Integrating Gene Expression with Summary Association Statistics to Identify Genes Associated with 30 Complex Traits

Abstract

Although genome-wide association studies (GWASs) have identified thousands of risk loci for many complex traits and diseases, the causal variants and genes at these loci remain largely unknown. Here, we introduce a method for estimating the local genetic correlation between gene expression and a complex trait and utilize it to estimate the genetic correlation due to predicted expression between pairs of traits. We integrated gene expression measurements from 45 expression panels with summary GWAS data to perform 30 multi-tissue transcriptome-wide association studies (TWASs). We identified 1,196 genes whose expression is associated with these traits; of these, 168 reside more than 0.5 Mb away from any previously reported GWAS significant variant. We then used our approach to find 43 pairs of traits with significant genetic correlation at the level of predicted expression; of these, eight were not found through genetic correlation at the SNP level. Finally, we used bi-directional regression to find evidence that BMI causally influences triglyceride levels and that triglyceride levels causally influence low-density lipoprotein. Together, our results provide insight into the role of gene expression in the susceptibility of complex traits and diseases.

Integrating Genome-Wide Association and Blood Genomic Profiling Data to Characterise Migraine Risk Loci

“…The TWAS statistic was more significant than the best eQTL at two loci (LRP1-STAT6 and FHL5-UFL1), supporting the presence of secondary association signals identified through GWAS and demonstrating the utility of combining cis SNP association signals into a single, interpretable statistic. Furthermore, the cis effects on expression was largely consistent across tissues, including whole blood and five other tissues at the FHL5-UFL1 locus, in line with previous analyses of multi-tissue effects of genetic variation of gene expression (382,383). Taken together with our blood genomic profiling data, these results suggest migraine-associated gene expression and eSNPs in whole blood will provide reliable surrogate information for the relevant migraine-related pathogenic cell types.…”

The Genetics of Multiple Sclerosis

“…In this regard, computational strategies integrating gene expression measurements with summary GWAS data have been recently developed to identify genes whose cis-regulated expression is associated with complex traits, an approach called transcriptome-wide association study (TWAS) (48,49). In addition, transcriptomic studies in relevant tissue samples from MS patients can also help identifying specific genetic signatures associated with disease susceptibility or progression.…”

Integrating genome-wide association and transcriptome predicted model identify novel target genes with osteoporosis

“…Instead of testing millions of SNPs in GWAS, TWAS evaluates the association of predicted expression for thousands of genes, greatly reducing the burden of multiple comparisons in statistical inference. This approach has been shown to have the potential to identify the genes responsible for GWAS-identified associations for complex traits and provide mechanistic insight regarding genes being regulated via disease-associated genetic variants (Mancuso et al 2017;Gusev et al 2018;Lu et al 2018;Wu et al 2018;Atkins et al 2019). In this paper, we conducted transcriptome-wide association study to identify genes associated with OP by integrating gene expression from the Genotype-Tissue Expression (GTEx) and GWAS summary data from the Genetic Factors for Osteoporosis (GEFOS) Consortium, and then evaluated the biological patterns of expression-trait association by COLOC method.…”