Bayesian genomic selection: the effect of haplotype length and priors

Abstract

Breeding values for animals with marker data are estimated using a genomic selection approach where data is analyzed using Bayesian multi-marker association models. Fourteen model scenarios with varying haplotype lengths, hyper parameter and prior distributions were compared to find the scenario expected to give the most correct genomic estimated breeding values for animals with marker information only. Five-fold cross validation was performed to assess the ability of models to estimate breeding values for animals in generation 3. In each of the five subsets, 20% of phenotypic records in generation 3 were left out. Correlations between breeding values estimated on full data and on subsets for the "leave-out" animals varied between 0.77–0.99. Regression coefficients of breeding values from full data on breeding values from subsets ranged from 0.78–1.01. Single-SNP marker models didn't perform well. Correlations were 0.77–0.89 and predicted breeding values were biased. In addition the models seemed to over fit the genomic part of the variation. Highest correlations and most unbiased results were obtained when SNP markers were joined into haplotypes. Especially the scenarios with 5-SNP haplotypes gave promising results (distance between adjacent SNPs is 0.1 cM evenly over the genome). All correlations were 0.99 and regression coefficients were 0.99–1.01. Models with 5-SNP markers seemed robust to hyper parameter and prior changes. Haplotypes up to 40 SNPs also gave good results. However, longer haplotypes are expected to have less predictive ability over several generations and therefore the 5-SNP haplotypes are expected to give the best predictions for generations 4–6.

Comparison of analyses of the QTLMAS XII common dataset. I: Genomic selection

“…Two contributions [16,17] analysed data with Bayesian methods and in total submitted GEBVs from five different models. GEBVs from all models were more accurate (Table 6) than the non-Bayesian models.…”

“…Following the internal validation by [17], it can be seen that, for this data, it was an advantage to fit effects of haplotypes rather than effects of single SNPs with the model used in [17]. It must be noted that the data were provided with known haplotypes.…”

“…This creates a strong dependency between the data used for model development and validation, which could be avoided by using the phenotypic data in generation G t4 rather than EBVs. Villumsen et al [17] used a 5-fold cross validation where in each of the five validation sets, 20% of the data in G t4 , were taken as test data. The advantage of cross validation is that it makes it possible to retain training data as large as possible, while obtaining the amount of total test data as large as required (with maximal total test data equal to the whole data).…”

Genomic breeding value prediction: methods and procedures

“…In this case, the marker haplotypes are treated to be alike-in-state (AIS; e.g. Villumsen and Janss, 2008). The assumption that copies of each unique haplotype carry the same QTL allele can be relaxed by treating haplotypes to be identical-by-descent (IBD; .…”

Field Screening of Primary and Secondary Tritipyrum Genotypes using Selection Indices based on BLUP under Saline and Normal Conditions

“…Large efforts have been made in the past to develop methods for hybrid prediction, with the goal to facilitate hybrid breeding by obtaining better hybrids with fewer crosses (Xu et al, 2014). The accuracy of the predicted breeding value of traits with high heritability was better than for those with low heritability (Goddard et al, 2006;Villumsen and Janss, 2009). The accuracy of the predicted breeding value of traits with high heritability was better than for those with low heritability (Goddard et al, 2006;Villumsen and Janss, 2009).…”