Browsing by Subject "Whole Genome Sequencing"

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    ItemOpen Access
    A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies.
    (Nature methods, 2022-12) Li, Zilin; Li, Xihao; Zhou, Hufeng; Gaynor, Sheila M; Selvaraj, Margaret Sunitha; Arapoglou, Theodore; Quick, Corbin; Liu, Yaowu; Chen, Han; Sun, Ryan; Dey, Rounak; Arnett, Donna K; Auer, Paul L; Bielak, Lawrence F; Bis, Joshua C; Blackwell, Thomas W; Blangero, John; Boerwinkle, Eric; Bowden, Donald W; Brody, Jennifer A; Cade, Brian E; Conomos, Matthew P; Correa, Adolfo; Cupples, L Adrienne; Curran, Joanne E; de Vries, Paul S; Duggirala, Ravindranath; Franceschini, Nora; Freedman, Barry I; Göring, Harald HH; Guo, Xiuqing; Kalyani, Rita R; Kooperberg, Charles; Kral, Brian G; Lange, Leslie A; Lin, Bridget M; Manichaikul, Ani; Manning, Alisa K; Martin, Lisa W; Mathias, Rasika A; Meigs, James B; Mitchell, Braxton D; Montasser, May E; Morrison, Alanna C; Naseri, Take; O'Connell, Jeffrey R; Palmer, Nicholette D; Peyser, Patricia A; Psaty, Bruce M; Raffield, Laura M; Redline, Susan; Reiner, Alexander P; Reupena, Muagututi'a Sefuiva; Rice, Kenneth M; Rich, Stephen S; Smith, Jennifer A; Taylor, Kent D; Taub, Margaret A; Vasan, Ramachandran S; Weeks, Daniel E; Wilson, James G; Yanek, Lisa R; Zhao, Wei; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium; TOPMed Lipids Working Group; Rotter, Jerome I; Willer, Cristen J; Natarajan, Pradeep; Peloso, Gina M; Lin, Xihong
    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.
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    ItemOpen Access
    Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies.
    (Nature genetics, 2023-01) Li, Xihao; Quick, Corbin; Zhou, Hufeng; Gaynor, Sheila M; Liu, Yaowu; Chen, Han; Selvaraj, Margaret Sunitha; Sun, Ryan; Dey, Rounak; Arnett, Donna K; Bielak, Lawrence F; Bis, Joshua C; Blangero, John; Boerwinkle, Eric; Bowden, Donald W; Brody, Jennifer A; Cade, Brian E; Correa, Adolfo; Cupples, L Adrienne; Curran, Joanne E; de Vries, Paul S; Duggirala, Ravindranath; Freedman, Barry I; Göring, Harald HH; Guo, Xiuqing; Haessler, Jeffrey; Kalyani, Rita R; Kooperberg, Charles; Kral, Brian G; Lange, Leslie A; Manichaikul, Ani; Martin, Lisa W; McGarvey, Stephen T; Mitchell, Braxton D; Montasser, May E; Morrison, Alanna C; Naseri, Take; O'Connell, Jeffrey R; Palmer, Nicholette D; Peyser, Patricia A; Psaty, Bruce M; Raffield, Laura M; Redline, Susan; Reiner, Alexander P; Reupena, Muagututi'a Sefuiva; Rice, Kenneth M; Rich, Stephen S; Sitlani, Colleen M; Smith, Jennifer A; Taylor, Kent D; Vasan, Ramachandran S; Willer, Cristen J; Wilson, James G; Yanek, Lisa R; Zhao, Wei; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Lipids Working Group; Rotter, Jerome I; Natarajan, Pradeep; Peloso, Gina M; Li, Zilin; Lin, Xihong
    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.
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    ItemOpen Access
    Whole genome sequence analysis of blood lipid levels in >66,000 individuals.
    (Nature communications, 2022-10) Selvaraj, Margaret Sunitha; Li, Xihao; Li, Zilin; Pampana, Akhil; Zhang, David Y; Park, Joseph; Aslibekyan, Stella; Bis, Joshua C; Brody, Jennifer A; Cade, Brian E; Chuang, Lee-Ming; Chung, Ren-Hua; Curran, Joanne E; de Las Fuentes, Lisa; de Vries, Paul S; Duggirala, Ravindranath; Freedman, Barry I; Graff, Mariaelisa; Guo, Xiuqing; Heard-Costa, Nancy; Hidalgo, Bertha; Hwu, Chii-Min; Irvin, Marguerite R; Kelly, Tanika N; Kral, Brian G; Lange, Leslie; Li, Xiaohui; Lisa, Martin; Lubitz, Steven A; Manichaikul, Ani W; Michael, Preuss; Montasser, May E; Morrison, Alanna C; Naseri, Take; O'Connell, Jeffrey R; Palmer, Nicholette D; Palmer, Nicholette D; Peyser, Patricia A; Reupena, Muagututia S; Smith, Jennifer A; Sun, Xiao; Taylor, Kent D; Tracy, Russell P; Tsai, Michael Y; Wang, Zhe; Wang, Yuxuan; Bao, Wei; Wilkins, John T; Yanek, Lisa R; Zhao, Wei; Arnett, Donna K; Blangero, John; Boerwinkle, Eric; Bowden, Donald W; Chen, Yii-Der Ida; Correa, Adolfo; Cupples, L Adrienne; Dutcher, Susan K; Ellinor, Patrick T; Fornage, Myriam; Gabriel, Stacey; Germer, Soren; Gibbs, Richard; He, Jiang; Kaplan, Robert C; Kardia, Sharon LR; Kim, Ryan; Kooperberg, Charles; Loos, Ruth JF; Viaud-Martinez, Karine A; Mathias, Rasika A; McGarvey, Stephen T; Mitchell, Braxton D; Nickerson, Deborah; North, Kari E; Psaty, Bruce M; Redline, Susan; Reiner, Alexander P; Vasan, Ramachandran S; Rich, Stephen S; Willer, Cristen; Rotter, Jerome I; Rader, Daniel J; Lin, Xihong; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium; Peloso, Gina M; Natarajan, Pradeep
    Blood lipids are heritable modifiable causal factors for coronary artery disease. Despite well-described monogenic and polygenic bases of dyslipidemia, limitations remain in discovery of lipid-associated alleles using whole genome sequencing (WGS), partly due to limited sample sizes, ancestral diversity, and interpretation of clinical significance. Among 66,329 ancestrally diverse (56% non-European) participants, we associate 428M variants from deep-coverage WGS with lipid levels; ~400M variants were not assessed in prior lipids genetic analyses. We find multiple lipid-related genes strongly associated with blood lipids through analysis of common and rare coding variants. We discover several associated rare non-coding variants, largely at Mendelian lipid genes. Notably, we observe rare LDLR intronic variants associated with markedly increased LDL-C, similar to rare LDLR exonic variants. In conclusion, we conducted a systematic whole genome scan for blood lipids expanding the alleles linked to lipids for multiple ancestries and characterize a clinically-relevant rare non-coding variant model for lipids.