Biology subjects

Eric R Gamazon

Publications and source records attributed to Eric R Gamazon.

6 recordsLinked to original sources

bioRxiv2016

Distant regulatory effects of genetic variation in multiple human tissues

Understanding the genetics of gene regulation provides information on the cellular mechanisms through which genetic variation influences complex traits. Expression quantitative trait loci, or eQTLs, are enriched for polymorphisms that have been found to be associated with disease risk. While most analyses of human data has focused on regulation of expression by nearby variants (cis-eQTLs), distal or trans-eQTLs may have broader effects on the transcriptome and important phenotypic consequences, necessitating a comprehensive study of the effects of genetic variants on distal gene transcription levels. In this work, we identify trans-eQTLs in the Genotype Tissue Expression (GTEx) project data1, consisting of 449 individuals with RNA-sequencing data across 44 tissue types. We find 81 genes with a trans-eQTL in at least one tissue, and we demonstrate that trans-eQTLs are more likely than cis-eQTLs to have effects specific to a single tissue. We evaluate the genomic and functional properties of trans-eQTL variants, identifying strong enrichment in enhancer elements and Piwi-interacting RNA clusters. Finally, we describe three tissue-specific regulatory loci underlying relevant disease associations: 9q22 in thyroid that has a role in thyroid cancer, 5q31 in skeletal muscle, and a previously reported master regulator near KLF14 in adipose. These analyses provide a comprehensive characterization of trans-eQTLs across human tissues, which contribute to an improved understanding of the tissue-specific cellular mechanisms of regulatory genetic variation.

Genomics↗

bioRxiv2016

Local genetic effects on gene expression across 44 human tissues

Expression quantitative trait locus (eQTL) mapping provides a powerful means to identify functional variants influencing gene expression and disease pathogenesis. We report the identification of cis-eQTLs from 7,051 post-mortem samples representing 44 tissues and 449 individuals as part of the Genotype-Tissue Expression (GTEx) project. We find a cis-eQTL for 88% of all annotated protein-coding genes, with one-third having multiple independent effects. We identify numerous tissue-specific cis-eQTLs, highlighting the unique functional impact of regulatory variation in diverse tissues. By integrating large-scale functional genomics data and state-of-the-art fine-mapping algorithms, we identify multiple features predictive of tissue-specific and shared regulatory effects. We improve estimates of cis-eQTL sharing and effect sizes using allele specific expression across tissues. Finally, we demonstrate the utility of this large compendium of cis-eQTLs for understanding the tissue-specific etiology of complex traits, including coronary artery disease. The GTEx project provides an exceptional resource that has improved our understanding of gene regulation across tissues and the role of regulatory variation in human genetic diseases.

Genomics↗

bioRxiv2016

Genetic predictors of gene expression associated with risk of bipolar disorder

Bipolar disorder (BD) affects the quality of life of approximately 1% of the population and represents a major public health concern. It is known to be highly heritable but large-scale genome-wide association studies (GWAS) have discovered only a handful of markers associated with the disease. Furthermore, the biological mechanisms underlying these markers need to be elucidated. We recently published a gene-level association test, PrediXcan that integrates transcriptome regulation data to characterize the function of these markers in a tissue specific manner. In this study, we developed prediction models for mRNA levels in 10 brain regions using data from the GTEx project and performed PrediXcan analysis in WTCCC as well as in an independent cohort, GAIN. We replicate the association between predicted expression of PTPRE and BD risk in whole blood and recapitulate the association in brain tissues. PTPRE encodes the protein tyrosine phosphatase, receptor type E, that is known to be involved in RAS signaling and activation of voltage-gated K+ channels. We also found a new genome-wide significant association between lower predicted expression of BBX (bobby sox homolog) in the anterior cingulate cortex region of the brain and increased risk of BD (pWTCCC = 7.02 x 10-6, pGAIN = 4.68 x 10-3, pmeta = 1.11 x 10-7). In sum, we used our mechanistically informed approach, PrediXcan, to identify and replicate two novel genome-wide significant genes using existing GWAS studies.

Genetics↗

bioRxiv2016

SNP-based heritability estimation: measurement noise, population stratification and stability

Siddharth Krishna Kumar1 and co-authors claim to have shown that \"GCTA applied to current SNP data cannot produce reliable or stable estimates of heritability.\" Given the numerous recent studies on the genetic architecture of complex traits that are based on this methodology, these claims have important implications for the field. Through an investigation of the stability of the likelihood function under phenotype perturbation and an analysis of its dependence on the spectral properties of the genetic relatedness matrix, our study characterizes the properties of an important approach to the analysis of GWAS data and identified crucial errors in the authors analyses, invalidating their main conclusions.

Genomics↗

bioRxiv2016

An Ancestry Based Approach for Detecting Interactions

IBackgroundEpistasis and gene-environment interactions are known to contribute significantly to variation of complex phenotypes in model organisms. However, their identification in human association studies remains challenging for myriad reasons. In the case of epistatic interactions, the large number of potential interacting sets of genes presents computational, multiple hypothesis correction, and other statistical power issues. In the case of gene-environment interactions, the lack of consistently measured environmental covariates in most disease studies precludes searching for interactions and creates difficulties for replicating studies.\n\nResultsIn this work, we develop a new statistical approach to address these issues that leverages genetic ancestry in admixed populations. We applied our method to gene expression and methylation data from African American and Latino admixed individuals respectively, identifying nine interactions that were significant at p < 5x10-8, we show that two of the interactions in methylation data replicate, and the remaining six are significantly enriched for low p-values (p < 1.8x10-6).\n\nConclusionWe show that genetic ancestry can be a useful proxy for unknown and unmeasured covariates in the search for interaction effects. These results have important implications for our understanding of the genetic architecture of complex traits.

Genetics↗

bioRxiv2015

PrediXcan: Trait Mapping Using Human Transcriptome Regulation

Genome-wide association studies (GWAS) have identified thousands of variants robustly associated with complex traits. However, the biological mechanisms underlying these associations are, in general, not well understood. We propose a gene-based association method called PrediXcan that directly tests the molecular mechanisms through which genetic variation affects phenotype. The approach estimates the component of gene expression determined by an individuals genetic profile and correlates the \"imputed\" gene expression with the phenotype under investigation to identify genes involved in the etiology of the phenotype. The genetically regulated gene expression is estimated using whole-genome tissue-dependent prediction models trained with reference transcriptome datasets. PrediXcan enjoys the benefits of gene-based approaches such as reduced multiple testing burden, more comprehensive annotation of gene function compared to that derived from single variants, and a principled approach to the design of follow-up experiments while also integrating knowledge of regulatory function. Since no actual expression data are used in the analysis of GWAS data - only in silico expression - reverse causality problems are largely avoided. PrediXcan harnesses reference transcriptome data for disease mapping studies. Our results demonstrate that PrediXcan can detect known and novel genes associated with disease traits and provide insights into the mechanism of these associations.

Genomics↗