|Title||Whole genome sequence analyses of eGFR in 23,732 people representing multiple ancestries in the NHLBI trans-omics for precision medicine (TOPMed) consortium.|
|Publication Type||Journal Article|
|Year of Publication||2021|
|Authors||Lin, BM, Grinde, KE, Brody, JA, Breeze, CE, Raffield, LM, Mychaleckyj, JC, Thornton, TA, Perry, JA, Baier, LJ, Fuentes, Lde Las, Guo, X, Heavner, BD, Hanson, RL, Hung, Y-J, Qian, H, Hsiung, CA, Hwang, S-J, 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, WH-H, 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, Der Chen, IYii-, Correa, A, L Cupples, A, He, J, Kardia, SLr, Kooperberg, C, Mathias, RA, Mitchell, BD, Nickerson, DA, Turner, ST, Vasan, RS, Rotter, JI, Levy, D, Kramer, HJ, Köttgen, A, Rich, SS, Lin, D-Y, Browning, SR, Franceschini, N|
|Date Published||2021 Jan|
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; METTL8, rs116951054, MAF 0.09%, P = 4.5 × 10; and MATK, rs539182790, MAF 0.05%, P = 3.4 × 10). We also replicated two known loci for common variants (rs2461702, MAF=0.49, P = 1.2 × 10, nearest gene GATM, and rs71147340, MAF=0.34, P = 3.3 × 10, 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.
|PubMed Central ID||PMC7804602|
|Grant List||R01 HL120393 / HL / NHLBI NIH HHS / United States |
R01 DK117445 / DK / NIDDK NIH HHS / United States
HHSN268201800001C / HL / NHLBI NIH HHS / United States
R21 HL123677 / HL / NHLBI NIH HHS / United States
R01 MD012765 / MD / NIMHD NIH HHS / United States
R01 HL117626 / HL / NHLBI NIH HHS / United States
R01 HL149683 / HL / NHLBI NIH HHS / United States
K01 AG059898 / AG / NIA NIH HHS / United States
R01 HL131136 / HL / NHLBI NIH HHS / United States
R01 HG009974 / HG / NHGRI NIH HHS / United States
R21 HL140385 / HL / NHLBI NIH HHS / United States