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Integrated genome-wide analysis of expression quantitative trait loci aids interpretation of genomic association studies.

ABSTRACT: BACKGROUND:Identification of single nucleotide polymorphisms (SNPs) associated with gene expression levels, known as expression quantitative trait loci (eQTLs), may improve understanding of the functional role of phenotype-associated SNPs in genome-wide association studies (GWAS). The small sample sizes of some previous eQTL studies have limited their statistical power. We conducted an eQTL investigation of microarray-based gene and exon expression levels in whole blood in a cohort of 5257 individuals, exceeding the single cohort size of previous studies by more than a factor of 2. RESULTS:We detected over 19,000 independent lead cis-eQTLs and over 6000 independent lead trans-eQTLs, targeting over 10,000 gene targets (eGenes), with a false discovery rate (FDR)?

SUBMITTER: Joehanes R

PROVIDER: S-EPMC5264466 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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Integrated genome-wide analysis of expression quantitative trait loci aids interpretation of genomic association studies.

Joehanes Roby R Zhang Xiaoling X Huan Tianxiao T Yao Chen C Ying Sai-Xia SX Nguyen Quang Tri QT Demirkale Cumhur Yusuf CY Feolo Michael L ML Sharopova Nataliya R NR Sturcke Anne A Schäffer Alejandro A AA Heard-Costa Nancy N Chen Han H Liu Po-Ching PC Wang Richard R Woodhouse Kimberly A KA Tanriverdi Kahraman K Freedman Jane E JE Raghavachari Nalini N Dupuis Josée J Johnson Andrew D AD O'Donnell Christopher J CJ Levy Daniel D Munson Peter J PJ

Genome biology 20170125 1

<h4>Background</h4>Identification of single nucleotide polymorphisms (SNPs) associated with gene expression levels, known as expression quantitative trait loci (eQTLs), may improve understanding of the functional role of phenotype-associated SNPs in genome-wide association studies (GWAS). The small sample sizes of some previous eQTL studies have limited their statistical power. We conducted an eQTL investigation of microarray-based gene and exon expression levels in whole blood in a cohort of 52 ...[more]

PMID: 28122634

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Project description:There is considerable evidence that human genetic variation influences gene expression. Genome-wide studies have revealed that mRNA levels are associated with genetic variation in or close to the gene coding for those mRNA transcripts - cis effects, and elsewhere in the genome - trans effects. The role of genetic variation in determining protein levels has not been systematically assessed. Using a genome-wide association approach we show that common genetic variation influences levels of clinically relevant proteins in human serum and plasma. We evaluated the role of 496,032 polymorphisms on levels of 42 proteins measured in 1200 fasting individuals from the population based InCHIANTI study. Proteins included insulin, several interleukins, adipokines, chemokines, and liver function markers that are implicated in many common diseases including metabolic, inflammatory, and infectious conditions. We identified eight Cis effects, including variants in or near the IL6R (p = 1.8x10(-57)), CCL4L1 (p = 3.9x10(-21)), IL18 (p = 6.8x10(-13)), LPA (p = 4.4x10(-10)), GGT1 (p = 1.5x10(-7)), SHBG (p = 3.1x10(-7)), CRP (p = 6.4x10(-6)) and IL1RN (p = 7.3x10(-6)) genes, all associated with their respective protein products with effect sizes ranging from 0.19 to 0.69 standard deviations per allele. Mechanisms implicated include altered rates of cleavage of bound to unbound soluble receptor (IL6R), altered secretion rates of different sized proteins (LPA), variation in gene copy number (CCL4L1) and altered transcription (GGT1). We identified one novel trans effect that was an association between ABO blood group and tumour necrosis factor alpha (TNF-alpha) levels (p = 6.8x10(-40)), but this finding was not present when TNF-alpha was measured using a different assay , or in a second study, suggesting an assay-specific association. Our results show that protein levels share some of the features of the genetics of gene expression. These include the presence of strong genetic effects in cis locations. The identification of protein quantitative trait loci (pQTLs) may be a powerful complementary method of improving our understanding of disease pathways.

Project description:BackgroundAlzheimer's disease is a debilitating and highly heritable neurological condition. As such, genetic studies have sought to understand the genetic architecture of Alzheimer's disease since the 1990s, with successively larger genome-wide association studies (GWAS) and meta-analyses. These studies started with a small sample size of 1086 individuals in 2007, which was able to identify only the APOE locus. In 2013, the International Genomics of Alzheimer's Project (IGAP) did a meta-analysis of all existing GWAS using data from 74 046 individuals, which stood as the largest Alzheimer's disease GWAS until 2018. This meta-analysis discovered 19 susceptibility loci for Alzheimer's disease in populations of European ancestry.Recent developmentsThree new Alzheimer's disease GWAS published in 2018 and 2019, which used larger sample sizes and proxy phenotypes from biobanks, have substantially increased the number of known susceptibility loci in Alzheimer's disease to 40. The first, an updated GWAS from IGAP, included 94 437 individuals and discovered 24 susceptibility loci. Although IGAP sought to increase sample size by recruiting additional clinical cases and controls, the two other studies used parental family history of Alzheimer's disease to define proxy cases and controls in the UK Biobank for a genome-wide association by proxy, which was meta-analysed with data from GWAS of clinical Alzheimer's disease to attain sample sizes of 388 324 and 534 403 individuals. These two studies identified 27 and 29 susceptibility loci, respectively. However, the three studies were not independent because of the large overlap in their participants, and interpretation can be challenging because different variants and genes were highlighted by each study, even in the same locus. Furthermore, neither the variant with the strongest Alzheimer's disease association nor the nearest gene are necessarily causal. This situation presents difficulties for experimental studies, drug development, and other future research. WHERE NEXT?: The ultimate goal of understanding the genetic architecture of Alzheimer's disease is to characterise novel biological pathways that underly Alzheimer's disease pathogenesis and to identify novel drug targets. GWAS have successfully contributed to the characterisation of the genetic architecture of Alzheimer's disease, with the identification of 40 susceptibility loci; however, this does not equate to the discovery of 40 Alzheimer's disease genes. To identify Alzheimer's disease genes, these loci need to be mapped to variants and genes through functional genomics studies that combine annotation of variants, gene expression, and gene-based or pathway-based analyses. Such studies are ongoing and have validated several genes at Alzheimer's disease loci, but greater sample sizes and cell-type specific data are needed to map all GWAS loci.

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Integrated genome-wide analysis of expression quantitative trait loci aids interpretation of genomic association studies.

Publications

Integrated genome-wide analysis of expression quantitative trait loci aids interpretation of genomic association studies.

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