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Metabolomics is intermediate stage between genotype and phenotype, and therefore useful for breeding. Objective was to investigate heritabilities and accuracies of genetic evaluation of malting quality (MQ) traits by integrating both genomic and metabolomic information. In total, 2,430 plots of 562 malting spring barley lines from three years and two locations were included. Five MQ traits were measured in wort produced from each plot. Metabolomic features used were 24,018 Nuclear Magnetic Resonance intensities measured on each wort sample. Methods for statistical analyses were genomic best linear unbiased prediction (GBLUP) and metabolomic-genomic best linear unbiased prediction (MGBLUP). Accuracies of predicted breeding values were compared using two cross-validation strategies: leave-one-year-out (LOYO) and leave-one-line-out (LOLO). Plot heritabilities ranged from 0.060 to 0.279 for GBLUP, and increased to range from 0.123 to 0.283 for MGBLUP. LOLO scheme yielded higher accuracies than LOYO scheme, and MGBLUP yielded higher accuracies than GBLUP regardless of cross-validation scheme.Author SummaryMetabolomics is intermediate stage between genotype and phenotype, and therefore useful for breeding. We carried out a study on 2,430 plots of 562 malting spring barley lines and 24,018 Nuclear Magnetic Resonance intensities measured on each wort sample, to investigate the prediction of breeding values of five malting quality traits by integrating genomic and metabolomic information using a metabolomics-genomic model. Results showed an increase in prediction accuracy for this model compared to a genomic model.

Genomic evaluations for crossbred animals introduce challenges not encountered for purebred animals. We propose to include effects of local breed proportions (LBP) based on detected breed of origin of alleles in a genomic model for crossbred dairy cows. We tested different segment length for modelling the LBP: 1, 20, 100 and 500 markers, for genomic prediction based on data of first lactation protein yields of 5,176 Danish crossbred dairy cows with contribution from three dairy breeds. The models that included LBP had slightly higher crossvalidation predictive ability, 0.390 for all segment lengths compared with 0.385 for a model without LBP effects. The LBP models also had less dispersion bias. Inclusion of LBP in genomic predictions can therefore improve predictions even when considering the local effects in segments of hundreds of markers.

Genomic predictions have been applied for dairy cattle for more than a decade with great success, but genomic estimated breeding values (GEBV) are not widely available for crossbred dairy cows. The large reference populations already in place for genomic evaluations of many pure breeds makes it interesting to use the accurate solutions, in particular the estimated marker effects, from these evaluations for calculation of GEBV for crossbred heifers and cows. Effects of marker alleles in crossbred animals can depend on breed origin of the alleles (BOA). Therefore, our aim was to investigate if reliable GEBV for crossbred dairy cows can be obtained by combining estimated marker effects from purebred evaluations based on BOA. We used data on 5,467 Danish crossbred dairy cows with contributions from Holstein, Jersey, and Red Dairy Cattle breeds. We assessed BOA assignment on their genotypes and found that we could assign 99.3% of the alleles to a definite breed of origin. We compared GEBV for 2 traits, protein yield and interval between first and last insemination of cows, with 2 models that both combine estimated marker effects from the genomic evaluations of the pure breeds: a breed of origin model that accounts for BOA and a breed proportion model that only accounts for genomic breed proportions in the crossbred animals. We accounted for the difference in level between the purebred evaluations by including intercepts in the models based on phenotypic averages. The predictive ability for protein yield was significantly higher from the breed of origin model, 0.45 compared with 0.43 from the breed proportion model. Furthermore, for the breed proportion model, the GEBVs had level bias, which made comparison across groups with different breed composition skewed. We therefore concluded that reliable genomic predictions for crossbred dairy cows can be obtained by combining estimated marker effects from the genomic evaluations of purebreds using a model that accounts for BOA.

A recently developed single-step method allows genetic evaluation based on information from phenotypes, pedigree and markers simultaneously. This paper compared reliabilities of predicted breeding values obtained from single-step method and the traditional pedigree-based method for two litter size traits, total number of piglets born (TNB), and litter size at five days after birth (Ls 5) in Danish Landrace and Yorkshire pigs. The results showed that the single-step method combining phenotypic and genotypic information provided more accurate predictions than the pedigree-based method, not only for genotyped animals but also for non-genotyped animals.

In association mapping, haplotype-based methods are generally regarded to provide higher power and increased precision than methods based on single markers. For haplotype-based association mapping most studies use a fixed haplotype effect in the model. However, an increase in haplotype length increases the number of parameters in the model, resulting in low accuracy of the estimates especially for the low-frequency haplotypes. Modeling of haplotype effects can be improved if they are assumed to be random effects, as only one parameter i.e. haplotype variance needs to be estimated compared to estimating the effects of all different haplotypes in a fixed haplotype model. Using simulated data, we investigated statistical models where haplotypes were fitted either as a fixed or random effect and we compared them for the power, precision, and type I error. We investigated five haplotype lengths of 2, 4, 6, 10 and 20. The simulated data resembled the Danish Holstein cattle pedigree representing a complex relationship structure and QTL effects of different sizes were simulated. We observed that the random haplotype models had high power and very low type I error rates (after Bonferroni correction), while the fixed haplotype models had lower power and excessively high type I errors. A haplotype length of 4 to 6 gave the best results for random model in the present study. Though the present study was conducted on data structure more frequent in livestock, our findings on random vs. fixed haplotype effects in association mapping models are applicable to data from other species with similar pedigree structure

In the field of molecular evolution genome substitution models with neighbour dependent substitution rates have recently received much attention. It is well-known that substitution of nucleotides does not occur independently of neighbouring nucleotides, but there has been less focus on the phenomenon that this substitution process is also not time-reversible. In this paper I construct a pseudo-likelihood type method for inference in non-reversible substitution models with neighbour dependent substitution rates. I also construct an EM-algorithm for maximising the pseudo-likelihood. For human-mouse aligned sequence data a number of different models are investigated, where I show that strand-symmetric models are appropriate, and that overlapping di-nucleotide models do not fit the data well.

Dominance and imprinting effects which may be important for complex traits are usually ignored in the genomic prediction of complex traits in livestock. The objectives of this study were to estimate genetic parameters of additive, dominance and imprinting effects and assess genomic predictions considering dominance and imprinting effects for lean meat Percentage (LMP) and average daily gain (ADG) in Danish Duroc pigs.Corrected phenotypes of 8,113 genotyped pigs in both breeding and multiplier herds were used for the analysis. Four genomic BLUP models were compared with regard to prediction accuracy and bias: 1) additive model (GA), 2) additive and dominance model (GAD), 3) additive and imprinting model (GAI), 4) additive, dominance and imprinting model (GADI).Based on GADI model, the estimated heritabilities were 0.37 (LMP) and 0.21 (ADG). GADI resulted to the smallest estimates of additive, litter and residual variances, respectively, while GA led the largest. Dominance is significant and accounted for about 3% of the phenotypic variances while imprinting is not significant and accounted for less than 1% of the phenotypic variances. Among all the models, GADI led to the highest reliability (51.4%) of predicted total genetic values (GTV) for LMP, and GAD led to the highest (27.7%) for ADG. The most unbiased predictions of GTV were provided by GAD model.The results indicate that dominance effects significantly contribute to the genetic variation of LMP and ADG in Danish Duroc pigs, but imprinting effects are not. Models including dominance effects can improve accuracy and reduce bias of genomic predictions.

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A single-step method allows genetic evaluation using information of phenotypes, pedigree, and markers from genotyped and nongenotyped individuals simultaneously. This paper compared genomic predictions obtained from a single-step BLUP (SSBLUP) method, a genomic BLUP (GBLUP) method, a selection index blending (SELIND) method, and a traditional pedigree-based method (BLUP) for total number of piglets born (TNB), litter size at d 5 after birth (LS5), and mortality rate before d 5 (Mort; including stillbirth) in Danish Landrace and Yorkshire pigs. Data sets of 778,095 litters from 309,362 Landrace sows and 472,001 litters from 190,760 Yorkshire sows were used for the analysis. There were 332,795 Landrace and 207,255 Yorkshire animals in the pedigree data, among which 3,445 Landrace pigs (1,366 boars and 2,079 sows) and 3,372 Yorkshire pigs (1,241 boars and 2,131 sows) were genotyped with the Illumina PorcineSNP60 BeadChip. The results showed that the 3 methods with marker information (SSBLUP, GBLUP, and SELIND) produced more accurate predictions for genotyped animals than the pedigree-based method. For genotyped animals, the average of reliabilities for all traits in both breeds using traditional BLUP was 0.091, which increased to 0.171 when using GBLUP and to 0.179 when using SELIND and further increased to 0.209 when using SSBLUP. Furthermore, the average reliability of EBV for nongenotyped animals was increased from 0.091 for traditional BLUP to 0.105 for the SSBLUP. The results indicate that the SSBLUP is a good approach to practical genomic prediction of litter size and piglet mortality in Danish Landrace and Yorkshire populations.

We hypothesized that (i) non-genetic correlations between litter size (LS) and daily gain are different from zero, and (ii) modelling non-genetic correlations changes estimates of genetic (co)variances of the traits compared to when they are ignored. We tested these hypotheses using a new multi-trait model that separates genetic and non-genetic correlations by fitting correlations between the residual effects for one trait and non-residual effects of the other trait.We estimated variances by fitting models with and without these non-genetic correlations to LS and daily gain in DanBred Landrace pigs. We found that all non-genetic correlations between LS and daily gain were significantly different from zero. Estimates of genetic (co)variances changed significantly when non-genetic correlations were ignored. Clearly, multi-trait models with production traits and litter size traits should include correlations between residual effects for one trait and non-residual effects of the other trait to separate genetic correlations from non-genetic correlations.

The DMU-package for Analyzing Multivariate Mixed Models has been developed over a period of more than 25 years. This paper gives an overview of new features and the recent developments around the DMU-package, including: Genomic prediction (SNP-BLUP, G-BLUP and “One-Step”), Genome-wide association studies, Survival models and double hierarchical generalized linear mixed models