Just Jensen

Genotype by Environment Interaction in Nordic Dairy Cattle Studied Using Reaction Norms

Genotype by environment interaction for production and fertility was studied by use of a reaction norm model. Milk recording data, comprising 927 929 records, were analysed to predict reaction norms for young bulls of the Nordic Red dairy breeds. Random regressions were estimated for each bull, regressing phenotypic values of daughters on herd environment. The phenotypic measures were 305 days kg protein production and days open in first lactation. The herd environment was defined as the herd-year average of protein production and days open, respectively. Heritability of protein production and days open and genetic correlation between the two traits were estimated as functions of the herd environment. The results showed that the genetic parameters change over environments, which are measured on a continuous scale across countries. Grouping of observations is avoided and thereby the problem of genetic connectedness between groups or countries may be avoided. Although significant genetic variation for the slope of the reaction norm was found, there was little reranking of sires, except between extreme environments. More appropriate models and methods need to be developed for further studies of genetic variation in reaction norms.

Multivariate Bayesian analysis of Gaussian, right censored Gaussian, ordered categorical and binary traits using Gibbs sampling

A fully Bayesian analysis using Gibbs sampling and data augmentation in a multivariate model of Gaussian, right censored, and grouped Gaussian traits is described. The grouped Gaussian traits are either ordered categorical traits (with more than two categories) or binary traits, where the grouping is determined via thresholds on the underlying Gaussian scale, the liability scale. Allowances are made for unequal models, unknown covariance matrices and missing data. Having outlined the theory, strategies for implementation are reviewed. These include joint sampling of location parameters; efficient sampling from the fully conditional posterior distribution of augmented data, a multivariate truncated normal distribution; and sampling from the conditional inverse Wishart distribution, the fully conditional posterior distribution of the residual covariance matrix. Finally, a simulated dataset was analysed to illustrate the methodology. This paper concentrates on a model where residuals associated with liabilities of the binary traits are assumed to be independent. A Bayesian analysis using Gibbs sampling is outlined for the model where this assumption is relaxed.

Influence of breed, parity, and stage of lactation on lactational performance and relationship between body fatness and live weight

The aim of this study was to characterise the effects of genotype, parity and nutrition on performance and the relationship between body condition and body weight. A total of 657 lactations from 322 cows were used. Three breeds were used, Danish Holstein, Danish Red and Jersey. Each breed was subdivided into two lines selected to differ in milk yield. Within line cows were randomly assigned to either a normal or low energy density total mixed ration. Three 1-week periods representing early lactation, peak milk yield and late lactation were identified for the analyses. For the analysis of the relationship between body weight and condition score, the dry period was also considered. There were significant effects of breed on all performance measures but no effect of line (with the exception of condition score). Cows fed the normal energy density diet had higher milk yield, fat percentage and condition score and weighed more than cows on the low feeding treatment. There was a highly significant relationship between body weight and condition score. In all periods except the dry period, there were significant effects of breed (P<0.001) and parity (P<0.05) on the intercept of the relation between body weight and condition score. However, there was no significant effect of breed or parity on the slope of the relationship between body weight and condition score.

Genome-Wide Association Study Reveals Genetic Architecture of Eating Behavior in Pigs and Its Implications for Humans Obesity by Comparative Mapping

This study was aimed at identifying genomic regions controlling feeding behavior in Danish Duroc boars and its potential implications for eating behavior in humans. Data regarding individual daily feed intake (DFI), total daily time spent in feeder (TPD), number of daily visits to feeder (NVD), average duration of each visit (TPV), mean feed intake per visit (FPV) and mean feed intake rate (FR) were available for 1130 boars. All boars were genotyped using the Illumina Porcine SNP60 BeadChip. The association analyses were performed using the GenABEL package in the R program. Sixteen SNPs were found to have moderate genome-wide significance (p<5E-05) and 76 SNPs had suggestive (p<5E-04) association with feeding behavior traits. MSI2 gene on chromosome (SSC) 14 was very strongly associated with NVD. Thirty-six SNPs were located in genome regions where QTLs have previously been reported for behavior and/or feed intake traits in pigs. The regions: 64–65 Mb on SSC 1, 124–130 Mb on SSC 8, 63–68 Mb on SSC 11, 32–39 Mb and 59–60 Mb on SSC 12 harbored several signifcant SNPs. Synapse genes (GABRR2, PPP1R9B, SYT1, GABRR1, CADPS2, DLGAP2 and GOPC), dephosphorylation genes (PPM1E, DAPP1, PTPN18, PTPRZ1, PTPN4, MTMR4 and RNGTT) and positive regulation of peptide secretion genes (GHRH, NNAT and TCF7L2) were highly significantly associated with feeding behavior traits. This is the first GWAS to identify genetic variants and biological mechanisms for eating behavior in pigs and these results are important for genetic improvement of pig feed efficiency. We have also conducted pig-human comparative gene mapping to reveal key genomic regions and/or genes on the human genome that may influence eating behavior in human beings and consequently affect the development of obesity and metabolic syndrome. This is the first translational genomics study of its kind to report potential candidate genes for eating behavior in humans.

Partitioning additive genetic variance into genomic and remaining polygenic components for complex traits in dairy cattle

BackgroundLow cost genotyping of individuals using high density genomic markers were recently introduced as genomic selection in genetic improvement programs in dairy cattle. Most implementations of genomic selection only use marker information, in the models used for prediction of genetic merit. However, in other species it has been shown that only a fraction of the total genetic variance can be explained by markers. Using 5217 bulls in the Nordic Holstein population that were genotyped and had genetic evaluations based on progeny, we partitioned the total additive genetic variance into a genomic component explained by markers and a remaining component explained by familial relationships. The traits analyzed were production and fitness related traits in dairy cattle. Furthermore, we estimated the genomic variance that can be attributed to individual chromosomes and we illustrate methods that can predict the amount of additive genetic variance that can be explained by sets of markers with different density.ResultsThe amount of additive genetic variance that can be explained by markers was estimated by an analysis of the matrix of genomic relationships. For the traits in the analysis, most of the additive genetic variance can be explained by 44 K informative SNP markers. The same amount of variance can be attributed to individual chromosomes but surprisingly the relation between chromosomal variance and chromosome length was weak. In models including both genomic (marker) and familial (pedigree) effects most (on average 77.2%) of total additive genetic variance was explained by genomic effects while the remaining was explained by familial relationships.ConclusionsMost of the additive genetic variance for the traits in the Nordic Holstein population can be explained using 44 K informative SNP markers. By analyzing the genomic relationship matrix it is possible to predict the amount of additive genetic variance that can be explained by a reduced (or increased) set of markers. For the population analyzed the improvement of genomic prediction by increasing marker density beyond 44 K is limited.

A comparison of bivariate and univariate QTL mapping in livestock populations

This study presents a multivariate, variance component-based QTL mapping model implemented via restricted maximum likelihood (REML). The method was applied to investigate bivariate and univariate QTL mapping analyses, using simulated data. Specifically, we report results on the statistical power to detect a QTL and on the precision of parameter estimates using univariate and bivariate approaches. The model and methodology were also applied to study the effectiveness of partitioning the overall genetic correlation between two traits into a component due to many genes of small effect, and one due to the QTL. It is shown that when the QTL has a pleiotropic effect on two traits, a bivariate analysis leads to a higher statistical power of detecting the QTL and to a more precise estimate of the QTLs map position, in particular in the case when the QTL has a small effect on the trait. The increase in power is most marked in cases where the contributions of the QTL and of the polygenic components to the genetic correlation have opposite signs. The bivariate REML analysis can successfully partition the two components contributing to the genetic correlation between traits.

Genetic improvement of livestock for organic farming systems

Abstract Organic farming which experienced a constant rise over the last two decades is a system based on sustainability and on a concept tending towards functional integrity. Legislation as well as the wish to produce separately from conventional farming raise the question whether organic farming should be conducted completely apart from conventional farming or not. This paper discusses the aspects that affect animal breeding under these circumstances, e.g., maintaining genetic diversity by using local breeds and possible G×E interactions which might occur when breeds adapted to conventional farming systems are used in organic farming. Ways of modelling G×E are presented, moreover examples of G×E in dairy cattle, swine, and poultry are given. Trends in selection index theory—designing multi-trait breeding goals including functional traits on one hand, and developing methods for using customised selection indices on the other hand—support breeding work for organic farming systems. It is concluded that before the technical issues can be addressed, all parties involved, farmers, consumers as well as legislators, have to agree on the socio-cultural conditions under which organic farming should be conducted.

Genetic parameters for different measures of feed efficiency and related traits in boars of three pig breeds

Residual feed intake (RFI) is commonly used as a measure of feed efficiency at a given level of production. A total of 16,872 pigs with their pedigree traced back as far as possible was used to estimate genetic parameters for RFI, growth performance, food conversion ratio (FCR), body conformation, and feeding behavior traits in 3 Danish breeds [Duroc (DD), Landrace (LL), and Yorkshire (YY)]. Two measures of RFI were considered: residual feed intake 1 (RFI1) was calculated based on regression of daily feed intake (DFI) from 30 to 100 kg on initial test weight and ADG from 30 to 100 kg (ADG2). Residual feed intake 2 (RFI2) was as RFI1, except it was also regressed with respect to backfat (BF). The estimated heritabilities for RFI1 and RFI2 were 0.34 and 0.38 in DD, 0.34 and 0.36 in LL, and 0.39 and 0.40 in YY, respectively. The heritabilities ranged from 0.32 (DD) to 0.54 (LL) for ADG2, from 0.54 (DD) to 0.67 (LL) for BF, and from 0.13 (DD) to 0.19 (YY) for body conformation. Feeding behavior traits including DFI, number of visits to feeder per day (NVD), total time spent eating per day (TPD), feed intake rate (FR), feed intake per visit (FPV), and time spent eating per visit (TPV) were moderately to highly heritable. Residual feed intake 2 was genetically independent of ADG2 and BF in all breeds, except it had low genetic correlation to ADG2 in YY (0.2). Residual feed intake 1 was also genetically independent of ADG2 in DD and LL. Both RFI traits had strong genetic correlations with DFI (0.85 to 0.96) and FCR (0.76 to 0.99). They had low or no genetic correlations with feeding behavior traits. Unfavorable genetic correlations were found between ADG2 and both BF and DFI. Among feeding behavior traits, DFI had low genetic correlations to other traits in all breeds. High and negative genetic correlations were also found between TPD with FR (-0.79 in YY to -0.88 in DD), NVD, and TPD (-0.91 in DD to -0.94 in YY) and between NVD and FPV (-0.83 in DD to -0.91 in YY) in all breeds. The genetic trend for feed efficiency was favorable in all breeds regardless of the definition of feed efficiency used. In summary, RFI1 and RFI2 were heritable and selection for reduced RFI2 can be performed without adversely affecting ADG and BF and could replace FCR in the selection index for the Danish pig breeds. Selection could also be based on RFI1 for breeds with fewer concerns about a negative effect of BF or for breeds that do not have BF records.

Mixture model for inferring susceptibility to mastitis in dairy cattle: a procedure for likelihood-based inference

A Gaussian mixture model with a finite number of components and correlated random effects is described. The ultimate objective is to model somatic cell count information in dairy cattle and to develop criteria for genetic selection against mastitis, an important udder disease. Parameter estimation is by maximum likelihood or by an extension of restricted maximum likelihood. A Monte Carlo expectation-maximization algorithm is used for this purpose. The expectation step is carried out using Gibbs sampling, whereas the maximization step is deterministic. Ranking rules based on the conditional probability of membership in a putative group of uninfected animals, given the somatic cell information, are discussed. Several extensions of the model are suggested.

Genomic dissection and prediction of heading date in perennial ryegrass.

BackgroundGenomic selection (GS) has become a commonly used technology in animal breeding. In crops, it is expected to significantly improve the genetic gains per unit of time. So far, its implementation in plant breeding has been mainly investigated in species farmed as homogeneous varieties. Concerning crops farmed in family pools, only a few theoretical studies are currently available. Here, we test the opportunity to implement GS in breeding of perennial ryegrass, using real data from a forage breeding program. Heading date was chosen as a model trait, due to its high heritability and ease of assessment. Genome Wide Association analysis was performed to uncover the genetic architecture of the trait. Then, Genomic Prediction (GP) models were tested and prediction accuracy was compared to the one obtained in traditional Marker Assisted Selection (MAS) methods.ResultsSeveral markers were significantly associated with heading date, some locating within or proximal to genes with a well-established role in floral regulation. GP models gave very high accuracies, which were significantly better than those obtained through traditional MAS. Accuracies were higher when predictions were made from related families and from larger training populations, whereas predicting from unrelated families caused the variance of the estimated breeding values to be biased downwards.ConclusionsWe have demonstrated that there are good perspectives for GS implementation in perennial ryegrass breeding, and that problems resulting from low linkage disequilibrium (LD) can be reduced by the presence of structure and related families in the breeding population. While comprehensive Genome Wide Association analysis is difficult in species with extremely low LD, we did identify variants proximal to genes with a known role in flowering time (e.g. CONSTANS and Phytochrome C).