Rare-variant association testing

STAAR Methods

STAAR and its extensions use functional annotation to improve rare-variant association analysis for whole-genome sequencing studies.

Overview

STAAR Methods

The STAAR family of methods combines statistical genetics and functional genomics so rare variants can be tested in biologically meaningful sets. The ecosystem spans single-study analysis, biobank-scale pipelines, meta-analysis, multi-trait analysis, single-cell-informed noncoding tests, and time-to-event outcomes.

Annotation principal components and dynamic annotation weighting.
Scalable analysis for very large WGS cohorts with continuous and binary traits.
Extensions for meta-analysis, multi-trait discovery, cell-type-informed noncoding regions, and survival outcomes.
Software implementations designed for consortium-scale genetic studies.

STAAR GitHub STAARpipeline GitHub metaSTAAR GitHub

Publications

Related work

Representative publications connected to this project.

2020

Dynamic incorporation of multiple in silico functional annotations empowers rare variant association analysis of large whole-genome sequencing studies at scale.

Li X, Li Z, Zhou H, Gaynor SM, Liu Y, Chen H, Sun R, Dey R, Arnett DK, Aslibekyan S, Ballantyne CM, Bielak LF, Blangero J, Boerwinkle E, Bowden DW, Broome JG, Conomos MP, Correa A, Cupples LA, Curran JE, Freedman BI, Guo X, Hindy G, Irvin MR, Kardia SLR, Kathiresan S, Khan AT, Kooperberg CL, Laurie CC, Liu XS, Mahaney MC, Manichaikul AW, Martin LW, Mathias RA, McGarvey ST, Mitchell BD, Montasser ME, Moore J, Morrison AC, O'Connell JR, Palmer ND, Pampana A, Peralta JM, Peyser PA, Psaty BM, Redline S, Rice KM, Rich SS, Smith JA, Tiwari HK, Tsai MY, Vasan RS, Wang FF, Weeks DE, Weng Z, Wilson JG, Yanek LR, TOPMed Consortium, Neale BM, Sunyaev SR, Abecasis GR, Rotter JI, Willer CJ, Peloso GM, Natarajan P, Lin X.

Nat Genet. 2020;52(9):969-983.

2023

Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies.

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, Goring HH, 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, TOPMed Consortium, Rotter JI, Natarajan P, Peloso GM, Li Z, Lin X.

Nat Genet. 2023;55(1):154-164.

DOI PubMed

2022

A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies.

Li Z, Li X, Zhou H, Gaynor SM, Selvaraj MS, Arapoglou T, Quick C, Liu Y, Chen H, Sun R, Dey R, Arnett DK, Auer PL, Bielak LF, Bis JC, Blackwell T, Blangero J, Boerwinkle E, Bowden DW, Brody JA, Cade BE, Conomos MP, Correa A, Cupples LA, Curran JE, de Vries PS, Duggirala R, Franceschini N, Freedman BI, Goring HH, Guo X, Kalyani RR, Kooperberg C, Kral BG, Lange L, Lin B, Manichaikul A, Manning A, Martin LW, Mathias R, Meigs JB, Mitchell BD, Montasser ME, Morrison AC, Naseri T, O'Connell J, Palmer ND, Peyser PA, Psaty BM, Raffield LM, Redline S, Reiner AP, Reupena MS, Rice KM, Rich S, Smith J, Taylor KD, Taub M, Vasan RS, Weeks D, Wilson J, Yanek L, Zhao W, TOPMed Group, Rotter J, Willer C, Natarajan P, Peloso G, Lin X.

Nat Methods. 2022;19:1599-1611.

DOI