title: SparRec: An effective matrix completion framework of missing data imputation for GWAS.
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.
authors.affiliation: Research Center for Management Science and Data Analytics, School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai 200433, China.
authors.forename: Bo
authors.initials: B
authors.lastname: Jiang
authors.affiliation: Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong.
authors.forename: Shiqian
authors.initials: S
authors.lastname: Ma
authors.affiliation: Department of Computer Science, Arkansas State University, Jonesboro, Arkansas 72467, United States of America.|The UALR/UAMS Joint Graduate Program in Bioinformatics, Little Rock, Arkansas 72204, United States of America.
authors.forename: Jason
authors.initials: J
authors.lastname: Causey
authors.affiliation: College of Computer, National University of Defense Technology, Changsha 410073, China.
authors.forename: Linbo
authors.initials: L
authors.lastname: Qiao
authors.affiliation: Molecular Biosciences Program, Arkansas State University, Jonesboro, Arkansas 72467, United States of America.
authors.forename: Matthew Price
authors.initials: MP
authors.lastname: Hardin
authors.affiliation: Department of Computer Science, Arkansas State University, Jonesboro, Arkansas 72467, United States of America.
authors.forename: Ian
authors.initials: I
authors.lastname: Bitts
authors.affiliation: The University of Tennessee Health Science Center (UTHSC) rBIO Core Lab, Cancer Research Building Rm258, 19 S Manassas St. Memphis, TN 38163, United States of America.
authors.forename: Daniel
authors.initials: D
authors.lastname: Johnson
authors.affiliation: Department of Industrial and Systems Engineering, University of Minnesota, Minneapolis, Minnesota 55455, United States of America.
authors.forename: Shuzhong
authors.initials: S
authors.lastname: Zhang
authors.affiliation: Department of Computer Science, Arkansas State University, Jonesboro, Arkansas 72467, United States of America.|The UALR/UAMS Joint Graduate Program in Bioinformatics, Little Rock, Arkansas 72204, United States of America.|College of Computer, National University of Defense Technology, Changsha 410073, China.
authors.forename: Xiuzhen
authors.initials: X
authors.lastname: Huang
mesh_terms: D000818:Animals; D055106:Genome-Wide Association Study / Q000295:instrumentation / Q000379:methods*; D006801:Humans; D008957:Models, Genetic; D012984:Software