Whole-exome sequence analysis of anthropometric traits illustrates challenges in identifying effects of rare genetic variants.

Kristin L Young ,Barbara Mcknight,Silvia Leal ,E.l.i.z.a.b.e.t.h.a Regan ,B.r.a.x.t.o.n.d Mitchell ,K.e.r.r.i.l Wiggins ,Hanfei Xu ,Robert Kraaij ,J.e.n.n.i.f.e.r.a Brody ,J.e.r.o.m.e.i Rotter ,D.a.w.o.o.d Darbar ,A.l.b.e.r.t.v Smith ,S.t.e.p.h.e.n.s Rich ,K.a.t.h.l.e.e.n Barnes ,Elise Lim ,L Adrienne Cupples ,C.o.r.n.e.l.i.a.m Van Duijn ,Ivana Prokić ,K.a.r.i.e North ,D.i Zhang ,Petra Lenzini ,Stella Aslibekyan ,Suchitra Chavan ,Jon Smith ,M.e.h.e.r.p.r.e.e.t.h.i Boorgula ,J.e.r.o.e.n Van Rooij ,Ran Tao ,A.l.e.x.a.n.d.e.r.p Reiner ,N.a.n.c.y.l Heard-Costa ,Dan-Yu Lin ,B.r.i.d.g.e.t.m Lin ,S.c.o.t.t Weiss ,S.u.s.a.n Redline ,Xuan Deng ,Bertha Hidalgo ,Najaf Amin ,Fernando Rivadeneira ,Jeff Haessler ,Meg Mcdonald ,E.r.i.n.j Buth ,Rasika A Mathias ,D.o.n.n.a Arnett ,W.o.n.j.i Kim ,J.a.i.g Broome ,Ben Heavner ,Michael Province ,P.a.t.r.i.c.i.a.a Peyser ,R.i.t.a.r Kalyani ,Mariaelisa Graff ,L.i.s.a.r Yanek ,M.a.r.g.u.e.r.i.t.e.r Irvin ,Charles Kooperberg ,Ping An ,S.h.a.r.o.n.l.r Kardia ,A.l.i.s.o.n.e Fohner ,Devanshi Jain ,Carolina Medina‐Gomez ,C.o.l.l.e.e.n Sitlani ,M.a.r.i.z.a De Andrade ,Xiuqing Guo ,Virginia Fisher ,C.h.i.n.g.-T.i Liu

doi:10.1016/j.xhgg.2022.100163

Abstract

Anthropometric traits, measuring body size and shape, are highly heritable and significant clinical risk factors for cardiometabolic disorders. These traits have been extensively studied in genome-wide association studies (GWASs), with hundreds of genome-wide significant loci identified. We performed a whole-exome sequence analysis of the genetics of height, body mass index (BMI) and waist/hip ratio (WHR). We meta-analyzed single-variant and gene-based associations of whole-exome sequence variation with height, BMI, and WHR in up to 22,004 individuals, and we assessed replication of our findings in up to 16,418 individuals from 10 independent cohorts from Trans-Omics for Precision Medicine (TOPMed). We identified four trait associations with single-nucleotide variants (SNVs; two for height and two for BMI) and replicated the LECT2 gene association with height. Our expression quantitative trait locus (eQTL) analysis within previously reported GWAS loci implicated CEP63 and RFT1 as potential functional genes for known height loci. We further assessed enrichment of SNVs, which were monogenic or syndromic variants within loci associated with our three traits. This led to the significant enrichment results for height, whereas we observed no Bonferroni-corrected significance for all SNVs. With a sample size of ∼20,000 whole-exome sequences in our discovery dataset, our findings demonstrate the importance of genomic sequencing in genetic association studies, yet they also illustrate the challenges in identifying effects of rare genetic variants.

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