Browsing by Subject "Selection signatures"

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    Genome-wide mapping and functional analysis of genes determining the meat quality in pigs
    (2014) Stratz, Patrick; Bennewitz, Jörn
    In chapter one QTL were mapped and tested for pairwise epistasis for meat quality traits in three connected porcine F2 crosses comprising around 1000 individuals. The crosses were derived from Chinese Meishan, European Wild Boar and Piétrain. The animals were genotyped genomewide for approximately 250 genetic markers and phenotyped for seven meat quality traits. QTL mapping was done using a multi-QTL multi-allele model. It considered additive (a), dominance (d) and imprinting (i) effects. The major gene RYR1:G.1843C>T affecting the meat quality was included as a cofactor in the model. The mapped QTL were tested for possible epistatic effects between the main effects, leading to nine orthogonal forms of epistasis (aa, ad, da, di, id, ai, ia, dd and ii). Numerous QTL were found; the most interesting are located on chromosome SSC6. Epistasis was significant (FDR q-value<0.2) for the pairwise QTL on SSC12 and SSC14 for pH 24 h after slaughter and for the QTL on SSC2 and SSC5 for rigour. In chapter two around 500 progeny tested Piétrain sires were genotyped with the PorcineSNP60 BeadChip. After data filtering around 48k SNPs were useable in this sample. These SNPs were used to conduct a genome-wide association analysis for growth, muscularity and meat quality traits. Because it is known, that a mutation in the RYR1 gene located on chromosome 6 shows a major effect on meat quality, this mutation was included in the models. Single-marker and multi-marker association analysis were performed. The results revealed between one and eight significant associations per trait with P-value<0.00005. Of special interest are SNPs located on SSC6, 10 and 15. In chapter three a literature search was conducted to search putative candidate genes in the vicinity of significant SNPs found in the association analysis. MYOD1 was suggested as putative candidate gene. The expression of MYOD1 was measured in muscle tissue from 20 Piétrain sires. Growth, muscularity and meat quality traits were available. DNA was isolated out of blood tissue to genotype the SNP ASGA0010149:g. 47980126G>A. Significant Correlations (FDR q-value<0.15) between the expression of MYOD1 and growth and muscularity traits were found. Association between the traits, respectively MYOD1, and ASGA0010149:g. 47980126G>A was tested, but was only significant (FDR q-value<0.15) for two muscularity traits. In chapter four the LD structure in the genome of the Piétrain pigs was characterized using data from the PorcineSNP60 BeadChip. The Relative Extended Haplotype Homozygosity test was conducted genome-wide to search for selection signatures using core haplotypes above a frequency of 0.25. The test was also conduct in targeted regions, where significant SNPs were already found in association analysis. A small subdivision of the population with regard to the geographical origin of the individuals was observed. As a measure of the extent of linkage disequilibrium, r2 was calculated genome-wide for SNP pairs with a distance 5Mb and was on average 0.34. Six selection signatures having a P-value<0.001 were genome-wide detected, located on SSC1, 2, 6 and 17. In targeted regions, it was possible to successfully annotate nine SNPs to core regions. Strong evidence for recent selection was not found in those regions. Three selection signatures with P-value<0.1 were detected on SSC2, 5 and 16. To reduce the costs of genomic selection, selection candidates can be genotyped with an SNP panel of reduced density (384 SNPs). The aim of chapter five was to investigate two strategies for the selection of SNPs to be considered in the above mentioned SNP panel, using 895 progeny tested and genotyped German Piétrain boars. In the first strategy equal spaced SNPs were selected, which were used to impute the high density genotypes. In the second strategy SNPs were selected based on results of association analysis. Direct genomic values were estimated with GBLUP from deregressed estimated breeding values. Accuracies of direct genomic values for the two strategies were obtained from cross validation. A regression approach to correct for the upward bias of the cross validation accuracy of the direct genomic values was used. The first strategy resulted in more accurate direct genomic values. This implies that imputation is beneficial even if only 384 SNPs are genotyped for the selection candidates.