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- Melzer, Nina, et al.
(författare)
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Integrating milk metabolite profile information for the prediction of traditional milk traits based on SNP information for Holstein cows
- 2013
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Ingår i: PLOS ONE. - San Fransisco, USA : Public Library Science. - 1932-6203. ; 8:8
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Tidskriftsartikel (refereegranskat)abstract
- In this study the benefit of metabolome level analysis for the prediction of genetic value of three traditional milk traits was investigated. Our proposed approach consists of three steps: First, milk metabolite profiles are used to predict three traditional milk traits of 1,305 Holstein cows. Two regression methods, both enabling variable selection, are applied to identify important milk metabolites in this step. Second, the prediction of these important milk metabolite from single nucleotide polymorphisms (SNPs) enables the detection of SNPs with significant genetic effects. Finally, these SNPs are used to predict milk traits. The observed precision of predicted genetic values was compared to the results observed for the classical genotype-phenotype prediction using all SNPs or a reduced SNP subset (reduced classical approach). To enable a comparison between SNP subsets, a special invariable evaluation design was implemented. SNPs close to or within known quantitative trait loci (QTL) were determined. This enabled us to determine if detected important SNP subsets were enriched in these regions. The results show that our approach can lead to genetic value prediction, but requires less than 1% of the total amount of (40,317) SNPs., significantly more important SNPs in known QTL regions were detected using our approach compared to the reduced classical approach. Concluding, our approach allows a deeper insight into the associations between the different levels of the genotype-phenotype map (genotype-metabolome, metabolome-phenotype, genotype-phenotype).
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- Melzer, Nina, et al.
(författare)
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Investigating a complex genotype-phenotype map for the development of methods to predict genetic values based on genome-wide marker data – a simulation study for the livestock perspective
- 2013
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Ingår i: Archiv für Tierzucht. - Dummerstorf, Germany : Leibniz Institute for Farm Animal Biology (FBN). - 0003-9438. ; 56:38, s. 380-398
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Tidskriftsartikel (refereegranskat)abstract
- Phenotypic variation can partly be explained by genetic variation, such as variation in single nucleotide polymorphism (SNP) genotypes. Genomic selection methods seek to predict genetic values (breeding values) based on SNP genotypes. To develop and to optimize these methods, simulated data are often used, which follow a rather simple genotype-phenotype map. Is the conventional approach for data simulation in this field an appropriate basis to optimize such methods in view of experimental data? Here, we present an alternative approach, striving to simulate more realistic data based on a genotype-phenotype map which includes a simulated metabolome level. This level was used to simulate genetic values, implicitly including additive and non-additive genetic effects, whereas in a conventional approach additive and dominance effects were explicitly simulated and assembled to genetic values. For both simulation approaches, different scenarios regarding numbers of quantitative trait loci (QTLs) and SNPs were analysed using fastBayesB as prediction method. We observed that our alternative map showed a smaller prediction precision (at least 3.75 %) compared to the conventional approach in all investigated scenarios. The observed degree of linearity is at least 94.12 % of the conventional approach or less. Additionally, we present results for different simulated data and experimental data to allow a comparison on a purely conceptual level. Concluding, simulating a more complex genotype-phenotype map including a molecular level, allows to study processing of variation from the genetic to the phenotype level in more detail and may prepare the ground for modern methods of genomic selection.
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- Melzer, Nina, et al.
(författare)
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Investigating associations between milk metabolite profiles and milk traits of Holstein cows
- 2013
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Ingår i: Journal of Dairy Science. - New York, USA : Elsevier. - 0022-0302 .- 1525-3198. ; 96:3, s. 1521-34
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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- Wittenburg, Dörte, et al.
(författare)
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Milk metabolites and their genetic variability
- 2013
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Ingår i: Journal of Dairy Science. - New York, USA : Elsevier. - 0022-0302 .- 1525-3198. ; 96:4, s. 2557-69
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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- Wittenburg, Dörte, et al.
(författare)
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Milk metabolites and their genetic variability
- 2012
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Ingår i: Book of Abstracts of the 63rd Annual Meeting of the European Federation of Animal Science. - Wageningen : Wageningen Academic Publishers. - 9789086862061 ; , s. 94-94
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Konferensbidrag (refereegranskat)
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