@article {8664, title = {Whole genome sequence analyses of eGFR in 23,732 people representing multiple ancestries in the NHLBI trans-omics for precision medicine (TOPMed) consortium.}, journal = {EBioMedicine}, volume = {63}, year = {2021}, month = {2021 Jan}, pages = {103157}, 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~{\texttimes}~10; METTL8, rs116951054, MAF 0.09\%, P~=~4.5~{\texttimes}~10; and MATK, rs539182790, MAF 0.05\%, P~=~3.4~{\texttimes}~10). We also replicated two known loci for common variants (rs2461702, MAF=0.49, P~=~1.2~{\texttimes}~10, nearest gene GATM, and rs71147340, MAF=0.34, P~=~3.3~{\texttimes}~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.

}, issn = {2352-3964}, doi = {10.1016/j.ebiom.2020.103157}, 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 Guo, Xiuqing and Heavner, Benjamin D and Hanson, Robert L and Hung, Yi-Jen and Qian, Huijun and Hsiung, Chao A and Hwang, Shih-Jen and Irvin, Margaret R and Jain, Deepti and Kelly, Tanika N and Kobes, Sayuko and Lange, Leslie and Lash, James P and Li, Yun and Liu, Xiaoming and Mi, Xuenan and Musani, Solomon K and Papanicolaou, George J and Parsa, Afshin and Reiner, Alex P and Salimi, Shabnam and Sheu, Wayne H-H and Shuldiner, Alan R and Taylor, Kent D and Smith, Albert V and Smith, Jennifer A and Tin, Adrienne and Vaidya, Dhananjay and Wallace, Robert B and Yamamoto, Kenichi and Sakaue, Saori and Matsuda, Koichi and Kamatani, Yoichiro and Momozawa, Yukihide and Yanek, Lisa R and Young, Betsi A and Zhao, Wei and Okada, Yukinori and Abecasis, Gonzalo and Psaty, Bruce M and Arnett, Donna K and Boerwinkle, Eric and Cai, Jianwen and Yii-Der Chen, Ida and Correa, Adolfo and Cupples, L Adrienne and He, Jiang and Kardia, Sharon Lr and Kooperberg, Charles and Mathias, Rasika A and Mitchell, Braxton D and Nickerson, Deborah A and Turner, Steve T and Vasan, Ramachandran S and Rotter, Jerome I and Levy, Daniel and Kramer, Holly J and K{\"o}ttgen, Anna and Rich, Stephen S and Lin, Dan-Yu and Browning, Sharon R and Franceschini, Nora} } @article {9500, title = {Whole Genome Sequencing Based Analysis of Inflammation Biomarkers in the Trans-Omics for Precision Medicine (TOPMed) Consortium.}, journal = {bioRxiv}, year = {2023}, month = {2023 Sep 12}, abstract = {

Inflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38,465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program. We identified 22 distinct single-variant associations across 6 traits - E-selectin, intercellular adhesion molecule 1, interleukin-6, lipoprotein-associated phospholipase A2 activity and mass, and P-selectin - that remained significant after conditioning on previously identified associations for these inflammatory biomarkers. We further expanded upon known biomarker associations by pairing the single-variant analysis with a rare variant set-based analysis that further identified 19 significant rare variant set-based associations with 5 traits. These signals were distinct from both significant single variant association signals within TOPMed and genetic signals observed in prior studies, demonstrating the complementary value of performing both single and rare variant analyses when analyzing quantitative traits. We also confirm several previously reported signals from semi-quantitative proteomics platforms. Many of these signals demonstrate the extensive allelic heterogeneity and ancestry-differentiated variant-trait associations common for inflammation biomarkers, a characteristic we hypothesize will be increasingly observed with well-powered, large-scale analyses of complex traits.

}, doi = {10.1101/2023.09.10.555215}, author = {Jiang, Min-Zhi and Gaynor, Sheila M and Li, Xihao and Van Buren, Eric and Stilp, Adrienne and Buth, Erin and Wang, Fei Fei and Manansala, Regina and Gogarten, Stephanie M and Li, Zilin and Polfus, Linda M and Salimi, Shabnam and Bis, Joshua C and Pankratz, Nathan and Yanek, Lisa R and Durda, Peter and Tracy, Russell P and Rich, Stephen S and Rotter, Jerome I and Mitchell, Braxton D and Lewis, Joshua P and Psaty, Bruce M and Pratte, Katherine A and Silverman, Edwin K and Kaplan, Robert C and Avery, Christy and North, Kari and Mathias, Rasika A and Faraday, Nauder and Lin, Honghuang and Wang, Biqi and Carson, April P and Norwood, Arnita F and Gibbs, Richard A and Kooperberg, Charles and Lundin, Jessica and Peters, Ulrike and Dupuis, Jos{\'e}e and Hou, Lifang and Fornage, Myriam and Benjamin, Emelia J and Reiner, Alexander P and Bowler, Russell P and Lin, Xihong and Auer, Paul L and Raffield, Laura M} }