Using gene networks to identify genes and pathways involved in milk production traits in Polish Holstein dairy cattle T., Wojdak-Maksymiec K., Szyda J. (2016): Using gene networks to identify genes and pathways involved in milk production traits in Polish Holstein dairy cattle. Czech J. Anim. Sci., 61: 526-538.
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When analyzing phenotypes undergoing a complex mode of inheritance, it is of great interest to switch the scope from single genes to gene pathways, which form better defined functional units. We used gene networks to search for physiological processes and underlying genes responsible for complex traits recorded in dairy cattle. Major problems addressed included loss of information from multiple single nucleotide polymorphisms (SNPs) located within or close to the same gene, ignoring information on linkage disequilibrium and validation of the obtained gene network. 2601 bulls genotyped by the Illumina BovineSNP50 BeadChip were used. SNP effects were estimated using a mixed model, then underlying gene effects were estimated and tested for significance, subsequently a gene network was constructed and the functional information represented by the network was retrieved. The networks were validated by repeating the above-mentioned analyses after permutation of bulls’ pseudophenotypes. Effects of 4345 genes were estimated, what makes 16.4% of all genes mapped to the UMD3.1 reference genome. Assuming the maximum 10% type I error rate, for milk yield 50 different gene ontology (GO) terms and three pathways defined by the Kyoto Encyclopedia of Genes and Genomes (KEGG) were significantly overrepresented in the real data as compared to the permuted data sets, while for fat yield nine of the GO terms were significantly overrepresented in the real data network, although none of the KEGG pathways reached the significance level. In turn, for protein yield 28 of the GO terms and six KEGG pathways were significantly overrepresented in the real data. Based on the physiological information we identified sets of loci involved in the determination of milk yield (224 genes), fat yield (72 genes), and protein yield (546 genes). Among the genes some have large effects and have already been reported in previous studies, whereas some others represent novel discoveries and thus most probably genes with medium or small effects on trait variation.
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