Biology subjects

Andrew A Brown

Publications and source records attributed to Andrew A Brown.

5 recordsLinked to original sources

bioRxiv2016

Quantifying the regulatory effect size of cis-acting genetic variation using allelic fold change

Mapping cis-acting expression quantitative trait loci (cis-eQTL) has become a popular approach for characterizing proximal genetic regulatory variants. However, measures used for quantifying the effect size of cis-eQTLs have been inconsistent and poorly defined. In this paper, we describe log allelic fold change (aFC) as a biologically interpretable and mathematically convenient unit that represents the magnitude of expression change associated with a given genetic variant. This measure is mathematically independent from expression level and allele frequency, applicable to multi-allelic variants, and generalizable to multiple independent variants. We provide tools and guidelines for estimating aFC from eQTL and allelic expression data sets, and apply it to GTEx data. We show that aFC estimates independently derived from eQTL and allelic expression data are highly consistent, and identify technical and biological correlates of eQTL effect size. We generalize aFC to analyze genes with two eQTLs in GTEx, and show that in nearly all cases these eQTLs are independent in their regulatory activity. In summary, aFC is a solid measure of cis-regulatory effect size that allows quantitative interpretation of cellular regulatory events from population data, and it is a valuable approach for investigating novel aspects of eQTL data sets.

Bioinformatics↗

bioRxiv2016

Estimating the causal tissues for complex traits and diseases

Interpretation of biological causes of the predisposing markers identified through Genome Wide Association Studies (GWAS) remains an open question1. One direct and powerful way to assess the genetic causality behind GWAS is through expression quantitative trait loci (eQTLs)2. Here we describe a novel approach to estimate the tissues giving rise to the genetic causality behind a wide variety of GWAS traits, using the cis-eQTLs identified in 44 tissues of the GTEx consortium3,4. We have adapted the Regulatory Trait Concordance (RTC) score5, to on the one hand measure the tissue sharing probabilities of eQTLs, and also to calculate the probability that a GWAS and an eQTL variant tag the same underlying functional effect. We show that our tissue sharing estimates significantly correlate with commonly used estimates of tissue sharing. By normalizing the GWAS-eQTL probabilities with the tissue sharing estimates of the eQTLs, we can estimate the tissues from which GWAS genetic causality arises. Our approach not only indicates the gene mediating individual GWAS signals, but also can highlight tissues where the genetic causality for an individual trait is manifested.

Genetics↗

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↗

bioRxiv2014

Transcriptome Sequencing Reveals Widespread Gene-Gene and Gene-Environment Interactions

Understanding the genetic architecture of gene expression is an intermediate step to understand the genetic architecture of complex diseases. RNA-seq technologies have improved the quantification of gene expression and allow to measure allelic specific expression (ASE)1-3. ASE is hypothesized to result from the direct effect of cis regulatory variants, but a proper estimation of the causes of ASE has not been performed to date. In this study we take advantage of a sample of twins to measure the relative contribution of genetic and environmental effects on ASE and we found substantial effects of gene x gene (GxG) and gene x environment (GxE) interactions. We propose a model where ASE requires genetic variability in cis, a difference in the sequence of both alleles, but the magnitude of the ASE effect depends on trans genetic and environmental factors that interact with the cis genetic variants. We uncover large GxG and GxE effects on gene expression and likely complex phenotypes that currently remain elusive.

Genomics↗