Abstract
Our study focused on quantifying functional similarities between complex traits recorded in dairy cattle: milk yield, fat yield, protein yield, somatic cell score and stature. Similarities were calculated based on gene sets forming gene networks and on gene ontology term sets underlying genes estimated as significant for the analysed traits. Gene networks were obtained by the Bisogenet and Gene Set Linkage Analysis (GSLA) software. The highest similarity was observed between milk yield and fat yield. A very low degree of similarity was attributed to protein yield and stature when using gene sets as a similarity criterion, as well as to protein yield and fat yield when using sets of gene ontology terms. Pearson correlation coefficients between gene effect estimates, representing additive polygenic similarities, were highest for protein yield and milk yield, and the lowest in case of protein yield and somatic cell score. Using the 50 K Illumina SNP chip from the national genomic selection data set only the most significant gene-trait associations can be retrieved, while enhancing it by the functional information contained in interaction data stored in public data bases and by metabolic pathways information facilitates a better characterization of the functional background of the traits and furthermore — trait comparison. The most interesting result of our study was that the functional similarity observed between protein yield and milk-/fat yields contradicted moderate genetic correlations estimated earlier for the same population based on a multivariate mixed model. The discrepancy indicates that an infinitesimal model assumed in that study reflects an averaged correlation due to polygenes, but fails to reveal the functional background underlying the traits, which is due to the cumulative composition of many genes involved in metabolic pathways, which appears to differ between protein-fat yield and protein-milk yield pairs.Electronic supplementary materialThe online version of this article (doi:10.1007/s13353-015-0306-5) contains supplementary material, which is available to authorized users.
Highlights
In genetic analysis of complex traits the focus has been shifted from single genes identified via genome-wide association studies (GWAS) to genes identified via a functional analysis (Evangelou et al 2014; Visscher et al 2012)
Our study focused on quantifying functional similarities between complex traits recorded in dairy cattle: milk yield, fat yield, protein yield, somatic cell score and stature
For size and non-return rate for cows and heifers no gene effect exceeded the 20 % significance threshold and the traits were not used for further analysis
Summary
In genetic analysis of complex traits the focus has been shifted from single genes identified via genome-wide association studies (GWAS) to genes identified via a functional analysis (Evangelou et al 2014; Visscher et al 2012). In our study we were interested in the incorporation of functional information from gene network analysis into the assessment of similarity between selected quantitative traits. Pszczola et al (2013) used the so-called predictor traits with widely available records in cattle populations, e.g. fat–proteincorrected milk, to enhance the accuracy of genomic prediction for other traits with less phenotypic information available such as, e.g. dry matter intake. This can be considered as a within-species phenolog approach on an additive polygenic basis, i.e. with the underlying assumption of an infinitesimal mode of inheritance of phenotypes, with identification of neither particular genes nor the pathways. Our goal was to compare similarities between traits based on the functional information gathered through gene networks and assuming an underlying complex mode of inheritance
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