@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 = {<p>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.</p>},
	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 = {<p>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.</p>},
	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}
}
