title: Toward genomic prediction from whole-genome sequence data: impact of sequencing design on genotype imputation and accuracy of predictions.
abstract: Genomic prediction from whole-genome sequence data is attractive, as the accuracy of genomic prediction is no longer bounded by extent of linkage disequilibrium between DNA markers and causal mutations affecting the trait, given the causal mutations are in the data set. A cost-effective strategy could be to sequence a small proportion of the population, and impute sequence data to the rest of the reference population. Here, we describe strategies for selecting individuals for sequencing, based on either pedigree relationships or haplotype diversity. Performance of these strategies (number of variants detected and accuracy of imputation) were evaluated in sequence data simulated through a real Belgian Blue cattle pedigree. A strategy (AHAP), which selected a subset of individuals for sequencing that maximized the number of unique haplotypes (from single-nucleotide polymorphism panel data) sequenced gave good performance across a range of variant minor allele frequencies. We then investigated the optimum number of individuals to sequence by fold coverage given a maximum total sequencing effort. At 600 total fold coverage (x 600), the optimum strategy was to sequence 75 individuals at eightfold coverage. Finally, we investigated the accuracy of genomic predictions that could be achieved. The advantage of using imputed sequence data compared with dense SNP array genotypes was highly dependent on the allele frequency spectrum of the causative mutations affecting the trait. When this followed a neutral distribution, the advantage of the imputed sequence data was small; however, when the causal mutations all had low minor allele frequencies, using the sequence data improved the accuracy of genomic prediction by up to 30%.
authors.affiliation: Unit of Animal Genomics, Faculty of Veterinary Medicine and Centre for Biomedical Integrative Genoproteomics, University of Liège, Liège, Belgium.
authors.forename: T
authors.initials: T
authors.lastname: Druet
authors.forename: I M
authors.initials: IM
authors.lastname: Macleod
authors.forename: B J
authors.initials: BJ
authors.lastname: Hayes
mesh_terms: D000818:Animals; D002417:Cattle; D003362:Cost-Benefit Analysis; D005819:Genetic Markers / Q000235:genetics*; D016678:Genome; D055106:Genome-Wide Association Study; D005838:Genotype; D006239:Haplotypes; D006801:Humans; D010375:Pedigree; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D017422:Sequence Analysis, DNA
chemical_list: D005819:Genetic Markers
references.citation: J Anim Sci. 2012 Oct;90(10):3375-84
references.pmid: 23038744
references.citation: Am J Hum Genet. 2007 Nov;81(5):1084-97
references.pmid: 17924348
references.citation: J Anim Breed Genet. 2013 Feb;130(1):20-31
references.pmid: 23317062
references.citation: Genetics. 2010 Mar;184(3):789-98
references.pmid: 20008575
references.citation: Genome Res. 2011 Jun;21(6):940-51
references.pmid: 21460063
references.citation: Nature. 2009 Jan 22;457(7228):369
references.pmid: 19158758
references.citation: Proc Natl Acad Sci U S A. 2012 May 15;109(20):7693-8
references.pmid: 22529356
references.citation: Genetica. 2009 Jun;136(2):245-57
references.pmid: 18704696
references.citation: BMC Bioinformatics. 2008 Sep 08;9:364
references.pmid: 18778480
references.citation: Science. 2009 Apr 24;324(5926):528-32
references.pmid: 19390050
references.citation: BMC Genomics. 2009 Apr 24;10:181
references.pmid: 19393053
references.citation: Nat Genet. 2012 Mar 25;44(5):483-9
references.pmid: 22446960
references.citation: Proc Natl Acad Sci U S A. 2008 Nov 11;105(45):17312-7
references.pmid: 18981413
references.citation: BMC Genet. 2012 Jun 13;13:44
references.pmid: 22694746
references.citation: J Dairy Sci. 2010 Nov;93(11):5443-54
references.pmid: 20965360
references.citation: Genet Sel Evol. 2011 May 17;43:18
references.pmid: 21575265
references.citation: J Dairy Sci. 2012 Jul;95(7):4114-29
references.pmid: 22720968
references.citation: Genet Epidemiol. 2010 Dec;34(8):816-34
references.pmid: 21058334
references.citation: Genome Res. 2011 Jun;21(6):952-60
references.pmid: 20980557
references.citation: BMC Genomics. 2009 Apr 24;10:179
references.pmid: 19393048
references.citation: Proc Natl Acad Sci U S A. 2011 Nov 1;108(44):18026-31
references.pmid: 22003128
references.citation: Genetics. 1985 Mar;109(3):611-31
references.pmid: 3979817
references.citation: J Anim Breed Genet. 2007 Dec;124(6):342-55
references.pmid: 18076471
references.citation: Genetics. 2010 Jun;185(2):623-31
references.pmid: 20308278
references.citation: Genetics. 2014 Dec;198(4):1671-84
references.pmid: 25233989
references.citation: Genet Sel Evol. 2010 Feb 19;42:5
references.pmid: 20170500
references.citation: PLoS Genet. 2012;8(5):e1002685
references.pmid: 22570636
references.citation: Mol Biol Evol. 2013 Sep;30(9):2209-23
references.pmid: 23842528
references.citation: Nat Genet. 2010 Jul;42(7):565-9
references.pmid: 20562875
references.citation: Genet Res (Camb). 2011 Jun;93(3):203-19
references.pmid: 24725775
references.citation: Genet Sel Evol. 2009 Nov 24;41:51
references.pmid: 19930712