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 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, Ben Heavner, Robert L. Hanson, Yi‐Jen Hung, Huijun Qian, Chao A. Hsiung, Shih‐Jen Hwang, M.R. Irvin, Deepti Jain, Tanika N. Kelly, Sayuko Kobes, Leslie A. Lange, James P. Lash, Yun Li, Xiaoming Liu, Xuenan Mi, Solomon K. Musani, George 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, Ramachandran S. Vasan, Jerome I. Rotter, Daniel Levy, Holly Kramer, Anna Köttgen, TOPMed Kidney Working Group, Stephen S. Rich, D. Y. Lin, Sharon R. Browning, Nora Franceschini
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.