Trait-Associated SNPs Are More Likely to Be eQTLs: Annotation to Enhance Discovery from GWAS

By Dan L. Nicolae, E. Gamazon, Wei Zhang, S. Duan, M. Dolan, Nancy J. Cox, G. Gibson. PLoS Genetics, 2010

1,330 citations recorded by the source corpus.

Although genome-wide association studies (GWAS) of complex traits have yielded more reproducible associations than had been discovered using any other approach, the loci characterized to date do not account for much of the heritability to such traits and, in general, have not led to improved understanding of the biology underlying complex phenotypes. Using a web site we developed to serve results of expression quantitative trait locus (eQTL) studies in lymphoblastoid cell lines from HapMap samples (http://www.scandb.org), we show that single nucleotide polymorphisms (SNPs) associated with complex traits (from http://www.genome.gov/gwastudies/) are significantly more likely to be eQTLs than minor-allele-frequency–matched SNPs chosen from high-throughput GWAS platforms. These findings are robust across a range of thresholds for establishing eQTLs (p-values from 10−4–10−8), and a broad spectrum of human complex traits. Analyses of GWAS data from the Wellcome Trust studies confirm that annotating SNPs with a score reflecting the strength of the evidence that the SNP is an eQTL can improve the ability to discover true associations and clarify the nature of the mechanism driving the associations. Our results showing that trait-associated SNPs are more likely to be eQTLs and that application of this information can enhance discovery of trait-associated SNPs for complex phenotypes raise the possibility that we can utilize this information both to increase the heritability explained by identifiable genetic factors and to gain a better understanding of the biology underlying complex traits.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/7fcabcb1dff389a3c8750c983b1b34753cbce59e

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Citations in the corpus

Cancer Genetics and Epigenetics: Two Sides of the Same Coin? (2012, background): “Further, several genome-wide expression quantitative trait loci studies in humans have demonstrated a link between genetic variation and changes in gene regulation (Nica et al., 2010; Nicolae et al., 2010).”
Genetic identification of brain cell types underlying schizophrenia (2017, neutral): “We decided to extend the default window size as a large fraction of trait-associated SNPs are located just outside genes in regions likely to regulate gene expression 3,47 .”
Linking disease associations with regulatory information in the human genome (2012, background): “Several recent analyses of associated regions use these types of functional data in order to identify functional loci in individual diseases (Lou et al. 2009; Carvajal-Carmona et al. 2011; Harismendy et al. 2011; Paul et al. 2011).”
The GTEx Consortium atlas of genetic regulatory effects across human tissues (2019, methodology): “To analyze the mediating role of cis-regulation of gene expression on complex traits (35, 42), we used two complementary approaches, QTLEnrich (43) and Stratified LD score regression (S-LDSC) (11).”
Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016, background): “Significant GWAS loci are enriched for eQTL (Nicolae et al., 2010) and meQTL (Zhang et al.”