Rare Variants Association Analysis in Large-Scale Sequencing Studies at the Single Locus Level
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Univ Arizona, Epidemiol & BiostatIssue Date
2016-06-29
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Rare Variants Association Analysis in Large-Scale Sequencing Studies at the Single Locus Level 2016, 12 (6):e1004993 PLOS Computational BiologyJournal
PLOS Computational BiologyRights
Copyright © 2016 Jeng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
Genetic association analyses of rare variants in next-generation sequencing (NGS) studies are fundamentally challenging due to the presence of a very large number of candidate variants at extremely low minor allele frequencies. Recent developments often focus on pooling multiple variants to provide association analysis at the gene instead of the locus level. Nonetheless, pinpointing individual variants is a critical goal for genomic researches as such information can facilitate the precise delineation of molecular mechanisms and functions of genetic factors on diseases. Due to the extreme rarity of mutations and high-dimensionality, significances of causal variants cannot easily stand out from those of noncausal ones. Consequently, standard false-positive control procedures, such as the Bonferroni and false discovery rate (FDR), are often impractical to apply, as a majority of the causal variants can only be identified along with a few but unknown number of noncausal variants. To provide informative analysis of individual variants in large-scale sequencing studies, we propose the Adaptive False-Negative Control (AFNC) procedure that can include a large proportion of causal variants with high confidence by introducing a novel statistical inquiry to determine those variants that can be confidently dispatched as noncausal. The AFNC provides a general framework that can accommodate for a variety of models and significance tests. The procedure is computationally efficient and can adapt to the underlying proportion of causal variants and quality of significance rankings. Extensive simulation studies across a plethora of scenarios demonstrate that the AFNC is advantageous for identifying individual rare variants, whereas the Bonferroni and FDR are exceedingly over-conservative for rare variants association studies. In the analyses of the CoLaus dataset, AFNC has identified individual variants most responsible for gene-level significances. Moreover, single-variant results using the AFNC have been successfully applied to infer related genes with annotation information.Note
Open Access JournalISSN
1553-7358PubMed ID
27355347Version
Final published versionSponsors
National Institutes of Health [P01 CA142538]Additional Links
http://dx.plos.org/10.1371/journal.pcbi.1004993ae974a485f413a2113503eed53cd6c53
10.1371/journal.pcbi.1004993
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Except where otherwise noted, this item's license is described as Copyright © 2016 Jeng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License.
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