title: Two-Variance-Component Model Improves Genetic Prediction in Family Datasets.
abstract: Genetic prediction based on either identity by state (IBS) sharing or pedigree information has been investigated extensively with best linear unbiased prediction (BLUP) methods. Such methods were pioneered in plant and animal-breeding literature and have since been applied to predict human traits, with the aim of eventual clinical utility. However, methods to combine IBS sharing and pedigree information for genetic prediction in humans have not been explored. We introduce a two-variance-component model for genetic prediction: one component for IBS sharing and one for approximate pedigree structure, both estimated with genetic markers. In simulations using real genotypes from the Candidate-gene Association Resource (CARe) and Framingham Heart Study (FHS) family cohorts, we demonstrate that the two-variance-component model achieves gains in prediction r(2) over standard BLUP at current sample sizes, and we project, based on simulations, that these gains will continue to hold at larger sample sizes. Accordingly, in analyses of four quantitative phenotypes from CARe and two quantitative phenotypes from FHS, the two-variance-component model significantly improves prediction r(2) in each case, with up to a 20% relative improvement. We also find that standard mixed-model association tests can produce inflated test statistics in datasets with related individuals, whereas the two-variance-component model corrects for inflation.
authors.affiliation: Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Epidemiology, Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
authors.forename: George
authors.initials: G
authors.lastname: Tucker
authors.affiliation: Department of Epidemiology, Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
authors.forename: Po-Ru
authors.initials: PR
authors.lastname: Loh
authors.affiliation: Faculty of Veterinary and Agricultural Science, University of Melbourne, Melbourne, VIC 3010, Australia; Dairy Futures Cooperative Research Centre, La Trobe University, Bundoora, VIC 3083, Australia.
authors.forename: Iona M
authors.initials: IM
authors.lastname: MacLeod
authors.affiliation: Dairy Futures Cooperative Research Centre, La Trobe University, Bundoora, VIC 3083, Australia; BioSciences Research Division, Department of Environment and Primary Industries, Melbourne, VIC 3083, Australia; Biosciences Research Centre, La Trobe University, Melbourne, VIC 3083, Australia.
authors.forename: Ben J
authors.initials: BJ
authors.lastname: Hayes
authors.affiliation: Faculty of Veterinary and Agricultural Science, University of Melbourne, Melbourne, VIC 3010, Australia; BioSciences Research Division, Department of Environment and Primary Industries, Melbourne, VIC 3083, Australia.
authors.forename: Michael E
authors.initials: ME
authors.lastname: Goddard
authors.affiliation: Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Computer Science and Artificial Intelligence Laboratory, Cambridge, MA 02139, USA.
authors.forename: Bonnie
authors.initials: B
authors.lastname: Berger
authors.affiliation: Department of Epidemiology, Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Department of Biostatistics, Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA. Electronic address: aprice@hsph.harvard.edu.
authors.forename: Alkes L
authors.initials: AL
authors.lastname: Price
mesh_terms: D002318:Cardiovascular Diseases / Q000175:diagnosis* / Q000235:genetics; D003198:Computer Simulation; D066264:Datasets as Topic; D005190:Family; D056726:Genetic Association Studies; D005819:Genetic Markers; D055106:Genome-Wide Association Study; D023281:Genomics / Q000379:methods; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D025341:Principal Component Analysis; D040641:Quantitative Trait Loci; D012641:Selection, Genetic / Q000235:genetics
chemical_list: D005819:Genetic Markers