A predictive assessment of genetic correlations between traits in chickens using markers. Issue 1 (December 2017)

• Record Type: Journal Article
• Authors: Momen, Mehdi; Mehrgardi, Ahmad; Sheikhy, Ayoub; Esmailizadeh, Ali; Fozi, Masood; Kranis, Andreas; Valente, Bruno; Rosa, Guilherme; Gianola, Daniel
• Is Part Of: Genetics, selection, evolution. Volume 49:Issue 1(2017)
• Page Start: 1
• Page End: 14
• Publication Date: 2017-12
• Subjects: Livestock -- Breeding -- Periodicals; Animal genetics -- Periodicals; Livestock -- Genetics -- Periodicals; Evolution -- Periodicals; 576.505
• DOI: 10.1186/s12711-017-0290-9
• Languages: English
• ISSNs: 1297-9686

Abstract

Abstract Background Genomic selection has been successfully implemented in plant and animal breeding programs to shorten generation intervals and accelerate genetic progress per unit of time. In practice, genomic selection can be used to improve several correlated traits simultaneously via multiple-trait prediction, which exploits correlations between traits. However, few studies have explored multiple-trait genomic selection. Our aim was to infer genetic correlations between three traits measured in broiler chickens by exploring kinship matrices based on a linear combination of measures of pedigree and marker-based relatedness. A predictive assessment was used to gauge genetic correlations. Methods A multivariate genomic best linear unbiased prediction model was designed to combine information from pedigree and genome-wide markers in order to assess genetic correlations between three complex traits in chickens, i.e. body weight at 35 days of age (BW), ultrasound area of breast meat (BM) and hen-house egg production (HHP). A dataset with 1351 birds that were genotyped with the 600 K Affymetrix platform was used. A kinship kernel (K ) was constructed asK = λ G + (1 − λ )A, whereA is the numerator relationship matrix, measuring pedigree-based relatedness, andG is a genomic relationship matrix. The weight (λ ) assigned to each source of information varied over the gridλ = (0, 0.2, 0.4, 0.6, 0.8, 1). Maximum likelihood estimates of heritability and genetic correlations were obtained at eachλ, and the "optimum"λ was determined using cross-validation. Results Estimates of genetic correlations were affected by the weight placed on the source of information used to buildK . For example, the genetic correlation between BW–HHP and BM–HHP changed markedly whenλ varied from 0 (onlyA used for measuring relatedness) to 1 (only genomic information used). Asλ increased, predictive correlations (correlation between observed phenotypes and predicted breeding values) increased and mean-squared predictive error decreased. However, the improvement in predictive ability was not monotonic, with an optimum found at some 0 < λ < 1, i.e., when both sources of information were used together. Conclusions Our findings indicate that multiple-trait prediction may benefit from combining pedigree and marker information. Also, it appeared that expected correlated responses to selection computed from standard theory may differ from realized responses. The predictive assessment provided a metric for performance evaluation as well as a means for expressing uncertainty of outcomes of multiple-trait selection.