Whole genome sequence analyses of eGFR in 23,732 people representing multiple ancestries in the NHLBI trans-omics for precision medicine (TOPMed) consortium

Bridget M. Lin; Kelsey E. Grinde; Jennifer A. Brody; Charles E. Breeze; Laura M. Raffield; Josyf C. Mychaleckyj; Timothy A. Thornton; James A. Perry; Leslie J. Baier; Lisa de las Fuentes; Xiuqing Guo; Benjamin D. Heavner; Robert L. Hanson; Yi Jen Hung; Huijun Qian; Chao A. Hsiung; Shih Jen Hwang; Margaret R. Irvin; Deepti Jain; Tanika N. Kelly; Sayuko Kobes; Leslie Lange; James P. Lash; Yun Li; Xiaoming Liu; Xuenan Mi; Solomon K. Musani; George J. Papanicolaou; Afshin Parsa; Alex P. Reiner; Shabnam Salimi; Wayne H.H. Sheu; Alan R. Shuldiner; Kent D. Taylor; Albert V. Smith; Jennifer A. Smith; Adrienne Tin; Dhananjay Vaidya; Robert B. Wallace; Kenichi Yamamoto; Saori Sakaue; Koichi Matsuda; Yoichiro Kamatani; Yukihide Momozawa; Lisa R. Yanek; Betsi A. Young; Wei Zhao; Yukinori Okada; Gonzalo Abecasis; Bruce M. Psaty; Donna K. Arnett; Eric Boerwinkle; Jianwen Cai; Ida Yii-Der Chen; Adolfo Correa; L. Adrienne Cupples; Jiang He; Sharon LR Kardia; Charles Kooperberg; Rasika A. Mathias; Braxton D. Mitchell; Deborah A. Nickerson; Steve T. Turner; Vasan S. Ramachandran; Jerome I. Rotter; Daniel Levy; Holly J. Kramer; Anna Köttgen; Trans-Omics for Precision Medicine (TOPMed) Consortium NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium; Kidney Working Group TOPMed Kidney Working Group; Stephen S. Rich; Dan Yu Lin; Sharon R. Browning; Nora Franceschini

doi:10.1016/j.ebiom.2020.103157

Whole genome sequence analyses of eGFR in 23,732 people representing multiple ancestries in the NHLBI trans-omics for precision medicine (TOPMed) consortium

Bridget M. Lin, Kelsey E. Grinde, Jennifer A. Brody, Charles E. Breeze, Laura M. Raffield, Josyf C. Mychaleckyj, Timothy A. Thornton, James A. Perry, Leslie J. Baier, Lisa de las Fuentes, Xiuqing Guo, Benjamin D. Heavner, Robert L. Hanson, Yi Jen Hung, Huijun Qian, Chao A. Hsiung, Shih Jen Hwang, Margaret R. Irvin, Deepti Jain, Tanika N. KellySayuko Kobes, Leslie Lange, James P. Lash, Yun Li, Xiaoming Liu, Xuenan Mi, Solomon K. Musani, George J. Papanicolaou, Afshin Parsa, Alex P. Reiner, Shabnam Salimi, Wayne H.H. Sheu, Alan R. Shuldiner, Kent D. Taylor, Albert V. Smith, Jennifer A. Smith, Adrienne Tin, Dhananjay Vaidya, Robert B. Wallace, Kenichi Yamamoto, Saori Sakaue, Koichi Matsuda, Yoichiro Kamatani, Yukihide Momozawa, Lisa R. Yanek, Betsi A. Young, Wei Zhao, Yukinori Okada, Gonzalo Abecasis, Bruce M. Psaty, Donna K. Arnett, Eric Boerwinkle, Jianwen Cai, Ida Yii-Der Chen, Adolfo Correa, L. Adrienne Cupples, Jiang He, Sharon LR Kardia, Charles Kooperberg, Rasika A. Mathias, Braxton D. Mitchell, Deborah A. Nickerson, Steve T. Turner, Vasan S. Ramachandran, Jerome I. Rotter, Daniel Levy, Holly J. Kramer, Anna Köttgen, Trans-Omics for Precision Medicine (TOPMed) Consortium NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Kidney Working Group TOPMed Kidney Working Group, Stephen S. Rich, Dan Yu Lin, Sharon R. Browning, Nora Franceschini

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

17 Scopus citations

Abstract

Background: Genetic factors that influence kidney traits have been understudied for low frequency and ancestry-specific variants. Methods: We combined whole genome sequencing (WGS) data from 23,732 participants from 10 NHLBI Trans-Omics for Precision Medicine (TOPMed) Program multi-ethnic studies to identify novel loci for estimated glomerular filtration rate (eGFR). Participants included European, African, East Asian, and Hispanic ancestries. We applied linear mixed models using a genetic relationship matrix estimated from the WGS data and adjusted for age, sex, study, and ethnicity. Findings: When testing single variants, we identified three novel loci driven by low frequency variants more commonly observed in non-European ancestry (PRKAA2, rs180996919, minor allele frequency [MAF] 0.04%, P = 6.1 × 10⁻¹¹; METTL8, rs116951054, MAF 0.09%, P = 4.5 × 10⁻⁹; and MATK, rs539182790, MAF 0.05%, P = 3.4 × 10⁻⁹). We also replicated two known loci for common variants (rs2461702, MAF=0.49, P = 1.2 × 10⁻⁹, nearest gene GATM, and rs71147340, MAF=0.34, P = 3.3 × 10⁻⁹, CDK12). Testing aggregated variants within a gene identified the MAF gene. A statistical approach based on local ancestry helped to identify replication samples for ancestry-specific variants. Interpretation: This study highlights challenges in studying variants influencing kidney traits that are low frequency in populations and more common in non-European ancestry.

Original language	English
Article number	103157
Journal	EBioMedicine
Volume	63
DOIs	https://doi.org/10.1016/j.ebiom.2020.103157
State	Published - Jan 2021

Bibliographical note

Publisher Copyright:
© 2020 The Author(s)

Funding

Whole genome sequencing (WGS) for the Trans-Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung and Blood Institute (NHLBI). Centralized read mapping and genotype calling, along with variant quality metrics and filtering were provided by the TOPMed Informatics Research Center (3R01HL-117626–02S1; contract HHSN268201800002I). Phenotype harmonization, data management, sample-identity QC, and general study coordination were provided by the TOPMed Data Coordinating Center (3R01HL-120393–02S1; contract HHSN268201800001I). We gratefully acknowledge the studies and participants who provided biological samples and data for TOPMed. Additional acknowledgements are included in the Supplementary Data. Individuals who contributed to the overall conduct of TOPMed are available at: https://www.nhlbiwgs.org/topmed-banner-authorship. NF is funded by the NIH grants: R01 DK117445, R01 MD012765 and R21 HL140385. SS is supported by the NIH K01AG059898. KEG was supported by the National Science Foundation Graduate Research Fellowship Program under grant no. DGE-1256082. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services. The Analysis Commons was funded by R01HL131136. SS is supported by NIH K01AG059898. Funding: see acknowledgements Whole genome sequencing (WGS) for the Trans-Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung and Blood Institute (NHLBI). Centralized read mapping and genotype calling, along with variant quality metrics and filtering were provided by the TOPMed Informatics Research Center (3R01HL-117626–02S1; contract HHSN268201800002I). Phenotype harmonization, data management, sample-identity QC, and general study coordination were provided by the TOPMed Data Coordinating Center (3R01HL-120393–02S1; contract HHSN268201800001I). We gratefully acknowledge the studies and participants who provided biological samples and data for TOPMed. Additional acknowledgements are included in the Supplementary Data. Individuals who contributed to the overall conduct of TOPMed are available at: https://www.nhlbiwgs.org/topmed-banner-authorship . NF is funded by the NIH grants: R01 DK117445, R01 MD012765 and R21 HL140385. SS is supported by the NIH K01AG059898. KEG was supported by the National Science Foundation Graduate Research Fellowship Program under grant no. DGE-1256082. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services. The Analysis Commons was funded by R01HL131136. SS is supported by NIH K01AG059898.

Funders	Funder number
TOPMed Informatics Research Center	3R01HL-120393–02S1, HHSN268201800001I, HHSN268201800002I, 3R01HL-117626–02S1
National Science Foundation Arctic Social Science Program	DGE-1256082
National Science Foundation Arctic Social Science Program
National Institutes of Health (NIH)	R01 DK117445, K01AG059898, R21 HL140385
National Institutes of Health (NIH)
U.S. Department of Health and Human Services
National Heart, Lung, and Blood Institute (NHLBI)	R01HL131136
National Heart, Lung, and Blood Institute (NHLBI)
National Institute on Minority Health and Health Disparities (NIMHD)	R01MD012765
National Institute on Minority Health and Health Disparities (NIMHD)

Keywords

Ancestry-specific variants
Kidney traits
Rare variants
Whole genome sequencing

ASJC Scopus subject areas

General Biochemistry, Genetics and Molecular Biology

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10.1016/j.ebiom.2020.103157

Cite this

Lin, B. M., Grinde, K. E., Brody, J. A., Breeze, C. E., Raffield, L. M., Mychaleckyj, J. C., Thornton, T. A., Perry, J. A., Baier, L. J., de las Fuentes, L., Guo, X., Heavner, B. D., Hanson, R. L., Hung, Y. J., Qian, H., Hsiung, C. A., Hwang, S. J., Irvin, M. R., Jain, D., ... Franceschini, N. (2021). Whole genome sequence analyses of eGFR in 23,732 people representing multiple ancestries in the NHLBI trans-omics for precision medicine (TOPMed) consortium. EBioMedicine, 63, Article 103157. https://doi.org/10.1016/j.ebiom.2020.103157

@article{418657c7da8f4867bd199b53737bcdcd,

title = "Whole genome sequence analyses of eGFR in 23,732 people representing multiple ancestries in the NHLBI trans-omics for precision medicine (TOPMed) consortium",

abstract = "Background: Genetic factors that influence kidney traits have been understudied for low frequency and ancestry-specific variants. Methods: We combined whole genome sequencing (WGS) data from 23,732 participants from 10 NHLBI Trans-Omics for Precision Medicine (TOPMed) Program multi-ethnic studies to identify novel loci for estimated glomerular filtration rate (eGFR). Participants included European, African, East Asian, and Hispanic ancestries. We applied linear mixed models using a genetic relationship matrix estimated from the WGS data and adjusted for age, sex, study, and ethnicity. Findings: When testing single variants, we identified three novel loci driven by low frequency variants more commonly observed in non-European ancestry (PRKAA2, rs180996919, minor allele frequency [MAF] 0.04\%, P = 6.1 × 10−11; METTL8, rs116951054, MAF 0.09\%, P = 4.5 × 10−9; and MATK, rs539182790, MAF 0.05\%, P = 3.4 × 10−9). We also replicated two known loci for common variants (rs2461702, MAF=0.49, P = 1.2 × 10−9, nearest gene GATM, and rs71147340, MAF=0.34, P = 3.3 × 10−9, CDK12). Testing aggregated variants within a gene identified the MAF gene. A statistical approach based on local ancestry helped to identify replication samples for ancestry-specific variants. Interpretation: This study highlights challenges in studying variants influencing kidney traits that are low frequency in populations and more common in non-European ancestry.",

keywords = "Ancestry-specific variants, Kidney traits, Rare variants, Whole genome sequencing",

author = "Lin, \{Bridget M.\} and Grinde, \{Kelsey E.\} and Brody, \{Jennifer A.\} and Breeze, \{Charles E.\} and Raffield, \{Laura M.\} and Mychaleckyj, \{Josyf C.\} and Thornton, \{Timothy A.\} and Perry, \{James A.\} and Baier, \{Leslie J.\} and \{de las Fuentes\}, Lisa and Xiuqing Guo and Heavner, \{Benjamin D.\} and Hanson, \{Robert L.\} and Hung, \{Yi Jen\} and Huijun Qian and Hsiung, \{Chao A.\} and Hwang, \{Shih Jen\} and Irvin, \{Margaret R.\} and Deepti Jain and Kelly, \{Tanika N.\} and Sayuko Kobes and Leslie Lange and Lash, \{James P.\} and Yun Li and Xiaoming Liu and Xuenan Mi and Musani, \{Solomon K.\} and Papanicolaou, \{George J.\} and Afshin Parsa and Reiner, \{Alex P.\} and Shabnam Salimi and Sheu, \{Wayne H.H.\} and Shuldiner, \{Alan R.\} and Taylor, \{Kent D.\} and Smith, \{Albert V.\} and Smith, \{Jennifer A.\} and Adrienne Tin and Dhananjay Vaidya and Wallace, \{Robert B.\} and Kenichi Yamamoto and Saori Sakaue and Koichi Matsuda and Yoichiro Kamatani and Yukihide Momozawa and Yanek, \{Lisa R.\} and Young, \{Betsi A.\} and Wei Zhao and Yukinori Okada and Gonzalo Abecasis and Psaty, \{Bruce M.\} and Arnett, \{Donna K.\} and Eric Boerwinkle and Jianwen Cai and \{Yii-Der Chen\}, Ida and Adolfo Correa and Cupples, \{L. Adrienne\} and Jiang He and Kardia, \{Sharon LR\} and Charles Kooperberg and Mathias, \{Rasika A.\} and Mitchell, \{Braxton D.\} and Nickerson, \{Deborah A.\} and Turner, \{Steve T.\} and Ramachandran, \{Vasan S.\} and Rotter, \{Jerome I.\} and Daniel Levy and Kramer, \{Holly J.\} and Anna K{\"o}ttgen and \{NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium\}, \{Trans-Omics for Precision Medicine (TOPMed) Consortium\} and \{TOPMed Kidney Working Group\}, \{Kidney Working Group\} and Rich, \{Stephen S.\} and Lin, \{Dan Yu\} and Browning, \{Sharon R.\} and Nora Franceschini",

note = "Publisher Copyright: {\textcopyright} 2020 The Author(s)",

year = "2021",

month = jan,

doi = "10.1016/j.ebiom.2020.103157",

language = "English",

volume = "63",

}

Lin, BM, Grinde, KE, Brody, JA, Breeze, CE, Raffield, LM, Mychaleckyj, JC, Thornton, TA, Perry, JA, Baier, LJ, de las Fuentes, L, Guo, X, Heavner, BD, Hanson, RL, Hung, YJ, Qian, H, Hsiung, CA, Hwang, SJ, Irvin, MR, Jain, D, Kelly, TN, Kobes, S, Lange, L, Lash, JP, Li, Y, Liu, X, Mi, X, Musani, SK, Papanicolaou, GJ, Parsa, A, Reiner, AP, Salimi, S, Sheu, WHH, Shuldiner, AR, Taylor, KD, Smith, AV, Smith, JA, Tin, A, Vaidya, D, Wallace, RB, Yamamoto, K, Sakaue, S, Matsuda, K, Kamatani, Y, Momozawa, Y, Yanek, LR, Young, BA, Zhao, W, Okada, Y, Abecasis, G, Psaty, BM, Arnett, DK, Boerwinkle, E, Cai, J, Yii-Der Chen, I, Correa, A, Cupples, LA, He, J, Kardia, SLR, Kooperberg, C, Mathias, RA, Mitchell, BD, Nickerson, DA, Turner, ST, Ramachandran, VS, Rotter, JI, Levy, D, Kramer, HJ, Köttgen, A, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, T-OFPMC, TOPMed Kidney Working Group, KWG, Rich, SS, Lin, DY, Browning, SR & Franceschini, N 2021, 'Whole genome sequence analyses of eGFR in 23,732 people representing multiple ancestries in the NHLBI trans-omics for precision medicine (TOPMed) consortium', EBioMedicine, vol. 63, 103157. https://doi.org/10.1016/j.ebiom.2020.103157

TY - JOUR

T1 - Whole genome sequence analyses of eGFR in 23,732 people representing multiple ancestries in the NHLBI trans-omics for precision medicine (TOPMed) consortium

AU - Lin, Bridget M.

AU - Grinde, Kelsey E.

AU - Brody, Jennifer A.

AU - Breeze, Charles E.

AU - Raffield, Laura M.

AU - Mychaleckyj, Josyf C.

AU - Thornton, Timothy A.

AU - Perry, James A.

AU - Baier, Leslie J.

AU - de las Fuentes, Lisa

AU - Guo, Xiuqing

AU - Heavner, Benjamin D.

AU - Hanson, Robert L.

AU - Hung, Yi Jen

AU - Qian, Huijun

AU - Hsiung, Chao A.

AU - Hwang, Shih Jen

AU - Irvin, Margaret R.

AU - Jain, Deepti

AU - Kelly, Tanika N.

AU - Kobes, Sayuko

AU - Lange, Leslie

AU - Lash, James P.

AU - Li, Yun

AU - Liu, Xiaoming

AU - Mi, Xuenan

AU - Musani, Solomon K.

AU - Papanicolaou, George J.

AU - Parsa, Afshin

AU - Reiner, Alex P.

AU - Salimi, Shabnam

AU - Sheu, Wayne H.H.

AU - Shuldiner, Alan R.

AU - Taylor, Kent D.

AU - Smith, Albert V.

AU - Smith, Jennifer A.

AU - Tin, Adrienne

AU - Vaidya, Dhananjay

AU - Wallace, Robert B.

AU - Yamamoto, Kenichi

AU - Sakaue, Saori

AU - Matsuda, Koichi

AU - Kamatani, Yoichiro

AU - Momozawa, Yukihide

AU - Yanek, Lisa R.

AU - Young, Betsi A.

AU - Zhao, Wei

AU - Okada, Yukinori

AU - Abecasis, Gonzalo

AU - Psaty, Bruce M.

AU - Arnett, Donna K.

AU - Boerwinkle, Eric

AU - Cai, Jianwen

AU - Yii-Der Chen, Ida

AU - Correa, Adolfo

AU - Cupples, L. Adrienne

AU - He, Jiang

AU - Kardia, Sharon LR

AU - Kooperberg, Charles

AU - Mathias, Rasika A.

AU - Mitchell, Braxton D.

AU - Nickerson, Deborah A.

AU - Turner, Steve T.

AU - Ramachandran, Vasan S.

AU - Rotter, Jerome I.

AU - Levy, Daniel

AU - Kramer, Holly J.

AU - Köttgen, Anna

AU - NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Trans-Omics for Precision Medicine (TOPMed) Consortium

AU - TOPMed Kidney Working Group, Kidney Working Group

AU - Rich, Stephen S.

AU - Lin, Dan Yu

AU - Browning, Sharon R.

AU - Franceschini, Nora

PY - 2021/1

Y1 - 2021/1

N2 - Background: Genetic factors that influence kidney traits have been understudied for low frequency and ancestry-specific variants. Methods: We combined whole genome sequencing (WGS) data from 23,732 participants from 10 NHLBI Trans-Omics for Precision Medicine (TOPMed) Program multi-ethnic studies to identify novel loci for estimated glomerular filtration rate (eGFR). Participants included European, African, East Asian, and Hispanic ancestries. We applied linear mixed models using a genetic relationship matrix estimated from the WGS data and adjusted for age, sex, study, and ethnicity. Findings: When testing single variants, we identified three novel loci driven by low frequency variants more commonly observed in non-European ancestry (PRKAA2, rs180996919, minor allele frequency [MAF] 0.04%, P = 6.1 × 10−11; METTL8, rs116951054, MAF 0.09%, P = 4.5 × 10−9; and MATK, rs539182790, MAF 0.05%, P = 3.4 × 10−9). We also replicated two known loci for common variants (rs2461702, MAF=0.49, P = 1.2 × 10−9, nearest gene GATM, and rs71147340, MAF=0.34, P = 3.3 × 10−9, CDK12). Testing aggregated variants within a gene identified the MAF gene. A statistical approach based on local ancestry helped to identify replication samples for ancestry-specific variants. Interpretation: This study highlights challenges in studying variants influencing kidney traits that are low frequency in populations and more common in non-European ancestry.

AB - Background: Genetic factors that influence kidney traits have been understudied for low frequency and ancestry-specific variants. Methods: We combined whole genome sequencing (WGS) data from 23,732 participants from 10 NHLBI Trans-Omics for Precision Medicine (TOPMed) Program multi-ethnic studies to identify novel loci for estimated glomerular filtration rate (eGFR). Participants included European, African, East Asian, and Hispanic ancestries. We applied linear mixed models using a genetic relationship matrix estimated from the WGS data and adjusted for age, sex, study, and ethnicity. Findings: When testing single variants, we identified three novel loci driven by low frequency variants more commonly observed in non-European ancestry (PRKAA2, rs180996919, minor allele frequency [MAF] 0.04%, P = 6.1 × 10−11; METTL8, rs116951054, MAF 0.09%, P = 4.5 × 10−9; and MATK, rs539182790, MAF 0.05%, P = 3.4 × 10−9). We also replicated two known loci for common variants (rs2461702, MAF=0.49, P = 1.2 × 10−9, nearest gene GATM, and rs71147340, MAF=0.34, P = 3.3 × 10−9, CDK12). Testing aggregated variants within a gene identified the MAF gene. A statistical approach based on local ancestry helped to identify replication samples for ancestry-specific variants. Interpretation: This study highlights challenges in studying variants influencing kidney traits that are low frequency in populations and more common in non-European ancestry.

KW - Ancestry-specific variants

KW - Kidney traits

KW - Rare variants

KW - Whole genome sequencing

UR - http://www.scopus.com/inward/record.url?scp=85098975319&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85098975319&partnerID=8YFLogxK

U2 - 10.1016/j.ebiom.2020.103157

DO - 10.1016/j.ebiom.2020.103157

M3 - Article

C2 - 33418499

AN - SCOPUS:85098975319

VL - 63

JO - EBioMedicine

JF - EBioMedicine

M1 - 103157

ER -

Whole genome sequence analyses of eGFR in 23,732 people representing multiple ancestries in the NHLBI trans-omics for precision medicine (TOPMed) consortium

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