Language
English
Publication Date
9-9-2025
Journal
Genome Biology
DOI
10.1186/s13059-025-03698-0
PMID
40926209
PMCID
PMC12418676
PubMedCentral® Posted Date
9-9-2025
PubMedCentral® Full Text Version
Post-print
Abstract
Background: Rare genetic variation provided by whole genome sequence datasets has been relatively less explored for its contributions to human traits. Meta-analysis of sequencing data offers advantages by integrating larger sample sizes from diverse cohorts, thereby increasing the likelihood of discovering novel insights into complex traits. Furthermore, emerging methods in genome-wide rare variant association testing further improve power and interpretability.
Results: Here, we conduct the largest meta-analysis of whole genome sequencing for low-density lipoprotein cholesterol (LDL-C), a therapeutic target for coronary artery disease, analyzing data from 246 K participants and integrating 1.23B variants from the UK Biobank and the Trans-Omics for Precision Medicine (TOPMed) program. We identify numerous rare coding and non-coding gene associations related to LDL-C, with replication across 86 K participants in All of Us. Our findings are based on single-variant analyses, rare coding and non-coding variant aggregation tests, and sliding window approaches. Through this comprehensive analysis, we identify 704 novel single-variant associations, 25 novel rare coding variant aggregates, 28 novel rare non-coding variant aggregates, and one novel sliding window aggregate.
Conclusions: This study provides a meta-analysis framework for large-scale whole genome sequence association analyses from diverse population groups, yielding novel rare non-coding variant associations.
Keywords
Humans, Cholesterol, LDL, Whole Genome Sequencing, Genome-Wide Association Study, Polymorphism, Single Nucleotide, Genome, Human, Genetic Variation
Published Open-Access
yes
Recommended Citation
Selvaraj, Margaret Sunitha; Li, Xihao; Li, Zilin; et al., "Whole Genome Sequence Analysis of Low-Density Lipoprotein Cholesterol Across 246 K Individuals" (2025). The Brown Foundation: Institute of Molecular Medicine. 28.
https://digitalcommons.library.tmc.edu/molecular_med/28