| Title: | Whole genome sequence analysis of low-density lipoprotein cholesterol across 246 K individuals |
| Journal: | Genome Biology |
| Published: | 9 Sep 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40926209/ |
| DOI: | https://doi.org/10.1186/s13059-025-03698-0 |
| URL: | https://link.springer.com/content/pdf/10.1186/s13059-025-03698-0.pdf |
Publication 19530
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Abstract
BackgroundRare 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.ResultsHere, 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.ConclusionsThis 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.</p>
7 Keywords
- Cholesterol, LDL
- Genetic Variation
- Genome, Human
- Genome-Wide Association Study
- Humans
- Polymorphism, Single Nucleotide
- Whole Genome Sequencing
82 Authors
- Margaret Sunitha Selvaraj
- Xihao Li
- Zilin Li
- Eric Van Buren
- Sara Haidermota
- Darina Postupaka
- Whitney Hornsby
- Joshua C. Bis
- Jennifer A. Brody
- Brian E. Cade
- Ren-Hua Chung
- Joanne E. Curran
- Scott M. Damrauer
- Lisa de las Fuentes
- Paul S. de Vries
- Ravindranath Duggirala
- Barry I. Freedman
- MariaElisa Graff
- Xiuqing Guo
- Bertha A. Hidalgo
- Lifang Hou
- Ryan Irvin
- Renae Judy
- Rita R. Kalyani
- Tanika N. Kelly
- Iain R. Konigsberg
- Brian G. Kral
- Lydia Coulter Kwee
- Daniel Levy
- Changwei Li
- Ani W. Manichaikul
- Lisa Warsinger Martin
- May E. Montasser
- Alanna C. Morrison
- Take Naseri
- Kari E. North
- Jeffrey R. O'Connell
- Nicholette D. Palmer
- Patricia A. Peyser
- Alex P. Reiner
- Svati H. Shah
- Roelof A. J. Smit
- Jennifer A. Smith
- Kent D. Taylor
- Hemant Tiwari
- Michael Y. Tsai
- Satupa'itea Viali
- Zhe Wang
- Yuxuan Wang
- Wei Zhao
- Donna K. Arnett
- John Blangero
- Eric Boerwinkle
- Donald W. Bowden
- Jenna C. Carlson
- Yii-Der Ida Chen
- Patrick T. Ellinor
- Myriam Fornage
- Jiang He
- Nancy Heard-Costa
- Robert C. Kaplan
- Sharon L. R. Kardia
- Charles Kooperberg
- William E. Kraus
- Leslie A. Lange
- Ruth J. F. Loos
- Braxton D. Mitchell
- Bruce M. Psaty
- Daniel J. Rader
- Susan Redline
- Stephen S. Rich
- Lisa R. Yanek
- Richard Gibbs
- Stacey Gabriel
- Karine A. Viaud-Martinez
- Susan K. Dutcher
- Soren Germer
- Ryan Kim
- Jerome I. Rotter
- Xihong Lin
- Gina M. Peloso
- Pradeep Natarajan
1 Application
| Application ID | Title |
|---|---|
| 7089 | Exome Sequencing of All Premature Coronary Artery Disease Participants in UK Biobank |
Enabling scientific discoveries that improve human health