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Please use this identifier to cite or link to this item:
http://ir.nhri.org.tw/handle/3990099045/15464
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| Title: | A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies |
| Authors: | 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, TW;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;Göring, HHH;Guo, X;Kalyani, RR;Kooperberg, C;Kral, BG;Lange, LA;Lin, BM;Manichaikul, A;Manning, AK;Martin, LW;Mathias, RA;Meigs, JB;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;Smith, JA;Taylor, KD;Taub, MA;Vasan, RS;Weeks, DE;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;Arking, D;Ashley-Koch, A;Aslibekyan, S;Assimes, T;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;Blue, N;Bowler, R;Broeckel, U;Broome, J;Brown, D, .;et al. |
| Contributors: | National Institute of Cancer Research;Institute of Population Health Sciences |
| Abstract: | 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 |
| Date: | 2022-10-27 |
| Relation: | Nature Methods. 2022 Oct 27;19:1599-1611. |
| Link to: | http://dx.doi.org/10.1038/s41592-022-01640-x |
| JIF/Ranking 2023: | http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=1548-7091&DestApp=IC2JCR |
| Cited Times(Scopus): | https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85143379527 |
| Appears in Collections: | [張憶壽] 期刊論文
[鍾仁華] 期刊論文
[熊昭] 期刊論文
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| SCP85143379527.pdf | | 11532Kb | Adobe PDF | 77 | View/Open |
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