Accuracy of Genomic EBV Using an Evenly Spaced, Low-density SNP Panel in Broiler Chickens

Whole-genome or genomic selection is based on associations of large number of markers across the genome with phenotype but will require use of small SNP panels to be cost effective in chickens. The potential loss of accuracy of genotyping selection candidates with an evenly-spaced low-density marker panel and imputation of high-density SNP genotypes was evaluated in a commercial broiler chicken line. Several methods were used to estimate marker effects. The loss in accuracy was less than 5% for different methods and traits. Thus, genomic selection using evenly-spaced low-density marker panels is a cost-effective choice for implementation of genomic selection

Estimated genomic heritabilities from the swine sample.

<p>  =  additive heritability,  =  dominance heritability, and  =  total heritability (or heritability in the broad sense).</p><p>Estimated genomic heritabilities from the swine sample.</p

Mixed model methods for genomic prediction and variance component estimation of additive and dominance effects using SNP markers.

We established a genomic model of quantitative trait with genomic additive and dominance relationships that parallels the traditional quantitative genetics model, which partitions a genotypic value as breeding value plus dominance deviation and calculates additive and dominance relationships using pedigree information. Based on this genomic model, two sets of computationally complementary but mathematically identical mixed model methods were developed for genomic best linear unbiased prediction (GBLUP) and genomic restricted maximum likelihood estimation (GREML) of additive and dominance effects using SNP markers. These two sets are referred to as the CE and QM sets, where the CE set was designed for large numbers of markers and the QM set was designed for large numbers of individuals. GBLUP and associated accuracy formulations for individuals in training and validation data sets were derived for breeding values, dominance deviations and genotypic values. Simulation study showed that GREML and GBLUP generally were able to capture small additive and dominance effects that each accounted for 0.00005-0.0003 of the phenotypic variance and GREML was able to differentiate true additive and dominance heritability levels. GBLUP of the total genetic value as the summation of additive and dominance effects had higher prediction accuracy than either additive or dominance GBLUP, causal variants had the highest accuracy of GREML and GBLUP, and predicted accuracies were in agreement with observed accuracies. Genomic additive and dominance relationship matrices using SNP markers were consistent with theoretical expectations. The GREML and GBLUP methods can be an effective tool for assessing the type and magnitude of genetic effects affecting a phenotype and for predicting the total genetic value at the whole genome level

Six definitions of genomic additive and dominance relationships.

<p>Six definitions of genomic additive and dominance relationships.</p

Genomic additive and dominance relationships by Definitions I-VI for parent-offspring (3518 pairs), full-sibs (1441 pairs) and half-sibs (23,628 pairs) of the swine sample.

<p>Genomic additive and dominance relationships by Definitions I-VI for parent-offspring (3518 pairs), full-sibs (1441 pairs) and half-sibs (23,628 pairs) of the swine sample.</p

Quantitative Genetics Model as the Unifying Model for Defining Genomic Relationship and Inbreeding Coefficient

<div><p>The traditional quantitative genetics model was used as the unifying approach to derive six existing and new definitions of genomic additive and dominance relationships. The theoretical differences of these definitions were in the assumptions of equal SNP effects (equivalent to across-SNP standardization), equal SNP variances (equivalent to within-SNP standardization), and expected or sample SNP additive and dominance variances. The six definitions of genomic additive and dominance relationships on average were consistent with the pedigree relationships, but had individual genomic specificity and large variations not observed from pedigree relationships. These large variations may allow finding least related genomes even within the same family for minimizing genomic relatedness among breeding individuals. The six definitions of genomic relationships generally had similar numerical results in genomic best linear unbiased predictions of additive effects (GBLUP) and similar genomic REML (GREML) estimates of additive heritability. Predicted SNP dominance effects and GREML estimates of dominance heritability were similar within definitions assuming equal SNP effects or within definitions assuming equal SNP variance, but had differences between these two groups of definitions. We proposed a new measure of genomic inbreeding coefficient based on parental genomic co-ancestry coefficient and genomic additive correlation as a genomic approach for predicting offspring inbreeding level. This genomic inbreeding coefficient had the highest correlation with pedigree inbreeding coefficient among the four methods evaluated for calculating genomic inbreeding coefficient in a Holstein sample and a swine sample.</p></div

Genomic additive and dominance relationships by Definitions I-VI for parent-offspring (239 pairs), full-sibs (48 pairs) and half-sibs (23,941) of the Holstein sample.

<p>Genomic additive and dominance relationships by Definitions I-VI for parent-offspring (239 pairs), full-sibs (48 pairs) and half-sibs (23,941) of the Holstein sample.</p

Statistical summary of diagonal values of additive and dominance relationships.

<p>Statistical summary of diagonal values of additive and dominance relationships.</p

Genomic and pedigree relationships of the Holstein sample.

<p>Genomic and pedigree relationships of the Holstein sample.</p

Genomic and pedigree relationships of the swine sample.

<p>Genomic and pedigree relationships of the swine sample.</p