Sequencing in over 50,000 cases identifies coding and structural variation underlying atrial fibrillation risk. - Choi, Seung Hoan; Jurgens, Sean J; Xiao, Ling; Hill, Matthew C; Haggerty, Christopher M; Sveinbjörnsson, Garðar; Morrill, Valerie N; Marston, Nicholas A; Weng, Lu-Chen; Pirruccello, James P; Arnar, David O; Gudbjartsson, Daniel Fannar; et al. (Nat Genet, 2025)
Atrial fibrillation (AF) is a prevalent and morbid abnormality of the heart rhythm with a strong genetic component. Here, we meta-analyzed genome and exome sequencing data from 36 studies that included 52,416 AF cases and 277,762 controls. In burden tests of rare coding variation, we identified novel associations between AF and the genes MYBPC3, LMNA, PKP2, FAM189A2 and KDM5B. We further identified associations between AF and rare structural variants owing to deletions in CTNNA3 and duplications of GATA4. We broadly replicated our findings in independent samples from MyCode, deCODE and UK Biobank. Finally, we found that CRISPR knockout of KDM5B in stem-cell-derived atrial cardiomyocytes led to a shortening of the action potential duration and widespread transcriptomic dysregulation of genes relevant to atrial homeostasis and conduction. Our results highlight the contribution of rare coding and structural variants to AF, including genetic links between AF and cardiomyopathies, and expand our understanding of the rare variant architecture for this common arrhythmia.
Keywords: Atrial Fibrillation; Case-Control Studies; Exome Sequencing; Genetic Predisposition to Disease; Genetic Variation; Genome-Wide Association Study; Humans; Myocytes, Cardiac; Risk Factors

A statistical framework for multi-trait rare variant analysis in large-scale whole-genome sequencing studies. - Li, Xihao; Chen, Han; Selvaraj, Margaret Sunitha; Van Buren, Eric; Zhou, Hufeng; Wang, Yuxuan; Sun, Ryan; McCaw, Zachary R; Yu, Zhi; Jiang, Min-Zhi; DiCorpo, Daniel; Gaynor, Sheila M; et al. (Nat Comput Sci, 2025)
Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally scalable analytical pipeline for functionally informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple traits, and further empowers rare variant association analysis by incorporating multiple functional annotations. We applied MultiSTAAR to jointly analyze three lipid traits in 61,838 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered and replicated new associations with lipid traits missed by single-trait analysis.
Keywords: Genetic Variation; Genome, Human; Genome-Wide Association Study; Humans; Lipids; Phenotype; Polymorphism, Single Nucleotide; Quantitative Trait Loci; Whole Genome Sequencing

Whole Genome Variable Number Tandem Repeat Analysis in Alzheimer Disease. - Heath, Alesha; McNerney, M Windy; Yesavage, Jerome (Neurol Genet, 2025)
BACKGROUND AND OBJECTIVES: Investigation into different allelic variants may yield new associative genes to predict late-onset Alzheimer disease (LOAD). Variable number tandem repeats (VNTRs) are important polymorphic components of the genome; however, they have been previously overlooked because of their complex genotyping. New software can now determine differing lengths of VNTRs; however, this has not been tested in a large case-control population. METHODS: We used VNTRseek to genotype over 200,000 tandem repeats in 9,501 cases and controls from the Alzheimer's Disease Sequencing Project. We first identified limiting factors of this analysis and then examined the association of VNTRs with AD diagnosis in a subset of non-Hispanic White participants. RESULTS: We found that VNTRs were highly associated with areas of the genome with a high number of previously identified variants. From our case-control analysis, we identified 9 VNTRs with a repeat allele length associated with LOAD including VNTRs on DSC3, NR2E3, CCNY, PKP4, GRAP, and MAP6. DISCUSSION: We were able to show the feasibility of this new type of analysis in large-scale whole-genome sequencing data and identify promising VNTRs that are associated with LOAD.