title: Genomic prediction based on runs of homozygosity.
abstract: BACKGROUND
  Genomic prediction is based on the accurate estimation of the genomic relationships among and between training animals and selection candidates in order to obtain accurate estimates of the genomic estimated breeding values (GEBV). Various methods have been used to predict GEBV based on population-wide linkage disequilibrium relationships (G IBS ) or sometimes on linkage analysis relationships (G LA ). Here, we propose a novel method to predict GEBV based on a genomic relationship matrix using runs of homozygosity (G ROH ). Runs of homozygosity were used to derive probabilities of multi-locus identity by descent chromosome segments. The accuracy and bias of the prediction of GEBV using G ROH were compared to those using G IBS and G LA . Comparisons were performed using simulated datasets derived from a random pedigree and a real pedigree of Italian Brown Swiss bulls. The comparison of accuracies of GEBV was also performed on data from 1086 Italian Brown Swiss dairy cattle.
  
  
  RESULTS
  Simulations with various thresholds of minor allele frequency for markers and quantitative trait loci showed that G ROH achieved consistently more accurate GEBV (0 to 4% points higher) than G IBS and G LA . The bias of GEBV prediction for simulated data was higher based on the real pedigree than based on a random pedigree. In the analyses with real data, G ROH and G LA had similar accuracies. However, G LA achieved a higher accuracy when the prediction was done on the youngest animals. The G IBS matrices calculated with and without standardized marker genotypes resulted in similar accuracies.
  
  
  CONCLUSIONS
  The present study proposes G ROH as a novel method to estimate genomic relationship matrices and predict GEBV based on runs of homozygosity and shows that it can result in higher or similar accuracies of GEBV prediction than G LA , except for the real data analysis with validation of young animals. Compared to G IBS , G ROH resulted in more accurate GEBV predictions.
authors.affiliation: Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, Ås, N-1432, Norway. tu.luan@nmbu.no.
authors.forename: Tu
authors.initials: T
authors.lastname: Luan
authors.affiliation: Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, Ås, N-1432, Norway. xijiang.yu@nmbu.no.
authors.forename: Xijiang
authors.initials: X
authors.lastname: Yu
authors.affiliation: Dipartimento di Scienze e Tecnologie Veterinarie per la Sicurezza Alimentare, Università degli Studi di Milano, Via Celoria 10, 20133, Milano, Italy. marlies.dolezal@gmail.com.
authors.forename: Marlies
authors.initials: M
authors.lastname: Dolezal
authors.affiliation: Dipartimento di Scienze e Tecnologie Veterinarie per la Sicurezza Alimentare, Università degli Studi di Milano, Via Celoria 10, 20133, Milano, Italy. alessandro.bagnato@unimi.it.
authors.forename: Alessandro
authors.initials: A
authors.lastname: Bagnato
authors.affiliation: Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, Ås, N-1432, Norway. theo.meuwissen@nmbu.no.
authors.forename: Theo He
authors.initials: TH
authors.lastname: Meuwissen
mesh_terms: D000818:Animals; D001947:Breeding; D002417:Cattle / Q000235:genetics*; D003198:Computer Simulation; D005787:Gene Frequency / Q000235:genetics*; D023281:Genomics / Q000379:methods*; D006720:Homozygote; D008297:Male; D008957:Models, Genetic; D010375:Pedigree; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci