Robin Wellmann

Bayesian models with dominance effects for genomic evaluation of quantitative traits.

Genomic selection refers to the use of dense, genome-wide markers for the prediction of breeding values (BV) and subsequent selection of breeding individuals. It has become a standard tool in livestock and plant breeding for accelerating genetic gain. The core of genomic selection is the prediction of a large number of marker effects from a limited number of observations. Various Bayesian methods that successfully cope with this challenge are known. Until now, the main research emphasis has been on additive genetic effects. Dominance coefficients of quantitative trait loci (QTLs), however, can also be large, even if dominance variance and inbreeding depression are relatively small. Considering dominance might contribute to the accuracy of genomic selection and serve as a guide for choosing mating pairs with good combining abilities. A general hierarchical Bayesian model for genomic selection that can realistically account for dominance is introduced. Several submodels are proposed and compared with respect to their ability to predict genomic BV, dominance deviations and genotypic values (GV) by stochastic simulation. These submodels differ in the way the dependency between additive and dominance effects is modelled. Depending on the marker panel, the inclusion of dominance effects increased the accuracy of GV by about 17% and the accuracy of genomic BV by 2% in the offspring. Furthermore, it slowed down the decrease of the accuracies in subsequent generations. It was possible to obtain accurate estimates of GV, which enables mate selection programmes.

Genomic selection using low density marker panels with application to a sire line in pigs

BackgroundGenomic selection has become a standard tool in dairy cattle breeding. However, for other animal species, implementation of this technology is hindered by the high cost of genotyping. One way to reduce the routine costs is to genotype selection candidates with an SNP (single nucleotide polymorphism) panel of reduced density. This strategy is investigated in the present paper. Methods are proposed for the approximation of SNP positions, for selection of SNPs to be included in the low-density panel, for genotype imputation, and for the estimation of the accuracy of genomic breeding values. The imputation method was developed for a situation in which selection candidates are genotyped with an SNP panel of reduced density but have high-density genotyped sires. The dams of selection candidates are not genotyped. The methods were applied to a sire line pig population with 895 German Piétrain boars genotyped with the PorcineSNP60 BeadChip.ResultsGenotype imputation error rates were 0.133 for a 384 marker panel, 0.079 for a 768 marker panel, and 0.022 for a 3000 marker panel. Error rates for markers with approximated positions were slightly larger. Availability of high-density genotypes for close relatives of the selection candidates reduced the imputation error rate. The estimated decrease in the accuracy of genomic breeding values due to imputation errors was 3% for the 384 marker panel and negligible for larger panels, provided that at least one parent of the selection candidates was genotyped at high-density.Genomic breeding values predicted from deregressed breeding values with low reliabilities were more strongly correlated with the estimated BLUP breeding values than with the true breeding values. This was not the case when a shortened pedigree was used to predict BLUP breeding values, in which the parents of the individuals genotyped at high-density were considered unknown.ConclusionsGenomic selection with imputation from very low- to high-density marker panels is a promising strategy for the implementation of genomic selection at acceptable costs. A panel size of 384 markers can be recommended for selection candidates of a pig breeding program if at least one parent is genotyped at high-density, but this appears to be the lower bound.

The contribution of dominance to the understanding of quantitative genetic variation

Knowledge of the genetic architecture of a quantitative trait is useful to adjust methods for the prediction of genomic breeding values and to discover the extent to which common assumptions in quantitative trait locus (QTL) mapping experiments and breeding value estimation are violated. It also affects our ability to predict the long-term response of selection. In this paper, we focus on additive and dominance effects of QTL. We derive formulae that can be used to estimate the number of QTLs that affect a quantitative trait and parameters of the distribution of their additive and dominance effects from variance components, inbreeding depression and results from QTL mapping experiments. It is shown that a lower bound for the number of QTLs depends on the ratio of squared inbreeding depression to dominance variance. That is, high inbreeding depression must be due to a sufficient number of QTLs because otherwise the dominance variance would exceed the true value. Moreover, the second moment of the dominance coefficient depends only on the ratio of dominance variance to additive variance and on the dependency between additive effects and dominance coefficients. This has implications on the relative frequency of overdominant alleles. It is also demonstrated how the expected number of large QTLs determines the shape of the distribution of additive effects. The formulae are applied to milk yield and productive life in Holstein cattle. Possible sources for a potential bias of the results are discussed.

Genome-wide association analysis for growth, muscularity and meat quality in Piétrain pigs

Improvement in growth and meat quality is one of the main objectives in sire line pig breeding programmes. Mapping quantitative trait loci for these traits using experimental crosses and a linkage-based approach has been performed frequently in the past. The Piétrain breed often was involved as a founder breed to establish the experimental crosses. This breed was selected for muscularity and leanness but shows relatively poor meat quality. It is frequently used as a sire line breed. With the advent of genome-wide and dense SNP chips in pig genomic research, it is possible to also conduct genome-wide association studies within the Piétrain breed. In this study, around 500 progeny-tested sires were genotyped with 60k SNPs. Data filtering showed that around 48k SNPs were useable in this sample. These SNPs were used to conduct a genome-wide association study for growth, muscularity and meat quality traits. Because it is known that a mutation in the RYR1 gene located on chromosome 6 shows a major effect on meat quality, this mutation was included in the models. Single-marker and multimarker association analyses were performed. The results revealed between zero and eight significant associations per trait with P < 5 × 10(-5) . Of special interest are SNPs located on SSC6, SSC10 and SSC15.

Optimum contribution selection for conserved populations with historic migration

BackgroundIn recent decades, local varieties of domesticated animal species have been frequently crossed with economically superior breeds which has resulted in considerable genetic contributions from migrants. Optimum contribution selection by maximizing gene diversity while constraining breeding values of the offspring or vice versa could eventually lead to the extinction of local breeds with historic migration because maximization of gene diversity or breeding values would be achieved by maximization of migrant contributions. Therefore, other objective functions are needed for these breeds.ResultsDifferent objective functions and side constraints were compared with respect to their ability to reduce migrant contributions, to increase the genome equivalents originating from native founders, and to conserve gene diversity. Additionally, a new method for monitoring the development of effective size for breeds with incomplete pedigree records was applied. Approaches were compared for Vorderwald cattle, Hinterwald cattle, and Limpurg cattle. Migrant contributions could be substantially decreased for these three breeds, but the potential to increase the native genome equivalents is limited.ConclusionsThe most promising approach was constraining migrant contributions while maximizing the conditional probability that two alleles randomly chosen from the offspring population are not identical by descent, given that both descend from native founders.

Using genome-wide association analysis to characterize environmental sensitivity of milk traits in dairy cattle.

Genotype-by-environment interaction (GxE) has been widely reported in dairy cattle. One way to analyze GxE is to apply reaction norm models. The first derivative of a reaction norm is the environmental sensitivity (ES). In the present study we conducted a large-scale, genome-wide association analysis to identify single-nucleotide polymorphisms (SNPs) that affect general production (GP) and ES of milk traits in the German Holstein population. Sire estimates for GP and for ES were calculated from approximately 13 million daughter records by the use of linear reaction norm models. The daughters were offspring from 2297 sires. Sires were genotyped for 54k SNPs. The environment was defined as the average milk energy yield performance of the herds at the time during which the daughter observations were recorded. The sire estimates were used as observations in a genome-wide association analysis, using 1797 sires. Significant SNPs were confirmed in an independent validation set (500 sires of the same population). To separate GxE scaling and other GxE effects, the observations were log-transformed in some analyses. Results from the reaction norm model revealed GxE effects. Numerous significant SNPs were validated for both GP and ES. Many SNPs that affect GP also affect ES. We showed that ES of milk traits is a typical quantitative trait, genetically controlled by many genes with small effects and few genes with larger effect. A log-transformation of the observation resulted in a reduced number of validated SNPs for ES, pointing to genes that not only caused scaling GxE effects. The results will have implications for breeding for robustness in dairy cattle.

Application of a Bayesian dominance model improves power in quantitative trait genome-wide association analysis

AbstractBackgroundMulti-marker methods, which fit all markers simultaneously, were originally tailored for genomic selection purposes, but have proven to be useful also in association analyses, especially the so-called BayesC Bayesian methods. In a recent study, BayesD extended BayesC towards accounting for dominance effects and improved prediction accuracy and persistence in genomic selection. The current study investigated the power and precision of BayesC and BayesD in genome-wide association studies by means of stochastic simulations and applied these methods to a dairy cattle dataset. MethodsThe simulation protocol was designed to mimic the genetic architecture of quantitative traits as realistically as possible. Special emphasis was put on the joint distribution of the additive and dominance effects of causative mutations. Additive marker effects were estimated by BayesC and additive and dominance effects by BayesD. The dependencies between additive and dominance effects were modelled in BayesD by choosing appropriate priors. A sliding-window approach was used. For each window, the R. Fernando window posterior probability of association was calculated and this was used for inference purpose. The power to map segregating causal effects and the mapping precision were assessed for various marker densities up to full sequence information and various window sizes.ResultsPower to map a QTL increased with higher marker densities and larger window sizes. This held true for both methods. Method BayesD had improved power compared to BayesC. The increase in power was between −2 and 8% for causative genes that explained more than 2.5% of the genetic variance. In addition, inspection of the estimates of genomic window dominance variance allowed for inference about the magnitude of dominance at significant associations, which remains hidden in BayesC analysis. Mapping precision was not substantially improved by BayesD.ConclusionsBayesD improved power, but precision only slightly. Application of BayesD needs large datasets with genotypes and own performance records as phenotypes. Given the current efforts to establish cow reference populations in dairy cattle genomic selection schemes, such datasets are expected to be soon available, which will enable the application of BayesD for association mapping and genomic prediction purposes.

Host Genome Influence on Gut Microbial Composition and Microbial Prediction of Complex Traits in Pigs

The aim of the present study was to analyze the interplay between gastrointestinal tract (GIT) microbiota, host genetics, and complex traits in pigs using extended quantitative-genetic methods. The study design consisted of 207 pigs that were housed and slaughtered under standardized conditions, and phenotyped for daily gain, feed intake, and feed conversion rate. The pigs were genotyped with a standard 60 K SNP chip. The GIT microbiota composition was analyzed by 16S rRNA gene amplicon sequencing technology. Eight from 49 investigated bacteria genera showed a significant narrow sense host heritability, ranging from 0.32 to 0.57. Microbial mixed linear models were applied to estimate the microbiota variance for each complex trait. The fraction of phenotypic variance explained by the microbial variance was 0.28, 0.21, and 0.16 for daily gain, feed conversion, and feed intake, respectively. The SNP data and the microbiota composition were used to predict the complex traits using genomic best linear unbiased prediction (G-BLUP) and microbial best linear unbiased prediction (M-BLUP) methods, respectively. The prediction accuracies of G-BLUP were 0.35, 0.23, and 0.20 for daily gain, feed conversion, and feed intake, respectively. The corresponding prediction accuracies of M-BLUP were 0.41, 0.33, and 0.33. Thus, in addition to SNP data, microbiota abundances are an informative source of complex trait predictions. Since the pig is a well-suited animal for modeling the human digestive tract, M-BLUP, in addition to G-BLUP, might be beneficial for predicting human predispositions to some diseases, and, consequently, for preventative and personalized medicine.

Short communication: Importance of introgression for milk traits in the German Vorderwald and Hinterwald cattle

The subject of the present study was to analyze the influence of genetic introgression on milk yield performance of the German local Vorderwald and Hinterwald cattle breeds. Deviations of milk yield, fat yield, and protein yield of cows as well as pedigree information were analyzed. A sire model was used to estimate genetic trend and effects of the migrant breeds. Migrant contributions to Vorderwald cattle were high and have been rising even in the recent past. The effects of these breeds on milk yield performance were positive. Montbéliarde cattle not only had the largest effect on milk production of Vorderwald cattle but also the highest genetic contribution to this breed. Genetic introgression with Montbéliarde continued until recently. This suggests that introgression of high-yielding breeds is still a preferred method for genetic improvement of local breeds, even though it diminishes their value for conservation. Hence, the current population management has too little focus on the preservation of genetic uniqueness. In comparison, migrant breed contributions to the Hinterwald cattle, a breed with a unique phenotype and an own niche, were moderate and almost constant over the time. For the Hinterwald cattle, no significant effect of migrant breeds could be detected, which suggests that population management has different priorities in different endangered breeds. We conclude that not only the registration of animals from local breeds but also the breeding programs themselves should be supported and need to be controlled.

The contribution of migrant breeds to the genetic gain of beef traits of German Vorderwald and Hinterwald cattle

During the past decades, migrant contributions have accumulated in many local breeds. Cross-breeding was carried out to mitigate the risk of inbreeding depression and to improve the performance of local breeds. However, breeding activities for local breeds were not as intensive and target oriented as for popular high-yielding breeds. Therefore, even if performance improved, the gap between the performance of local and popular breeds increased for many traits. Furthermore, the genetic originality of local breeds declined due to the increasing contributions of migrant breeds. This study examined the importance of migrant breed influences for the realization of breeding progress of beef traits of German Vorderwald and Hinterwald cattle. The results show that there is a high amount of migrant contributions and their effects on performance are substantial for most traits. The effect of the French cattle breed Montbéliard (p-value 0.014) on daily gain of Vorderwald bulls at test station was positive. The effects of Vorderwald ancestors (p-value for daily gain 0.007 and p-value for net gain 0.004) were positive for both traits under consideration in the population of Hinterwald cattle. Additionally, the effect of remaining breeds (p-value 0.030) on net gain of Hinterwald cattle in the field was also positive. The estimated effect of Fleckvieh ancestors on net gain of Hinterwald cattle was even larger but not significant. Breeding values adjusted for the effects of the migrant breeds showed little genetic trend.