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

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

Fine-tuning the predicted position of genes associated with economic traits in livestock

Different methods that estimate the position of a gene on a chromosome were tested in computer-simulated populations to determine their accuracy. Given the same amount of genetic information from the animals, one method performed better than the others. In situations where experimental costs were assumed to be equal but genetic information could vary, this method was no longer the most accurate. Further study of this method found that the animals’ genetic information must be used in a specific way in order to obtain the most accurate position of the gene. These methods will be useful in identifying genes and the genetic differences between animals that can be used for genetic improvement of livestock

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