Natural and artificial selection affect genome structure causing
genetic variation between breeds. Dense marker maps of thousand
SNP disseminated across the whole genome allow for the
investigation of chromosomal regions that differ between
breeds. Several statistical approaches have been proposed to
study selection signatures in livestock species. In this work, four
approaches were used to study selection signatures in a sample
of 496 sheep belonging to 20 Italian breeds, different for geographical
origin and production aptitude. The four approaches
were: I) Fst Outlier Detection (FOD), implemented in the LOSITAN
software. II) comparison of Breed LS means of the sum of
differences in SNP allele frequencies along sliding windows
(SNP_DIFF). III) Correspondence analysis (CA). VI) Canonical
Discriminant Analysis (CDA). Animal were genotyped with the
Illumina OvineSNP50 BeadChip. The first six chromosomes
were considered. After data editing, a total of 20,194 SNP were
retained for the analysis. The different approaches were able to
identify the same regions expressing variation between breeds.
On OAR6, for example, all methods highlighted a region located
between 35 and 41 Mb, where BMPR1b and ABCG2 loci map.
Moreover, SNP able to differentiate between breeds were also
detected at 76, 96 and 107 Mb, near to KIT, IL8 and SCD5 loci,
respectively. All methods were able to discriminate breeds and, in
general, a geographical pattern of variation has been detected.
However each approach may supply different kind of information.
FOD detected a relatively low number of markers in divergent
selection but it was able to identify loci under balanced
selection. CA and CDA decomposed the total variability of SNP
markers among breeds in different and uncorrelated variables
that could be useful for the identification of genes influencing
complex traits
