A gene-based association method for mapping traits using reference transcriptome data

By E. Gamazon, H. Wheeler, Kaanan P. Shah, S. Mozaffari, K. Aquino-Michaels, Robert J. Carroll, A. Eyler, Joshua Charles Denny, Dan L. Nicolae, Nancy J. Cox and 1 more. Nature Genetics, 2015

1,739 citations recorded by the source corpus.

Genome-wide association studies (GWAS) have identified thousands of variants robustly associated with complex traits. However, the biological mechanisms underlying these associations are, in general, not well understood. We propose a gene-based association method called PrediXcan that directly tests the molecular mechanisms through which genetic variation affects phenotype. The approach estimates the component of gene expression determined by an individual's genetic profile and correlates 'imputed' gene expression with the phenotype under investigation to identify genes involved in the etiology of the phenotype. Genetically regulated gene expression is estimated using whole-genome tissue-dependent prediction models trained with reference transcriptome data sets. PrediXcan enjoys the benefits of gene-based approaches such as reduced multiple-testing burden and a principled approach to the design of follow-up experiments. Our results demonstrate that PrediXcan can detect known and new genes associated with disease traits and provide insights into the mechanism of these associations.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/fb156076888c08cc57c4796009a8e5567a29ea88

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Citations in the corpus

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).”
webTWAS: a resource for disease candidate susceptibility genes identified by transcriptome-wide association study (2021, methodology): “First proposed by Gamazon et al. (1) in 2015, the transcriptome-wide association study (TWAS) has emerged as a powerful method for investigating associations be-tween genetic variants and disease or disease-related complex traits.”
The GTEx Consortium atlas of genetic regulatory effects across human tissues (2019, background): “We also assessed the association between the genetically regulated component of expression or splicing and complex traits with PrediXcan (11, 41, 47).”
Integrating predicted transcriptome from multiple tissues improves association detection (2018, methodology): “To examine these mechanisms we developed PrediXcan [10], a method that 46 tests the mediating role of gene expression variation in complex traits.”
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”
Dissecting the genetics of complex traits using summary association statistics (2016, methodology): “Although originally proposed using individual-level data 29 , TWAS using predicted expression can also be performed using only summary association statistics and summary LD information 30,31 .”
Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics (2016, methodology): “We tested association between 217 these phenotypes and the predicted expression levels using elastic net models in 44 human tissues from 218 GTEx as described in the Methods section, and a whole blood model from the DGN cohort presented 219 in [11].”