title: A penalized linear mixed model for genomic prediction using pedigree structures.
abstract: Genetic Analysis Workshop 18 provided a platform for evaluating genomic prediction power based on single-nucleotide polymorphisms from single-nucleotide polymorphism array data and sequencing data. Also, Genetic Analysis Workshop 18 provided a diverse pedigree structure to be explored in prediction. In this study, we attempted to combine pedigree information with single-nucleotide polymorphism data to predict systolic blood pressure. Our results suggested that the prediction power based on pedigree information only could be unsatisfactory. Using additional information such as single-nucleotide polymorphism genotypes would improve prediction accuracy. In particular, the improvement can be significant when there exist a few single-nucleotide polymorphisms with relatively larger effect sizes. We also compared the prediction performance based on genome-wide association study data (ie, common variants) and sequencing data (ie, common variants plus low-frequency variants). The experimental result showed that inclusion of low frequency variants could not lead to improvement of prediction accuracy.
authors.affiliation: Department of Biostatistics, Yale School of Public Health, New Haven, CT 06520, USA.
authors.forename: Can
authors.initials: C
authors.lastname: Yang
authors.affiliation: Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.
authors.forename: Cong
authors.initials: C
authors.lastname: Li
authors.affiliation: Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.
authors.forename: Mengjie
authors.initials: M
authors.lastname: Chen
authors.affiliation: Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.
authors.forename: Xiaowei
authors.initials: X
authors.lastname: Chen
authors.affiliation: Department of Biostatistics, Yale School of Public Health, New Haven, CT 06520, USA.
authors.forename: Lin
authors.initials: L
authors.lastname: Hou
authors.affiliation: Department of Biostatistics, Yale School of Public Health, New Haven, CT 06520, USA ; Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.
authors.forename: Hongyu
authors.initials: H
authors.lastname: Zhao