Browsing by Author "NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium"

Now showing 1 - 6 of 6
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    A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies
    (Springer Nature, 2022) 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 H. H.; 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; Biostatistics and Health Data Science, School of Medicine
    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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    A high-resolution HLA reference panel capturing global population diversity enables multi-ancestry fine-mapping in HIV host response
    (Springer Nature, 2021) Luo, Yang; Kanai, Masahiro; Choi, Wanson; Li, Xinyi; Sakaue, Saori; Yamamoto, Kenichi; Ogawa, Kotaro; Gutierrez-Arcelus, Maria; Gregersen, Peter K.; Stuart, Philip E.; Elder, James T.; Forer, Lukas; Schönherr, Sebastian; Fuchsberger, Christian; Smith, Albert V.; Fellay, Jacques; Carrington, Mary; Haas, David W.; Guo, Xiuqing; Palmer, Nicholette D.; Chen, Yii-Der Ida; Rotter, Jerome I.; Taylor, Kent D.; Rich, Stephen S.; Correa, Adolfo; Wilson, James G.; Kathiresan, Sekar; Cho, Michael H.; Metspalu, Andres; Esko, Tonu; Okada, Yukinori; Han, Buhm; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium; McLaren, Paul J.; Raychaudhuri, Soumya; Obstetrics and Gynecology, School of Medicine
    Fine-mapping to plausible causal variation may be more effective in multi-ancestry cohorts, particularly in the MHC, which has population-specific structure. To enable such studies, we constructed a large (n = 21,546) HLA reference panel spanning five global populations based on whole-genome sequences. Despite population-specific long-range haplotypes, we demonstrated accurate imputation at G-group resolution (94.2%, 93.7%, 97.8% and 93.7% in admixed African (AA), East Asian (EAS), European (EUR) and Latino (LAT) populations). Applying HLA imputation to genome-wide association study data for HIV-1 viral load in three populations (EUR, AA and LAT), we obviated effects of previously reported associations from population-specific HIV studies and discovered a novel association at position 156 in HLA-B. We pinpointed the MHC association to three amino acid positions (97, 67 and 156) marking three consecutive pockets (C, B and D) within the HLA-B peptide-binding groove, explaining 12.9% of trait variance.
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    Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies
    (Springer Nature, 2023) 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 H. H.; 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; Biostatistics and Health Data Science, School of Medicine
    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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    Rare variants in long non-coding RNAs are associated with blood lipid levels in the TOPMed Whole Genome Sequencing Study
    (medRxiv, 2023-06-29) Wang, Yuxuan; Selvaraj, Margaret Sunitha; Li, Xihao; Li, Zilin; Holdcraft, Jacob A.; Arnett, Donna K.; Bis, Joshua C.; Blangero, John; Boerwinkle, Eric; Bowden, Donald W.; Cade, Brian E.; Carlson, Jenna C.; Carson, April P.; Chen, Yii-Der Ida; Curran, Joanne E.; de Vries, Paul S.; Dutcher, Susan K.; Ellinor, Patrick T.; Floyd, James S.; Fornage, Myriam; Freedman, Barry I.; Gabriel, Stacey; Germer, Soren; Gibbs, Richard A.; Guo, Xiuqing; He, Jiang; Heard-Costa, Nancy; Hildalgo, Bertha; Hou, Lifang; Irvin, Marguerite R.; Joehanes, Roby; Kaplan, Robert C.; Kardia, Sharon Lr.; Kelly, Tanika N.; Kim, Ryan; Kooperberg, Charles; Kral, Brian G.; Levy, Daniel; Li, Changwei; Liu, Chunyu; Lloyd-Jone, Don; Loos, Ruth Jf.; Mahaney, Michael C.; Martin, Lisa W.; Mathias, Rasika A.; Minster, Ryan L.; Mitchell, Braxton D.; Montasser, May E.; Morrison, Alanna C.; Murabito, Joanne M.; Naseri, Take; O'Connell, Jeffrey R.; Palmer, Nicholette D.; Preuss, Michael H.; Psaty, Bruce M.; Raffield, Laura M.; Rao, Dabeeru C.; Redline, Susan; Reiner, Alexander P.; Rich, Stephen S.; Ruepena, Muagututi'a Sefuiva; Sheu, Wayne H-H; Smith, Jennifer A.; Smith, Albert; Tiwari, Hemant K.; Tsai, Michael Y.; Viaud-Martinez, Karine A.; Wang, Zhe; Yanek, Lisa R.; Zhao, Wei; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium; Rotter, Jerome I.; Lin, Xihong; Natarajan, Pradeep; Peloso, Gina M.; Biostatistics and Health Data Science, School of Medicine
    Long non-coding RNAs (lncRNAs) are known to perform important regulatory functions. Large-scale whole genome sequencing (WGS) studies and new statistical methods for variant set tests now provide an opportunity to assess the associations between rare variants in lncRNA genes and complex traits across the genome. In this study, we used high-coverage WGS from 66,329 participants of diverse ancestries with blood lipid levels (LDL-C, HDL-C, TC, and TG) in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) program to investigate the role of lncRNAs in lipid variability. We aggregated rare variants for 165,375 lncRNA genes based on their genomic locations and conducted rare variant aggregate association tests using the STAAR (variant-Set Test for Association using Annotation infoRmation) framework. We performed STAAR conditional analysis adjusting for common variants in known lipid GWAS loci and rare coding variants in nearby protein coding genes. Our analyses revealed 83 rare lncRNA variant sets significantly associated with blood lipid levels, all of which were located in known lipid GWAS loci (in a ±500 kb window of a Global Lipids Genetics Consortium index variant). Notably, 61 out of 83 signals (73%) were conditionally independent of common regulatory variations and rare protein coding variations at the same loci. We replicated 34 out of 61 (56%) conditionally independent associations using the independent UK Biobank WGS data. Our results expand the genetic architecture of blood lipids to rare variants in lncRNA, implicating new therapeutic opportunities.
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    Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program
    (Springer Nature, 2021) Taliun, Daniel; Harris, Daniel N.; Kessler, Michael D.; Carlson, Jedidiah; Szpiech, Zachary A.; Torres, Raul; Gagliano Taliun, Sarah A.; Corvelo, André; Gogarten, Stephanie M.; Kang, Hyun Min; Pitsillides, Achilleas N.; LeFaive, Jonathon; Lee, Seung-Been; Tian, Xiaowen; Browning, Brian L.; Das, Sayantan; Emde, Anne-Katrin; Clarke, Wayne E.; Loesch, Douglas P.; Shetty, Amol C.; Blackwell, Thomas W.; Smith, Albert V.; Wong, Quenna; Liu, Xiaoming; Conomos, Matthew P.; Bobo, Dean M.; Aguet, François; Albert, Christine; Alonso, Alvaro; Ardlie, Kristin G.; Arking, Dan E.; Aslibekyan, Stella; Auer, Paul L.; Barnard, John; Barr, R. Graham; Barwick, Lucas; Becker, Lewis C.; Beer, Rebecca L.; Benjamin, Emelia J.; Bielak, Lawrence F.; Blangero, John; Boehnke, Michael; Bowden, Donald W.; Brody, Jennifer A.; Burchard, Esteban G.; Cade, Brian E.; Casella, James F.; Chalazan, Brandon; Chasman, Daniel I.; Chen, Yii-Der Ida; Cho, Michael H.; Choi, Seung Hoan; Chung, Mina K.; Clish, Clary B.; Correa, Adolfo; Curran, Joanne E.; Custer, Brian; Darbar, Dawood; Daya, Michelle; de Andrade, Mariza; DeMeo, Dawn L.; Dutcher, Susan K.; Ellinor, Patrick T.; Emery, Leslie S.; Eng, Celeste; Fatkin, Diane; Fingerlin, Tasha; Forer, Lukas; Fornage, Myriam; Franceschini, Nora; Fuchsberger, Christian; Fullerton, Stephanie M.; Germer, Soren; Gladwin, Mark T.; Gottlieb, Daniel J.; Guo, Xiuqing; Hall, Michael E.; He, Jiang; Heard-Costa, Nancy L.; Heckbert, Susan R.; Irvin, Marguerite R.; Johnsen, Jill M.; Johnson, Andrew D.; Kaplan, Robert; Kardia, Sharon L. R.; Kelly, Tanika; Kelly, Shannon; Kenny, Eimear E.; Kiel, Douglas P.; Klemmer, Robert; Konkle, Barbara A.; Kooperberg, Charles; Köttgen, Anna; Lange, Leslie A.; Lasky-Su, Jessica; Levy, Daniel; Lin, Xihong; Lin, Keng-Han; Liu, Chunyu; Loos, Ruth J. F.; Garman, Lori; Gerszten, Robert; Lubitz, Steven A.; Lunetta, Kathryn L.; Mak, Angel C. Y.; Manichaikul, Ani; Manning, Alisa K.; Mathias, Rasika A.; McManus, David D.; McGarvey, Stephen T.; Meigs, James B.; Meyers, Deborah A.; Mikulla, Julie L.; Minear, Mollie A.; Mitchell, Braxton D.; Mohanty, Sanghamitra; Montasser, May E.; Montgomery, Courtney; Morrison, Alanna C.; Murabito, Joanne M.; Natale, Andrea; Natarajan, Pradeep; Nelson, Sarah C.; North, Kari E.; O'Connell, Jeffrey R.; Palmer, Nicholette D.; Pankratz, Nathan; Peloso, Gina M.; Peyser, Patricia A.; Pleiness, Jacob; Post, Wendy S.; Psaty, Bruce M.; Rao, D. C.; Redline, Susan; Reiner, Alexander P.; Roden, Dan; Rotter, Jerome I.; Ruczinski, Ingo; Sarnowski, Chloé; Schoenherr, Sebastian; Schwartz, David A.; Seo, Jeong-Sun; Seshadri, Sudha; Sheehan, Vivien A.; Sheu, Wayne H.; Shoemaker, M. Benjamin; Smith, Nicholas L.; Smith, Jennifer A.; Sotoodehnia, Nona; Stilp, Adrienne M.; Tang, Weihong; Taylor, Kent D.; Telen, Marilyn; Thornton, Timothy A.; Tracy, Russell P.; Van Den Berg, David J.; Vasan, Ramachandran S.; Viaud-Martinez, Karine A.; Vrieze, Scott; Weeks, Daniel E.; Weir, Bruce S.; Weiss, Scott T.; Weng, Lu-Chen; Willer, Cristen J.; Zhang, Yingze; Zhao, Xutong; Arnett, Donna K.; Ashley-Koch, Allison E.; Barnes, Kathleen C.; Boerwinkle, Eric; Gabriel, Stacey; Gibbs, Richard; Rice, Kenneth M.; Rich, Stephen S.; Silverman, Edwin K.; Qasba, Pankaj; Gan, Weiniu; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium; Papanicolaou, George J.; Nickerson, Deborah A.; Browning, Sharon R.; Zody, Michael C.; Zöllner, Sebastian; Wilson, James G.; Cupples, L. Adrienne; Laurie, Cathy C.; Jaquish, Cashell E.; Hernandez, Ryan D.; O'Connor, Timothy D.; Abecasis, Gonçalo R.; Epidemiology, Richard M. Fairbanks School of Public Health
    The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes)1. In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%.
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    Whole Genome Sequencing Analysis of Body Mass Index Identifies Novel African Ancestry-Specific Risk Allele
    (medRxiv, 2023-08-22) Zhang, Xinruo; Brody, Jennifer A.; Graff, Mariaelisa; Highland, Heather M.; Chami, Nathalie; Xu, Hanfei; Wang, Zhe; Ferrier, Kendra; Chittoor, Geetha; Josyula, Navya S.; Li, Xihao; Li, Zilin; Allison, Matthew A.; Becker, Diane M.; Bielak, Lawrence F.; Bis, Joshua C.; Boorgula, Meher Preethi; Bowden, Donald W.; Broome, Jai G.; Buth, Erin J.; Carlson, Christopher S.; Chang, Kyong-Mi; Chavan, Sameer; Chiu, Yen-Feng; Chuang, Lee-Ming; Conomos, Matthew P.; DeMeo, Dawn L.; Du, Margaret; Duggirala, Ravindranath; Eng, Celeste; Fohner, Alison E.; Freedman, Barry I.; Garrett, Melanie E.; Guo, Xiuqing; Haiman, Chris; Heavner, Benjamin D.; Hidalgo, Bertha; Hixson, James E.; Ho, Yuk-Lam; Hobbs, Brian D.; Hu, Donglei; Hui, Qin; Hwu, Chii-Min; Jackson, Rebecca D.; Jain, Deepti; Kalyani, Rita R.; Kardia, Sharon L. R.; Kelly, Tanika N.; Lange, Ethan M.; LeNoir, Michael; Li, Changwei; Marchand, Loic Le; McDonald, Merry-Lynn N.; McHugh, Caitlin P.; Morrison, Alanna C.; Naseri, Take; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium; O'Connell, Jeffrey; O'Donnell, Christopher J.; Palmer, Nicholette D.; Pankow, James S.; Perry, James A.; Peters, Ulrike; Preuss, Michael H.; Rao, D. C.; Regan, Elizabeth A.; Reupena, Sefuiva M.; Roden, Dan M.; Rodriguez-Santana, Jose; Sitlani, Colleen M.; Smith, Jennifer A.; Tiwari, Hemant K.; Vasan, Ramachandran S.; Wang, Zeyuan; Weeks, Daniel E.; Wessel, Jennifer; Wiggins, Kerri L.; Wilkens, Lynne R.; Wilson, Peter W. F.; Yanek, Lisa R.; Yoneda, Zachary T.; Zhao, Wei; Zöllner, Sebastian; Arnett, Donna K.; Ashley-Koch, Allison E.; Barnes, Kathleen C.; Blangero, John; Boerwinkle, Eric; Burchard, Esteban G.; Carson, April P.; Chasman, Daniel I.; Chen, Yii-Der Ida; Curran, Joanne E.; Fornage, Myriam; Gordeuk, Victor R.; He, Jiang; Heckbert, Susan R.; Hou, Lifang; Irvin, Marguerite R.; Kooperberg, Charles; Minster, Ryan L.; Mitchell, Braxton D.; Nouraie, Mehdi; Psaty, Bruce M.; Raffield, Laura M.; Reiner, Alexander P.; Rich, Stephen S.; Rotter, Jerome I.; Shoemaker, M. Benjamin; Smith, Nicholas L.; Taylor, Kent D.; Telen, Marilyn J.; Weiss, Scott T.; Zhang, Yingze; Heard-Costa, Nancy; Sun, Yan V.; Lin, Xihong; Cupples, L. Adrienne; Lange, Leslie A.; Liu, Ching-Ti; Loos, Ruth J. F.; North, Kari E.; Justice, Anne E.; Biostatistics and Health Data Science, School of Medicine
    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 × 10−9). 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.