title: Comparison of single-trait and multiple-trait genomic prediction models.
abstract: BACKGROUND
  In this study, a single-trait genomic model (STGM) is compared with a multiple-trait genomic model (MTGM) for genomic prediction using conventional estimated breeding values (EBVs) calculated using a conventional single-trait and multiple-trait linear mixed models as the response variables. Three scenarios with and without missing data were simulated; no missing data, 90% missing data in a trait with high heritability, and 90% missing data in a trait with low heritability. The simulated genome had a length of 500 cM with 5000 equally spaced single nucleotide polymorphism markers and 300 randomly distributed quantitative trait loci (QTL). The true breeding values of each trait were determined using 200 of the QTLs, and the remaining 100 QTLs were assumed to affect both the high (trait I with heritability of 0.3) and the low (trait II with heritability of 0.05) heritability traits. The genetic correlation between traits I and II was 0.5, and the residual correlation was zero.
  
  
  RESULTS
  The results showed that when there were no missing records, MTGM and STGM gave the same reliability for the genomic predictions for trait I while, for trait II, MTGM performed better that STGM. When there were missing records for one of the two traits, MTGM performed much better than STGM. In general, the difference in reliability of genomic EBVs predicted using the EBV response variables estimated from either the multiple-trait or single-trait models was relatively small for the trait without missing data. However, for the trait with missing data, the EBV response variable obtained from the multiple-trait model gave a more reliable genomic prediction than the EBV response variable from the single-trait model.
  
  
  CONCLUSIONS
  These results indicate that MTGM performed better than STGM for the trait with low heritability and for the trait with a limited number of records. Even when the EBV response variable was obtained using the multiple-trait model, the genomic prediction using MTGM was more reliable than the prediction using the STGM.
authors.forename: Gang
authors.initials: G
authors.lastname: Guo
authors.forename: Fuping
authors.initials: F
authors.lastname: Zhao
authors.forename: Yachun
authors.initials: Y
authors.lastname: Wang
authors.forename: Yuan
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: National Center for Molecular Genetics and Breeding of Animal, Institute of Animal Sciences, Chinese academy of Agricultural Sciences, Beijing 100193, China. lxdu@263.net.
authors.forename: Lixin
authors.initials: L
authors.lastname: Du
authors.forename: Guosheng
authors.initials: G
authors.lastname: Su
mesh_terms: D000818:Animals; D001947:Breeding; D005260:Female; D023281:Genomics / Q000379:methods*; D016014:Linear Models; D008297:Male; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci