F. Reinhardt

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.

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.

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.

Reaction norms and genotype‐by‐environment interaction in the German Holstein dairy cattle

Reaction norm random regression sire models were used to study genotype-by-environment interactions (G × E) in the German Holstein dairy cattle population. Around 2300 sires with a minimum of 50 daughters per sire and seven first-lactation test day observations per daughter were analysed. Corrected test day records for milk yield, protein yield, fat yield and somatic cell score (SCS) were used. Herd test day solutions for milk traits, milk energy yield or SCS were used as environmental descriptors. Second-order orthogonal polynomial regressions were applied to the sire effects. The results revealed significant slope variances of the reaction norms, which caused a non-constant additive genetic variance across the environmental ranges considered. This pointed to the presence of minor G × E effects. The additive genetic variance increased when the environment improved, that is, higher (lower) herd test day solutions for milk traits (SCS). This was also influenced by pure scaling effects, because the non-genetic variance increased in an improved environment and the heritability was less influenced by the environment. The G × E effects caused very little reranking of the sires for the environmental range considered in this study.

Novel Use of Derived Genotype Probabilities to Discover Significant Dominance Effects for Milk Production Traits in Dairy Cattle

The estimation of dominance effects requires the availability of direct phenotypes, i.e., genotypes and phenotypes in the same individuals. In dairy cattle, classical QTL mapping approaches are, however, relying on genotyped sires and daughter-based phenotypes like breeding values. Thus, dominance effects cannot be estimated. The number of dairy bulls genotyped for dense genome-wide marker panels is steadily increasing in the context of genomic selection schemes. The availability of genotyped cows is, however, limited. Within the current study, the genotypes of male ancestors were applied to the calculation of genotype probabilities in cows. Together with the cows’ phenotypes, these probabilities were used to estimate dominance effects on a genome-wide scale. The impact of sample size, the depth of pedigree used in deriving genotype probabilities, the linkage disequilibrium between QTL and marker, the fraction of variance explained by the QTL, and the degree of dominance on the power to detect dominance were analyzed in simulation studies. The effect of relatedness among animals on the specificity of detection was addressed. Furthermore, the approach was applied to a real data set comprising 470,000 Holstein cows. To account for relatedness between animals a mixed-model two-step approach was used to adjust phenotypes based on an additive genetic relationship matrix. Thereby, considerable dominance effects were identified for important milk production traits. The approach might serve as a powerful tool to dissect the genetic architecture of performance and functional traits in dairy cattle.

Genome-wide association analysis to identify genotype × environment interaction for milk protein yield and level of somatic cell score as environmental descriptors in German Holsteins.

Genotype by environment interaction (G × E) has been widely reported in dairy cattle. If the environment can be measured on a continuous scale, reaction norms can be applied to study G × E. The average herd milk production level has frequently been used as an environmental descriptor because it is influenced by the level of feeding or the feeding regimen. Another important environmental factor is the level of udder health and hygiene, for which the average herd somatic cell count might be a descriptor. In the present study, we conducted a genome-wide association analysis to identify single nucleotide polymorphisms (SNP) that affect intercept and slope of milk protein yield reaction norms when using the average herd test-day solution for somatic cell score as an environmental descriptor. Sire estimates for intercept and slope of the reaction norms were calculated from around 12 million daughter records, using linear reaction norm models. Sires were genotyped for ~54,000 SNP. The sire estimates were used as observations in the association analysis, using 1,797 sires. Significant SNP were confirmed in an independent validation set consisting of 500 sires. A known major gene affecting protein yield was included as a covariable in the statistical model. Sixty (21) SNP were confirmed for intercept with P ≤ 0.01 (P ≤ 0.001) in the validation set, and 28 and 11 SNP, respectively, were confirmed for slope. Most but not all SNP affecting slope also affected intercept. Comparison with an earlier study revealed that SNP affecting slope were, in general, also significant for slope when the environment was modeled by the average herd milk production level, although the two environmental descriptors were poorly correlated.