title: Compression distance can discriminate animals by genetic profile, build relationship matrices and estimate breeding values.
abstract: BACKGROUND
  Genetic relatedness is currently estimated by a combination of traditional pedigree-based approaches (i.e. numerator relationship matrices, NRM) and, given the recent availability of molecular information, using marker genotypes (via genomic relationship matrices, GRM). To date, GRM are computed by genome-wide pair-wise SNP (single nucleotide polymorphism) correlations.
  
  
  RESULTS
  We describe a new estimate of genetic relatedness using the concept of normalised compression distance (NCD) that is borrowed from Information Theory. Analogous to GRM, the resultant compression relationship matrix (CRM) exploits numerical patterns in genome-wide allele order and proportion, which are known to vary systematically with relatedness. We explored properties of the CRM in two industry cattle datasets by analysing the genetic basis of yearling weight, a phenotype of moderate heritability. In both Brahman (Bos indicus) and Tropical Composite (Bos taurus by Bos indicus) populations, the clustering inferred by NCD was comparable to that based on SNP correlations using standard principal component analysis approaches. One of the versions of the CRM modestly increased the amount of explained genetic variance, slightly reduced the 'missing heritability' and tended to improve the prediction accuracy of breeding values in both populations when compared to both NRM and GRM. Finally, a sliding window-based application of the compression approach on these populations identified genomic regions influenced by introgression of taurine haplotypes.
  
  
  CONCLUSIONS
  For these two bovine populations, CRM reduced the missing heritability and increased the amount of explained genetic variation for a moderately heritable complex trait. Given that NCD can sensitively discriminate closely related individuals, we foresee CRM having possible value for estimating breeding values in highly inbred populations.
authors.affiliation: CSIRO Agriculture, Computational and Systems Biology, 306 Carmody Road, St. Lucia, Brisbane, QLD, 4075, Australia. nick.hudson@csiro.au.
authors.forename: Nicholas J
authors.initials: NJ
authors.lastname: Hudson
authors.affiliation: CSIRO Agriculture, Computational and Systems Biology, 306 Carmody Road, St. Lucia, Brisbane, QLD, 4075, Australia. Laercio.Portoneto@csiro.au.
authors.forename: Laercio
authors.initials: L
authors.lastname: Porto-Neto
authors.affiliation: CSIRO Agriculture, Computational and Systems Biology, 306 Carmody Road, St. Lucia, Brisbane, QLD, 4075, Australia. James.Kijas@csiro.au.
authors.forename: James W
authors.initials: JW
authors.lastname: Kijas
authors.affiliation: CSIRO Agriculture, Computational and Systems Biology, 306 Carmody Road, St. Lucia, Brisbane, QLD, 4075, Australia. toni.reverter-gomez@csiro.au.
authors.forename: Antonio
authors.initials: A
authors.lastname: Reverter
mesh_terms: D000465:Algorithms; D000483:Alleles; D000818:Animals; D001947:Breeding; D003198:Computer Simulation; D014644:Genetic Variation; D055106:Genome-Wide Association Study; D023281:Genomics / Q000379:methods; D005838:Genotype; D008957:Models, Genetic; D010375:Pedigree; D020641:Polymorphism, Single Nucleotide; D015203:Reproducibility of Results