Department of Biostatistics, University of North Carolina, Chapel Hill, NC, United States.
Department of Mathematics, Statistics, and Computer Science, Macalester College, St. Paul, MN, United States.
EBioMedicine. 2021 Jan;63:103157. doi: 10.1016/j.ebiom.2020.103157. Epub 2021 Jan 6.
Genetic factors that influence kidney traits have been understudied for low frequency and ancestry-specific variants.
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.
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.
This study highlights challenges in studying variants influencing kidney traits that are low frequency in populations and more common in non-European ancestry.
遗传因素对肾脏特征的影响在低频和特定祖先的变体方面研究不足。
我们结合了来自 NHLBI 跨组学精准医学(TOPMed)计划的 10 个多民族研究中 23732 名参与者的全基因组测序(WGS)数据,以确定估计肾小球滤过率(eGFR)的新基因座。参与者包括欧洲、非洲、东亚和西班牙裔血统。我们应用了线性混合模型,使用从 WGS 数据中估计的遗传关系矩阵,并根据年龄、性别、研究和种族进行了调整。
在测试单一变体时,我们确定了三个由低频变体驱动的新基因座,这些变体在非欧洲血统中更为常见(PRKAA2,rs180996919,次要等位基因频率 [MAF] 为 0.04%,P=6.1×10;METTL8,rs116951054,MAF 为 0.09%,P=4.5×10;和 MATK,rs539182790,MAF 为 0.05%,P=3.4×10)。我们还复制了两个常见变体的已知基因座(rs2461702,MAF=0.49,P=1.2×10,最近的基因 GATM,和 rs71147340,MAF=0.34,P=3.3×10,CDK12)。在基因内测试聚集变体确定了 MAF 基因。一种基于局部祖先的统计方法有助于识别特定祖先变体的复制样本。
本研究强调了研究在人群中低频且在非欧洲血统中更为常见的影响肾脏特征的变体所面临的挑战。