Rare variants analysis using penalization methods for whole genome sequence data

BMC Bioinformatics

Methodology Article

Akram Yazdani¹  Azam Yazdani¹  Eric Boerwinkle² 

[1]Human Genetics Center, University of Texas Health Science Center at Houston, TX, USA

[2]Human Genetics Center, University of Texas Health Science Center at Houston, TX, USA

[3]Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA

关键词: Penalization;    Linkage disequilibrium;    Principal component;    Rare variants;    Sparsity;   

DOI  :  10.1186/s12859-015-0825-4

 received in 2015-07-27, accepted in 2015-11-11,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundAvailability of affordable and accessible whole genome sequencing for biomedical applications poses a number of statistical challenges and opportunities, particularly related to the analysis of rare variants and sparseness of the data. Although efforts have been devoted to address these challenges, the performance of statistical methods for rare variants analysis still needs further consideration.ResultWe introduce a new approach that applies restricted principal component analysis with convex penalization and then selects the best predictors of a phenotype by a concave penalized regression model, while estimating the impact of each genomic region on the phenotype. Using simulated data, we show that the proposed method maintains good power for association testing while keeping the false discovery rate low under a verity of genetic architectures. Illustrative data analyses reveal encouraging result of this method in comparison with other commonly applied methods for rare variants analysis.ConclusionBy taking into account linkage disequilibrium and sparseness of the data, the proposed method improves power and controls the false discovery rate compared to other commonly applied methods for rare variant analyses.

【 授权许可】

CC BY   
© Yazdani et al. 2015