RATIONALE: Genome-wide association studies have identified over 100 genetic loci for atrial fibrillation (AF); recent work described an association between loss-of-function (LOF) variants in TTN and early-onset AF. OBJECTIVE: We sought to determine the contribution of rare and common genetic variation to AF risk in the general population. METHODS: The UK Biobank is a population-based study of 500 000 individuals including a subset with genome-wide genotyping and exome sequencing. In this case-control study, we included AF cases and controls of genetically determined white-European ancestry; analyses were performed using a logistic mixed-effects model adjusting for age, sex, the first 4 principal components of ancestry, empirical relationships, and case-control imbalance. An exome-wide, gene-based burden analysis was performed to examine the relationship between AF and rare, high-confidence LOF variants in genes with ≥10 LOF carriers. A polygenic risk score for AF was estimated using the LDpred algorithm. We then compared the contribution of AF polygenic risk score and LOF variants to AF risk. RESULTS: The study included 1546 AF cases and 41 593 controls. In an analysis of 9099 genes with sufficient LOF variant carriers, a significant association between AF and rare LOF variants was observed in a single gene, TTN (odds ratio, 2.71, P=2.50×10-8). The association with AF was more significant (odds ratio, 6.15, P=3.26×10-14) when restricting to LOF variants located in exons highly expressed in cardiac tissue (TTNLOF). Overall, 0.44% of individuals carried TTNLOF variants, of whom 14% had AF. Among individuals in the highest 0.44% of the AF polygenic risk score only 9.3% had AF. In contrast, the AF polygenic risk score explained 4.7% of the variance in AF susceptibility, while TTNLOF variants only accounted for 0.2%. CONCLUSIONS: Both monogenic and polygenic factors contribute to AF risk in the general population. While rare TTNLOF variants confer a substantial AF penetrance, the additive effect of many common variants explains a larger proportion of genetic susceptibility to AF.
RATIONALE: Genome-wide association studies have identified over 100 genetic loci for atrial fibrillation (AF); recent work described an association between loss-of-function (LOF) variants in TTN and early-onset AF. OBJECTIVE: We sought to determine the contribution of rare and common genetic variation to AF risk in the general population. METHODS: The UK Biobank is a population-based study of 500 000 individuals including a subset with genome-wide genotyping and exome sequencing. In this case-control study, we included AF cases and controls of genetically determined white-European ancestry; analyses were performed using a logistic mixed-effects model adjusting for age, sex, the first 4 principal components of ancestry, empirical relationships, and case-control imbalance. An exome-wide, gene-based burden analysis was performed to examine the relationship between AF and rare, high-confidence LOF variants in genes with ≥10 LOF carriers. A polygenic risk score for AF was estimated using the LDpred algorithm. We then compared the contribution of AF polygenic risk score and LOF variants to AF risk. RESULTS: The study included 1546 AF cases and 41 593 controls. In an analysis of 9099 genes with sufficient LOF variant carriers, a significant association between AF and rare LOF variants was observed in a single gene, TTN (odds ratio, 2.71, P=2.50×10-8). The association with AF was more significant (odds ratio, 6.15, P=3.26×10-14) when restricting to LOF variants located in exons highly expressed in cardiac tissue (TTNLOF). Overall, 0.44% of individuals carried TTNLOF variants, of whom 14% had AF. Among individuals in the highest 0.44% of the AF polygenic risk score only 9.3% had AF. In contrast, the AF polygenic risk score explained 4.7% of the variance in AF susceptibility, while TTNLOF variants only accounted for 0.2%. CONCLUSIONS: Both monogenic and polygenic factors contribute to AF risk in the general population. While rare TTNLOF variants confer a substantial AF penetrance, the additive effect of many common variants explains a larger proportion of genetic susceptibility to AF.
Authors: Seung Hoan Choi; Lu-Chen Weng; Carolina Roselli; Honghuang Lin; Christopher M Haggerty; M Benjamin Shoemaker; John Barnard; Dan E Arking; Daniel I Chasman; Christine M Albert; Mark Chaffin; Nathan R Tucker; Jonathan D Smith; Namrata Gupta; Stacey Gabriel; Lauren Margolin; Marisa A Shea; Christian M Shaffer; Zachary T Yoneda; Eric Boerwinkle; Nicholas L Smith; Edwin K Silverman; Susan Redline; Ramachandran S Vasan; Esteban G Burchard; Stephanie M Gogarten; Cecelia Laurie; Thomas W Blackwell; Gonçalo Abecasis; David J Carey; Brandon K Fornwalt; Diane T Smelser; Aris Baras; Frederick E Dewey; Cashell E Jaquish; George J Papanicolaou; Nona Sotoodehnia; David R Van Wagoner; Bruce M Psaty; Sekar Kathiresan; Dawood Darbar; Alvaro Alonso; Susan R Heckbert; Mina K Chung; Dan M Roden; Emelia J Benjamin; Michael F Murray; Kathryn L Lunetta; Steven A Lubitz; Patrick T Ellinor Journal: JAMA Date: 2018-12-11 Impact factor: 56.272
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