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
N1 - Funding Information:
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
Funding Information:
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.
Publisher Copyright:
© 2020 The Author(s)
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
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U2 - 10.1016/j.ebiom.2020.103157
DO - 10.1016/j.ebiom.2020.103157
M3 - Article
C2 - 33418499
AN - SCOPUS:85098975319
SN - 2352-3964
VL - 63
JO - EBioMedicine
JF - EBioMedicine
M1 - 103157
ER -