A framework for interpreting genome-wide association studies of psychiatric disorders

Genome-wide association studies (GWAS) have yielded a plethora of new findings in the past 3 years. By early 2009, GWAS on 47 samples of subjects with attention-deficit hyperactivity disorder, autism, bipolar disorder, major depressive disorder and schizophrenia will be completed. Taken together, these GWAS constitute the largest biological experiment ever conducted in psychiatry (59 000 independent cases and controls, 7700 family trios and >40 billion genotypes). We know that GWAS can work, and the question now is whether it will work for psychiatric disorders. In this review, we describe these studies, the Psychiatric GWAS Consortium for meta-analyses of these data, and provide a logical framework for interpretation of some of the conceivable outcomes

Methods for detecting associations between phenotype and aggregations of rare variants

Although genome-wide association studies have uncovered variants associated with more than 150 traits, the percentage of phenotypic variation explained by these associations remains small. This has led to the search for the dark matter that explains this missing genetic component of heritability. One potential explanation for dark matter is rare variants, and several statistics have been devised to detect associations resulting from aggregations of rare variants in relatively short regions of interest, such as candidate genes. In this paper we investigate the feasibility of extending this approach in an agnostic way, in which we consider all variants within a much broader region of interest, such as an entire chromosome or even the entire exome. Our method searches for subsets of variant sites using either Markov chain Monte Carlo or genetic algorithms. The analysis was performed with knowledge of the Genetic Analysis Workshop 17 answers

LocusZoom: regional visualization of genome-wide association scan results

Summary: Genome-wide association studies (GWAS) have revealed hundreds of loci associated with common human genetic diseases and traits. We have developed a web-based plotting tool that provides fast visual display of GWAS results in a publication-ready format. LocusZoom visually displays regional information such as the strength and extent of the association signal relative to genomic position, local linkage disequilibrium (LD) and recombination patterns and the positions of genes in the region

Challenges and directions: an analysis of Genetic Analysis Workshop 17 data by collapsing rare variants within family data

Recent studies suggest that the traditional case-control study design does not have sufficient power to discover rare risk variants. Two different methods—collapsing and family data—are suggested as alternatives for discovering these rare variants. Compared with common variants, rare variants have unique characteristics. In this paper, we assess the distribution of rare variants in family data. We notice that a large number of rare variants exist only in one or two families and that the association result is largely shaped by those families. Therefore we explore the possibility of integrating both the collapsing method and the family data method. This combinational approach offers a potential power boost for certain causal genes, including VEGFA, VEGFC, SIRT1, SREBF1, PIK3R3, VLDLR, PLAT, and FLT4, and thus deserves further investigation

Acculturation Is Associated With Hypertension in a Multiethnic Sample

Background: Hypertension varies in prevalence among race/ethnic groups in the United States. Within-ethnic group differences associated with acculturation have been less frequently examined. We studied the association of three measures of acculturation (language spoken at home, place of birth, and years living in the US) with hypertension in a population sample of 2619 white, 1898 African American, 1,494 Hispanic, and 803 Chinese participants in the Multiethnic Study of Atherosclerosis. Methods: Multivariate Poisson regression was used to estimate the association between the acculturation variables and hypertension. Results: Birthplace outside the US and speaking a non-English language at home were each associated with a lower prevalence of hypertension after adjustment for age, gender, and socioeconomic status (prevalence ratio [95% confidence intervals] 0.82 (0.77–0.87) for non-US born versus US born and 0.80 (0.74–0.85) for those not speaking English at home versus speakers of English at home, both P < .001). For participants born outside of the US, each 10-year increment of years in the US was associated with a higher prevalence of hypertension after adjustment for age, gender, and socioeconomic status (P for trend < .01). The associations between acculturation variables and hypertension were weakened after adjustment for race/ethnic category and risk factors for hypertension. Compared to US-born Hispanics, those born in Mexico or South America had lower prevalence of hypertension, but those born in the Caribbean and Central America had higher prevalence of hypertension. Conclusions: Acculturation and place of birth are associated with hypertension in a multiethnic sample.http://deepblue.lib.umich.edu/bitstream/2027.42/57776/1/Acculturation is associated with hypertension in a multiethnic sample.pd

JEPEGMIX2: improved gene-level joint analysis of eQTLs in cosmopolitan cohorts.

Motivation: To increase detection power, researchers use gene level analysis methods to aggregate weak marker signals. Due to gene expression controlling biological processes, researchers proposed aggregating signals for expression Quantitative Trait Loci (eQTL). Most gene-level eQTL methods make statistical inferences based on i) summary statistics from genome-wide association studies (GWAS) and ii) linkage disequilibrium (LD) patterns from a relevant reference panel. While most such tools assume homogeneous cohorts, our Gene-level Joint Analysis of functional SNPs in Cosmopolitan Cohorts (JEPEGMIX) method accommodates cosmopolitan cohorts by using heterogeneous panels. However, JEPGMIX relies on brain eQTLs from older gene expression studies and does not adjust for background enrichment in GWAS signals. Results: We propose JEPEGMIX2, an extension of JEPEGMIX. When compared to JPEGMIX, it uses i) cis-eQTL SNPs from the latest expression studies and ii) brains specific (sub)tissues and tissues other than brain. JEPEGMIX2 also i) avoids accumulating averagely enriched polygenic information by adjusting for background enrichment and ii), to avoid an increase in false positive rates for studies with numerous highly enriched (above the background) genes, it outputs gene q-values based on Holm adjustment of p-values. Supplementary information: Supplementary material is available at Bioinformatics online. Bioinformatics 2018; 34(2):286-28

A LASSO-based approach to analyzing rare variants in genetic association studies

Genetic markers with rare variants are spread out in the genome, making it necessary and difficult to consider them in genetic association studies. Consequently, wisely combining rare variants into “composite” markers may facilitate meaningful analyses. In this paper, we propose a novel approach of analyzing rare variant data by incorporating the least absolute shrinkage and selection operator technique. We applied this method to the Genetic Analysis Workshop 17 data, and our results suggest that this new approach is promising. In addition, we took advantage of having 200 phenotype replications and assessed the performance of our approach by means of repeated classification tree analyses. Our method and analyses were performed without knowledge of the underlying simulating model. Our method identified 38 markers (in 65 genes) that are significantly associated with the phenotype Affected and correctly identified two causal genes, SIRT1 and PDGFD

Detection of regulator genes and eQTLs in gene networks

Genetic differences between individuals associated to quantitative phenotypic traits, including disease states, are usually found in non-coding genomic regions. These genetic variants are often also associated to differences in expression levels of nearby genes (they are "expression quantitative trait loci" or eQTLs for short) and presumably play a gene regulatory role, affecting the status of molecular networks of interacting genes, proteins and metabolites. Computational systems biology approaches to reconstruct causal gene networks from large-scale omics data have therefore become essential to understand the structure of networks controlled by eQTLs together with other regulatory genes, and to generate detailed hypotheses about the molecular mechanisms that lead from genotype to phenotype. Here we review the main analytical methods and softwares to identify eQTLs and their associated genes, to reconstruct co-expression networks and modules, to reconstruct causal Bayesian gene and module networks, and to validate predicted networks in silico.Comment: minor revision with typos corrected; review article; 24 pages, 2 figure

Analysis of human mini-exome sequencing data from Genetic Analysis Workshop 17 using a Bayesian hierarchical mixture model

Next-generation sequencing technologies are rapidly changing the field of genetic epidemiology and enabling exploration of the full allele frequency spectrum underlying complex diseases. Although sequencing technologies have shifted our focus toward rare genetic variants, statistical methods traditionally used in genetic association studies are inadequate for estimating effects of low minor allele frequency variants. Four our study we use the Genetic Analysis Workshop 17 data from 697 unrelated individuals (genotypes for 24,487 autosomal variants from 3,205 genes). We apply a Bayesian hierarchical mixture model to identify genes associated with a simulated binary phenotype using a transformed genotype design matrix weighted by allele frequencies. A Metropolis Hasting algorithm is used to jointly sample each indicator variable and additive genetic effect pair from its conditional posterior distribution, and remaining parameters are sampled by Gibbs sampling. This method identified 58 genes with a posterior probability greater than 0.8 for being associated with the phenotype. One of these 58 genes, PIK3C2B was correctly identified as being associated with affected status based on the simulation process. This project demonstrates the utility of Bayesian hierarchical mixture models using a transformed genotype matrix to detect genes containing rare and common variants associated with a binary phenotype