@article {9543,
	title = {A statistical framework for powerful multi-trait rare variant analysis in large-scale whole-genome sequencing studies.},
	journal = {bioRxiv},
	year = {2023},
	month = {2023 Nov 02},
	abstract = {<p>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 (low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides) in 61,861 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered new associations with lipid traits missed by single-trait analysis, including rare variants within an enhancer of  and an intergenic region on chromosome 1.</p>},
	doi = {10.1101/2023.10.30.564764},
	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 Arnett, Donna K 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 and Konigsberg, Iain and Kooperberg, Charles and Kral, Brian G and Li, Changwei and Loos, Ruth J F and Mahaney, Michael C and Martin, Lisa W and Mathias, Rasika A and Minster, Ryan L and Mitchell, Braxton D and Montasser, May E and Morrison, Alanna C and Palmer, Nicholette D and Peyser, Patricia A and Psaty, Bruce M and Raffield, Laura 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 and Vasan, Ramachandran S and Wang, Zhe and Yanek, Lisa R and Yu, Bing 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 {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 = {<p>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.</p>},
	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}
}
