Jörn Bennewitz

Combined analysis of data from two granddaughter designs: A simple strategy for QTL confirmation and increasing experimental power in dairy cattle

A joint analysis of five paternal half-sib Holstein families that were part of two different granddaughter designs (ADR- or Inra-design) was carried out for five milk production traits and somatic cell score in order to conduct a QTL confirmation study and to increase the experimental power. Data were exchanged in a coded and standardised form. The combined data set (JOINT-design) consisted of on average 231 sires per grandsire. Genetic maps were calculated for 133 markers distributed over nine chromosomes. QTL analyses were performed separately for each design and each trait. The results revealed QTL for milk production on chromosome 14, for milk yield on chromosome 5, and for fat content on chromosome 19 in both the ADR- and the Inra-design (confirmed within this study). Some QTL could only be mapped in either the ADR- or in the Inra-design (not confirmed within this study). Additional QTL previously undetected in the single designs were mapped in the JOINT-design for fat yield (chromosome 19 and 26), protein yield (chromosome 26), protein content (chromosome 5), and somatic cell score (chromosome 2 and 19) with genomewide significance. This study demonstrated the potential benefits of a combined analysis of data from different granddaughter designs.

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.

A novel method for the estimation of the relative importance of breeds in order to conserve the total genetic variance

The need for conservation of farm animal genetic resources is widely accepted. A key question is the choice of breeds to be conserved. For this purpose, a core set of breeds was introduced in that the total genetic variance of a hypothetical quantitative trait was maximised (MVT core set). For each breed the relative contribution to the core set was estimated and the breeds were ranked for conservation priority according to their relative contribution. The method was based on average kinships between and within breeds and these can be estimated using genetic marker data. The method was compared to a recently published core set method that maximises the variance of a hypothetical population that could be obtained by interbreeding the conserved breeds (MVO core set). The results show that the MVT (MVO) core set favours breeds with a high (low) within breed kinship that are not related to other breeds. Following this, the MVT core set method suggests conserving breeds that show a large difference in the respective population mean of a hypothetical quantitative trait. This maximises the speed of achieving selection response for this hypothetical selection direction. Additionally, bootstrap based methods for the estimation of the breeds contribution to the core sets were introduced, substantially improving the accuracy of the contribution estimates.

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.

Genetic aspects regarding piglet losses and the maternal behaviour of sows. Part 1. Genetic analysis of piglet mortality and fertility traits in pigs.

In spite of the improvement in management and the breeding goal of increasing the number of piglets born alive, piglet mortality is still a substantial problem in pig breeding. The objective of the first part of the study was to estimate genetic parameters for different causes of piglet losses and to investigate the relationship to litter-size traits. Data were collected on a nucleus herd from January till December 2004. Records from 943 German Landrace sows with 1538 pure-bred litters and 13 971 individually weighted piglets were included. Four different causes of piglet losses (LOSS) were evaluated. Additional analysed traits were underweight and runting. Furthermore, the fertility traits number of piglets born alive, born in total and stillborn piglets as well as the individual birth and weaning weights were analysed. The different LOSS were treated as a binary trait and subsequently the heritabilities were estimated using a threshold model. The most important LOSS was crushing under the sow (12.4%). The survival rate and crushing had a heritability of h2 = 0.03. The fertility traits piglets born alive, born in total and stillborn piglets were analysed with a linear model and heritabilities rank from h2 = 0.05 (stillborn) to h2 = 0.10 (born alive). The estimated heritabilities for birth- and weaning weight were both h2 = 0.10. The genetic correlations between number of piglets born alive and each LOSS trait were analysed bivariately. Of all piglets born alive 84.3% survive the lactation period. Survival decreased with increasing litter size (rg = -0.54 up to -0.78) and the probability of being crushed under the sow increased.

Joint QTL analysis of three connected F2-crosses in pigs.

BackgroundNumerous QTL mapping resource populations are available in livestock species. Usually they are analysed separately, although the same founder breeds are often used. The aim of the present study was to show the strength of analysing F2-crosses jointly in pig breeding when the founder breeds of several F2-crosses are the same.MethodsThree porcine F2-crosses were generated from three founder breeds (i.e. Meishan, Pietrain and wild boar). The crosses were analysed jointly, using a flexible genetic model that estimated an additive QTL effect for each founder breed allele and a dominant QTL effect for each combination of alleles derived from different founder breeds. The following traits were analysed: daily gain, back fat and carcass weight. Substantial phenotypic variation was observed within and between crosses. Multiple QTL, multiple QTL alleles and imprinting effects were considered. The results were compared to those obtained when each cross was analysed separately.ResultsFor daily gain, back fat and carcass weight, 13, 15 and 16 QTL were found, respectively. For back fat, daily gain and carcass weight, respectively three, four, and five loci showed significant imprinting effects. The number of QTL mapped was much higher than when each design was analysed individually. Additionally, the test statistic plot along the chromosomes was much sharper leading to smaller QTL confidence intervals. In many cases, three QTL alleles were observed.ConclusionsThe present study showed the strength of analysing three connected F2-crosses jointly. In this experiment, statistical power was high because of the reduced number of estimated parameters and the large number of individuals. The applied model was flexible and was computationally fast.

Genomic breeding value estimation using nonparametric additive regression models

Genomic selection refers to the use of genomewide dense markers for breeding value estimation and subsequently for selection. The main challenge of genomic breeding value estimation is the estimation of many effects from a limited number of observations. Bayesian methods have been proposed to successfully cope with these challenges. As an alternative class of models, non- and semiparametric models were recently introduced. The present study investigated the ability of nonparametric additive regression models to predict genomic breeding values. The genotypes were modelled for each marker or pair of flanking markers (i.e. the predictors) separately. The nonparametric functions for the predictors were estimated simultaneously using additive model theory, applying a binomial kernel. The optimal degree of smoothing was determined by bootstrapping. A mutation-drift-balance simulation was carried out. The breeding values of the last generation (genotyped) was predicted using data from the next last generation (genotyped and phenotyped). The results show moderate to high accuracies of the predicted breeding values. A determination of predictor specific degree of smoothing increased the accuracy.

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.

The distribution of QTL additive and dominance effects in porcine F2 crosses

The present study used published quantitative trait loci (QTL) mapping data from three F2 crosses in pigs for 34 meat quality and carcass traits to derive the distribution of additive QTL effects as well as dominance coefficients. Dominance coefficients were calculated as the observed QTL dominance deviation divided by the absolute value of the observed QTL additive effect. The error variance of this ratio was approximated using the delta method. Mixtures of normal distributions (mixtures of normals) were fitted to the dominance coefficient using a modified EM-algorithm that considered the heterogeneous error variances of the data points. The results suggested clearly to fit one component which means that the dominance coefficients are normally distributed with an estimated mean (standard deviation) of 0.193 (0.312). For the additive effects mixtures of normals and a truncated exponential distribution were fitted. Two components were fitted by the mixtures of normals. The mixtures of normals did not predict enough QTL with small effects compared to the exponential distribution and to literature reports. The estimated rate parameter of the exponential distribution was 5.81 resulting in a mean effect of 0.172.

Need for sharp phenotypes in QTL detection for calving traits in dairy cattle.

The aim of the study was to investigate whether parity-specific phenotypes provide a clearer picture of quantitative trait loci (QTL) affecting calving traits in German Holsteins than breeding values estimated across parities. In experiment I, approximate daughter yield deviations were calculated by applying a univariate sire model assuming unrelated sires used as phenotypes in a QTL mapping study. These results were compared with those obtained using deregressed estimated breeding values obtained from the routine German sire evaluation (experiment II). In experiment I, 17 chromosome-wise significant QTL were found for the first parity, but only 12 for the second parity. Only three QTL for maternal stillbirth, located on BTA7, 15 and 23, showed an experiment-wise significance. Experiment II revealed 15 chromosome-wise significant QTL. The results differed markedly between first and second parity within experiment I, as well as between experiment I and II. The present study showed that parity-specific daughter yield deviations are beneficial for mapping QTL for calving traits. Furthermore, it is expected that the use of sharper phenotypes will also be advantageous for QTL fine mapping and the identification of candidate genes.