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 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

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

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

Improved nutrient digestibility and retention partially explains feed efficiency gains in pigs selected for low residual feed intake

Residual feed intake (RFI) is a unique measure of feed efficiency (FE) and an alternative to traditional measures. The RFI is defined as the difference between the actual feed intake of a pig and its expected feed intake based on a given amount of growth and backfat. Therefore, selecting pigs with a low RFI (LRFI) results in a more feed-efficient animal for a given rate of growth. Our objective was to determine the extent to which apparent total tract digestibility of nutrients and energy use and retention may explain FE differences between pigs divergently selected for LRFI or high RFI (HRFI). After 7 generations of selection, 12 HRFI and 12 LRFI pigs (62 ± 3 kg BW) were randomly assigned to metabolism crates. Pigs had free access to a standard diet based on corn (Zea mays) and soybean (Glycine max) meal containing 0.4% TiO2, an exogenous digestibility marker. After a 7-d acclimation, total urine and feces were collected for 72 h. Nutrient and energy digestibility, P digestibility, and N balance were then measured and calculated to determine differences between the RFI lines. As expected, ADFI was lower (2.0 vs. 2.6 kg; P \u3c 0.01), ADG did not differ, and FE was higher in the LRFI (P \u3c 0.001) compared to the HRFI pigs. The digestibility values for DM (87.3 vs. 85.9%), N (88.3 vs. 86.1%), and GE (86.9 vs. 85.4%) were higher (P ≤ 0.003) in the LRFI vs. HRFI pigs, respectively. The DE (16.59 vs. 16.32 MJ/kg DM) and ME (15.98 vs. 15.72 MJ/kg DM) values were also greater (P \u3c 0.001) in LRFI pigs. When correcting for ADFI, P digestibility did not differ between the lines. However, the LRFI pigs tended to have improved N retention (P = 0.08) compared to HRFI pigs (36.9 vs. 32.1 g/d). In conclusion, the higher energy and nutrient digestibility, use, and retention may partially explain the superior FE seen in pigs selected for LRFI