1. Whole-genome sequencing in 333,100 individuals reveals rare non-coding single variant and aggregate associations with height.
- Author
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Hawkes G, Beaumont RN, Li Z, Mandla R, Li X, Albert CM, Arnett DK, Ashley-Koch AE, Ashrani AA, Barnes KC, Boerwinkle E, Brody JA, Carson AP, Chami N, Chen YI, Chung MK, Curran JE, Darbar D, Ellinor PT, Fornage M, Gordeuk VR, Guo X, He J, Hwu CM, Kalyani RR, Kaplan R, Kardia SLR, Kooperberg C, Loos RJF, Lubitz SA, Minster RL, Naseri T, Viali S, Mitchell BD, Murabito JM, Palmer ND, Psaty BM, Redline S, Shoemaker MB, Silverman EK, Telen MJ, Weiss ST, Yanek LR, Zhou H, Liu CT, North KE, Justice AE, Locke JM, Owens N, Murray A, Patel K, Frayling TM, Wright CF, Wood AR, Lin X, Manning A, and Weedon MN
- Subjects
- Humans, Male, Female, Gene Frequency, Genome, Human, Genetic Variation, Phenotype, Whole Genome Sequencing, Body Height genetics, Polymorphism, Single Nucleotide, Genome-Wide Association Study
- Abstract
The role of rare non-coding variation in complex human phenotypes is still largely unknown. To elucidate the impact of rare variants in regulatory elements, we performed a whole-genome sequencing association analysis for height using 333,100 individuals from three datasets: UK Biobank (N = 200,003), TOPMed (N = 87,652) and All of Us (N = 45,445). We performed rare ( < 0.1% minor-allele-frequency) single-variant and aggregate testing of non-coding variants in regulatory regions based on proximal-regulatory, intergenic-regulatory and deep-intronic annotation. We observed 29 independent variants associated with height at P < 6 × 10 - 10 after conditioning on previously reported variants, with effect sizes ranging from -7cm to +4.7 cm. We also identified and replicated non-coding aggregate-based associations proximal to HMGA1 containing variants associated with a 5 cm taller height and of highly-conserved variants in MIR497HG on chromosome 17. We have developed an approach for identifying non-coding rare variants in regulatory regions with large effects from whole-genome sequencing data associated with complex traits., (© 2024. The Author(s).)
- Published
- 2024
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