Linear programming applied to dairy cattle selection

Paper 1 outlines a generalization to Hill\u27s equations for predicting response to selection. Equations are developed that account for multiple stage selection in either or both sexes and the flow of genes for animals selected at later stages. The asymptotic response to a single cycle of selection is shown to agree with classical selection theory. The equations applied to a dairy progeny testing scheme representative of an artificial insemination organization in the USA. The predicted asymptotic rates to a single cycle of selection were overestimated by 6% and the cumulative response to continuous selection over 20 years was overestimated by 8% when single stage male selection model was compared to two stage selection model;A linear programming model that accounts for the economic consequences of response to selection to the producer enterprise over a given planning horizon is described in Paper 2. A procedure is given in detail for defining upper lower bound constraints on variables that are correlated in the linear programming model. The optimal response to selection per year for the production traits was closest to their maximums achievable from a gene-flow model. Of all the non-production traits, days open had the greatest proportion of its maximum achievable from a gene-flow model. The linear programming model was used to compute relative economic weights (REV). The REVs for milk, fat, and protein production were considerably larger than the REVs for the non-production traits for all planning horizons. Somatic cell score had the largest REVs of the non-production traits in all planning horizons;In the third paper multiple-trait REML was used to estimate the heritabilities and the genetic and phenotypic correlations for 48- and 72-mo herd life from sire models incorporating sire relationships. Two traits were defined for 48- and 72-mo herd life, true herd life (THL) and functional herd life (FHL), which were adjusted for milk production prior to culling. The genetic correlations were used to compute weights for indirect prediction of true and functional herd-life PTA from linear-type traits PTA. (Abstract shortened by UMI.

Genome-wide association analysis reveals QTL and candidate mutations involved in white spotting in cattle

International audienceAbstractBackgroundWhite spotting of the coat is a characteristic trait of various domestic species including cattle and other mammals. It is a hallmark of Holstein–Friesian cattle, and several previous studies have detected genetic loci with major effects for white spotting in animals with Holstein–Friesian ancestry. Here, our aim was to better understand the underlying genetic and molecular mechanisms of white spotting, by conducting the largest mapping study for this trait in cattle, to date.ResultsUsing imputed whole-genome sequence data, we conducted a genome-wide association analysis in 2973 mixed-breed cows and bulls. Highly significant quantitative trait loci (QTL) were found on chromosomes 6 and 22, highlighting the well-established coat color genes KIT and MITF as likely responsible for these effects. These results are in broad agreement with previous studies, although we also report a third significant QTL on chromosome 2 that appears to be novel. This signal maps immediately adjacent to the PAX3 gene, which encodes a known transcription factor that controls MITF expression and is the causal locus for white spotting in horses. More detailed examination of these loci revealed a candidate causal mutation in PAX3 (p.Thr424Met), and another candidate mutation (rs209784468) within a conserved element in intron 2 of MITF transcripts expressed in the skin. These analyses also revealed a mechanistic ambiguity at the chromosome 6 locus, where highly dispersed association signals suggested multiple or multiallelic QTL involving KIT and/or other genes in this region.ConclusionsOur findings extend those of previous studies that reported KIT as a likely causal gene for white spotting, and report novel associations between candidate causal mutations in both the MITF and PAX3 genes. The sizes of the effects of these QTL are substantial, and could be used to select animals with darker, or conversely whiter, coats depending on the desired characteristics

Genome-wide association analysis reveals QTL and candidate mutations involved in white spotting in cattle

International audienceAbstractBackgroundWhite spotting of the coat is a characteristic trait of various domestic species including cattle and other mammals. It is a hallmark of Holstein–Friesian cattle, and several previous studies have detected genetic loci with major effects for white spotting in animals with Holstein–Friesian ancestry. Here, our aim was to better understand the underlying genetic and molecular mechanisms of white spotting, by conducting the largest mapping study for this trait in cattle, to date.ResultsUsing imputed whole-genome sequence data, we conducted a genome-wide association analysis in 2973 mixed-breed cows and bulls. Highly significant quantitative trait loci (QTL) were found on chromosomes 6 and 22, highlighting the well-established coat color genes KIT and MITF as likely responsible for these effects. These results are in broad agreement with previous studies, although we also report a third significant QTL on chromosome 2 that appears to be novel. This signal maps immediately adjacent to the PAX3 gene, which encodes a known transcription factor that controls MITF expression and is the causal locus for white spotting in horses. More detailed examination of these loci revealed a candidate causal mutation in PAX3 (p.Thr424Met), and another candidate mutation (rs209784468) within a conserved element in intron 2 of MITF transcripts expressed in the skin. These analyses also revealed a mechanistic ambiguity at the chromosome 6 locus, where highly dispersed association signals suggested multiple or multiallelic QTL involving KIT and/or other genes in this region.ConclusionsOur findings extend those of previous studies that reported KIT as a likely causal gene for white spotting, and report novel associations between candidate causal mutations in both the MITF and PAX3 genes. The sizes of the effects of these QTL are substantial, and could be used to select animals with darker, or conversely whiter, coats depending on the desired characteristics

Linear programming applied to dairy cattle selection

Paper 1 outlines a generalization to Hill's equations for predicting response to selection. Equations are developed that account for multiple stage selection in either or both sexes and the flow of genes for animals selected at later stages. The asymptotic response to a single cycle of selection is shown to agree with classical selection theory. The equations applied to a dairy progeny testing scheme representative of an artificial insemination organization in the USA. The predicted asymptotic rates to a single cycle of selection were overestimated by 6% and the cumulative response to continuous selection over 20 years was overestimated by 8% when single stage male selection model was compared to two stage selection model;A linear programming model that accounts for the economic consequences of response to selection to the producer enterprise over a given planning horizon is described in Paper 2. A procedure is given in detail for defining upper lower bound constraints on variables that are correlated in the linear programming model. The optimal response to selection per year for the production traits was closest to their maximums achievable from a gene-flow model. Of all the non-production traits, days open had the greatest proportion of its maximum achievable from a gene-flow model. The linear programming model was used to compute relative economic weights (REV). The REVs for milk, fat, and protein production were considerably larger than the REVs for the non-production traits for all planning horizons. Somatic cell score had the largest REVs of the non-production traits in all planning horizons;In the third paper multiple-trait REML was used to estimate the heritabilities and the genetic and phenotypic correlations for 48- and 72-mo herd life from sire models incorporating sire relationships. Two traits were defined for 48- and 72-mo herd life, true herd life (THL) and functional herd life (FHL), which were adjusted for milk production prior to culling. The genetic correlations were used to compute weights for indirect prediction of true and functional herd-life PTA from linear-type traits PTA. (Abstract shortened by UMI.)</p

Optimal cow replacement on New Zealand seasonal supply dairy farms : a thesis presented in partial fulfilment of the requirements for a Mastrate [sic] degree of Agricultural Science in Animal Science at Massey University

An intraherd best linear unbiased prediction (BLUP) model for predicting the future milkfat production of individual cows was developed. A major advantage of the BLUP technique was to enable prediction of the future milkfat production of freshening heifers, since relationships between animals were included in the model. These predictions of future performance were incorporated, along with various costs and revenues Qf production in New Zealand and calving date, into a model to arrive at an expected net revenue for each individual cow. Three models to rank cows on future profitability were developed and evaluated. Two models utilised dynamic programming procedures. One model estimated the annualised present value of the net returns of each cow and her replacement up to a predetermined planning horizon. The second model used the same criterion, but also a11owed optimal replacement to occur in future seasons. The third model utilised replacement model evaluation techniques and estimated the annualised present value of the net returns based on the remaining economic lifespan of individual cows. The models were tested over a large number of different situations. The effects of changes in the different economic parameters are discussed and the behaviour of each model is documented. The parameters directly associated with the cost of replacement had the greatest effect on the annual present value's (APV) of individual cows. The optimal rankings were affected by the price of the heifer replacement and the price of manufacturing beef, whereas milkfat price played an insignificant role. Varying the price of manufacturing beef and the price of the heifer replacement simultaneously had only a small effect on the ranking of the cows. The parameters such as interest rate and planning horizon also affected the APVs produced by the dynamic models. Increasing the planning horizon post 10 yeors coused o reduction in the voriotion between the APVs. It was concluded that the dynamic programming model which allowed optimal replacement in future seasons provided the best system for ranking cows on expected future income

Estimation of (co)variance components across breeds by a test-day model adapted to New Zealand dairy cattle.

peer reviewe

Estimation of test-day model (co)variance components across breeds using New Zealand dairy cattle data

In New Zealand, a large proportion of cows are currently crossbreds, mostly Holstein-Friesians (HF) x Jersey (JE). The genetic evaluation system for milk yields is considering the same additive genetic effects for all breeds. The objective was to model different additive effects according to parental breeds to obtain first estimates of correlations among breed-specific effects and to study the usefulness of this type of random regression test-day model. Estimates of (co) variance components for purebred HF and JE cattle in purebred herds were computed by using a single-breed model. This analysis showed differences between the 2 breeds, with a greater variability in the HF breed. (Co) variance components for purebred HF and JE and crossbred HF x JE cattle were then estimated by using a complete multibreed model in which computations of complete across-breed (co)variances were simplified by correlating only eigenvectors for HF and JE random regressions of the same order as obtained from the single-breed analysis. Parameter estimates differed more strongly than expected between the single-breed and multibreed analyses, especially for JE. This could be due to differences between animals and management in purebred and nonpurebred herds. In addition, the model used only partially accounted for heterosis. The multibreed analysis showed additive genetic differences between the HF and JE breeds, expressed as genetic correlations of additive effects in both breeds, especially in linear and quadratic Legendre polynomials (respectively, 0.807 and 0.604). The differences were small for overall milk production (0.926). Results showed that permanent environmental lactation curves were highly correlated across breeds; however, intraherd lactation curves were also affected by the breed-environment interaction. This result may indicate the existence of breed-specific competition effects that vary through the different lactation stages. In conclusion, a multibreed model similar to the one presented could optimally use the environmental and genetic parameters and provide breed-dependent additive breeding values. This model could also be a useful tool to evaluate crossbred dairy cattle populations like those in New Zealand. However, a routine evaluation would still require the development of an improved methodology. It would also be computationally very challenging because of the simultaneous presence of a large number of breeds

ht-phenos

Herd test phenotypes for 29,350 cows. Used for milk phenotype association analyses