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

Nicholas Mancuso; Huwenbo Shi; Pagé Goddard; Gleb Kichaev; Alexander Gusev; Bogdan Pasaniuc

doi:10.1016/j.ajhg.2017.01.031

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

Am J Hum Genet. 2017 Mar 2;100(3):473-487. doi: 10.1016/j.ajhg.2017.01.031. Epub 2017 Feb 23.

Authors

Nicholas Mancuso¹, Huwenbo Shi², Pagé Goddard³, Gleb Kichaev², Alexander Gusev⁴, Bogdan Pasaniuc⁵

Affiliations

¹ Department of Pathology & Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90024, USA. Electronic address: [email protected].
² Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90024, USA.
³ Department of Molecular, Cell, and Developmental Biology, University of California, Los Angeles, Los Angeles, CA 90024, USA.
⁴ Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
⁵ Department of Pathology & Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90024, USA; Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90024, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90024, USA. Electronic address: [email protected].

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.

Keywords: complex disease; complex trait; expression quantitative trait loci (eQTLs); genetic correlation; genetic covariance; genome-wide association study (GWAS); susceptibility gene; transcriptome-wide association study (TWAS).

MeSH terms

Body Mass Index
Gene Expression Profiling*
Genetic Predisposition to Disease
Genome-Wide Association Study*
Humans
Linkage Disequilibrium
Lipoproteins, LDL / blood
Models, Theoretical
Phenotype*
Polymorphism, Single Nucleotide
Quantitative Trait Loci
Sequence Analysis, RNA
Transcriptome*
Triglycerides / blood

Substances

Lipoproteins, LDL
Triglycerides

Abstract

MeSH terms

Substances

Grants and funding