well-powered eQTL mapping studies in populations with African ancestry can both impede the dissemination of eQTL mapping results that would otherwise benefit individuals with African ancestry and hinder the comparable analysis for understanding how gene regulation is shaped through evolution. We fill this critical knowledge gap by performing a large-scale in-depth eQTL mapping study on 1,032 African Americans (AA) and 801 European Americans (EA) in the GENOA cohort. We identified a total of 354,931 eSNPs in AA and 371,309 eSNPs in EA, with 112,316 eSNPs overlapped between the two. We found that eQTL harboring genes (eGenes) are enriched in metabolic pathways and tend to have higher SNP heritability compared to non-eGenes. We found that eGenes that are common in the two populations tend to be less conserved than eGenes that are unique to one population, which are less conserved than non-eGenes. Through conditional analysis, we found that eGenes in AA tend to harbor more independent eQTLs than eGenes in EA, suggesting potentially diverse genetic architecture underlying expression variation in the two populations. Finally, the large sample sizes in GENOA allow us to construct accurate expression prediction models in both AA and EA, facilitating powerful transcriptome-wide association studies. Overall, our results represent an important step toward revealing the genetic architecture underlying expression variation in African Americans.

Genome-Wide Analysis of MDR and XDR Tuberculosis from Belarus: Machine-Learning Approach

Emergence of drug-resistant microorganisms has been recognized as a serious threat to public health worldwide. This problem is extensively discussed in the context of tuberculosis treatment. Alterations in pathogen genomes are among the main mechanisms by which microorganisms exhibit drug resistance. Analysis of 144 M. tuberculosis strains of different phenotypes including drug susceptible, MDR, and XDR isolated in Belarus was fulfilled in this paper. A wide range of machine learning methods that can discover SNPs related to drug-resistance in the whole bacteria genomes was investigated. Besides single-SNP testing approaches, methods that allow detecting joint effects from interacting SNPs were considered. We proposed a framework for automated selection of the best performing statistical model in terms of recall, precision, and accuracy to identify drug resistance-associated mutations. Analysis of whole-genome sequences often leads to situations where the number of treated features exceeds the number of available observations. For this reason, special attention is paid to fair evaluation of the model prediction quality and minimizing the risk of overfitting while estimating the underlying parameters. Results of our experiments aimed at identifying top-scoring resistance mutations to the major first-line and second-line anti-TB drugs are presented.

Gene-level associations in suicide attempter families show overrepresentation of synaptic genes and genes differentially expressed in brain development

Suicidal behavior (SB) has a complex etiology involving different polygenic and environmental components. Here we used an excess of significant markers (ESM) test to study gene-level associations in previous genome-wide association studies (GWAS) SNP data from a family-based sample, having medically severe suicide attempt (SA) as main outcome in the offspring. In SA without major psychiatric disorders (N = 498), a screening of 5,316 genes across the genome suggested association 17 genes (at fdr < 0.05). Genes RETREG1 (a.k.a. FAM134B), GSN, GNAS, and CACNA1D were particularly robust to different methodological variations. Comparison with the more widely used Multi-marker Analysis of GenoMic Annotation (MAGMA) methods, mainly supported RETREG1, GSN, RNASEH2B, UBE2H, and CACNA1D by using the "mean" model, and ranked 13 of the same genes as ESM among its top-17. Complementing the ESM screen by using MAGMA to analyze 17,899 genes, we observed excess of genes with p < .05 by using the "top" model, and the "mean" model suggested additional genes with genome-wide fdr < 0.25. Overrepresentation analysis of 10 selected gene sets using all genes with p < .05, showed significant results for synaptic genes, genes differentially expressed in brain development and for ~12% of the SA polygenic association genes identified previously in this sample. Exploratory analysis linked some of the ESM top-17 genes to psychotropic drugs and we examined the allelic heterogeneity in the previous SA candidate GRIN2B. This study complemented previous GWAS on SB outcomes, implicating both previous candidate (e.g., GRIN2B and GNAS) and novel genes in SA outcomes, as well as synaptic functions and brain development.

A Model of Compound Heterozygous, Loss-of-Function Alleles Is Broadly Consistent with Observations from Complex-Disease GWAS Datasets

The genetic component of complex disease risk in humans remains largely unexplained. A corollary is that the allelic spectrum of genetic variants contributing to complex disease risk is unknown. Theoretical models that relate population genetic processes to the maintenance of genetic variation for quantitative traits may suggest profitable avenues for future experimental design. Here we use forward simulation to model a genomic region evolving under a balance between recurrent deleterious mutation and Gaussian stabilizing selection. We consider multiple genetic and demographic models, and several different methods for identifying genomic regions harboring variants associated with complex disease risk. We demonstrate that the model of gene action, relating genotype to phenotype, has a qualitative effect on several relevant aspects of the population genetic architecture of a complex trait. In particular, the genetic model impacts genetic variance component partitioning across the allele frequency spectrum and the power of statistical tests. Models with partial recessivity closely match the minor allele frequency distribution of significant hits from empirical genome-wide association studies without requiring homozygous effect sizes to be small. We highlight a particular gene-based model of incomplete recessivity that is appealing from first principles. Under that model, deleterious mutations in a genomic region partially fail to complement one another. This model of gene-based recessivity predicts the empirically observed inconsistency between twin and SNP based estimated of dominance heritability. Furthermore, this model predicts considerable levels of unexplained variance associated with intralocus epistasis. Our results suggest a need for improved statistical tools for region based genetic association and heritability estimation.

Gene action determines how mutations affect phenotype. When placed in an evolutionary context, the details of the genotype-to-phenotype model can impact the maintenance of genetic variation for complex traits. Likewise, non-equilibrium demographic history may affect patterns of genetic variation. Here, we explore the impact of genetic model and population growth on distribution of genetic variance across the allele frequency spectrum underlying risk for a complex disease. Using forward-in-time population genetic simulations, we show that the genetic model has important impacts on the composition of variation for complex disease risk in a population. We explicitly simulate genome-wide association studies (GWAS) and perform heritability estimation on population samples. A particular model of gene-based partial recessivity, based on allelic non-complementation, aligns well with empirical results. This model is congruent with the dominance variance estimates from both SNPs and twins, and the minor allele frequency distribution of GWAS hits.