Banner

Quantitative trait loci affecting growth performance in F2 intercross between Golden Montazah and White Leghorn chickens.

The 3rd International Conference On Biotechnology Applications In Agriculture (ICBAA), held in Moshtohor-Sharm El-Sheikh, Benha University, Egypt, 5-9 April 2016 • 2016
Back
Publication Information
Authors 47) Abdel Alal, M.H, Khalil, M.H., Iraqi, M.M., El-Moghazy, Gihan M.
Keywords Chickens, QTL, microsatellite markers, growth traits, additive effects, dominance effects
Journal The 3rd International Conference On Biotechnology Applications In Agriculture (ICBAA), held in Moshtohor-Sharm El-Sheikh, Benha University, Egypt, 5-9 April 2016
Publisher Annals of Agric. Mosht., Egypt
Volume I
Issue Special I|ssue
Pages Animal Biotechnology, 1-16
publication.type International
Paper Link Open Link
Supplementary Materials Not Available
Abstract
Quantitative trait loci (QTL) for body weights (BW) at 4, 8, 12, 16 weeks of age and daily gains (DG) at
intervals of 0-4, 4-8, 8-12 and 12-16 weeks were identified in F2 crossbred population produced by crossing
males of Golden Montazah (GM) with females of White Leghorn (WL). Phenotypic data were analyzed using
multi-traits animal model including the genetic group, sex and hatch as fixed effects and the additive genetic and
common environmental effects as random effects. After parentage checking and F2 genotyping, data of 1011
chicks of F2 were genotyped using 43 genetic markers in nine autosomal linkage groups, Z chromosome and the
genotypes were used for QTL analysis. A mixed model included the sex and hatch as fixed effects along with
the additive and dominance effects of QTL as random effects were used for QTL analysis. The heritability
estimates for growth traits were high. The genotypic and phenotypic correlations between growth traits were
positive and high. The total map length was 1901 cM (ranging from 25 to 568 cM), with an average spacing of
markers of 24.39 cM (ranging from 7.8 to 24.3 cM). A total of 34 QTL were detected for BW traits. These QTL
were distributed over five distinct regions on 10 chromosomes, and their effects ranged from 1.2 to 13.8% of the
phenotypic variation. A total of 19 significant genome QTL that affected BW traits were located on seven
macro-chromosomes (1, 2, 3, 4, 6, 8 and Z) and one micro-chromosome (11). A total of 14 significant QTL
were detected for DG traits, distributed over 7 distinct regions on 6 chromosomes, and their effects ranged from
2 to 8.9% of the phenotypic variation. A total of 11 significant genome QTL affecting DG traits were located on
five macro-chromosomes (1, 2, 3, 4 and 8) and there was statistical evidence for two QTL on chromosome 4.
The proportions of phenotypic variation explained by significant and suggestive QTL for BW traits at 4, 8, 12
and 16 weeks were 21.1, 30.8, 29.3 and 25.4%, respectively. The proportions of phenotypic variation explained
by significant and suggestive QTL for DG traits during 0-4, 4-8, 8-12 and 12-16 weeks were 25.9, 29.1, 9.35
and 3.9%, respectively. The largest proportion of the phenotypic variation explained by a QTL was 8.9% for
DG4-8 at 428 cM on chromosome 4. The additive effects of QTL on growth traits were positive, while the
dominance effects were generally negative or not significant. A QTL for BW at 12 weeks of age segregating on
chromosome 4 at 179 cM had the largest additive effect (205.7 ± 22.2 g) and explained 13.8% of the phenotypic
variation. The largest dominance effect (−188.1 ± 55.0 g) was for QTL of BW at 16 weeks of age segregation on
chromosome 4 at 139 cM and the QTL effect accounted for 6.5% of the phenotypic variation. The total trait
variances explained by QTL for each growth trait were 21.1, 30.8, 31.7, 25.4, 25.9, 29.1, 9.35 and 3.9 % in
BW4, BW8, BW12, BW16, DG04, DG48, DG812 and DG1216, respectively.