Detection and utilisation of quantitative trait loci in dairy cattle

The focus of the thesis is on the detection of quantitative trait loci (QTL) in dairy cattle and their utilisation in breeding programmes. Analysis of one bovine chromosome for quantitative trait loci for milk production traits is described and a QTL for protein percent was identified that was significant at the 1% level.When analysing the chromosome it was observed that the degree of precision in estimating QTL location and size (or variance) is poor. Through stochastic simulation, the effect of incorrect parameter estimates for quantitative trait locus effect, and position on genetic response from marker assisted selection is investigated.From this study it was concluded that studies should be undertaken to verify estimates of QTL and location to enable optimal use of marker assisted selection. Strategies to confirm the existence and size of quantitative trait loci identified in a genome scan are outlined. Also through stochastic simulation the effect of reducing flanking-marker bracket size was found to increase the genetic response from marker assisted selection.Simulation is used to estimate improvements in rate of genetic gain from marker assisted selection for two scenarios, the current situation and a futuristic setting. The increase in rate of genetic gain with marker assisted selection is some 5% in the current situation with the potential of 30% with improvements in level of genetic variance identified and the identified loci being in disequilibrium with marker alleles.The general discussion of this thesis addresses the use of significance levels in quantitative trait loci detection, experimental designs to identify further quantitative trait loci in the New Zealand dairy industry, and the current and possible future application of marker assisted selection in dairy breeding programmes.</p

QTL detection for milk production traits in goats using a longitudinal model

Summary Eight paternal half-sib families were used to identify chromosomal regions associated with variation in the lactation curves of dairy goats. DNA samples from 162 animals were amplified by PCR for 37 microsatellite markers, from Capra hircus autosomes CHI3, CHI6, CHI14 and CHI20. Milk samples were collected during 6 years, and there were 897 records for milk yield (MY) and 814 for fat (FP) and protein percentage (PP). The analysis was conducted in two stages. First, a random regression model with several fixed effects was fitted to describe the lactation function, using a scale (α) plus four shape parameters: β and γ, both associated with a decrease in the slope of the curve, and δ and φ that are related to the increase in slope. Predictions of α, β, γ, δ and φ were regressed using an interval mapping model, and F-tests were used to test for quantitative trait loci (QTL) effects. Significant (p < 0.05) QTLs were found for: (i) MY: CHI6 at 70-80 cM for all parameters; CHI14 at 14 cM for δ and φ; (ii) FP: CHI14, at 63 cM was associated with β; CHI20, at 72 cM, showed association with α; (iii) PP: chromosomal regions associated with β were found at 59 cM in CHI3 and at 55 cM in CHI20 with α and γ. Analyses using more families and more animals will be useful to confirm or to reject these findings. © 2008 Blackwell Verlag, Berlin.Fil: Roldán, D.L.. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación en Ciencias Veterinarias y Agronómicas. Instituto de Genética; ArgentinaFil: Rabasa, Alicia Elvira. Universidad Nacional de Tucumán. Facultad de Agronomía y Zootecnia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; ArgentinaFil: Saldaño, S.. Universidad Nacional de Tucumán. Facultad de Agronomía y Zootecnia; ArgentinaFil: Holgado, F.. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Tucuman-santiago del Estero. Campo Experimental Regional Leales; ArgentinaFil: Poli, M. A.. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación en Ciencias Veterinarias y Agronómicas. Instituto de Genética; ArgentinaFil: Cantet, Rodolfo Juan Carlos. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Animal; Argentin

Selective genotyping to detect QTL for multiple traits in outbred populations

Selective genotyping (i.e., genotyping of individuals that are extreme for a quantitative trait) has been undertaken in many studies but has usually been limited to one trait of interest. This paper outlines the implications of selective genotyping when applied to a daughter design. Formulas are presented that enable unbiased estimation of QTL effects for the selectively genotyped and correlated traits. Formulas were verified using simulation. Further, formulas are presented to enable power calculations to be undertaken for the selectively genotyped trait and correlated traits. These algorithms are demonstrated with a numerical example

Effect of Live Weight and Differing Economic Values on Responses to Selection for Milk Fat, Protein, Volume, and Live Weight

Five traits of major economic importance in the New Zealand dairy industry are milk volume, milk fat, milk protein, live weight, and survival. This study evaluated the impact of live weight as a trait in the selection objectives for the New Zealand dairy industry. Live weight of the lactating cow is an important measure because it reflects feeding costs via maintenance feed and salvage values of cows to be culled. In addition, selection responses were evaluated for differing relative economic values for milk protein and milk fat, and selection indexes that included or excluded phenotypic and genotypic correlations between traits were compared. Inclusion of live weight, with a negative economic value in a four-trait selection index with milk, milk fat, and protein resulted in higher economic response. Protein response to selection was not more than 2% when the relative economic value for the ratio of protein to milk fat exceeded 5:1 in a two-trait model; however, milk fat response decreased by over 10%. When a negative relative economic value was assigned to milk fat, economic returns were lower because of lower milk fat responses and the lack of higher protein responses compared with the same ratio for relative economic value but a positive weight for milk fat. Accounting for phenotypic and genetic correlations in deriving selection index weight improved economic response 5%

Effect of inaccurate parameter estimates on genetic response to marker assisted selection in an outbred population.

The effect of inaccurate estimates of variance and of the location of the quantitative trait locus on the genetic response to marker-assisted selection was studied by simulation of an adult multiple ovulation and embryo transfer nucleus breeding scheme. Two genetic models were simulated for the quantitative trait locus: a total of 10 alleles or 2 distinct alleles per base parent. For both models, the locus explained either 5 or 10% of phenotypic variance. A polygenic component was simulated, and the two genetic components were summed to 35% heritability for a trait measured on females. Overestimation of variance of the quantitative trait locus had minimal effect on genetic gain for marker-assisted selection over the short term, but decreased long-term response. The long-term loss was reduced when variance of the quantitative trait locus was reestimated after four generations of marker-assisted selection. Selection for favorable alleles at a nonexistent quantitative trait locus resulted in first generation losses of 3 and 7% for postulated quantitative trait loci, explaining 5 and 10% of variance, respectively. The larger the degree of error in location, the larger was the genetic loss compared with the correct location scenario. For the largest simulated location error of 15 cM, genetic superiority of marker-assisted selection was reduced by 80% in the first generation. We concluded that studies should be undertaken to verify estimates of quantitative trait locus and location to make optimal use of marker-assisted selection