Identification of QTLs in chicken chromosomoes 2 and 4 for growth and carcass traits (Gallus gallus)

Author(s)

Erica Elias Baron

• Document type: Doctoral Thesis
• Press: Piracicaba.
• Institution: Universidade de São Paulo (USP). Escola Superior de Agricultura Luiz de Queiroz (ESALA/BC)
• Defense date: 2004-09-15
• Advisor: Luiz Lehmann Coutinho

Abstract

The goal of animal breeding is to achieve genetic progress. Until recently animals were selected after genetic evaluations based on phenotypic data. Finding new or more efficient ways to select for desirable traits is the most important reason for the interest in developing methods to find genes. Animal breeders are mostly interested in economic traits, which have a quantitative nature. These traits are usually influenced by a number of loci which are named quantitative trait loci or QTLs. The objective of the thesis was to identify QTLs on chromosomes 2 and 4 that affect growth and carcass traits. Two chicken lines, one broiler (TT) and another layer (CC), were used to produced and F2 population. Selective genotyping was used to identify regions containing potential QTL. The selected regions were then typed in all individuals of the most informative families. A total of 614 individual from 06 full-sib families were genotyped. The linkage map were constructed using the CRI-MAP program. Phenotypic data were adjusted to significative non-genetic effects in ANOVA analysis. A interval mapping method were used to map QTLs, under linecross and half-sib analysis. Under the linecross model it is assumed that two lines, although they may segregate at the marker loci, are fixed for alternative alleles the QTL affecting the traits of interest. Ten QTLs were found in chromosome 2 with, at least, suggestive linkage under linecross analysis. In chromosome 4, eleven QTLs were found under this same analysis. Under half-sib analysis, 3 and 5 QTLs were identified in chromosomes 2 and 4, respectively. These results support the use of selective genotyping strategy to map QTL and the assumption of linecross analyses are more powerful than half-sib analyses to our population structure (AU)