Identifying rare variants for quantitative traits in extreme samples of population via Kullback-Leibler distance
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METHODOLOGY ARTICLE
Open Access
Identifying rare variants for quantitative traits in extreme samples of population via Kullback-Leibler distance Yang Xiang1,2,3, Xinrong Xiang4 and Yumei Li1,2,3*
Abstract Background: The rapid development of sequencing technology and simultaneously the availability of large quantities of sequence data has facilitated the identification of rare variant associated with quantitative traits. However, existing statistical methods depend on certain assumptions and thus lacking uniform power. The present study focuses on mapping rare variant associated with quantitative traits. Results: In the present study, we proposed a two-stage strategy to identify rare variant of quantitative traits using phenotype extreme selection design and Kullback-Leibler distance, where the first stage was association analysis and the second stage was fine mapping. We presented a statistic and a linkage disequilibrium measure for the first stage and the second stage, respectively. Theory analysis and simulation study showed that (1) the power of the proposed statistic for association analysis increased with the stringency of the sample selection and was affected slightly by non-causal variants and opposite effect variants, (2) the statistic here achieved higher power than three commonly used methods, and (3) the linkage disequilibrium measure for fine mapping was independent of the frequencies of non-causal variants and simply dependent on the frequencies of causal variants. Conclusions: We conclude that the two-stage strategy here can be used effectively to mapping rare variant associated with quantitative traits. Keywords: Quantitative trait, Rare variant, Association analysis, Fine mapping, Extreme phenotype
Background Thanks to the rapid development of sequencing technology and the lowering of sequencing costs in the last decade, the availability of large quantities of sequence data provides an unprecedented opportunity for researchers to investigate the role of rare variants in complex traits [1–4]. But due to the low minor allele frequency (MAF < 5%) and thus resulting in weak linkage disequilibrium (LD) with nearby markers, detecting rare variant (RV) * Correspondence: [email protected]; [email protected] 1 School of Mathematics and Computational Science, Huaihua University, Huaihua, Hunan 418008, People’s Republic of China 2 Key Laboratory of Research and Utilization of Ethnomedicinal Plant Resources of Hunan Province, Huaihua University, Huaihua 418008, China Full list of author information is available at the end of the article
association with complex traits faces great challenges [5–8]. One challenge is that detection of rare causal variants with traditional designs usually requires a large sample, which will be the high cost [3, 6]. Thus costeffective design should be considered to reduce sample size. Another challenge is that the statistical power with test statistics of single-marker tests is generally low in genetic association studies of rare variants with more moderate or weak genetic effect
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