Year: 2016
Type: article
Abstract: Genome-wide association studies present computational challenges for missing data imputation, while the advances of genotype technologies are generating datasets of large sample sizes with sample sets genotyped on multiple SNP chips. We present a new framework SparRec (Sparse Recovery) for imputation, with the following properties: (1) The optimization models of SparRec, based on low-rank and low number of co-clusters of matrices, are different from current statistics methods. While our low-rank matrix completion (LRMC) model is similar to Mendel-Impute, our matrix co-clustering factorization (MCCF) model is completely new. (2) SparRec, as other matrix completion methods, is flexible to be applied to missing data imputation for large meta-analysis with different cohorts genotyped on different sets of SNPs, even when there is no reference panel. This kind of meta-analysis is very challenging for current statistics based methods. (3) SparRec has consistent performance and achieves high recovery accuracy even when the missing data rate is as high as 90%. Compared with Mendel-Impute, our low-rank based method achieves similar accuracy and efficiency, while the co-clustering based method has advantages in running time. The testing results show that SparRec has significant advantages and competitive performance over other state-of-the-art existing statistics methods including Beagle and fastPhase.
Source: Scientific Reports
DOI: 10.1038/srep35534
Institutions: Shanghai University of Finance and Economics, Chinese University of Hong Kong, University of Arkansas at Little Rock, Arkansas State University, University of Arkansas for Medical Sciences, National University of Defense Technology, University of Tennessee Health Science Center, University of Minnesota, University of Minnesota System
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,690
Awards: Gradient Methods for Solving Big Data (Tensor) Optimization Problems, Partnerships for Biomedical Research in Arkansas, G2683385012, Partnerships for Biomedical Research in Arkansas, G3445821079, G3641676048, III: EAGER: Novel algorithms for de novo transcriptome assembly using RNA-seq data and for metagenome assembly, G528198475, EAGER: Building a Starting Core for No-Boundary Education and Research Network, G8561488226
OpenAlex ID: W2535629435