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Genomic evaluations using similarity between haplotypes
Author(s) -
Hickey J.M.,
Kinghorn B.P.,
Tier B.,
Clark S.A.,
Werf J.H.J.,
Gorjanc G.
Publication year - 2013
Publication title -
journal of animal breeding and genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.689
H-Index - 51
eISSN - 1439-0388
pISSN - 0931-2668
DOI - 10.1111/jbg.12020
Subject(s) - haplotype , haplotype estimation , imputation (statistics) , genetics , similarity (geometry) , biology , computational biology , covariance , genomics , genotype , computer science , genome , statistics , mathematics , artificial intelligence , gene , missing data , image (mathematics)
Summary Long‐range phasing and haplotype library imputation methodologies are accurate and efficient methods to provide haplotype information that could be used in prediction of breeding value or phenotype. Modelling long haplotypes as independent effects in genomic prediction would be inefficient due to the many effects that need to be estimated and phasing errors, even if relatively low in frequency, exacerbate this problem. One approach to overcome this is to use similarity between haplotypes to model covariance of genomic effects by region or of animal breeding values. We developed a simple method to do this and tested impact on genomic prediction by simulation. Results show that the diagonal and off‐diagonal elements of a genomic relationship matrix constructed using the haplotype similarity method had higher correlations with the true relationship between pairs of individuals than genomic relationship matrices built using unphased genotypes or assumed unrelated haplotypes. However, the prediction accuracy of such haplotype‐based prediction methods was not higher than those based on unphased genotype information.