Unraveling the genetic background of bovine milk fat composition

Identification of genomic regions, and preferably individual genes, responsible for genetic variation in bovine milk fat composition enhances the understanding of biological pathways involved in fatty acid synthesis and is expected to increase opportunities for changing bovine milk fat composition by means of selective breeding. This thesis aimed to unravel the genetic background of bovine milk fat composition by detection, confirmation and fine-mapping of quantitative trait loci (QTL) for milk fatty acids in Dutch Holstein Friesian cattle. In addition, causal relations between fatty acids were explored. For this study roughly 2,000 dairy cows were genotyped with 50,000 DNA markers and phenotyped for individual fatty acids in both winter and summer milk samples using gas chromatography. Genome-wide association studies (GWAS) showed that milk fat composition has a complex genetic background with three major QTL that explain a relatively large fraction of the genetic variation of several milk fatty acids, and many QTL that explain a relatively small fraction of the genetic variation. Results from the GWAS for summer milk fatty acids confirmed most associations that were detected in the winter milk samples. Moving from linkage analysis toward GWAS confirmed and refined the size of previously detected QTL regions and resulted in new QTL regions. Performing GWAS based on individual fatty acids resulted in additional QTL as compared to GWAS based on fat percentage or yield. This shows that refinement of complex phenotypes into underlying components results in better links between genes and phenotypes. By increasing the marker density, the QTL on BTA19 was refined to a linkage disequilibrium block that contained 2 genes: coiled-coil domain containing 57 and fatty acid synthase. A search for causal relations between fatty acids resulted in a pathway from C4:0 to C12:0, which resembled the de novo synthesis pathway. Causal relation between the QTL on BTA19 and de novo fatty acids showed that the QTL affects C4:0, C6:0, C8:0, C10:0 and C14:0 directly, while C12:0 was indirectly affected by the QTL through its effect on C10:0. The potential of GWAS based on MIR predicted fatty acids was explored but failed to detect some QTL and resulted in additional QTL that were not detected based on GC measurements. Therefore, MIR predicted phenotypes add complexity to the genotype-phenotype relationship, and renders MIR predicted phenotypes less appropriate to identify candidate genes and to infer the biological background of traits.</p

Eco-hydrologische systeembeschrijving van het landgoed "De Wildenborch"

Background - In this study we perform a genome-wide association study (GWAS) for bovine milk fatty acids from summer milk samples. This study replicates a previous study where we performed a GWAS for bovine milk fatty acids based on winter milk samples from the same population. Fatty acids from summer and winter milk are genetically similar traits and we therefore compare the regions detected in summer milk to the regions previously detected in winter milk GWAS to discover regions that explain genetic variation in both summer and winter milk. Results - The GWAS of summer milk samples resulted in 51 regions associated with one or more milk fatty acids. Results are in agreement with most associations that were previously detected in a GWAS of fatty acids from winter milk samples, including eight ‘new’ regions that were not considered in the individual studies. The high correlation between the –log10(P-values) and effects of SNPs that were found significant in both GWAS imply that the effects of the SNPs were similar on winter and summer milk fatty acids. Conclusions - The GWAS of fatty acids based on summer milk samples was in agreement with most of the associations detected in the GWAS of fatty acids based on winter milk samples. Associations that were in agreement between both GWAS are more likely to be involved in fatty acid synthesis compared to regions detected in only one GWAS and are therefore worthwhile to pursue in fine-mapping studies

Genetic Variation in Vitamin B-12 Content of Bovine Milk and Its Association with SNP along the Bovine Genome

Vitamin B-12 (also called cobalamin) is essential for human health and current intake levels of vitamin B-12 are considered to be too low. Natural enrichment of the vitamin B-12 content in milk, an important dietary source of vitamin B-12, may help to increase vitamin B-12 intake. Natural enrichment of the milk vitamin B-12 content could be achieved through genetic selection, provided there is genetic variation between cows with respect to the vitamin B-12 content in their milk. A substantial amount of genetic variation in vitamin B-12 content was detected among raw milk samples of 544 first-lactation Dutch Holstein Friesian cows. The presence of genetic variation between animals in vitamin B-12 content in milk indicates that the genotype of the cow affects the amount of vitamin B-12 that ends up in her milk and, consequently, that the average milk vitamin B-12 content of the cow population can be increased by genetic selection. A genome-wide association study revealed significant association between 68 SNP and vitamin B-12 content in raw milk of 487 first-lactation Dutch Holstein Friesian cows. This knowledge facilitates genetic selection for milk vitamin B-12 content. It also contributes to the understanding of the biological mechanism responsible for the observed genetic variation in vitamin B-12 content in milk. None of the 68 significantly associated SNP were in or near known candidate genes involved in transport of vitamin B-12 through the gastrointestinal tract, uptake by ileum epithelial cells, export from ileal cells, transport through the blood, uptake from the blood, intracellular processing, or reabsorption by the kidneys. Probably, associations relate to genes involved in alternative pathways of well-studied processes or to genes involved in less well-studied processes such as ruminal production of vitamin B-12 or secretion of vitamin B-12 by the mammary glan

Cobalt(II)-disulfide compounds with the unusual PF2O2– anion. ligand-dependent redox conversion to a cobalt(III)-thiolate complex

Metals in Catalysis, Biomimetics & Inorganic Material

Genomic prediction across populations, using pre-selected markers and differential weight models

Genomic prediction (GP) in numerically small breeds is limited due to the requirement for a large reference set. Across breed prediction has not been very successful either. Our objective was to test alternative models for across breed and multi-breed GP in a small Jersey population, utilizing prior information on marker causality. We used data on 596 Jersey bulls from new Zealand and 5503 Holstein bulls from the Netherlands, all of which had deregressed proofs for stature. Two sets of genotype data were used, one containing 357 potential causal markers identified from a multi-breed meta-GWAS on stature (top markers), while the other contained 48,912 markers on the custom 50k chip, excluding the top markers. We used models in which only one GRM (either top markers, 50k, or top plus 50k markers combined) was fitted, and models in which two GRMs (both the top and 50k) were fitted simultaneously, however with different variance components to weight the GRMs differently. Moreover, we estimated the genetic correlation(s) between the breeds (for each GRM) using a multi-trait GP model, which implicitly weights the contribution of one breed’s information to another. Across breed, we observed low accuracies of GP when the 50k markers were fitted alone (0.06) or when the top markers were added to 50k (0.15). Higher accuracy was obtained when only the top markers were fitted (0.21), whereas the highest accuracy was obtained when fitting 50k and top markers simultaneously as two independent GRMs (0.25). Multi-breed prediction outperformed both within and across breed prediction with accuracies ranging from 0.34 to 0.45, with the same trend as in across breed prediction. Based on our results, the best approach for across and multi-breed GP is to fit models that are able to isolate and differentially weight the most important markers for the trait. Keywords: Across breed genomic prediction, marker pre-selection, multi-trait model, sequence data

Accuracy of genomic prediction of dry matter intake in Dutch Holsteins using sequence variants from meta-analyses

We evaluated the accuracy of biology informed genomic prediction for dry matter intake in 2,162 Dutch Holstein cows. Sequence variants were selected from meta-analyses including GWAS summary statistics for QTL and metabolomic QTL in several dairy and crossbred beef populations. Selected variants were prioritized in GBLUP models in a five-fold cross-validation. The accuracies were compared to genomic prediction based on routine 50k genotype data. The average accuracy for the 50k scenario was 0.683. Adding selected sequence variants in the GBLUP model did not improve the accuracies for dry matter intake. Next steps will include testing Bayesian variable selection methods to prioritize variants in genomic prediction for dry matter intake