Pinpointing Rare Causal Variants with the Adaptive Combination of Q-values Method
Date Issued
2016
Date
2016
Author(s)
Li, Jen-Yi
Abstract
In the past decade, genome-wide association analyses have identified thousands of single-nucleotide polymorphisms (SNPs) associated with complex diseases. With the improvement of next-generation sequencing technology, geneticists have observed more inherited information on human chromosome. Searching for rare causal variants (minor allele frequency < 1%) gradually becomes possible. In order to pinpoint rare causal variants in a large number of variants, statistical approaches such as the BE (backward elimination) procedure and the ADA method (the adaptive combination of P-values method), have been developed. It has been shown that the signal-to-noise ratio of variants identified by ADA is larger than that of variants identified by BE. In this study, we propose an ADAQ method (‘adaptive combination of Q-values method’) to further increase the probability that a finding is genuine. With synonymous / non-synonymous annotations for variants, we first allocate all variants into a non-synonymous group and a synonymous group, and transform two groups of per-site P-values into Benjamini-Hochberg Q-values, respectively. We then remove the variants with larger Q-values that are more likely to be neutral. Comprehensive simulations have shown that ADAQ has an even larger positive predictive value than ADA. Moreover, we applied ADAQ to the Genetic Analysis Workshop 17 (GAW 17) data sets. It controls the number of false positives more effectively and generates a larger positive predictive value than ADA. Therefore, we recommend using ADAQ to pinpoint individual rare causal variants, when synonymous / non-synonymous annotations for variants are available.
Subjects
neutral variants
rare variants
causal variants
non-synonymous variants
next-generation sequencing
SDGs
Type
thesis
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