Quantitative genetics: detecting selection.
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Animals↗
Biomedical subjects
M E Goddard
Publications and source records attributed to M E Goddard.
Prediction of total genetic value using genome-wide dense marker maps.
Recent advances in molecular genetic techniques will make dense marker maps available and genotyping many individuals for these markers feasible. Here we attempted to estimate the effects of approximately 50,000 marker haplotypes simultaneously from a limited number of phenotypic records. A genome of 1000 cM was simulated with a marker spacing of 1 cM. The markers surrounding every 1-cM region were combined into marker haplotypes. Due to finite population size N(e) = 100, the marker haplotypes were in linkage disequilibrium with the QTL located between the markers. Using least squares, all haplotype effects could not be estimated simultaneously. When only the biggest effects were included, they were overestimated and the accuracy of predicting genetic values of the offspring of the recorded animals was only 0.32. Best linear unbiased prediction of haplotype effects assumed equal variances associated to each 1-cM chromosomal segment, which yielded an accuracy of 0.73, although this assumption was far from true. Bayesian methods that assumed a prior distribution of the variance associated with each chromosome segment increased this accuracy to 0.85, even when the prior was not correct. It was concluded that selection on genetic values predicted from markers could substantially increase the rate of genetic gain in animals and plants, especially if combined with reproductive techniques to shorten the generation interval.
Animals↗
Estimates of genetic parameters for daily somatic cell count of Australian dairy cattle.
Genetic parameters for daily somatic cell counts (SCC) of the first three parities were estimated for Australian Dairy Cattle. Most of the data analyses were carried out with a sire random regression model. The estimates were compared with those from conventional ten-trait analyses and animal models. In the first-parity estimates of heritabilities (h2) were low (0.04 to 0.05) at the beginning of the lactation and higher (0.11 to 0.13) at the end. The average h2 estimated from random regression sire model, random regression animal model and conventional multitrait sire model were 0.09, 0.09, and 0.08, respectively, in the first lactation. The average h2 were 0.09 and 0.11 in the second and third parities, respectively. Genetic correlations between daily log(e) SCC within parity were high for adjacent tests (nearly 1) and low (as low as 0.30) between the beginning and the end of the lactation. Generally, the genetic correlations between parities depend on how far apart they are and on whether they are on the same day in any two parities. Across parities, on average, genetic correlations between parities 1 and 3 were the lowest and those between 1 and 2 intermediate, while those between 2 and 3 were the highest. The estimated environmental correlations were lower than the genetic correlations, but the trends were generally similar. Differences in genetic parameter estimates due to model were small, except for some genetic correlations. The high residual error variances, the low h2, and the inconsistency in genetic correlations that were observed particularly at the beginning of the first lactation suggest that log(e) SCC early in the first lactation may be related to a spike in SCC as result of infection and (or) onset of lactation while SCC later in lactation represents a sustained response to infection. Accounting for the variation in heritabilities and correlations should improve the accuracy of genetic evaluations for SCC based on test day records.
Animals↗
Fine mapping of quantitative trait loci using linkage disequilibria with closely linked marker loci.
A multimarker linkage disequilibrium mapping method was developed for the fine mapping of quantitative trait loci (QTL) using a dense marker map. The method compares the expected covariances between haplotype effects given a postulated QTL position to the covariances that are found in the data. The expected covariances between the haplotype effects are proportional to the probability that the QTL position is identical by descent (IBD) given the marker haplotype information, which is calculated using the genedropping method. Simulation results showed that a QTL was correctly positioned within a region of 3, 1.5, or 0.75 cM in 70, 62, and 68%, respectively, of the replicates using markers spaced at intervals of 1, 0.5, and 0.25 cM, respectively. These results were rather insensitive to the number of generations since the QTL occurred and to the effective population size, except that 10 generations yielded rather poor estimates of the QTL position. The position estimates of this multimarker disequilibrium mapping method were more accurate than those from a single marker transmission disequilibrium test. A general approach for identifying QTL is suggested, where several stages of disequilibrium mapping are used with increasingly dense marker spacing.
Chromosome Mapping↗
Multiple-trait mapping of quantitative trait loci after selective genotyping using logistic regression.
Experiments to map QTL usually measure several traits, and not uncommonly genotype only those animals that are extreme for some trait(s). Analysis of selectively genotyped, multiple-trait data presents special problems, and most simple methods lead to biased estimates of the QTL effects. The use of logistic regression to estimate QTL effects is described, where the genotype is treated as the dependent variable and the phenotype as the independent variable. In this way selection on phenotype does not bias the results. If normally distributed errors are assumed, the logistic-regression analysis is almost equivalent to a maximum-likelihood analysis, but can be carried out with standard statistical packages. Analysis of a simulated half-sib experiment shows that logistic regression can estimate the effect and position of a QTL without bias and confirms the increased power achieved by multiple-trait analysis.
Genotype↗
Consensus and debate in the definition of breeding objectives.
The breeding objective is usually to increase the profit of the firm, industry, or society that is investing in a breeding program. This objective should include long-term genetic gain and nonadditive genetic changes such as inbreeding depression and possibly a weighting against the variability of outcome. The breeding objective is described by a profit function that takes genetic values as input and produces profit as outcome. This profit function may be a bioeconomic model of the farm. The traits in the profit function should relate as directly as possible to all sources of income and costs. The profit function can include variables controlled by management decisions if these interact with genetic merit. The differences between genotypes should be evaluated when management variables are optimized for each genotype. In the long term, mean profit is expected to be close to zero, and all costs are assumed to be variable costs. Under these conditions, the relative economic weights are the same, regardless of how the farm is constrained how the unit for which profit is calculated, whether the perspective is that of individual producers, an industry, or consumers. However, if price signals are not passed along the chain from consumers to seed-stock breeders, the economic weights become distorted. The use to which breeding objectives are put, the perspective from which they are calculated, the rescaling and biological versus economic objectives, and the special problems associated with the inclusion of length of herdlife in the objective are discussed.
Animals↗
Covariance functions across herd production levels for test day records on milk, fat, and protein yields.
Multiple-trait BLUP evaluations of test day records require a large number of genetic parameters. This study estimated covariances with a reduced model that included covariance functions in two dimensions (stage of lactation and herd production level) and all three yield traits. Records came from all six states in Australia, were evenly distributed across the herd production levels, but decreased with increasing lactation stage from 9693 records for the 1st mo of lactation to 4199 records for the 10th mo. Using the variance component estimation package and a bivariate animal model, 1176 genetic (co)variances and 312 environmental (co)variances were estimated for 48 traits (1, 4, 7, and 10 mo of lactation; herd production levels of < 20, 20 to 22, 22 to 24, > 24 kg of milk/d; and milk, fat, and protein yields). The genetic (co)variances could be predicted by a multiplicative model that included 1) a term dependent on which yields (milk, fat, or protein) were involved in the covariance, 2) the covariance functions for month of lactation and herd production level, and 3) a covariance function for the interaction between these. This model required only 27 parameters instead of the 1176 (co)variances. For the environmental (co)variances, a model was fitted that contained several additional covariance functions. This model reduced the number of parameters from 312 to 71. For the same trait at the same production level, genetic correlations between test days ranged from 0.59 to 1, and environmental correlations ranged from 0.17 to 0.48. Genetic correlations between milk and fat, milk and protein, and fat and protein were 0.38, 0.83, 0.59, respectively, and correlations between the herd production levels ranged from 0.79 to 0.97. Failure to consider herd production level in a test day model evaluation might result, for instance, in overweighting of early lactation information from high production herds compared with information coming from bulls tested across all production levels.
Animals↗
The use of covariance functions and random regressions for genetic evaluation of milk production based on test day records.
In the analysis of test day records for dairy cattle, covariance functions allow a continuous change of variances and covariances of test day yields on different lactation days. The equivalence between covariance functions as an infinite dimensional extension of multivariate models and random regression models is shown in this paper. A canonical transformation procedure is proposed for random regression models in large-scale genetic evaluations. Two methods were used to estimate covariance function coefficients for first parity test day yields of Holsteins: 1) a two-step procedure fitting covariance functions to matrices with estimated genetic and residual covariances between predetermined periods of lactation and 2) REML directly from data with a random regression model. The first method gave more reliable estimates, particularly for the periphery of the trajectory. The goodness of fit of a random regression model based on covariables describing the shape of the lactation curve was nearly the same as random regression on Legendre polynomials. In the latter model, two and three regression coefficients were sufficient to fit the covariance structure for additive genetic and permanent environment, respectively. The eigenfunction pattern revealed the possibility of selection for persistency. Covariance functions can be usefully implemented in large-scale test day models by means of random regressions.
Analysis of Variance↗
Estimation of effects of quantitative trait loci in large complex pedigrees.
A method was derived to estimate effects of quantitative trait loci (QTL) using incomplete genotype information in large outbreeding populations with complex pedigrees. The method accounts for background genes by estimating polygenic effects. The basic equations used are very similar to the usual linear mixed model equations for polygenic models, and segregation analysis was used to estimate the probabilities of the QTL genotypes for each animal. Method R was used to estimate the polygenic heritability simultaneously with the QTL effects. Also, initial allele frequencies were estimated. The method was tested in a simulated data set of 10,000 animals evenly distributed over 10 generations, where 0, 400 or 10,000 animals were genotyped for a candidate gene. In the absence of selection, the bias of the QTL estimates was < 2%. Selection biased the estimate of the Aa genotype slightly, when zero animals were genotyped. Estimates of the polygenic heritability were 0.251 and 0.257, in absence and presence of selection, respectively, while the simulated value was 0.25. Although not tested in this study, marker information could be accommodated by adjusting the transmission probabilities of the genotypes from parent to offspring according to the marker information. This renders a QTL mapping study in large multi-generation pedigrees possible.
Animals↗
A computationally feasible test day model for genetic evaluation of yield traits in the United States.
A multitrait analysis of test day yields is proposed that includes 60 traits [3 yield traits (milk, fat, and protein), 2 parity groups (first and later) per yield trait, and 10 stages of lactation per parity]. To reduce the computations needed for the 60 traits, test day effects are estimated within the herd before analysis across herds, the rank of t he genetic (co)variance matrix is reduced, a canonical transformation is used with missing values replace by their expectations, and a repeatability model is applied to allow inclusion of parities after second. Historical 305-d records are included through their correlations with test day effects. Possible benefits from this model include 1) more accurate estimation of environmental effects from including the influence of particular days of recording, 2) optimal use of information from all test days (especially for lactations with long intervals from calving to first test or between tests), 3) improved accuracy of evaluations for component yields through contributions from information for milk yield, and 4) greater stability of bull evaluations from accounting for genetic differences among daughters in the shape of lactation curve and maturity rate.
Animals↗
Selection for carcass and feedlot traits considering alternative slaughter end points and optimized management.
Profit was defined as a function of the genotype of animals and variables controlled by management. Alternative parameterizations of management variables were examined to compare the effect of controlling age at slaughter, weight at slaughter, or fat depth at slaughter. The various parameterizations are shown to result in equivalent economic weights for genetic variables, provided management variables are optimized for the current genotype. The implication is that economic weights and selection indexes can be conveniently calculated for age constant end points even though commercial production may involve weight or backfat depth constant slaughter points. An example of selection for profit in the feedlot phase of beef production is presented. Three genotype-management combinations were considered. Economic weights and subsequent selection index weights were shown to depend on both average genotypic means and management (feeding and marketing program) factors.
Abattoirs↗
Genetic parameters for milk yield, survival, workability, and type traits for Australian dairy cattle.
Genetic parameters, such as heritabilities and genetic correlations were estimated for milk yield, survival, workability, and type traits for Australian Holstein-Friesian and Jersey cattle. All analyses were performed using multivariate REML with a sire model. Heritabilities for lactation yield traits were moderate, ranging from .20 to .28, and heritabilities for mean test day deviations were approximately .40 higher. Heritabilities for survival (probability of surviving to the next lactation) were low, ranging from .02 to .08. Genetic correlations between survival scores were high, ranging from .37 to .98, in particular between adjacent survival scores (on average .91 and .97 for Holstein-Friesians and Jerseys). Heritabilities for stayabilities were larger, ranging from .03 to .22. On average, genetic correlations between stayabilities were very high, ranging from .66 to .99. For milking speed, temperament, and likeability, heritability estimates ranged from .18 (for likeability in Holstein-Friesians) to .29 (for milking speed in Jerseys). Undesirable scores for milking speed and temperament had negative genetic correlations with stayabilities (correlations approximately -.20). Heritabilities for type traits were all moderate (.11 to .42), and genetic correlations among type traits and between type traits and production traits were large. Phenotypic correlations between type traits and stayabilities were low. Generally, genetic correlations between type traits and stayabilities were low although the standard errors of those estimates were large.
Animals↗
Comparison of selection methods at the same level of inbreeding.
Animal geneticists predict higher genetic responses to selection by increasing the accuracy of selection using BLUP with information on relatives. Comparison of different selection methods is usually made with the same total number tested and with the same number of parents and mating structure so as to give some acceptable (low) level of inbreeding. Use of family information by BLUP results in the individuals selected being more closely related, and the levels of inbreeding are increased, thereby breaking the original restriction on inbreeding. An alternative is to compare methods at the same level of inbreeding. This would allow more intense selection (fewer males selected) with the less accurate methods. Stochastic simulation shows that, at the same level of inbreeding, differences between the methods are much smaller than if inbreeding is unrestricted. If low to moderate inbreeding levels are targeted, as in a closed line of limited size, then selection on phenotype can yield higher genetic responses than selection on BLUP. Extra responses by BLUP are at the expense of extra inbreeding. The results derived here show that selection on BLUP of breeding values may not be optimal in all cases. Thus, current theory and teaching on selection methods are queried. Revision of the methodology and a reappraisal of the optimization results of selection theory are required.
Animals↗
Optimal effective population size for the global population of black and white dairy cattle.
The replacement of other black and white cattle strains by the North American Holstein breed, which itself is dominated by a small number of elite sires, has reduced the genetic diversity of the global population. Intense selection on a global basis leads to rapid genetic improvement but reduces effective population size. The optimal global effective population size was chosen to maximize the net present value of all future benefits from the breeding program. Two separate discount rates were used to reflect concerns about the long-term costs of small effective population size. This led to a higher optimal number of bull-sires than in past analyses. The optimum was sensitive to the magnitude of inbreeding depression and to the discount rates, but not to the variance caused by new mutations and the size of the world population. The genetic correlation between the breeding objectives of different AI studs controls the extent to which they all select the same sires of sons and, hence, affects the global effective population size. The prediction is made that different countries will select partially different sires, but genetically isolated strains will not reemerge. A better global breeding program is likely when selection of sires takes account of inbreeding depression and small genotype by environment interactions.
Animals↗
A comparison of two oral rehydration solutions in experimental models of dehydration and diarrhoea in calves.
Two oral rehydration solutions (ORS 1 and ORS 2) were evaluated in isolated intestinal loops of anaesthetised calves, in an experimental model of dehydration in the calf, in calves with experimentally induced diarrhoea and in 164 calves with clinical diarrhoea. The studies in isolated intestinal loops indicated that water absorption was significantly greater from ORS 2 than from ORS 1. After the intraperitoneal administration of hypertonic mannitol combined with intravenous diuretics, the plasma volume of calves was reduced by about 30 per cent, and was more rapidly expanded after treatment with ORS 2 than ORS 1. The plasma volume remained significantly reduced (P less than 0.01) three hours after dosing with ORS 1 whereas after treatment with ORS 2 it was not significantly different from the initial value. Acidosis was corrected to a significantly (P less than 0.01) greater extent after treatment with ORS 2, and peripheral perfusion also returned to normal more rapidly in calves given ORS 2. In newly purchased calves in which diarrhoea was induced experimentally with an E coli challenge, base deficit and diarrhoea were corrected more rapidly in the calves receiving ORS 2. When the solutions were tested in the treatment of 164 clinical cases of diarrhoea and dehydration there was no statistically significant difference in mortality between the formulations, although the overall mortality was 4.8 per cent in the calves treated with ORS 2, compared with 8.6 per cent in the calves treated with ORS 1. It was concluded that ORS 2 performed better than ORS 1 especially in the expansion of plasma volume and the correction of acidosis.
Acidosis↗
A model of acute infectious neonatal diarrhoea.
Oral inoculation of neonatal MFI mice with enterotoxigenic strains of Escherichia coli that possessed the K99 or F41 antigen or both resulted in severe diarrhoea with high mortality. The diarrhoea was associated with increased fluid in the gut, greatly increased numbers of E. coli in gut homogenates and reduced weight gain compared to control animals. Further studies with strain B44 demonstrated greatly increased numbers of E. coli on the surface of the intestinal mucosa and haemo-concentration. The infection was transmissible between litter-mates. There was no evidence of invasion of the intestinal tissue of infected animals. Gnotobiotic Balb C mice and endotoxin-resistant mice were susceptible to oral inoculation with bovine enterotoxigenic E. coli strains, but neonatal rats were not susceptible to infection with enterotoxigenic E. coli strains B44 or 431. Porcine strains of E. coli that possessed K88 or 987P antigen did not infect neonatal MFI mice but an "atypical" porcine strain (431) which possessed both K99 and F41 antigens caused diarrhoea and a high mortality. The disease in neonatal mice resembled acute diarrhoea caused by these bacteria in other species, particularly the calf, and the model should be of value in assessing the efficacy of therapeutic agents.
Animals↗
Assessment of heat stress in dairy cattle in Papua New Guinea.
The effects of breed (Friesian, AIS, Sindi-Sahiwal and Jersey), time of day (am v pm) and season of the year (hot or cool) on rectal temperature, pulse rate and respiratory rate were examined in the Highlands and Lowlands of Papua New Guinea. Two types of variation were investigated. Between-cow variation was the basis for the comparison of sites, seasons and breeds. Between-day variation was used to assess the effect of environmental temperatures on the animal parameters. Respiration rate and rectal temperature appeared more sensitive as indicators of heat stress than pulse rate and using these criteria the highlands climate was suitable for dairy cattle. Jerseys and Sahiwals showed lower respiratory rates compared with Friesians and AIS and their increase in rectal temperature during the day was less than the other two breeds but was not significantly so. The relationships between respiration rate and rectal temperature are discussed.
Animals↗
A multivariate analysis of the genetics of fearfulness in potential guide dogs.
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Animals↗