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

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.

SUBMITTER: Young KL

PROVIDER: S-EPMC9772568 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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

Young Kristin L KL Fisher Virginia V Deng Xuan X Brody Jennifer A JA Graff Misa M Lim Elise E Lin Bridget M BM Xu Hanfei H Amin Najaf N An Ping P Aslibekyan Stella S Fohner Alison E AE Hidalgo Bertha B Lenzini Petra P Kraaij Robert R Medina-Gomez Carolina C Prokić Ivana I Rivadeneira Fernando F Sitlani Colleen C Tao Ran R van Rooij Jeroen J Zhang Di D Broome Jai G JG Buth Erin J EJ Heavner Benjamin D BD Jain Deepti D Smith Albert V AV Barnes Kathleen K Boorgula Meher Preethi MP Chavan Sameer S Darbar Dawood D De Andrade Mariza M Guo Xiuqing X Haessler Jeffrey J Irvin Marguerite R MR Kalyani Rita R RR Kardia Sharon L R SLR Kooperberg Charles C Kim Wonji W Mathias Rasika A RA McDonald Merry-Lynn ML Mitchell Braxton D BD Peyser Patricia A PA Regan Elizabeth A EA Redline Susan S Reiner Alexander P AP Rich Stephen S SS Rotter Jerome I JI Smith Jennifer A JA Weiss Scott S Wiggins Kerri L KL Yanek Lisa R LR Arnett Donna D Heard-Costa Nancy L NL Leal Suzanne S Lin Danyu D McKnight Barbara B Province Michael M van Duijn Cornelia M CM North Kari E KE Cupples L Adrienne LA Liu Ching-Ti CT

HGG advances 20221125 1

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 variat ...[more]

PMID: 36568030

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Project description:BackgroundWhole-genome sequencing and imputation methodologies have enabled the study of the effects of genomic variants with low to very low minor allele frequency (MAF) on variation in complex traits. Our objective was to estimate the proportion of variance explained by imputed sequence variants classified according to their MAF compared with the variance explained by the pedigree-based additive genetic relationship matrix for 17 traits in Nordic Holstein dairy cattle.ResultsImputed sequence variants were grouped into seven classes according to their MAF (0.001-0.01, 0.01-0.05, 0.05-0.1, 0.1-0.2, 0.2-0.3, 0.3-0.4 and 0.4-0.5). The total contribution of all imputed sequence variants to variance in deregressed estimated breeding values or proofs (DRP) for different traits ranged from 0.41 [standard error (SE) = 0.026] for temperament to 0.87 (SE = 0.011) for milk yield. The contribution of rare variants (MAF < 0.01) to the total DRP variance explained by all imputed sequence variants was relatively small (a maximum of 12.5% for the health index). Rare and low-frequency variants (MAF < 0.05) contributed a larger proportion of the explained DRP variances (>13%) for health-related traits than for production traits (<11%). However, a substantial proportion of these variance estimates across different MAF classes had large SE, especially when the variance explained by a MAF class was small. The proportion of DRP variance that was explained by all imputed whole-genome sequence variants improved slightly compared with variance explained by the 50 k Illumina markers, which are routinely used in bovine genomic prediction. However, the proportion of DRP variance explained by imputed sequence variants was lower than that explained by pedigree relationships, ranging from 1.5% for milk yield to 37.9% for the health index.ConclusionsImputed sequence variants explained more of the variance in DRP than the 50 k markers for most traits, but explained less variance than that captured by pedigree-based relationships. Although in humans partitioning variants into groups based on MAF and linkage disequilibrium was used to estimate heritability without bias, many of our bovine estimates had a high SE. For a reliable estimate of the explained DRP variance for different MAF classes, larger sample sizes are needed.

Dataset Information

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

Publications

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

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