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Indian Journal of Animal Research

  • Chief EditorM. R. Saseendranath

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Research Article

Indian Journal of Animal Research, volume 52 issue 3 (march 2018) : 331-336

Assessment of genetic diversity in goat genetic groups of Kerala, (India) using morphobiometric markers

G. Radhika, K.C. Raghavan, K.A. Mercey, C. Sunanda, P.M. Rojan
1Kerala Veterinary and Animal Sciences University, Pookode- 673576, Wayanad, Kerala, India.
Cite article:- Radhika G., Raghavan K.C., Mercey K.A., Sunanda C., Rojan P.M. (2018). Assessment of genetic diversity in goat genetic groups of Kerala, (India) using morphobiometric markers. Indian Journal of Animal Research. 52(3): 331-336. doi: 10.18805/ijar.B-3119.

ABSTRACT

Geographical isolation and human interventions resulted in formation of subpopulations among native breeds, which showed differences in morphological or biometric traits. Crossbreeding of indigenous goats with exotic breeds resulted in combining desirable characteristics of many breeds in a composite population. Malabari crossbreds (CB), which had the inheritance of Malabari, Saanen, Alpine and Boer goat breeds were available at University Goat Farm, Mannuthy, Kerala, India. The native goats of Kerala namely, Attappady Black (AB) and populations of Malabari goats from Kannur, Calicut, Thrissur and Malappuram districts (MK, MC, MT and MM) were analysed along with Malabari crossbreds (CB) for genetic diversity. Morphological data were collected from 1776 animals and biometric data from 572 animals representing six goat populations. Qualitative morphological traits analysed were coat colour, presence or absence of horns, tassels, beard, pattern of ear and hair at forequarters and hindquarters. Quantitative biometric traits assessed were chest girth, height at withers, body length and body weight collected from female animals of one to three years of age. Least squares means calculated from SPSS, canonical discriminant analysis by CANDISC, Mahalanobis distance and dendrogram constructed by PROC CLUSTER were used to analyse biometric variables. Canonical discriminant analysis conducted for biometric data identified three statistically significant canonical variables (P<0.0001), with CAN1 explaining 57.2 per cent of total variance. Multivariate analytical techniques thus confirmed the existence of six different populations. The discriminatory capacity of biometric variables chosen for the study was also justified.  

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