Theo H. E. Meuwissen

Accuracy of Genomic Selection Using Different Methods to Define Haplotypes

Genomic selection uses total breeding values for juvenile animals, predicted from a large number of estimated marker haplotype effects across the whole genome. In this study the accuracy of predicting breeding values is compared for four different models including a large number of markers, at different marker densities for traits with heritabilities of 50 and 10%. The models estimated the effect of (1) each single-marker allele [single-nucleotide polymorphism (SNP)1], (2) haplotypes constructed from two adjacent marker alleles (SNP2), and (3) haplotypes constructed from 2 or 10 markers, including the covariance between haplotypes by combining linkage disequilibrium and linkage analysis (HAP_IBD2 and HAP_IBD10). Between 119 and 2343 polymorphic SNPs were simulated on a 3-M genome. For the trait with a heritability of 10%, the differences between models were small and none of them yielded the highest accuracies across all marker densities. For the trait with a heritability of 50%, the HAP_IBD10 model yielded the highest accuracies of estimated total breeding values for juvenile and phenotyped animals at all marker densities. It was concluded that genomic selection is considerably more accurate than traditional selection, especially for a low-heritability trait.

Accurate Prediction of Genetic Values for Complex Traits by Whole-Genome Resequencing

Whole-genome resequencing technology has improved rapidly during recent years and is expected to improve further such that the sequencing of an entire human genome sequence for

Prediction of identity by descent probabilities from marker-haplotypes

1000 is within reach. Our main aim here is to use whole-genome sequence data for the prediction of genetic values of individuals for complex traits and to explore the accuracy of such predictions. This is relevant for the fields of plant and animal breeding and, in human genetics, for the prediction of an individuals risk for complex diseases. Here, population history and genomic architectures were simulated under the Wright–Fisher population and infinite-sites mutation model, and prediction of genetic value was by the genomic selection approach, where a Bayesian nonlinear model was used to predict the effects of individual SNPs. The Bayesian model assumed a priori that only few SNPs are causative, i.e., have an effect different from zero. When using whole-genome sequence data, accuracies of prediction of genetic value were >40% increased relative to the use of dense ∼30K SNP chips. At equal high density, the inclusion of the causative mutations yielded an extra increase of accuracy of 2.5–3.7%. Predictions of genetic value remained accurate even when the training and evaluation data were 10 generations apart. Best linear unbiased prediction (BLUP) of SNP effects does not take full advantage of the genome sequence data, and nonlinear predictions, such as the Bayesian method used here, are needed to achieve maximum accuracy. On the basis of theoretical work, the results could be extended to more realistic genome and population sizes.

The Accuracy of Genomic Selection in Norwegian Red Cattle Assessed by Cross-Validation

The prediction of identity by descent (IBD) probabilities is essential for all methods that map quantitative trait loci (QTL). The IBD probabilities may be predicted from marker genotypes and/or pedigree information. Here, a method is presented that predicts IBD probabilities at a given chromosomal location given data on a haplotype of markers spanning that position. The method is based on a simplification of the coalescence process, and assumes that the number of generations since the base population and effective population size is known, although effective size may be estimated from the data. The probability that two gametes are IBD at a particular locus increases as the number of markers surrounding the locus with identical alleles increases. This effect is more pronounced when effective population size is high. Hence as effective population size increases, the IBD probabilities become more sensitive to the marker data which should favour finer scale mapping of the QTL. The IBD probability prediction method was developed for the situation where the pedigree of the animals was unknown (i.e. all information came from the marker genotypes), and the situation where, say T, generations of unknown pedigree are followed by some generations where pedigree and marker genotypes are known.

Genomic selection using different marker types and densities

Genomic Selection (GS) is a newly developed tool for the estimation of breeding values for quantitative traits through the use of dense markers covering the whole genome. For a successful application of GS, accuracy of the prediction of genomewide breeding value (GW-EBV) is a key issue to consider. Here we investigated the accuracy and possible bias of GW-EBV prediction, using real bovine SNP genotyping (18,991 SNPs) and phenotypic data of 500 Norwegian Red bulls. The study was performed on milk yield, fat yield, protein yield, first lactation mastitis traits, and calving ease. Three methods, best linear unbiased prediction (G-BLUP), Bayesian statistics (BayesB), and a mixture model approach (MIXTURE), were used to estimate marker effects, and their accuracy and bias were estimated by using cross-validation. The accuracies of the GW-EBV prediction were found to vary widely between 0.12 and 0.62. G-BLUP gave overall the highest accuracy. We observed a strong relationship between the accuracy of the prediction and the heritability of the trait. GW-EBV prediction for production traits with high heritability achieved higher accuracy and also lower bias than health traits with low heritability. To achieve a similar accuracy for the health traits probably more records will be needed.

Mapping multiple QTL using linkage disequilibrium and linkage analysis information and multitrait data

With the availability of high-density marker maps and cost-effective genotyping, genomic selection methods may provide faster genetic gain than can be achieved by current selection methods based on phenotypes and the pedigree. Here we investigate some of the factors driving the accuracy of genomic selection, namely marker density and marker type (i.e., microsatellite and SNP markers), and the use of marker haplotypes versus marker genotypes alone. Different densities were tested with marker densities equivalent to 2, 1, 0.5, and 0.25N(e) markers/morgan using microsatellites and 8, 4, 2, and 1N(e) markers/morgan using SNP, where 1N(e) markers/morgan means 100 markers per morgan, if effective size (N(e)) is 100. Marker characteristics and linkage disequilibria were obtained by simulating a population over 1,000 generations to achieve a mutation drift balance. The marker designs were evaluated for their accuracy of predicting breeding values from either estimating marker effects or estimating effects of haplotypes based upon combining 2 markers. Using microsatellites as direct marker effects, the accuracy of selection increased from 0.63 to 0.83 as the density increased from 0.25N(e)/morgan to 2N(e)/morgan. Using SNP markers as direct marker effects, the accuracy of selection increased from 0.69 to 0.86 as the density increased from 1N(e)/morgan to 8N(e)/morgan. The SNP markers required a 2 to 3 times greater density compared with using microsatellites to achieve a similar accuracy. The biases that genomic selection EBV often show are due to the prediction of marker effects instead of QTL effects, and hence, genomic selection EBV may need rescaling for practical use. Using haplotypes resulted in similar or reduced accuracies compared with using direct marker effects. In practical situations, this means that it is advantageous to use direct marker effects, because this avoids the estimation of marker phases with the associated errors. In general, the results showed that the accuracy remained responsive with small bias to increasing marker density at least up to 8N(e) SNP/morgan, where the effective population size was 100 and with the genomic model assumed. For a 30-morgan genome and N(e) = 100, this implies that about approximately 24,000 SNP are needed.

Testing strategies for genomic selection in aquaculture breeding programs

A multi-locus QTL mapping method is presented, which combines linkage and linkage disequilibrium (LD) information and uses multitrait data. The method assumed a putative QTL at the midpoint of each marker bracket. Whether the putative QTL had an effect or not was sampled using Markov chain Monte Carlo (MCMC) methods. The method was tested in dairy cattle data on chromosome 14 where the DGAT1 gene was known to be segregating. The DGAT1 gene was mapped to a region of 0.04 cM, and the effects of the gene were accurately estimated. The fitting of multiple QTL gave a much sharper indication of the QTL position than a single QTL model using multitrait data, probably because the multi-locus QTL mapping reduced the carry over effect of the large DGAT1 gene to adjacent putative QTL positions. This suggests that the method could detect secondary QTL that would, in single point analyses, remain hidden under the broad peak of the dominant QTL. However, no indications for a second QTL affecting dairy traits were found on chromosome 14.

Assessing the contribution of breeds to genetic diversity in conservation schemes

BackgroundGenomic selection is a selection method where effects of dense genetic markers are first estimated in a test population and later used to predict breeding values of selection candidates. The aim of this paper was to investigate genetic gains, inbreeding and the accuracy of selection in a general genomic selection scheme for aquaculture, where the test population consists of sibs of the candidates.MethodsThe selection scheme started after simulating 4000 generations in a Fisher-Wright population with a size of 1000 to create a founder population. The basic scheme had 3000 selection candidates, 3000 tested sibs of the candidates, 100 full-sib families, a trait heritability of 0.4 and a marker density of 0.5Ne/M. Variants of this scheme were also analysed.ResultsThe accuracy of selection in generation 5 was 0.823 for the basic scheme when the sib-testing was performed every generation. The accuracy was hardly reduced by selection, probably because the increased frequency of favourable alleles compensated for the Bulmer effect. When sib-testing was performed only in the first generation, in order to reduce costs, accuracy of selection in generation 5 dropped to 0.304, the main reduction occurring in the first generation. The genetic level in generation 5 was 6.35σa when sib-testing was performed every generation, which was 72%, 12% and 9% higher than when sib-testing was performed only in the first generation, only in the first three generations or every second generation, respectively. A marker density above 0.5Ne/M hardly increased accuracy of selection further. For the basic scheme, rates of inbreeding were reduced by 81% in these schemes compared to traditional selection schemes, due to within-family selection. Increasing the number of sibs to 6000 hardly affected the accuracy of selection, and increasing the number of candidates to 6000 increased genetic gain by 10%, mainly because of increased selection intensity.ConclusionVarious strategies were evaluated to reduce the amount of sib-testing and genotyping, but all resulted in loss of selection accuracy and thus of genetic gain. Rates of inbreeding were reduced by 81% in genomic selection schemes compared to traditional selection schemes for the parameters of the basic scheme, due to within-family selection.

Accelerating Improvement of Livestock with Genomic Selection

The quantitative assessment of genetic diversity within and between populations is important for decision making in genetic conservation plans. In this paper we define the genetic diversity of a set of populations, S, as the maximum genetic variance that can be obtained in a random mating population that is bred from the set of populations S. First we calculated the relative contribution of populations to a core set of populations in which the overlap of genetic diversity was minimised. This implies that the mean kinship in the core set should be minimal. The above definition of diversity differs from Weitzman diversity in that it attempts to conserve the founder population (and thus minimises the loss of alleles), whereas Weitzman diversity favours the conservation of many inbred lines. The former is preferred in species where inbred lines suffer from inbreeding depression. The application of the method is illustrated by an example involving 45 Dutch poultry breeds. The calculations used were easy to implement and not computer intensive. The method gave a ranking of breeds according to their contributions to genetic diversity. Losses in genetic diversity ranged from 2.1% to 4.5% for different subsets relative to the entire set of breeds, while the loss of founder genome equivalents ranged from 22.9% to 39.3%.

Mating schemes for optimum contribution selection with constrained rates of inbreeding

Three recent breakthroughs have resulted in the current widespread use of DNA information: the genomic selection (GS) methodology, which is a form of marker-assisted selection on a genome-wide scale, and the discovery of large numbers of single-nucleotide markers and cost effective methods to genotype them. GS estimates the effect of thousands of DNA markers simultaneously. Nonlinear estimation methods yield higher accuracy, especially for traits with major genes. The marker effects are estimated in a genotyped and phenotyped training population and are used for the estimation of breeding values of selection candidates by combining their genotypes with the estimated marker effects. The benefits of GS are greatest when selection is for traits that are not themselves recorded on the selection candidates before they can be selected. In the future, genome sequence data may replace SNP genotypes as markers. This could increase GS accuracy because the causative mutations should be included in the data.