Norbert Reinsch

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.

A mammary gland EST showing linkage disequilibrium to a milk production QTL on bovine Chromosome 14

As part of a genome scan, ESTs derived from mammary gland tissue of a lactating cow were used as candidate genes for quantitative trait loci (QTL), affecting milk production traits. Resource families were genotyped with 247 microsatellite markers and 4 polymorphic ESTs. It was shown by linkage analysis that one of these ESTs, KIEL_E8, mapped to the centromeric region of bovine Chromosome (Chr) 14. Regression analysis revealed the presence of a QTL, with significant effect on milk production, in this chromosome region, and analysis of variance showed no significant interaction of marker genotype and family. The estimated significant differences between homozygous marker genotypes were 140 kg milk, −5.02 kg fat yield, and 2.58 kg protein yield for the first 100 days of lactation. Thus, there was strong evidence for a complete or nearly complete linkage disequilibrium between KIEL_E8 and the QTL. To identify the biological function of KIEL_E8, we extended the sequence for 869 bp by 5′-RACE. A 560-bp fragment of this shows a 90.9% similarity to a gene encoding a cysteine- and histidine-rich cytoplasmic protein in mouse. Although such a protein may have a regulatory function for lactation and a linkage disequilibrium between the EST marker and the QTL has been observed, it remains to be elucidated whether they are identical or not. Nevertheless, KIEL_E8 will be an efficient marker to perform marker-assisted selection in the Holstein-Friesian population.

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.

Development of a 44K SNP assay focussing on the analysis of a varroa‐specific defence behaviour in honey bees (Apis mellifera carnica)

Honey bees are exposed to a number of damaging pathogens and parasites. The most destructive among them, affecting mainly the brood, is Varroa destructor. A promising approach to prevent its spread is to breed for Varroa‐tolerant honey bees. A trait that has been shown to provide significant resistance against the Varroa mite is hygienic behaviour, a behavioural response of honey bee workers to brood diseases in general. This study reports the development of a 44K SNP assay, specifically designed for the analysis of hygienic behaviour of individual worker bees (Apis mellifera carnica) directed against V. destructor. Initially, 70 000 SNPs chosen from a large set of SNPs published by the Honey Bee Genome Project were validated for their suitability in the analysis of the Varroa resistance trait ‘uncapping of Varroa‐infested brood’. This was achieved by genotyping of pooled DNA samples of trait bearers and two trait‐negative controls using next‐generation sequencing. Approximately 36 000 of these validated SNPs and another 8000 SNPs not validated in this study were selected for the construction of a SNP assay. This assay will be employed in following experiments to analyse individualized DNA samples in order to identify quantitative trait loci (QTL) involved in the control of the investigated trait and to evaluate and possibly confirm QTL found in other studies. However, this assay is not just suitable to study Varroa tolerance, it is as well applicable to analyse any other trait in honey bees. In addition, because of its high density, this assay provides access into genomic selection with respect to several traits considered in honey bee breeding. It will become publicly available via AROS Applied Biotechnology AS, Aarhus, Denmark, before the end of the year 2011.

A whole genome scan for differences in recombination rates among three Bos taurus breeds

Abstract. Twenty paternal half-sib families of a granddaughter design were genotyped for 265 genetic markers, most of them microsatellites. These were 16 Holstein families, 3 Simmental families, and 1 Brown Swiss family. The number of sires per breed was 872, 170, and 32, respectively. Two-point recombination rates were estimated both jointly for all breeds and each single breed separately. Of 1168 marker intervals, 865 provided estimates for at least two breeds. Differences between breeds were tested by likelihood ratio tests. Four marker intervals, representing three genomic regions on BTA19, BTA24, and BTA27, show a significant impact of the breed at a false discovery rate of 0.23 and indicate a genetic component of observed heterogeneity of recombination. The variability of recombination rates between cattle breeds might not be a common feature of the whole genome, but rather might be restricted to certain chromosomal segments. Thus, attention should be paid to heterogeneities when pooling data of such regions from different breeds.

Expression profiling of a high-fertility mouse line by microarray analysis and qPCR

BackgroundIn a recent study it was demonstrated that a largely increased ovulation number is responsible for high prolificacy in two mouse lines selected for fertility performance. The objective of the present study was to identify genes that are involved in increasing the ovulation number in one of these lines, FL1. For differential expression profiling, ovaries of FL1 and of a non-selected control line, DUKsi, both lines derived from the same genetic pool, were analyzed with microarray analysis and quantitative polymerase chain reaction (qPCR). Ovaries from 30 animals of each line were collected at the metestrous stage, combined to 6 pools each, and processed for microarray analysis.ResultsThe actual number of ova shed in FL1 exceeded that of the DUKsi control line more than twofold (26.6 vs. 12.9). 148 differentially expressed ovarian transcripts could be identified, 74 of them up- and 74 down-regulated. Of these, 47 significantly mapped to specific Gene Ontology (GO) terms representing different biological processes as steroid metabolism, folliculogenesis, immune response, intracellular signal transduction (particularly of the G protein signaling cascade), regulation of transcription and translation, cell cycle and others. qPCR was used to re-evaluate selected transcripts and to estimate inter-individual variation of expression levels. These data significantly correlated with microarray data in 12 out of 15 selected transcripts but revealed partly large variations of expression levels between individuals.Conclusion(1) The abundance of numerous ovarian transcripts was significantly different in FL1 compared to the non-selected control line DUKsi thus suggesting that at least some of the respective genes and corresponding biological processes are involved in improving reproductive traits, particularly by increasing the number of ovulation. (2) Selective qPCR re-evaluation largely confirmed the microarray data and in addition demonstrated that sample pooling can be beneficial to find out group-specific expression profiles despite of large inter-individual variation. (3) The present data will substantially help ongoing genetic association studies to identify candidate genes and causative mutations responsible for increased fertility performance in mice.

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.

Quantitative trait loci mapping of calving and conformation traits on Bos taurus autosome 18 in the German Holstein population

Linkage, linkage disequilibrium, and combined linkage and linkage disequilibrium analyses were performed to map quantitative trait loci (QTL) affecting calving and conformation traits on Bos taurus autosome 18 (BTA18) in the German Holstein population. Six paternal half-sib families consisting of a total of 1,054 animals were genotyped on 28 genetic markers in the telomeric region on BTA18 spanning approximately 30 Mb. Calving traits, body type traits, and udder type traits were investigated. Using univariately estimated breeding values, maternal and direct effects on calving ease and stillbirth were analyzed separately for first- and further-parity calvings. The QTL initially identified by separate linkage and linkage disequilibrium analyses could be confirmed by a combined linkage and linkage disequilibrium analysis for udder composite index, udder depth, fore udder attachment, front teat placement, body depth, rump angle, and direct effects on calving ease and stillbirth. Concurrence of QTL peaks and a similar shape of restricted log-likelihood ratio profiles were observed between udder type traits and for body depth and calving traits, respectively. Association analyses were performed for markers flanking the most likely QTL positions by applying a mixed model including a fixed allele effect of the maternally inherited allele and a random polygenic effect. Results indicated that microsatellite marker DIK4234 (located at 53.3 Mb) is associated with maternal effects on stillbirth, direct effects on calving ease, and body depth. A comparison of effects for maternally inherited DIK4234 alleles indicated a favorable, positive correlation of maternal and direct effects on calving. Additionally, the association of maternally inherited DIK4234 marker alleles with body depth implied that conformation traits might provide the functional background of the QTL for calving traits. For udder type traits, the strong coincidence of QTL peaks and the position of the QTL in a region previously reported to harbor QTL for somatic cell score indicated that effects of QTL for udder type traits might be correlated with effects of QTL for udder health traits on BTA18. Our results suggest that loci in the middle to telomeric region on BTA18 with effect on conformation traits may also contribute to the genetic variance of calving and udder health traits. Further analyses are required to identify the causal mutations affecting conformation and calving traits and to investigate the correlation of effects for loci associated with conformation, calving, and udder health traits.

Genome-Wide Association Study of a Varroa-Specific Defense Behavior in Honeybees (Apis mellifera)

Honey bees are exposed to many damaging pathogens and parasites. The most devastating is Varroa destructor, which mainly affects the brood. A promising approach for preventing its spread is to breed Varroa-resistant honey bees. One trait that has been shown to provide significant resistance against the Varroa mite is hygienic behavior, which is a behavioral response of honeybee workers to brood diseases in general. Here, we report the use of an Affymetrix 44K SNP array to analyze SNPs associated with detection and uncapping of Varroa-parasitized brood by individual worker bees (Apis mellifera). For this study, 22 000 individually labeled bees were video-monitored and a sample of 122 cases and 122 controls was collected and analyzed to determine the dependence/independence of SNP genotypes from hygienic and nonhygienic behavior on a genome-wide scale. After false-discovery rate correction of the P values, 6 SNP markers had highly significant associations with the trait investigated (α < 0.01). Inspection of the genomic regions around these SNPs led to the discovery of putative candidate genes.