@article {9253, title = {A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies.}, journal = {Nat Methods}, volume = {19}, year = {2022}, month = {2022 Dec}, pages = {1599-1611}, abstract = {

Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare-variant (RV) associations with complex human diseases and traits. Variant-set analysis is a powerful approach to study RV association. However, existing methods have limited ability in analyzing the noncoding genome. We propose a computationally efficient and robust noncoding RV association detection framework, STAARpipeline, to automatically annotate a whole-genome sequencing study and perform flexible noncoding RV association analysis, including gene-centric analysis and fixed window-based and dynamic window-based non-gene-centric analysis by incorporating variant functional annotations. In gene-centric analysis, STAARpipeline uses STAAR to group noncoding variants based on functional categories of genes and incorporate multiple functional annotations. In non-gene-centric analysis, STAARpipeline uses SCANG-STAAR to incorporate dynamic window sizes and multiple functional annotations. We apply STAARpipeline to identify noncoding RV sets associated with four lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several of them in an additional 9,123 TOPMed samples. We also analyze five non-lipid TOPMed traits.

}, keywords = {Genetic Variation, Genome, Genome-Wide Association Study, Humans, Phenotype, Whole Genome Sequencing}, issn = {1548-7105}, doi = {10.1038/s41592-022-01640-x}, author = {Li, Zilin and Li, Xihao and Zhou, Hufeng and Gaynor, Sheila M and Selvaraj, Margaret Sunitha and Arapoglou, Theodore and Quick, Corbin and Liu, Yaowu and Chen, Han and Sun, Ryan and Dey, Rounak and Arnett, Donna K and Auer, Paul L and Bielak, Lawrence F and Bis, Joshua C and Blackwell, Thomas W and Blangero, John and Boerwinkle, Eric and Bowden, Donald W and Brody, Jennifer A and Cade, Brian E and Conomos, Matthew P and Correa, Adolfo and Cupples, L Adrienne and Curran, Joanne E and de Vries, Paul S and Duggirala, Ravindranath and Franceschini, Nora and Freedman, Barry I and G{\"o}ring, Harald H H and Guo, Xiuqing and Kalyani, Rita R and Kooperberg, Charles and Kral, Brian G and Lange, Leslie A and Lin, Bridget M and Manichaikul, Ani and Manning, Alisa K and Martin, Lisa W and Mathias, Rasika A and Meigs, James B and Mitchell, Braxton D and Montasser, May E and Morrison, Alanna C and Naseri, Take and O{\textquoteright}Connell, Jeffrey R and Palmer, Nicholette D and Peyser, Patricia A and Psaty, Bruce M and Raffield, Laura M and Redline, Susan and Reiner, Alexander P and Reupena, Muagututi{\textquoteright}a Sefuiva and Rice, Kenneth M and Rich, Stephen S and Smith, Jennifer A and Taylor, Kent D and Taub, Margaret A and Vasan, Ramachandran S and Weeks, Daniel E and Wilson, James G and Yanek, Lisa R and Zhao, Wei and Rotter, Jerome I and Willer, Cristen J and Natarajan, Pradeep and Peloso, Gina M and Lin, Xihong} } @article {9158, title = {Whole genome sequence association analysis of fasting glucose and fasting insulin levels in diverse cohorts from the NHLBI TOPMed program.}, journal = {Commun Biol}, volume = {5}, year = {2022}, month = {2022 07 28}, pages = {756}, abstract = {

The genetic determinants of fasting glucose (FG) and fasting insulin (FI) have been studied mostly through genome arrays, resulting in over 100 associated variants. We extended this work with high-coverage whole genome sequencing analyses from fifteen cohorts in NHLBI{\textquoteright}s Trans-Omics for Precision Medicine (TOPMed) program. Over 23,000 non-diabetic individuals from five race-ethnicities/populations (African, Asian, European, Hispanic and Samoan) were included. Eight variants were significantly associated with FG or FI across previously identified regions MTNR1B, G6PC2, GCK, GCKR and FOXA2. We additionally characterize suggestive associations with FG or FI near previously identified SLC30A8, TCF7L2, and ADCY5 regions as well as APOB, PTPRT, and ROBO1. Functional annotation resources including the Diabetes Epigenome Atlas were compiled for each signal (chromatin states, annotation principal components, and others) to elucidate variant-to-function hypotheses. We provide a catalog of nucleotide-resolution genomic variation spanning intergenic and intronic regions creating a foundation for future sequencing-based investigations of glycemic traits.

}, keywords = {Diabetes Mellitus, Type 2, Fasting, Glucose, Humans, Insulin, National Heart, Lung, and Blood Institute (U.S.), Nerve Tissue Proteins, Polymorphism, Single Nucleotide, Precision Medicine, Receptors, Immunologic, United States}, issn = {2399-3642}, doi = {10.1038/s42003-022-03702-4}, author = {DiCorpo, Daniel and Gaynor, Sheila M and Russell, Emily M and Westerman, Kenneth E and Raffield, Laura M and Majarian, Timothy D and Wu, Peitao and Sarnowski, Chloe and Highland, Heather M and Jackson, Anne and Hasbani, Natalie R and de Vries, Paul S and Brody, Jennifer A and Hidalgo, Bertha and Guo, Xiuqing and Perry, James A and O{\textquoteright}Connell, Jeffrey R and Lent, Samantha and Montasser, May E and Cade, Brian E and Jain, Deepti and Wang, Heming and D{\textquoteright}Oliveira Albanus, Ricardo and Varshney, Arushi and Yanek, Lisa R and Lange, Leslie and Palmer, Nicholette D and Almeida, Marcio and Peralta, Juan M and Aslibekyan, Stella and Baldridge, Abigail S and Bertoni, Alain G and Bielak, Lawrence F and Chen, Chung-Shiuan and Chen, Yii-Der Ida and Choi, Won Jung and Goodarzi, Mark O and Floyd, James S and Irvin, Marguerite R and Kalyani, Rita R and Kelly, Tanika N and Lee, Seonwook and Liu, Ching-Ti and Loesch, Douglas and Manson, JoAnn E and Minster, Ryan L and Naseri, Take and Pankow, James S and Rasmussen-Torvik, Laura J and Reiner, Alexander P and Reupena, Muagututi{\textquoteright}a Sefuiva and Selvin, Elizabeth and Smith, Jennifer A and Weeks, Daniel E and Xu, Huichun and Yao, Jie and Zhao, Wei and Parker, Stephen and Alonso, Alvaro and Arnett, Donna K and Blangero, John and Boerwinkle, Eric and Correa, Adolfo and Cupples, L Adrienne and Curran, Joanne E and Duggirala, Ravindranath and He, Jiang and Heckbert, Susan R and Kardia, Sharon L R and Kim, Ryan W and Kooperberg, Charles and Liu, Simin and Mathias, Rasika A and McGarvey, Stephen T and Mitchell, Braxton D and Morrison, Alanna C and Peyser, Patricia A and Psaty, Bruce M and Redline, Susan and Shuldiner, Alan R and Taylor, Kent D and Vasan, Ramachandran S and Viaud-Martinez, Karine A and Florez, Jose C and Wilson, James G and Sladek, Robert and Rich, Stephen S and Rotter, Jerome I and Lin, Xihong and Dupuis, Jos{\'e}e and Meigs, James B and Wessel, Jennifer and Manning, Alisa K} } @article {9239, title = {Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies.}, journal = {Nat Genet}, volume = {55}, year = {2023}, month = {2023 Jan}, pages = {154-164}, abstract = {

Meta-analysis of whole genome sequencing/whole exome sequencing (WGS/WES) studies provides an attractive solution to the problem of collecting large sample sizes for discovering rare variants associated with complex phenotypes. Existing rare variant meta-analysis approaches are not scalable to biobank-scale WGS data. Here we present MetaSTAAR, a powerful and resource-efficient rare variant meta-analysis framework for large-scale WGS/WES studies. MetaSTAAR accounts for relatedness and population structure, can analyze both quantitative and dichotomous traits and boosts the power of rare variant tests by incorporating multiple variant functional annotations. Through meta-analysis of four lipid traits in 30,138 ancestrally diverse samples from 14 studies of the Trans Omics for Precision Medicine (TOPMed) Program, we show that MetaSTAAR performs rare variant meta-analysis at scale and produces results comparable to using pooled data. Additionally, we identified several conditionally significant rare variant associations with lipid traits. We further demonstrate that MetaSTAAR is scalable to biobank-scale cohorts through meta-analysis of TOPMed WGS data and UK Biobank WES data of ~200,000 samples.

}, keywords = {Exome Sequencing, Genome-Wide Association Study, Lipids, Phenotype, Whole Genome Sequencing}, issn = {1546-1718}, doi = {10.1038/s41588-022-01225-6}, author = {Li, Xihao and Quick, Corbin and Zhou, Hufeng and Gaynor, Sheila M and Liu, Yaowu and Chen, Han and Selvaraj, Margaret Sunitha and Sun, Ryan and Dey, Rounak and Arnett, Donna K and Bielak, Lawrence F and Bis, Joshua C and Blangero, John and Boerwinkle, Eric and Bowden, Donald W and Brody, Jennifer A and Cade, Brian E and Correa, Adolfo and Cupples, L Adrienne and Curran, Joanne E and de Vries, Paul S and Duggirala, Ravindranath and Freedman, Barry I and G{\"o}ring, Harald H H and Guo, Xiuqing and Haessler, Jeffrey and Kalyani, Rita R and Kooperberg, Charles and Kral, Brian G and Lange, Leslie A and Manichaikul, Ani and Martin, Lisa W and McGarvey, Stephen T and Mitchell, Braxton D and Montasser, May E and Morrison, Alanna C and Naseri, Take and O{\textquoteright}Connell, Jeffrey R and Palmer, Nicholette D and Peyser, Patricia A and Psaty, Bruce M and Raffield, Laura M and Redline, Susan and Reiner, Alexander P and Reupena, Muagututi{\textquoteright}a Sefuiva and Rice, Kenneth M and Rich, Stephen S and Sitlani, Colleen M and Smith, Jennifer A and Taylor, Kent D and Vasan, Ramachandran S and Willer, Cristen J and Wilson, James G and Yanek, Lisa R and Zhao, Wei and Rotter, Jerome I and Natarajan, Pradeep and Peloso, Gina M and Li, Zilin and Lin, Xihong} } @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} }