Dörte Wittenburg

Including non-additive genetic effects in Bayesian methods for the prediction of genetic values based on genome-wide markers

BackgroundMolecular marker information is a common source to draw inferences about the relationship between genetic and phenotypic variation. Genetic effects are often modelled as additively acting marker allele effects. The true mode of biological action can, of course, be different from this plain assumption. One possibility to better understand the genetic architecture of complex traits is to include intra-locus (dominance) and inter-locus (epistasis) interaction of alleles as well as the additive genetic effects when fitting a model to a trait. Several Bayesian MCMC approaches exist for the genome-wide estimation of genetic effects with high accuracy of genetic value prediction. Including pairwise interaction for thousands of loci would probably go beyond the scope of such a sampling algorithm because then millions of effects are to be estimated simultaneously leading to months of computation time. Alternative solving strategies are required when epistasis is studied.MethodsWe extended a fast Bayesian method (fBayesB), which was previously proposed for a purely additive model, to include non-additive effects. The fBayesB approach was used to estimate genetic effects on the basis of simulated datasets. Different scenarios were simulated to study the loss of accuracy of prediction, if epistatic effects were not simulated but modelled and vice versa.ResultsIf 23 QTL were simulated to cause additive and dominance effects, both fBayesB and a conventional MCMC sampler BayesB yielded similar results in terms of accuracy of genetic value prediction and bias of variance component estimation based on a model including additive and dominance effects. Applying fBayesB to data with epistasis, accuracy could be improved by 5% when all pairwise interactions were modelled as well. The accuracy decreased more than 20% if genetic variation was spread over 230 QTL. In this scenario, accuracy based on modelling only additive and dominance effects was generally superior to that of the complex model including epistatic effects.ConclusionsThis simulation study showed that the fBayesB approach is convenient for genetic value prediction. Jointly estimating additive and non-additive effects (especially dominance) has reasonable impact on the accuracy of prediction and the proportion of genetic variation assigned to the additive genetic source.

Investigating associations between milk metabolite profiles and milk traits of Holstein cows

In the field of dairy cattle research, it is of great interest to improve the detection and prevention of diseases (e.g., mastitis and ketosis) and monitor specific traits related to the state of health and management. During the standard milk performance test, traditional milk traits are monitored, and quality and quantity are screened. In addition to the standard test, it is also now possible to analyze milk metabolites in a high-throughput manner and to consider them in connection with milk traits to identify functionally important metabolites that can also serve as biomarker candidates. We present a study in which 190 milk metabolites and 14 milk traits of 1,305 Holstein cows on 18 commercial farms were investigated to characterize interrelations of milk metabolites between each other, to milk traits from the milk standard performance test, and to influencing factors such as farm and sire effect (half-sib structure). The effect of influencing factors (e.g., farm) varied among metabolites and traditional milk traits. The investigations of associations between metabolites and milk traits revealed groups of metabolites that show, for example, positive correlations to protein and casein, and negative correlations to lactose and pH. On the other hand, groups of metabolites jointly associated with the investigated milk traits can be identified and functionally discussed. To enable a multivariate investigation, 2 machine learning methods were applied to detect important metabolites that are highly correlated with the investigated traditional milk traits. For somatic cell score, uracil, lactic acid, and 9 other important metabolites were detected. Lactic acid has already been proposed as a biomarker candidate for mastitis in the recent literature. In conclusion, we found sets of metabolites eligible to predict milk traits, enabling the analysis of milk traits from a metabolic perspective and discussion of the possible functional background for some of the detected associations.

Milk metabolites and their genetic variability

The composition of milk is crucial to evaluate milk performance and quality measures. Milk components partly contribute to breeding scores, and they can be assessed to judge metabolic and energy status of the cow as well as to serve as predictive markers for diseases. In addition to the milk composition measures (e.g., fat, protein, lactose) traditionally recorded during milk performance test via infrared spectroscopy, novel techniques, such as gas chromatography-mass spectrometry, allow for a further analysis of milk into its metabolic components. Gas chromatography-mass spectrometry is suitable for measuring several hundred metabolites with high throughput, and thus it is applicable to study sources of genetic and nongenetic variation of milk metabolites in dairy cows. Heritability and mode of inheritance of metabolite measurements were studied in a linear mixed model approach including expected (pedigree) and realized (genomic) relationship between animals. The genetic variability of 190 milk metabolite intensities was analyzed from 1,295 cows held on 18 farms in Mecklenburg-Western Pomerania, Germany. Besides extensive pedigree information, genotypic data comprising 37,180 single nucleotide polymorphism markers were available. Goodness of fit and significance of genetic variance components based on likelihood ratio tests were investigated with a full model, including marker- and pedigree-based genetic effects. Broad-sense heritability varied from zero to 0.699, with a median of 0.125. Significant additive genetic variance was observed for highly heritable metabolites, but dominance variance was not significantly present. As some metabolites are particularly favorable for human nutrition, for instance, future research should address the identification of locus-specific genetic effects and investigate metabolites as the molecular basis of traditional milk performance test traits.

Analysis of birth weight variability in pigs with respect to liveborn and total born offspring

Reduction in the variability of piglet birth weight within litter and increased piglet survival are key objective in schemes aiming to improve sow prolificacy. In previous studies, variation in birth weight was described by the sample standard deviation of birth weights within one litter, and the genetic impact has been proved. In this study, we additionally considered the sex effect on piglets birth weight and on its variability. The sample variance of birth weights per litter separated by sex was assigned as a trait of the sow. Different transformations of the trait were fitted by linear and generalized linear mixed models. Based on 1111 litters from Landrace sows, the estimates of heritability for the different measures ranged from 11 to 12%. We analysed the influence of including birth weight of stillborn piglets on the variability of birth weight within litter. With omitted stillborns, the heritability was estimated approximately 2% higher than that in investigations of all born piglets, and the impact of sex on birth weight variability was increased. Because the proportion of intrapartum deaths is rather high, it is recommended to consider the total number of piglets born per litter when analysing birth weight variation.

Genomic additive and dominance variance of milk performance traits

Milk performance traits are likely influenced by both additive and non-additive (e.g. dominance) genetic effects. Genetic variation can be partitioned using genomic information. The objective of this study was to estimate genetic variance components of production and milk component traits (e.g. acetone, fatty acids), which are particularly important for milk processing or which can provide information on the health status of cows. A genomic relationship approach was applied to phenotypic and genetic information of 1295 Holstein cows for estimating additive genetic and dominance variance components. Most of the 17 investigated traits were mainly affected by additive genetic effects, but protein content and casein content also showed a significant contribution of dominance. The ratio of dominance to additive variance was estimated as 0.64 for protein content and 0.56 for casein content. This ratio was highest for SCS (1.36) although dominance was not significant. Dominance effects were negligible in other moderately heritable milk traits.

Integrating milk metabolite profile information for the prediction of traditional milk traits based on SNP information for Holstein cows.

In this study the benefit of metabolome level analysis for the prediction of genetic value of three traditional milk traits was investigated. Our proposed approach consists of three steps: First, milk metabolite profiles are used to predict three traditional milk traits of 1,305 Holstein cows. Two regression methods, both enabling variable selection, are applied to identify important milk metabolites in this step. Second, the prediction of these important milk metabolite from single nucleotide polymorphisms (SNPs) enables the detection of SNPs with significant genetic effects. Finally, these SNPs are used to predict milk traits. The observed precision of predicted genetic values was compared to the results observed for the classical genotype-phenotype prediction using all SNPs or a reduced SNP subset (reduced classical approach). To enable a comparison between SNP subsets, a special invariable evaluation design was implemented. SNPs close to or within known quantitative trait loci (QTL) were determined. This enabled us to determine if detected important SNP subsets were enriched in these regions. The results show that our approach can lead to genetic value prediction, but requires less than 1% of the total amount of (40,317) SNPs., significantly more important SNPs in known QTL regions were detected using our approach compared to the reduced classical approach. Concluding, our approach allows a deeper insight into the associations between the different levels of the genotype-phenotype map (genotype-metabolome, metabolome-phenotype, genotype-phenotype).

Statistical tools to detect genetic variation for a sex dimorphism in piglet birth weight.

Sex differences in birth weight contribute to within-litter variability, which itself is connected to piglet survival. Therefore, we studied whether the sex difference in piglet birth weight is a genetically variable sex dimorphism. For that purpose a linear mixed model including sex-specific additive genetic effects was set up. A hypothesis testing problem was defined to detect whether these genetic effects significantly differ between sexes. In a second step, the effect of sex-linked genes was studied explicitly by partitioning the additive genetic effects into autosomal and gonosomal effects. Furthermore, a definition of heritability for the sex difference of a randomly chosen pair of littermates with opposite sex was given. The proposed models were applied separately to a Landrace and Large White data set. Significant genetic variability for the sex dimorphism was found in Landrace (P = 0.03) but not in Large White (P = 0.10). Heritability estimates were at 3 to 5% depending on the model. The X-chromosomal genetic variation was not significant (P > 0.18) at all, whereas the Y-chromosome significantly (P < 0.01) contributed to the genetic variation in Landrace with a corresponding SD of 34 g. It can be concluded that the sex dimorphism of piglet birth weight is genetically variable and a potential target of genetic improvement.

Analyses of conformational performance differentiation among functional breeding goals in the Menorca horse breed

Abstract. Phenotypic variation can partly be explained by genetic variation, such as variation in single nucleotide polymorphism (SNP) genotypes. Genomic selection methods seek to predict genetic values (breeding values) based on SNP genotypes. To develop and to optimize these methods, simulated data are often used, which follow a rather simple genotype-phenotype map. Is the conventional approach for data simulation in this field an appropriate basis to optimize such methods in view of experimental data? Here, we present an alternative approach, striving to simulate more realistic data based on a genotype-phenotype map which includes a simulated metabolome level. This level was used to simulate genetic values, implicitly including additive and non-additive genetic effects, whereas in a conventional approach additive and dominance effects were explicitly simulated and assembled to genetic values. For both simulation approaches, different scenarios regarding numbers of quantitative trait loci (QTLs) and SNPs were analysed using fastBayesB as prediction method. We observed that our alternative map showed a smaller prediction precision (at least 3.75 %) compared to the conventional approach in all investigated scenarios. The observed degree of linearity is at least 94.12 % of the conventional approach or less. Additionally, we present results for different simulated data and experimental data to allow a comparison on a purely conceptual level. Concluding, simulating a more complex genotype-phenotype map including a molecular level, allows to study processing of variation from the genetic to the phenotype level in more detail and may prepare the ground for modern methods of genomic selection.Abstract. The endangered Menorca horse from the Balearic Islands (Spain) is selected for its economically viable traits, such as conformation and dressage performance, while maintaining the maximum possible genetic variability. The aim of this work was to describe the morphology of the Menorca horse and to analyse the conformational variation among the different performance aptitudes of the males (classical dressage, Menorca dressage and leisure) using 47 body measurements and 10 body indices. The data consists of 147 females and 200 males. The Menorca horse can be characterised as an animal of quadrangular format, slim figure with thin and long limbs, with the greatest values for height and length found in the males. The Duncan and principal component analysis with non-linear iterative partial least-squares algorithm discriminates the Menorca dressage group mainly by its differential hind limb angulations. Both the classical and Menorca dressage groups showed well-balanced body proportions for sport performance.

Covariance Between Genotypic Effects and its Use for Genomic Inference in Half-Sib Families

In livestock, current statistical approaches utilize extensive molecular data, e.g., single nucleotide polymorphisms (SNPs), to improve the genetic evaluation of individuals. The number of model parameters increases with the number of SNPs, so the multicollinearity between covariates can affect the results obtained using whole genome regression methods. In this study, dependencies between SNPs due to linkage and linkage disequilibrium among the chromosome segments were explicitly considered in methods used to estimate the effects of SNPs. The population structure affects the extent of such dependencies, so the covariance among SNP genotypes was derived for half-sib families, which are typical in livestock populations. Conditional on the SNP haplotypes of the common parent (sire), the theoretical covariance was determined using the haplotype frequencies of the population from which the individual parent (dam) was derived. The resulting covariance matrix was included in a statistical model for a trait of interest, and this covariance matrix was then used to specify prior assumptions for SNP effects in a Bayesian framework. The approach was applied to one family in simulated scenarios (few and many quantitative trait loci) and using semireal data obtained from dairy cattle to identify genome segments that affect performance traits, as well as to investigate the impact on predictive ability. Compared with a method that does not explicitly consider any of the relationship among predictor variables, the accuracy of genetic value prediction was improved by 10–22%. The results show that the inclusion of dependence is particularly important for genomic inference based on small sample sizes.

Expression variation of the porcine ADRB2 has a complex genetic background

Porcine adrenergic receptor beta 2 (ADRB2) gene exhibits differential allelic expression in skeletal muscle, and its genetic variation has been associated with muscle pH. Exploring the molecular–genetic background of expression variation for porcine ADRB2 will provide insight into the mechanisms driving its regulatory divergence and may also contribute to unraveling the genetic basis of muscle-related traits in pigs. In the present study, we therefore examined haplotype effects on the expression of porcine ADRB2 in four tissues: longissimus dorsi muscle, liver, subcutaneous fat, and spleen. The diversity and structure of haplotypes of the proximal gene region segregating in German commercial breeds were characterized. Seven haplotypes falling into three clades were identified. Two clades including five haplotypes most likely originated from introgression of Asian genetics during formation of modern breeds. Expression analyses revealed that the Asian-derived haplotypes increase expression of the porcine ADRB2 compared to the major, wild-type haplotype independently of tissue type. In addition, several tissue-specific differences in the expression of the Asian-derived haplotypes were found. Inspection of haplotype sequences showed that differentially expressed haplotypes exhibit polymorphisms in a polyguanine tract located in the core promoter region. These findings demonstrate that expression variation of the porcine ADRB2 has a complex genetic basis and suggest that the promoter polyguanine tract is causally involved. This study highlights the challenges of finding causal genetic variants underlying complex traits.