Non-hierarchical cluster analysis for determination of resistance to worm infection in meat sheep.

Abstract

The aim of this study was to determine the resistance to worm infection in Santa Inês sheep by combining different sets of gastrointestinal parasite resistance indicator traits, using the k-means algorithm. Records from 221 animals reared in the Mid-North sub-region of Brazil were used. The following phenotypes were used: hematocrit (HCT); white blood cell count; red blood cell count (RBC); hemoglobin (HGB); platelets; mean corpuscular hemoglobin; mean corpuscular volume; mean corpuscular hemoglobin concentration; fecal egg count (FEC); coloration of the ocular mucosa (FAMACHA score); body condition score (BCS); withers height; and rump height. Two files with phenotypic information of animals were edited: complete, including all traits, and reduced, in which only FAMACHA score, HCT, FEC, and BCS were used. For determination of worm resistance, three groups were formed using the k-means non-hierarchical clustering by combining the traits of the complete and reduced analyses. The animals of the group in which individuals had the lowest values for FEC and FAMACHA score, as well as the highest values for HCT, RBC, HGB, and BCS were classified as resistant. In the group with opposite values for the aforementioned traits, the animals were classified as sensitive. The animals of the group with values between the other two groups were classified as moderately resistant. The results obtained in complete and reduced analyses were equivalent. Thus, it is possible to identify animals of the Santa Inês sheep breed according to their status of resistance to worm infection based on a reduced trait set.