@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 {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 {9376, title = {Whole-Genome Sequencing Analysis of Human Metabolome in Multi-Ethnic Populations.}, journal = {Nat Commun}, volume = {14}, year = {2023}, month = {2023 May 30}, pages = {3111}, abstract = {

Circulating metabolite levels may reflect the state of the human organism in health and disease, however, the genetic architecture of metabolites is not fully understood. We have performed a whole-genome sequencing association analysis of both common and rare variants in up to 11,840 multi-ethnic participants from five studies with up to 1666 circulating metabolites. We have discovered 1985 novel variant-metabolite associations, and validated 761 locus-metabolite associations reported previously. Seventy-nine novel variant-metabolite associations have been replicated, including three genetic loci located on the X chromosome that have demonstrated its involvement in metabolic regulation. Gene-based analysis have provided further support for seven metabolite-replicated loci pairs and their biologically plausible genes. Among those novel replicated variant-metabolite pairs, follow-up analyses have revealed that 26 metabolites have colocalized with 21 tissues, seven metabolite-disease outcome associations have been putatively causal, and 7 metabolites might be regulated by plasma protein levels. Our results have depicted the genetic contribution to circulating metabolite levels, providing additional insights into understanding human disease.

}, keywords = {Ethnicity, Genome-Wide Association Study, Humans, Metabolome, Polymorphism, Single Nucleotide, Quantitative Trait Loci}, issn = {2041-1723}, doi = {10.1038/s41467-023-38800-2}, author = {Feofanova, Elena V and Brown, Michael R and Alkis, Taryn and Manuel, Astrid M and Li, Xihao and Tahir, Usman A and Li, Zilin and Mendez, Kevin M and Kelly, Rachel S and Qi, Qibin and Chen, Han and Larson, Martin G and Lemaitre, Rozenn N and Morrison, Alanna C and Grieser, Charles and Wong, Kari E and Gersztern, Robert E and Zhao, Zhongming and Lasky-Su, Jessica and Yu, Bing} } @article {9899, title = {A statistical framework for multi-trait rare variant analysis in large-scale whole-genome sequencing studies.}, journal = {Nat Comput Sci}, volume = {5}, year = {2025}, month = {2025 Feb}, pages = {125-143}, abstract = {

Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally scalable analytical pipeline for functionally informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple traits, and further empowers rare variant association analysis by incorporating multiple functional annotations. We applied MultiSTAAR to jointly analyze three lipid traits in 61,838 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered and replicated new associations with lipid traits missed by single-trait analysis.

}, keywords = {Genetic Variation, Genome, Human, Genome-Wide Association Study, Humans, Lipids, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Whole Genome Sequencing}, issn = {2662-8457}, doi = {10.1038/s43588-024-00764-8}, author = {Li, Xihao and Chen, Han and Selvaraj, Margaret Sunitha and Van Buren, Eric and Zhou, Hufeng and Wang, Yuxuan and Sun, Ryan and McCaw, Zachary R and Yu, Zhi and Jiang, Min-Zhi and DiCorpo, Daniel and Gaynor, Sheila M and Dey, Rounak and Arnett, Donna K and Benjamin, Emelia J and Bis, Joshua C and Blangero, John and Boerwinkle, Eric and Bowden, Donald W and Brody, Jennifer A and Cade, Brian E and Carson, April P and Carlson, Jenna C and Chami, Nathalie and Chen, Yii-Der Ida and Curran, Joanne E and de Vries, Paul S and Fornage, Myriam and Franceschini, Nora and Freedman, Barry I and Gu, Charles and Heard-Costa, Nancy L and He, Jiang and Hou, Lifang and Hung, Yi-Jen and Irvin, Marguerite R and Kaplan, Robert C and Kardia, Sharon L R and Kelly, Tanika N and Konigsberg, Iain and Kooperberg, Charles and Kral, Brian G and Li, Changwei and Li, Yun and Lin, Honghuang and Liu, Ching-Ti and Loos, Ruth J F and Mahaney, Michael C and Martin, Lisa W and Mathias, Rasika A and Mitchell, Braxton D and Montasser, May E and Morrison, Alanna C and Naseri, Take and North, Kari E and Palmer, Nicholette D and Peyser, Patricia A and Psaty, Bruce M and Redline, Susan and Reiner, Alexander P and Rich, Stephen S and Sitlani, Colleen M and Smith, Jennifer A and Taylor, Kent D and Tiwari, Hemant K and Vasan, Ramachandran S and Viali, Satupa{\textquoteright}itea and Wang, Zhe and Wessel, Jennifer and Yanek, Lisa R and Yu, Bing and Dupuis, Jos{\'e}e and Meigs, James B and Auer, Paul L and Raffield, Laura M and Manning, Alisa K and Rice, Kenneth M and Rotter, Jerome I and Peloso, Gina M and Natarajan, Pradeep and Li, Zilin and Liu, Zhonghua and Lin, Xihong} } @article {9942, title = {Whole genome sequencing analysis of body mass index identifies novel African ancestry-specific risk allele.}, journal = {Nat Commun}, volume = {16}, year = {2025}, month = {2025 Apr 11}, pages = {3470}, abstract = {

Obesity is a major public health crisis associated with high mortality rates. Previous genome-wide association studies (GWAS) investigating body mass index (BMI) have largely relied on imputed data from European individuals. This study leveraged whole-genome sequencing (WGS) data from 88,873 participants from the Trans-Omics for Precision Medicine (TOPMed) Program, of which 51\% were of non-European population groups. We discovered 18 BMI-associated signals (P < 5 {\texttimes} 10), including two secondary signals. Notably, we identified and replicated a novel low-frequency single nucleotide polymorphism (SNP) in MTMR3 that was common in individuals of African descent. Using a diverse study population, we further identified two novel secondary signals in known BMI loci and pinpointed two likely causal variants in the POC5 and DMD loci. Our work demonstrates the benefits of combining WGS and diverse cohorts in expanding current catalog of variants and genes confer risk for obesity, bringing us one step closer to personalized medicine.

}, keywords = {Adult, African People, Alleles, Body Mass Index, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Male, Middle Aged, Obesity, Polymorphism, Single Nucleotide, Population Groups, Whole Genome Sequencing}, issn = {2041-1723}, doi = {10.1038/s41467-025-58420-2}, author = {Zhang, Xinruo and Brody, Jennifer A and Graff, Mariaelisa and Highland, Heather M and Chami, Nathalie and Xu, Hanfei and Wang, Zhe and Ferrier, Kendra R and Chittoor, Geetha and Josyula, Navya Shilpa and Meyer, Mariah and Gupta, Shreyash and Li, Xihao and Li, Zilin and Allison, Matthew A and Becker, Diane M and Bielak, Lawrence F and Bis, Joshua C and Boorgula, Meher Preethi and Bowden, Donald W and Broome, Jai G and Buth, Erin J and Carlson, Christopher S and Chang, Kyong-Mi and Chavan, Sameer and Chiu, Yen-Feng and Chuang, Lee-Ming and Conomos, Matthew P and DeMeo, Dawn L and Du, Mengmeng and Duggirala, Ravindranath and Eng, Celeste and Fohner, Alison E and Freedman, Barry I and Garrett, Melanie E and Guo, Xiuqing and Haiman, Chris and Heavner, Benjamin D and Hidalgo, Bertha and Hixson, James E and Ho, Yuk-Lam and Hobbs, Brian D and Hu, Donglei and Hui, Qin and Hwu, Chii-Min and Jackson, Rebecca D and Jain, Deepti and Kalyani, Rita R and Kardia, Sharon L R and Kelly, Tanika N and Lange, Ethan M and LeNoir, Michael and Li, Changwei and Le Marchand, Lo{\"\i}c and McDonald, Merry-Lynn N and McHugh, Caitlin P and Morrison, Alanna C and Naseri, Take and O{\textquoteright}Connell, Jeffrey and O{\textquoteright}Donnell, Christopher J and Palmer, Nicholette D and Pankow, James S and Perry, James A and Peters, Ulrike and Preuss, Michael H and Rao, D C and Regan, Elizabeth A and Reupena, Sefuiva M and Roden, Dan M and Rodriguez-Santana, Jose and Sitlani, Colleen M and Smith, Jennifer A and Tiwari, Hemant K and Vasan, Ramachandran S and Wang, Zeyuan and Weeks, Daniel E and Wessel, Jennifer and Wiggins, Kerri L and Wilkens, Lynne R and Wilson, Peter W F and Yanek, Lisa R and Yoneda, Zachary T and Zhao, Wei and Z{\"o}llner, Sebastian and Arnett, Donna K and Ashley-Koch, Allison E and Barnes, Kathleen C and Blangero, John and Boerwinkle, Eric and Burchard, Esteban G and Carson, April P and Chasman, Daniel I and Ida Chen, Yii-Der and Curran, Joanne E and Fornage, Myriam and Gordeuk, Victor R and He, Jiang and Heckbert, Susan R and Hou, Lifang and Irvin, Marguerite R and Kooperberg, Charles and Minster, Ryan L and Mitchell, Braxton D and Nouraie, Mehdi and Psaty, Bruce M and Raffield, Laura M and Reiner, Alexander P and Rich, Stephen S and Rotter, Jerome I and Benjamin Shoemaker, M and Smith, Nicholas L and Taylor, Kent D and Telen, Marilyn J and Weiss, Scott T and Zhang, Yingze and Heard-Costa, Nancy and Sun, Yan V and Lin, Xihong and Cupples, L Adrienne and Lange, Leslie A and Liu, Ching-Ti and Loos, Ruth J F and North, Kari E and Justice, Anne E} }