Browsing by Person "Maushammer, Maria"

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Analyse komplexer Merkmale beim Schwein mittels SNP-Chip Genotypen, Darmmikrobiota- und Genexpressionsdaten
(2017) Maushammer, Maria; Bennewitz, Jörn
In the present scientific research, SNP chip genotypes, gut microbiota and gene expression data were used for analysing complex traits in a Piétrain population. These data were collected from around 200 performance tested sows and were used for genetic and microbial analyses of complex trait as well as for structural and functional meat quality traits. The gut microbiome plays a major role in the immune system development, state of health and energy supply of the host. Quantitative-genetic methods were applied to analyse the interrelationship between pig gut microbiota compositions, complex traits (daily gain, feed conversion and feed intake) and pig genomes. The specific aims were to characterize the gut microbiota of the pigs, to analyse the effects of host genetics on gut microbial composition, and to investigate the role of gut microbial composition on the host’s complex traits. The pigs were genotyped with a standard 60K SNP chip. Microbial composition was characterized by 16S rRNA gene amplicon sequencing technology. Ten out of 51 investigated bacterial genera showed a significant host heritability, ranging from 0.32 to 0.57. Conducting genome wide association analysis showed associations of 22 SNPs and six bacterial genera. The potential candidate genes identified are involved in the immune system, mucosa structure and secretion of digestive juice. These results show, that parts of the gut microbiota are heritable and that the gut microbiome can be seen as quantitative trait. Microbial mixed linear models were applied to estimate the microbiota variance for each of the investigated traits. The fraction of phenotypic variance explained by the microbial variance was 0.28, 0.21, and 0.16 for daily gain, feed conversion, and feed intake, respectively. The SNP data and the microbiota data were used to predict the phenotypes of the traits using both, genomic best linear unbiased prediction (G-BLUP) and microbial best linear unbiased prediction (M-BLUP) methods. The prediction accuracies of G-BLUP were 0.35, 0.23, and 0.20 for daily gain, feed conversion, and feed intake, respectively. The corresponding prediction accuracies of M-BLUP were 0.41, 0.33, and 0.33. Thus, the gut microbiota can be seen as an explaining variable for complex traits like daily gain, feed conversion and feed intake. In addition, in combination with meat quality traits, transcript levels of muscle tissue were analysed at time of slaughtering. This study should give an insight into the biological processes involved in meat quality characteristics. The aims were to functionally characterise differentially expressed genes, to link the functional information with structural information obtained from GWAS, and to identify potential candidate genes based on these results. An important meat quality trait is the intramuscular fat content, since it affects the juiciness, the taste and the tenderness of the meat. Another important trait is drip loss which causes not only a loss of weight but also a loss of important proteins. Both traits have an impact on the consumer acceptance of fresh meat products. For each of the two traits, eight discordant sibling pairs were selected out of the Piétrain sample and were used for genome-wide gene expression analyses. Thirty five and 114 genes were identified as differentially expressed and trait correlated genes for intramuscular fat content and drip loss, respectively. On the basis of functional annotation, gene groups belonging to the energy metabolism of the mitochondria, the immune response and the metabolism of fat, were associated with intramuscular fat content. Gene groups associated with protein ubiquitination, mitochondrial metabolism, and muscle structural proteins were associated with drip loss. Furthermore, genome-wide association analyses were carried out for these traits and their results were linked to the genome-wide expression analysis by functional annotation. In this context, intramuscular fat was related to muscle contraction, transmembrane transport and nucleotide binding. Drip loss was characterized by the endomembrane system, the energy generation of cells, and phosphorus metabolic processes. Three and four potential candidate genes were identified for intramuscular fat content and drip loss, respectively.