Year: 2022
Type: article
Abstract: A majority of the variants identified in genome-wide association studies fall in non-coding regions of the genome, indicating their mechanism of impact is mediated via gene expression. Leveraging this hypothesis, transcriptome-wide association studies (TWAS) have assisted in both the interpretation and discovery of additional genes associated with complex traits. However, existing methods for conducting TWAS do not take full advantage of the intra-individual correlation inherently present in multi-context expression studies and do not properly adjust for multiple testing across contexts. We introduce CONTENT-a computationally efficient method with proper cross-context false discovery correction that leverages correlation structure across contexts to improve power and generate context-specific and context-shared components of expression. We apply CONTENT to bulk multi-tissue and single-cell RNA-seq data sets and show that CONTENT leads to a 42% (bulk) and 110% (single cell) increase in the number of genetically predicted genes relative to previous approaches. We find the context-specific component of expression comprises 30% of heritability in tissue-level bulk data and 75% in single-cell data, consistent with cell-type heterogeneity in bulk tissue. In the context of TWAS, CONTENT increases the number of locus-phenotype associations discovered by over 51% relative to previous methods across 22 complex traits.
Source: Nature Communications
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $5,890
Funders: National Science Foundation, National Institutes of Health, National Heart, Lung, and Blood Institute, National Institute of Mental Health, National Human Genome Research Institute, National Institute of General Medical Sciences, National Institute of Allergy and Infectious Diseases, National Institute of Environmental Health Sciences, National Institute of Arthritis and Musculoskeletal and Skin Diseases
Awards: III: Medium: Detecting Low Dimensional Structures in Genomic Data, Fine mapping rheumatic disease variants using functional genomic sequencing, Rarely Common: Uncovering the dominant role of rare variants in the genetic architecture of complex human traits., Methods for Genomic Analysis in Heterogeneous Tissues, Fine mapping rheumatic disease variants using functional genomic sequencing, Improved methods for inference of genotype-specific response to environmental toxins, Epigenomics of asthma risk factors and clinical subtypes in minority children, Genomic Approaches to Population Health in Multi-Ethnic Hospital Systems, Leveraging functional data to predict disease risk in multi-ethnic populations, Utilizing single-cell Massively Parallel Reporter Assays to dissect gene by environment interactions, UCLA-Caltech Medical Scientist Training Program, G7159390087, Center for Multi- and Trans-ethnic Mapping of Mendelian and Complex Diseases, SINGLE-CELL MULTI-OMIC APPROACHES TO MECHANISTICALLY CHARACTERIZE PSYCHIATRIC DISORDER RISK LOCI IN THE HUMAN BRAIN, Identifying the genetic causes of depression in a deeply phenotyped population from South Korea, RESOURCE-BASED CENTER FOR THE ADVANCEMENT OF PRECISION MEDICINE IN RHEUMATOLOGY, Leveraging genetic variation to dissect gene regulatory networks of reprogramming to pluripotency, Training Grant in Genomic Analysis and Interpretation, Single-cell sequencing of peripheral blood cells in SLE patients
OpenAlex ID: W4297517261