@article {8778,
	title = {Identification of putative causal loci in whole-genome sequencing data via knockoff statistics.},
	journal = {Nat Commun},
	volume = {12},
	year = {2021},
	month = {2021 05 25},
	pages = {3152},
	abstract = {<p>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{\textquoteright}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.</p>},
	keywords = {Algorithms, Causality, Computer Simulation, Data Interpretation, Statistical, Datasets as Topic, Genetic Loci, Genetic Predisposition to Disease, Genome, Human, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Markov Chains, Models, Genetic, Polymorphism, Single Nucleotide, Reproducibility of Results, Whole Genome Sequencing},
	issn = {2041-1723},
	doi = {10.1038/s41467-021-22889-4},
	author = {He, Zihuai and Liu, Linxi and Wang, Chen and Le Guen, Yann and Lee, Justin and Gogarten, Stephanie and Lu, Fred and Montgomery, Stephen and Tang, Hua and Silverman, Edwin K and Cho, Michael H and Greicius, Michael and Ionita-Laza, Iuliana}
}
