Faculty, Staff and Student Publications
Publication Date
1-6-2022
Journal
American Journal of Human Genetics
Abstract
Large-scale gene sequencing studies for complex traits have the potential to identify causal genes with therapeutic implications. We performed gene-based association testing of blood lipid levels with rare (minor allele frequency < 1%) predicted damaging coding variation by using sequence data from >170,000 individuals from multiple ancestries: 97,493 European, 30,025 South Asian, 16,507 African, 16,440 Hispanic/Latino, 10,420 East Asian, and 1,182 Samoan. We identified 35 genes associated with circulating lipid levels; some of these genes have not been previously associated with lipid levels when using rare coding variation from population-based samples. We prioritize 32 genes in array-based genome-wide association study (GWAS) loci based on aggregations of rare coding variants; three (EVI5, SH2B3, and PLIN1) had no prior association of rare coding variants with lipid levels. Most of our associated genes showed evidence of association among multiple ancestries. Finally, we observed an enrichment of gene-based associations for low-density lipoprotein cholesterol drug target genes and for genes closest to GWAS index single-nucleotide polymorphisms (SNPs). Our results demonstrate that gene-based associations can be beneficial for drug target development and provide evidence that the gene closest to the array-based GWAS index SNP is often the functional gene for blood lipid levels.
Keywords
Alleles, Blood Glucose, Case-Control Studies, Computational Biology, Databases, Genetic, Diabetes Mellitus, Type 2, Exome, Genetic Predisposition to Disease, Genetic Variation, Genetics, Population, Genome-Wide Association Study, Humans, Lipid Metabolism, Lipids, Liver, Molecular Sequence Annotation, Multifactorial Inheritance, Open Reading Frames, Phenotype, Polymorphism, Single Nucleotide, exome sequencing, association, lipid, cholesterol, gene-based association
DOI
10.1016/j.ajhg.2021.11.021
PMID
34932938
PMCID
PMC8764201
PubMedCentral® Posted Date
12-20-2021
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Included in
Computational Biology Commons, Genomics Commons, Medical Genetics Commons, Molecular Genetics Commons, Public Health Commons