Integrative approaches for large-scale transcriptome-wide association studies

By Alexander Gusev, Arthur Ko, Huwenbo Shi, Gaurav Bhatia, Wonil Chung, Brenda W. J. H. Penninx, R. Jansen, E. D. de Geus, Dorret I. Boomsma, Fred A. Wright and 15 more. Nature Genetics, 2015

2,212 citations recorded by the source corpus.

Many genetic variants influence complex traits by modulating gene expression, thus altering the abundance of one or multiple proteins. Here we introduce a powerful strategy that integrates gene expression measurements with summary association statistics from large-scale genome-wide association studies (GWAS) to identify genes whose cis-regulated expression is associated with complex traits. We leverage expression imputation from genetic data to perform a transcriptome-wide association study (TWAS) to identify significant expression-trait associations. We applied our approaches to expression data from blood and adipose tissue measured in ∼3,000 individuals overall. We imputed gene expression into GWAS data from over 900,000 phenotype measurements to identify 69 new genes significantly associated with obesity-related traits (BMI, lipids and height). Many of these genes are associated with relevant phenotypes in the Hybrid Mouse Diversity Panel. Our results showcase the power of integrating genotype, gene expression and phenotype to gain insights into the genetic basis of complex traits.

Subfields: Cancer Research, Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/16bd79e17676c9a0115d1a31b76ee223ccd39b25

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Citations in the corpus

Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics (2016, methodology): “Methods similar in spirit to PrediXcan have been reported [10].”
Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses (2019, background): “…not hold colocalisation effectively tests whether the strongest signals for the two traits colocalise [10] which has been shown to be conservative [16]. e-CAVIAR [17] removes the assumption of a single causal variant per trait by integrating over the fine mapping posteriors for two traits, but…”
Genomic dissection of bipolar disorder and schizophrenia including 28 subphenotypes (2018, background): “We sought to identify additional disease-specific loci by comprehensively incorporating expression information with association results to perform fine-mapping and identify novel variants (Gamazon et al., 2015; Giambartolomei et al., 2014; Gusev et al., 2016; He et al., 2013).”
Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder (2018, positive): “TWAS and related methods have the advantage of aggregating the effects of multiple SNPs onto specific genes, reducing multiple comparisons and increasing power for association testing, although results can still be influenced by LD and pleiotropy (46, 47).”
webTWAS: a resource for disease candidate susceptibility genes identified by transcriptome-wide association study (2021, neutral): “PrediXcan, https://github.com/hakyim/PrediXcan/ TWAS-FUSION, http://gusevlab.org/projects/fusion/ UTMOST, https://github.com/Joker-Jerome/UTMOST/”
Colocalization of GWAS and eQTL Signals Detects Target Genes (2016, methodology): “PrediXscan [7] and TWAS [17] are examples of such methods, which impute gene expression followed by association of the imputed expression with trait.”
10 Years of GWAS Discovery: Biology, Function, and Translation. (2017, methodology): “One fertile area of method development is integrating data from GWASs and expression quantitative trait locus (eQTL) studies to identify associations between transcripts and complex traits.(56,61,62) These methods are useful for prioritizing genes from known GWAS loci for functional follow-up, detec”