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

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

doi:10.1016/j.xhgg.2022.100163

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

Kristin L. Young, Virginia Fisher, Xuan Deng, Jennifer A. Brody, Misa Graff, Elise Lim, Bridget M. Lin, Hanfei Xu, Najaf Amin, Ping An, Stella Aslibekyan, Alison E. Fohner, Bertha Hidalgo, Petra Lenzini, Robert Kraaij, Carolina Medina-Gomez, Ivana Prokić, Fernando Rivadeneira, Colleen Sitlani, Ran TaoJeroen van Rooij, Di Zhang, Jai G. Broome, Erin J. Buth, Benjamin D. Heavner, Deepti Jain, Albert V. Smith, Kathleen Barnes, Meher Preethi Boorgula, Sameer Chavan, Dawood Darbar, Mariza De Andrade, Xiuqing Guo, Jeffrey Haessler, Marguerite R. Irvin, Rita R. Kalyani, Sharon L.R. Kardia, Charles Kooperberg, Wonji Kim, Rasika A. Mathias, Merry Lynn McDonald, Braxton D. Mitchell, Patricia A. Peyser, Elizabeth A. Regan, Susan Redline, Alexander P. Reiner, Stephen S. Rich, Jerome I. Rotter, Jennifer A. Smith, Scott Weiss, Kerri L. Wiggins, Lisa R. Yanek, Donna Arnett, Nancy L. Heard-Costa, Suzanne Leal, Danyu Lin, Barbara McKnight, Michael Province, Cornelia M. van Duijn, Kari E. North, L. Adrienne Cupples, Ching Ti Liu

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Article number	100163
Journal	Human Genetics and Genomics Advances
Volume	4
Issue number	1
DOIs	https://doi.org/10.1016/j.xhgg.2022.100163
State	Published - Jan 12 2023

Keywords

body mass index
central obesity
exome sequencing
height

Access to Document

10.1016/j.xhgg.2022.100163

Cite this

Young, K. L., Fisher, V., Deng, X., Brody, J. A., Graff, M., Lim, E., Lin, B. M., Xu, H., Amin, N., An, P., Aslibekyan, S., Fohner, A. E., Hidalgo, B., Lenzini, P., Kraaij, R., Medina-Gomez, C., Prokić, I., Rivadeneira, F., Sitlani, C., ... Liu, C. T. (2023). Whole-exome sequence analysis of anthropometric traits illustrates challenges in identifying effects of rare genetic variants. Human Genetics and Genomics Advances, 4(1), [100163]. https://doi.org/10.1016/j.xhgg.2022.100163

@article{4ca59e6d91334854a567bb04c627c17a,

title = "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.",

keywords = "body mass index, central obesity, exome sequencing, height",

author = "Young, {Kristin L.} and Virginia Fisher and Xuan Deng and Brody, {Jennifer A.} and Misa Graff and Elise Lim and Lin, {Bridget M.} and Hanfei Xu and Najaf Amin and Ping An and Stella Aslibekyan and Fohner, {Alison E.} and Bertha Hidalgo and Petra Lenzini and Robert Kraaij and Carolina Medina-Gomez and Ivana Proki{\'c} and Fernando Rivadeneira and Colleen Sitlani and Ran Tao and {van Rooij}, Jeroen and Di Zhang and Broome, {Jai G.} and Buth, {Erin J.} and Heavner, {Benjamin D.} and Deepti Jain and Smith, {Albert V.} and Kathleen Barnes and Boorgula, {Meher Preethi} and Sameer Chavan and Dawood Darbar and {De Andrade}, Mariza and Xiuqing Guo and Jeffrey Haessler and Irvin, {Marguerite R.} and Kalyani, {Rita R.} and Kardia, {Sharon L.R.} and Charles Kooperberg and Wonji Kim and Mathias, {Rasika A.} and McDonald, {Merry Lynn} and Mitchell, {Braxton D.} and Peyser, {Patricia A.} and Regan, {Elizabeth A.} and Susan Redline and Reiner, {Alexander P.} and Rich, {Stephen S.} and Rotter, {Jerome I.} and Smith, {Jennifer A.} and Scott Weiss and Wiggins, {Kerri L.} and Yanek, {Lisa R.} and Donna Arnett and Heard-Costa, {Nancy L.} and Suzanne Leal and Danyu Lin and Barbara McKnight and Michael Province and {van Duijn}, {Cornelia M.} and North, {Kari E.} and Cupples, {L. Adrienne} and Liu, {Ching Ti}",

note = "Funding Information: The authors are grateful to the study participants and staff from all contributing studies. Funding support for “Building on GWAS for NHLBI-diseases: the US CHARGE consortium” was provided by the National Institutes of Health ( NIH ) through the American Recovery and Reinvestment Act of 2009 (ARRA) ( 5RC2HL102419 ). Data for “Building on GWAS for NHLBI-diseases: the US CHARGE consortium” was provided by Eric Boerwinkle on behalf of the ARIC Study, L.A.C., principal investigator for the FramHS, and Bruce Psaty, principal investigator for the CHS. Infrastructure for the CHARGE consortium was supported in part by NHLBI grant R01HL105756 . Sequencing was carried out at the Baylor College of Medicine Human Genome Sequencing Center and supported by NHGRI grants U54 HG003273 and UM1 HG008898 . The CHARGE Analysis Commons was supported by R01HL131136 . K.L.Y. was supported by KL2TR001109 and R21HL14041901 . C.-T.L. was supported by R01DK089256 and R01DK122503 . F.R. and C.M.-G. were supported by ZonMw grant VIDI 016.136.367 . C.M.v.D. used an exchange grant from Personalized Prevention of Chronic Diseases consortium ( PRECeDI ) ( H2020-MSCA-RISE-2014 ). B.D.H. and D.J. were supported by U01HL-120393 , contract HHSN268201800001I, through the TOPMed Data Coordinating Center (DCC). TOPMed Informatics Research Center (IRC) was supported by contract HHSN268201800002I. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NHLBI or the NIH. Publisher Copyright: {\textcopyright} 2022 The Authors",

year = "2023",

month = jan,

day = "12",

doi = "10.1016/j.xhgg.2022.100163",

language = "English",

volume = "4",

journal = "Human Genetics and Genomics Advances",

issn = "2666-2477",

number = "1",

}

Young, KL, Fisher, V, Deng, X, Brody, JA, Graff, M, Lim, E, Lin, BM, Xu, H, Amin, N, An, P, Aslibekyan, S, Fohner, AE, Hidalgo, B, Lenzini, P, Kraaij, R, Medina-Gomez, C, Prokić, I, Rivadeneira, F, Sitlani, C, Tao, R, van Rooij, J, Zhang, D, Broome, JG, Buth, EJ, Heavner, BD, Jain, D, Smith, AV, Barnes, K, Boorgula, MP, Chavan, S, Darbar, D, De Andrade, M, Guo, X, Haessler, J, Irvin, MR, Kalyani, RR, Kardia, SLR, Kooperberg, C, Kim, W, Mathias, RA, McDonald, ML, Mitchell, BD, Peyser, PA, Regan, EA, Redline, S, Reiner, AP, Rich, SS, Rotter, JI, Smith, JA, Weiss, S, Wiggins, KL, Yanek, LR, Arnett, D, Heard-Costa, NL, Leal, S, Lin, D, McKnight, B, Province, M, van Duijn, CM, North, KE, Cupples, LA & Liu, CT 2023, 'Whole-exome sequence analysis of anthropometric traits illustrates challenges in identifying effects of rare genetic variants', Human Genetics and Genomics Advances, vol. 4, no. 1, 100163. https://doi.org/10.1016/j.xhgg.2022.100163

TY - JOUR

T1 - Whole-exome sequence analysis of anthropometric traits illustrates challenges in identifying effects of rare genetic variants

AU - Young, Kristin L.

AU - Fisher, Virginia

AU - Deng, Xuan

AU - Brody, Jennifer A.

AU - Graff, Misa

AU - Lim, Elise

AU - Lin, Bridget M.

AU - Xu, Hanfei

AU - Amin, Najaf

AU - An, Ping

AU - Aslibekyan, Stella

AU - Fohner, Alison E.

AU - Hidalgo, Bertha

AU - Lenzini, Petra

AU - Kraaij, Robert

AU - Medina-Gomez, Carolina

AU - Prokić, Ivana

AU - Rivadeneira, Fernando

AU - Sitlani, Colleen

AU - Tao, Ran

AU - van Rooij, Jeroen

AU - Zhang, Di

AU - Broome, Jai G.

AU - Buth, Erin J.

AU - Heavner, Benjamin D.

AU - Jain, Deepti

AU - Smith, Albert V.

AU - Barnes, Kathleen

AU - Boorgula, Meher Preethi

AU - Chavan, Sameer

AU - Darbar, Dawood

AU - De Andrade, Mariza

AU - Guo, Xiuqing

AU - Haessler, Jeffrey

AU - Irvin, Marguerite R.

AU - Kalyani, Rita R.

AU - Kardia, Sharon L.R.

AU - Kooperberg, Charles

AU - Kim, Wonji

AU - Mathias, Rasika A.

AU - McDonald, Merry Lynn

AU - Mitchell, Braxton D.

AU - Peyser, Patricia A.

AU - Regan, Elizabeth A.

AU - Redline, Susan

AU - Reiner, Alexander P.

AU - Rich, Stephen S.

AU - Rotter, Jerome I.

AU - Smith, Jennifer A.

AU - Weiss, Scott

AU - Wiggins, Kerri L.

AU - Yanek, Lisa R.

AU - Arnett, Donna

AU - Heard-Costa, Nancy L.

AU - Leal, Suzanne

AU - Lin, Danyu

AU - McKnight, Barbara

AU - Province, Michael

AU - van Duijn, Cornelia M.

AU - North, Kari E.

AU - Cupples, L. Adrienne

AU - Liu, Ching Ti

N1 - Funding Information: The authors are grateful to the study participants and staff from all contributing studies. Funding support for “Building on GWAS for NHLBI-diseases: the US CHARGE consortium” was provided by the National Institutes of Health ( NIH ) through the American Recovery and Reinvestment Act of 2009 (ARRA) ( 5RC2HL102419 ). Data for “Building on GWAS for NHLBI-diseases: the US CHARGE consortium” was provided by Eric Boerwinkle on behalf of the ARIC Study, L.A.C., principal investigator for the FramHS, and Bruce Psaty, principal investigator for the CHS. Infrastructure for the CHARGE consortium was supported in part by NHLBI grant R01HL105756 . Sequencing was carried out at the Baylor College of Medicine Human Genome Sequencing Center and supported by NHGRI grants U54 HG003273 and UM1 HG008898 . The CHARGE Analysis Commons was supported by R01HL131136 . K.L.Y. was supported by KL2TR001109 and R21HL14041901 . C.-T.L. was supported by R01DK089256 and R01DK122503 . F.R. and C.M.-G. were supported by ZonMw grant VIDI 016.136.367 . C.M.v.D. used an exchange grant from Personalized Prevention of Chronic Diseases consortium ( PRECeDI ) ( H2020-MSCA-RISE-2014 ). B.D.H. and D.J. were supported by U01HL-120393 , contract HHSN268201800001I, through the TOPMed Data Coordinating Center (DCC). TOPMed Informatics Research Center (IRC) was supported by contract HHSN268201800002I. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NHLBI or the NIH. Publisher Copyright: © 2022 The Authors

PY - 2023/1/12

Y1 - 2023/1/12

N2 - 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.

AB - 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.

KW - body mass index

KW - central obesity

KW - exome sequencing

KW - height

UR - http://www.scopus.com/inward/record.url?scp=85144046616&partnerID=8YFLogxK

U2 - 10.1016/j.xhgg.2022.100163

DO - 10.1016/j.xhgg.2022.100163

M3 - Article

C2 - 36568030

AN - SCOPUS:85144046616

SN - 2666-2477

VL - 4

JO - Human Genetics and Genomics Advances

JF - Human Genetics and Genomics Advances

IS - 1

M1 - 100163

ER -

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

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this