title: Rare Variants Association Analysis in Large-Scale Sequencing Studies at the Single Locus Level.
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.
authors.affiliation: Department of Statistics, North Carolina State University, Raleigh, North Carolina, United States of America.
authors.forename: Xinge Jessie
authors.initials: XJ
authors.lastname: Jeng
authors.affiliation: Epidemiology and Biostatistics, University of Arizona, Tucson, Arizona, United States of America.
authors.forename: Zhongyin John
authors.initials: ZJ
authors.lastname: Daye
authors.affiliation: Department of Statistics, North Carolina State University, Raleigh, North Carolina, United States of America.
authors.forename: Wenbin
authors.initials: W
authors.lastname: Lu
authors.affiliation: Department of Statistics, North Carolina State University, Raleigh, North Carolina, United States of America.|Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, United States of America.|Department of Statistics, National Cheng-Kung University, Tainan, Taiwan.
authors.forename: Jung-Ying
authors.initials: JY
authors.lastname: Tzeng
mesh_terms: D002318:Cardiovascular Diseases / Q000235:genetics; D003198:Computer Simulation; D016208:Databases, Factual; D016503:Drug Delivery Systems; D005787:Gene Frequency / Q000235:genetics*; D056726:Genetic Association Studies; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D023281:Genomics; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D008957:Models, Genetic