COMPUTATIONAL ANALYSIS OF MOLECULAR SIGNATURES OF SELECTION AT PRODUCTION GENES FOR EGG AND MEAT IN LIVESTOCK AND WILD RELATIVES

Abstract

In Kenya, indigenous chicken form the majority of poultry. Their egg and meat production is low compared to commercial chicken. However there is variation among the indigenous chicken caused by evolutionary forces such as Natural Selection. The aim of this study was to model evolution and subsequent detection positive selection on genes for egg and meat production. Genes for egg production were prolactin, vasoactive intestinal peptide (vip) and intestinal peptide receptor (vipr) while genes for meat production were growth hormone (gh), growth hormone receptor (ghr), insulin growth factor 1(igf1) and insulin growth factor 1 receptor (igf1r). This was achieved by data mining of the sequences of these genes from databases followed by performing reciprocal BLASTp and BLOSUM62 substitution matrix using chicken sequence for each gene as the query. Homologues with an expectation value greater than 1e-10 were selected for each gene. Thereafter, Multiple Sequence Alignment was done using MUSCLE which uses an iterative algorithm. The alignments were edited using Seaview. MEGA6 was used to test for heterogeneity in substitution rate and for determining evolutionary model using lowest Akaike Information Criterion. Finally, phylogenetic trees were constructed using Nearest Neighbour Interchange with subtree pruning and regrafting of FastME followed by analysis for signatures of selection using PAML4. This led to inferred phylogenetic trees that modeled evolution of the genes in the different species and identification of positive selection on one amino acid site on igf1r. This is an advanced genetic technology that may be used to improve egg and meat production through artificial selection.