Empirical comparison between different methods for genomic prediction of number of piglets born alive in moderate sized breeding populations1

Abstract

Currently used multi-step methods to incorporate genomic information in the prediction of breeding values (BV) implicitly involve many assumptions which, if violated, may result in loss of information, inaccuracies and bias. To overcome this, single-step genomic best linear unbiased prediction (ssGBLUP) was proposed combining pedigree, phenotype and genotype of all individuals for genetic evaluation. Our objective was to implement ssGBLUP for genomic predictions in pigs and to compare the accuracy of ssGBLUP with that of multi-step methods with empirical data of moderately sized pig breeding populations. Different predictions were performed: conventional parent average (PA), direct genomic value (DGV) calculated with genomic BLUP (GBLUP), a GEBV obtained by blending the DGV with PA, and ssGBLUP. Data comprised individuals from a German Landrace (LR) and Large White (LW) population. The trait 'number of piglets born alive' (NBA) was available for 182,054 litters of 41,090 LR sows and 15,750 litters from 4534 LW sows. The pedigree contained 174,021 animals, of which 147,461 (26,560) animals were LR (LW) animals. In total, 526 LR and 455 LW animals were genotyped with the Illumina PorcineSNP60 BeadChip. After quality control and imputation, 495 LR (424 LW) animals with 44,368 (43,678) SNP on 18 autosomes remained for the analysis. Predictive abilities, i.e., correlations between de-regressed proofs and genomic BV, were calculated with a five-fold cross validation and with a forward prediction for young genotyped validation animals born after 2011. Generally, predictive abilities for LR were rather small (0.08 for GBLUP, 0.19 for GEBV and 0.18 for ssGBLUP). For LW, ssGBLUP had the greatest predictive ability (0.45). For both breeds, assessment of reliabilities for young genotyped animals indicated that genomic prediction outperforms PA with ssGBLUP providing greater reliabilities (0.40 for LR and 0.32 for LW) than GEBV (0.35 for LR and 0.29 for LW). Grouping of animals according to information sources revealed that genomic prediction had the highest potential benefit for genotyped animals without their own phenotype. Although, ssGBLUP did not generally outperform GBLUP or GEBV, the results suggest that ssGBLUP can be a useful and conceptually convincing approach for practical genomic prediction of NBA in moderately sized LR and LW populations.

A genome-wide association study of reproduction traits in four pig populations with different genetic backgrounds

“…The bold data in this column represent the significant level. 3) The associated gene in bold in this column represent these genes were associated with traits based on annotation. 4) down/up = the location of SNP in downstream/upstream of the nearest gene.…”

“…Reproductive traits, such as total number born (TNB) and number born alive (NBA), have been considered as the most important index included in the selection indices of pig breeding programs for evaluating sow productivity [2]. Up to the present, selection based on traditional breeding methods using best linear unbiased prediction has been successful in improving maternal reproductive traits [3]. However, the genetic architecture of reproductive traits is very complicated due to low heritability, minor genes, maternal effects and environmental factors [4], resulting in the difficulty deciphering the genetic architecture of reproduction traits.…”

Weighted single-step GWAS identified candidate genes associated with semen traits in a Duroc boar population

“…I denoted the identity matrix. The inverse of H matrix, a blend of pedigree and genetic marker derived matrices [7, 8], was calculated as where A denoted the pedigree derived relationship matrix; A 22 was a sub matrix of A corresponding to the genotyped individuals; G ω = 0.9 G + 0.1 A 22 , these weights were used to scale the genomic information to be compatible with the pedigree information and to control bias [51, 52]; was the genomic relationship matrix [53], where Z was a matrix of genotypes (with 0-2p, 1-2p, and 2-2p represented genotypes AA, Aa, and aa, respectively; p denoted the minor allele frequency (MAF)), D denoted a diagonal matrix contained the SNP weights, p i denoted MAF of the i th SNP, and m denoted the number of SNPs.…”