Accuracy of Genomic Prediction when Accounting for Population Structure and Polygenic Effects

Accuracy of genomic estimated breeding values obtained using the standard marker effect model was compared with models that account for population structure, either by applying a transmission disequilibrium test (TDT) approach or by fitting polygenic effects. The TDT approach was inferior to the standard model, whereas fitting polygenic effects in addition to marker effects increased the accuracy of estimated breeding values of the progeny of training individuals but also seven generations after training. Thus, fitting polygenic effects enhances utilization of genomic information both in the short and long-term

Bayesian Methods for Genomic Prediction and Genome-Wide Association Studies combining Information on Genotyped and Non-Genotyped Individuals

Genomic prediction involves using high-density marker genotypes to characterize the impact on performance of every region of the genome, and using that information to predict performance of genotyped selection candidates. This is a relatively new technology and is now gaining traction in personalized medicine and in various livestock industries. Our new approach promises to overcome serious limitations with existing techniques for genomic prediction

A Nested Mixture Model for Genomic Prediction Using Whole-Genome SNP Genotypes

We propose a novel model (BayesN) for genomic prediction, where multiple markers in a small segment are simultaneously fitted to jointly capture the effect of major genes (QTL) in the segment. Compared with BayesB, in which the effects of neighboring markers are a prioriassumed to be independent, BayesN gave higher accuracies of prediction and required less computing effort. BayesN is an accurate and practical method for analyzing high-density markers, especially for traits influenced by rare QTL allele

Analysis of Ten Generations of Selection for Residual Feed Intake in Yorkshire Pigs

Ten generations (G) of divergent selection for residual feed intake (RFI) was practiced in Yorkshire pigs. This study shows that feed efficiency based on RFI was moderately heritable and responded to selection. Pigs selected for increased feed efficiency from the low RFI line ate less, grew slightly slower, and were leaner than pigs from the high RFI line. Thus, the results of this study show that selection for decreased RFI can improve feed efficiency and can be included in an economic selection index in addition to growth for reducing feed cost

Improved Accuracy of Genomic Prediction for Traits with Rare QTL by Fitting Haplotypes

Genomic prediction estimates breeding values by exploiting linkage disequilibrium (LD) between quantitative trait loci (QTL) and single nucleotide polymorphisms (SNPs). High LD cannot occur when QTL and SNPs have different minor allele frequencies (MAF). Marker panels tend to use SNPs with high MAF and will have limited ability to predict rare QTL alleles. In practice, increasing SNP density has not improved prediction accuracy. A possible reason is that many traits are characterized by rare QTL. In that case, linear models fitting haplotypes could have advantage because haplotypes can be in complete LD with QTL alleles. SNP genotypes were simulated to resemble 600K chip for the bovine genome. Genomic breeding values were predicted using either SNP genotypes or non-overlapping haplotypes. When QTL had low MAF, the haplotype model had significantly higher accuracy than the SNP model. Results show that fitting haplotypes can improve the accuracy of genomic prediction for traits controlled by rare QTL

Genomic Prediction Using Linkage Disequilibrium and Co-segregation

A linear mixed model fitting both genome-wide cosegregation (CS) and linkage disequilibrium (LD) was developed to improve accuracy of genetic prediction for pedigreed populations of unrelated families that have half sibs represented in both training and validation. Cosegregation was modeled as the effects of genome-wide1-centimorgan haplotypes that one individual inherits from pedigree founders through identity-by-descent, while LD was modeled as allele substitution effects of all marker genotypes. Prediction accuracy of the LD-CS method was compared to the accuracy of three LD methods – GBLUP, BayesA and BayesB, using simulated datasets of varying numbers of paternal half sib families. Results show that the LD-CS method tended to have higher accuracy than any of the LD methods. With an increase in the number of families, the accuracy of the LD-CS method persisted, while the accuracy of the LD methods dropped. The results indicate that by fitting CS explicitly, the LD-CS method has higher and more consistent prediction accuracy than LD methods

A Least Squares Regression Model to Detect Quantitative Trait Loci with Polar Overdominance in a Cross of Outbred Breeds: Simulation

A least squares regression interval mapping model was derived to detect quantitative trait loci (QTL) with a unique mode of genomic imprinting, polar overdominance (POD), under a breed cross design model in outbred mammals. Tests to differentiate POD QTL from Mendelian, paternal or maternal expression QTL were also developed. To evaluate the power of the POD models and to determine the ability to differentiate POD from non-POD QTL, phenotypic data, marker data and a biallelic QTL were simulated on 512 F2 offspring. When tests for Mendelian versus parent-of-origin expression were performed, most POD QTL were classified as partially imprinted QTL. The application of the series of POD tests showed that more than 90% and 80% of medium and small POD QTL were declared as POD type. However, when breed-origin alleles were segregating in the grand parental breeds, the proportion of declared POD QTL decreased, which was more pronounced in a mating design with a small number of parents (F0 and F1). Non-POD QTL, i.e. with Mendelian or parent-of-origin expression (complete imprinting) inheritance, were well classified (>90%) as non-POD QTL, except for QTL with small effects and paternal or maternal expression in the design with a small number of parents, for which spurious POD QTL were declared

Genomic Selection of Purebred Animals for Crossbred Performance in the Presence of Dominant Gene Action

The primary objective of this study was to assess the performance of different genomic prediction models applied to the selection of purebreds for crossbred performancebased on high-density marker data. Our results suggest that in the presence of dominant gene action, selection based on the dominance model is superior to both the a breed-specific allele model and an additive model in terms of maximizing crossbred performance through purebred selection, especially when training is not updated each generatio

Total cost estimation for implementing genome-enabled selection in a multi-level swine production system

Background: Determining an animal’s genetic merit using genomic information can improve estimated breeding value (EBV) accuracy; however, the magnitude of the accuracy improvement must be large enough to recover the costs associated with implementing genome-enabled selection. One way to reduce costs is to genotype nucleus herd selection candidates using a low-density chip and to use high-density chip genotyping for animals that are used as parents in the nucleus breeding herd. The objective of this study was to develop a tool to estimate the cost structure associated with incorporating genome-enabled selection into multi-level commercial breeding programs. Results: For the purpose of this deterministic study, it was assumed that a commercial pig is created from a terminal line sire and a dam that is a cross between two maternal lines. It was also assumed that all male and female selection candidates from the 1000 sow maternal line nucleus herds were genotyped at low density and all animals used for breeding at high density. With the assumptions used in this analysis, it was estimated that genome-enabled selection costs for a maternal line would be approximately US$0.082 per weaned pig in the commercial production system. A total of US$0.164 per weaned pig is needed to incorporate genome-enabled selection into the two maternal lines. Similarly, for a 600 sow terminal line nucleus herd and genotyping only male selection candidates with the low-density panel, the cost per weaned pig in the commercial herd was estimated to be US$0.044. This means that US$0.21 per weaned pig produced at the commercial level and sired by boars obtained from the nucleus herd breeding program needs to be added to the genetic merit value in order to break even on the additional cost required when genome-enabled selection is used in both maternal lines and the terminal line. Conclusions: By modifying the input values, such as herd size and genotyping strategy, a flexible spreadsheet tool developed from this work can be used to estimate the additional costs associated with genome-enabled selection. This tool will aid breeders in estimating the economic viability of incorporating genome-enabled selection into their specific breeding program