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He, Zihuai; Liu, Linxi; Wang, Chen; Le Guen, Yann; Lee, Justin; Gogarten, Stephanie; Lu, Fred; Montgomery, Stephen; Tang, Hua; Silverman, Edwin K; Cho, Michael H; Greicius, Michael; Ionita-Laza, Iuliana
Nature communications, 05/2021, Letnik: 12, Številka: 1Journal Article
The analysis of whole-genome sequencing studies is challenging due to the large number of rare variants in noncoding regions and the lack of natural units for testing. We propose a statistical method to detect and localize rare and common risk variants in whole-genome sequencing studies based on a recently developed knockoff framework. It can (1) prioritize causal variants over associations due to linkage disequilibrium thereby improving interpretability; (2) help distinguish the signal due to rare variants from shadow effects of significant common variants nearby; (3) integrate multiple knockoffs for improved power, stability, and reproducibility; and (4) flexibly incorporate state-of-the-art and future association tests to achieve the benefits proposed here. In applications to whole-genome sequencing data from the Alzheimer's Disease Sequencing Project (ADSP) and COPDGene samples from NHLBI Trans-Omics for Precision Medicine (TOPMed) Program we show that our method compared with conventional association tests can lead to substantially more discoveries.
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in: SICRIS
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