MetaSTAAR

MetaSTAAR performs meta-analysis of whole genome sequencing (WGS) and whole exome sequencing (WES) data to discover rare variant associations with complex phenotypes.


Key Features:

  • Scalability: Capable of handling biobank-scale WGS data for large sequencing studies.
  • Resource Efficiency: Reduces computational burden relative to traditional methods for large-scale meta-analysis.
  • Relatedness and Population Structure: Incorporates models that account for relatedness among samples and population structure.
  • Trait Analysis Flexibility: Supports analysis of quantitative traits and dichotomous traits.
  • Annotation Integration: Integrates multiple variant functional annotations to enhance the power of rare variant tests.

Scientific Applications:

  • TOPMed lipid trait meta-analysis: Applied to lipid trait meta-analysis across 30,138 ancestrally diverse samples from 14 studies within the Trans Omics for Precision Medicine (TOPMed) Program, producing results comparable to pooled-data analyses and identifying conditionally significant rare variant associations.
  • Large-scale WGS/WES meta-analyses: Demonstrated scalability in meta-analyses combining TOPMed WGS data and UK Biobank WES data comprising approximately 200,000 samples.

Methodology:

Performs rare variant meta-analysis by integrating multiple variant functional annotations and applying models that account for relatedness and population structure for analysis of quantitative and dichotomous traits.

Topics

Details

License:
GPL-3.0
Cost:
Free of charge
Tool Type:
library
Operating Systems:
Mac, Linux, Windows
Programming Languages:
R, C++
Added:
2/22/2023
Last Updated:
2/22/2023

Operations

Data Inputs & Outputs

Publications

Li X, Quick C, Zhou H, Gaynor SM, Liu Y, Chen H, Selvaraj MS, Sun R, Dey R, Arnett DK, Bielak LF, Bis JC, Blangero J, Boerwinkle E, Bowden DW, Brody JA, Cade BE, Correa A, Cupples LA, Curran JE, de Vries PS, Duggirala R, Freedman BI, Göring HHH, Guo X, Haessler J, Kalyani RR, Kooperberg C, Kral BG, Lange LA, Manichaikul A, Martin LW, McGarvey ST, Mitchell BD, Montasser ME, Morrison AC, Naseri T, O’Connell JR, Palmer ND, Peyser PA, Psaty BM, Raffield LM, Redline S, Reiner AP, Reupena MS, Rice KM, Rich SS, Sitlani CM, Smith JA, Taylor KD, Vasan RS, Willer CJ, Wilson JG, Yanek LR, Zhao W, Abe N, Abecasis G, Aguet F, Albert C, Almasy L, Alonso A, Ament S, Anderson P, Anugu P, Applebaum-Bowden D, Ardlie K, Dan Arking, Ashley-Koch A, Aslibekyan S, Assimes T, Auer P, Avramopoulos D, Ayas N, Balasubramanian A, Barnard J, Barnes K, Barr RG, Barron-Casella E, Barwick L, Beaty T, Beck G, Becker D, Becker L, Beer R, Beitelshees A, Benjamin E, Benos T, Bezerra M, Blackwell T, Blue N, Bowler R, Broeckel U, Broome J, Brown D, Bunting K, Burchard E, Bustamante C, Buth E, Cardwell J, Carey V, Carrier J, Carson A, Carty C, Casaburi R, Casas Romero JP, Casella J, Castaldi P, Chaffin M, Chang C, Chang Y, Chasman D, Chavan S, Chen B, Chen W, Chen YI, Cho M, Choi SH, Chuang L, Chung M, Chung R, Clish C, Comhair S, Conomos M, Cornell E, Crandall C, Crapo J, Curtis J, Custer B, Damcott C, Darbar D, David S, Davis C, Daya M, de Andrade M, de las Fuentes L, DeBaun M, Deka R, DeMeo D, Devine S, Dinh H, Doddapaneni H, Duan Q, Dugan-Perez S, Durda JP, Dutcher SK, Eaton C, Ekunwe L, El Boueiz A, Ellinor P, Emery L, Erzurum S, Farber C, Farek J, Fingerlin T, Flickinger M, Fornage M, Franceschini N, Frazar C, Fu M, Fullerton SM, Fulton L, Gabriel S, Gan W, Gao S, Gao Y, Gass M, Geiger H, Gelb B, Geraci M, Germer S, Gerszten R, Ghosh A, Gibbs R, Gignoux C, Gladwin M, Glahn D, Gogarten S, Gong D, Graw S, Gray KJ, Grine D, Gross C, Gu CC, Guan Y, Gupta N, Hall M, Han Y, Hanly P, Harris D, Hawley NL, He J, Ben Heavner, Heckbert S, Hernandez R, Herrington D, Hersh C, Hidalgo B, Hixson J, Hobbs B, Hokanson J, Hong E, Hoth K, Hsiung C, Hu J, Hung Y, Huston H, Hwu CM, Irvin MR, Jackson R, Jain D, Jaquish C, Johnsen J, Johnson A, Johnson C, Johnston R, Jones K, Kang HM, Kaplan R, Kardia S, Kelly S, Kenny E, Kessler M, Khan A, Khan Z, Kim W, Kimoff J, Kinney G, Konkle B, Kramer H, Lange C, Lange E, Laurie C, Laurie C, LeBoff M, Lee J, Lee S, Lee W, LeFaive J, Levine D, Levy D, Lewis J, Li X, Li Y, Lin H, Lin H, Liu S, Liu Y, Liu Y, Loos RJF, Lubitz S, Lunetta K, Luo J, Magalang U, Mahaney M, Make B, Manning A, Manson J, Marton M, Mathai S, Mathias R, May S, McArdle P, McDonald M, McFarland S, McGoldrick D, McHugh C, McNeil B, Mei H, Meigs J, Menon V, Mestroni L, Metcalf G, Meyers DA, Mignot E, Mikulla J, Min N, Minear M, Minster RL, Moll M, Momin Z, Montgomery C, Muzny D, Mychaleckyj JC, Nadkarni G, Naik R, Nekhai S, Nelson SC, Neltner B, Nessner C, Nickerson D, Nkechinyere O, North K, O’Connor T, Ochs-Balcom H, Okwuonu G, Pack A, Paik DT, Pankow J, Papanicolaou G, Parker C, Peralta JM, Perez M, Perry J, Peters U, Phillips LS, Pleiness J, Pollin T, Post W, Becker JP, Boorgula MP, Preuss M, Qasba P, Qiao D, Qin Z, Rafaels N, Rajendran M, Rao DC, Rasmussen-Torvik L, Ratan A, Reed R, Reeves C, Regan E, Robillard R, Robine N, Dan Roden, Roselli C, Ruczinski I, Runnels A, Russell P, Ruuska S, Ryan K, Sabino EC, Saleheen D, Salimi S, Salvi S, Salzberg S, Sandow K, Sankaran VG, Santibanez J, Schwander K, Schwartz D, Sciurba F, Seidman C, Seidman J, Sériès F, Sheehan V, Sherman SL, Shetty A, Shetty A, Sheu WH, Shoemaker MB, Silver B, Silverman E, Skomro R, Smith AV, Smith J, Smith N, Smith T, Smoller S, Snively B, Snyder M, Sofer T, Sotoodehnia N, Stilp AM, Storm G, Streeten E, Su JL, Sung YJ, Sylvia J, Szpiro A, Taliun D, Tang H, Taub M, Taylor M, Taylor S, Telen M, Thornton TA, Threlkeld M, Tinker L, Tirschwell D, Tishkoff S, Tiwari H, Tong C, Tracy R, Tsai M, Vaidya D, Van Den Berg D, VandeHaar P, Vrieze S, Walker T, Wallace R, Walts A, Wang FF, Wang H, Wang J, Watson K, Watt J, Weeks DE, Weinstock J, Weir B, Weiss ST, Weng L, Wessel J, Williams K, Williams LK, Wilson C, Winterkorn L, Wong Q, Wu J, Xu H, Yang I, Yu K, Zekavat SM, Zhang Y, Zhao SX, Zhu X, Ziv E, Zody M, Zoellner S, Rotter JI, Natarajan P, Peloso GM, Li Z, Lin X. Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies. Nature Genetics. 2022;55(1):154-164. doi:10.1038/s41588-022-01225-6. PMID:36564505. PMCID:PMC10084891.

PMID: 36564505
Funding: - U.S. Department of Health & Human Services | NIH | National Cancer Institute: R35-CA197449, U19-CA203654 - U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute: U01-HG009088, U01-HG012064 - U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute: 75N92019D00031, BioData Catalyst Fellowship, F32-HL085989, HHSN268201500001I, HHSN268201500003I, HHSN268201600001C, HHSN268201600002C, HHSN268201600003C, HHSN268201600004C, HHSN268201600018C, HHSN268201700001I, HHSN268201700002I, HHSN268201700003I, HHSN268201700004I, HHSN268201700005I, HHSN268201800001I, HHSN268201800010I, HHSN268201800011I, HHSN268201800012I, HHSN268201800013I, HHSN268201800014I, HHSN268201800015I, HL436801, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169, NO1-HC-25195, R01-HL045522, R01-HL055673-18S1, R01-HL071051, R01-HL071205, R01HL071250, R01-HL092577-06S1, R01-HL093093, R01-HL104135-04S1, R01-HL113323, R01-HL113338, R01-HL127564, R01-HL133040, R01-HL142711, R01-HL153805, R01-HL67348, R01-HL92301, R01HL071251, R01HL071258, R01HL071259, R03-HL154284, R35-HL135818, R35-HL135824, U01-HL054472, U01-HL054473, U01-HL054495, U01-HL054509, U01-HL072524, U01-HL137162, U01-HL137181, U01-HL72518, HL087698, HL49762, HL59684, HL58625, HL071025, HL112064 - U.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases: DK063491, R01-DK071891, U01-DK085524 - U.S. Department of Health & Human Services | NIH | National Institute of Mental Health: R01-MH078111, R01-MH078143, R01-MH083824 - U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke: R01-NS058700 - U.S. Department of Health & Human Services | NIH | National Institute of Arthritis and Musculoskeletal and Skin Diseases: R01-AR48797 - U.S. Department of Health & Human Services | NIH | National Institute on Aging: P60-AG10484, R01-AG058921 - U.S. Department of Health & Human Services | NIH | National Center for Research Resources: M01-RR000052, M01-RR07122, UL1RR033176 - U.S. Department of Health & Human Services | National Institutes of Health: 75N92019D00031, 75N92020D00001, 75N92020D00002, 75N92020D00003, 75N92020D00004, 75N92020D00005, 75N92020D00006, 75N92020D00007 - American Heart Association: 18CDA34110116 - U.S. Department of Health & Human Services | NIH | National Center for Advancing Translational Sciences: KL2TR002490, UL1-TR-000040, UL1-TR-001079, UL1-TR-001420, UL1-TR-001881 - U.S. Department of Health & Human Services | NIH | National Institute of Nursing Research: NR0224103

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