Models to estimate genetic parameters in crossbred dairy cattle populations under selection

Estimates of genetic parameters needed to control breeding programs, have to be regularly updated, due to changing environments and ongoing selection and crossing of populations. Restricted maximum likelihood methods optimally provide these estimates, assuming that the statisticalgenetic model used is correct.Generally, a model for analysis of milk production data assumes only additive genetic effects and random sampling. These assumptions are rarely met. In many animal populations genetic material from other populations is used. Crossing of lines or breeds often gives rise to non-additive effects. Furthermore, most of the data used for genetic analysis come from populations under selection. The subject of this thesis was to determine whether or not models for genetic evaluation of dairy populations should account for non-additive effects and selection, and how this should be done.The influence of non-additive effects on the estimation of heritabilities and breeding values was studied in Chapter 2. A population having progeny that descended from sires and dams with various fractions of genes from two breeds was simulated. Additive breed effects and non-additive effects from breed crosses, were simulated. Data on performance were analyzed using mixed models, that accounted for fixed additive genetic group and random sire effects. Three additive models, with genetic groups defined according to 1) breed composition of the progeny, 2) breed composition of the sire and dam, or 3) linear regression on breed fraction, were compared with a non-additive model, with a linear regression on breed fraction, heterozygosity and recombination in the genome of the progeny. Variance components were estimated using restricted maximum likelihood.Additive genetic variance and heritability were overestimated for an additive model with progeny groups. Additive models gave biased estimates for breed differences, group effects and breeding values. Breed differences were overestimated when sire groups were used. Estimates for each parameter were unbiased using the non-additive model.In Chapter 3, the same models were applied to data of cows with variable proportions of genes from the Dutch Friesian and the Holstein Friesian (HF) populations. The data set contained 92,333 first lactation records (305 days milk production) of cows from 675 young sires and 307,050 records of cows from 202 proven sires. Estimates for heterosis varied from 2.5% (fat yield) to 0% (protein percentage). Recombination effects varied from -1.9% (protein yield) to 1.5% (fat percentage). Additive models with progeny groups overestimated genetic variance by 6%. Models with sire groups overestimated additive genetic values of imported HF sires by 33%. Using a nonadditive model, heritability estimates were .38 for milk yield, .80 for fat percentage and .70 for protein percentage. It was concluded that a nonadditive model was preferable for estimation of genetic variance and prediction of breeding values in crossbred dairy populations.In the fourth chapter, the effect of selection on estimation of additive genetic variance was studied. A population of size 40 was simulated 100 times, for ten generations. Five out of twenty males were selected at each generation and each male was mated to four females and had two progeny. The additive genetic variance(σ 2a )before selection was 10 and the initial heritability was .5. The genetic variance was reduced to 6.72 after ten generations of selection, due to covariances among animals, inbreeding and gametic disequilibrium. Reduction of variance was lower in another population simulated with size 400 and ten percent of the males selected. Restricted Maximum Likelihood was used to estimate σ2a using an animal model. The estimate of σ2awas empirically unbiased, when all data and all relationships were used. Omitting data from selected ancestors caused biased estimates of σ2adue to the fact that not all gametic disequilibrium was accounted for. Inbreeding and covariances were adjusted for, when additional relationships between assumed base animals were considered. Bias from gametic disequilibrium decreased slightly with the use of more relationship information. Estimates from data based on later generations only, were biased by selection. Mean estimates of genetic variance depended on the assumed base population and were insensitive to the number of subsequent generations with data.A method to estimate genetic parameters conditional to selection occurring before formation of the base population was investigated in Chapter 5. For this, simulated data from the same populations as in Chapter 4 was used. The method assumes base parents as fixed and a conditional variance is based upon the Mendelian sampling of gametes from the base parents. Selection was for five generations but only animals of generations 4 and 5 were assumed to have performance records and parents known. Additive genetic and residual variance were assumed to be 10. When 20 out of 200 sires were selected per generation, estimated genetic variance was 8.58 when base animals were assumed random, and it was 6.03 when they were fixed. Residual variance was overestimated in the latter case. When males of generation 4 were not selected to have progeny, estimated genetic variance was 9.91. It was concluded that estimates for genetic parameters with the conditional model were not biased by selection of base animals. However, the procedure with fixed base parents was biased when descendants of base animals were selected to have progeny.Genetic variance of milk production traits was estimated with a conditional model to account for selection of sires. In the HF subpopulation, which had been selected more intensively, genetic variance for milk yield was estimated about 8% higher compared to a random models that assumes no selection.Estimates of heritability for milk production traits were found to be high with a sire model, after correction for non-additive effects (Chapter 3) and selection of parents (Chapter 5). Preliminary results with an animal model, which accounted for non- random mating of sires, did not show lower estimates. More research is suggested to determine whether the cause for high heritabilities is genetic or environmental.Main conclusions- By not accounting for non-additive effects in genetic evaluation of crossbred populations, biased estimates of breeding values and additive genetic differences between crossbred groups are found. Records of crossbred dairy cattle should therefore be adjusted for systematic additive and non-additive breed effects.- Estimation of crossbreeding parameters from field data can provide low standard errors, although sampling correlation may be high for certain mating designs.- Estimates of genetic variance based on data from selected generations only were biased by selection. Mean estimates of genetic variance depended mostly on the assumed base population and were insensitive to the number of subsequent generations with data. Additional relationships adjust genetic variance estimates for covariances among animals, and forsome of the gametic disequilibrium.- Estimates for genetic parameters with a conditional model are not biased by selection of base animals, but a bias will be introduced when descendants of base animals have been selected to have progeny.- Heritability estimates of milk production traits in crossbred dairy cattle data were found to be higher as parameters currently assumed for genetic evaluation.</TT

Different models of genetic variation and their effect on genomic evaluation

<p>Abstract</p> <p>Background</p> <p>The theory of genomic selection is based on the prediction of the effects of quantitative trait loci (QTL) in linkage disequilibrium (LD) with markers. However, there is increasing evidence that genomic selection also relies on "relationships" between individuals to accurately predict genetic values. Therefore, a better understanding of what genomic selection actually predicts is relevant so that appropriate methods of analysis are used in genomic evaluations.</p> <p>Methods</p> <p>Simulation was used to compare the performance of estimates of breeding values based on pedigree relationships (Best Linear Unbiased Prediction, BLUP), genomic relationships (gBLUP), and based on a Bayesian variable selection model (Bayes B) to estimate breeding values under a range of different underlying models of genetic variation. The effects of different marker densities and varying animal relationships were also examined.</p> <p>Results</p> <p>This study shows that genomic selection methods can predict a proportion of the additive genetic value when genetic variation is controlled by common quantitative trait loci (QTL model), rare loci (rare variant model), all loci (infinitesimal model) and a random association (a polygenic model). The Bayes B method was able to estimate breeding values more accurately than gBLUP under the QTL and rare variant models, for the alternative marker densities and reference populations. The Bayes B and gBLUP methods had similar accuracies under the infinitesimal model.</p> <p>Conclusions</p> <p>Our results suggest that Bayes B is superior to gBLUP to estimate breeding values from genomic data. The underlying model of genetic variation greatly affects the predictive ability of genomic selection methods, and the superiority of Bayes B over gBLUP is highly dependent on the presence of large QTL effects. The use of SNP sequence data will outperform the less dense marker panels. However, the size and distribution of QTL effects and the size of reference populations still greatly influence the effectiveness of using sequence data for genomic prediction.</p

Development and validation of SUCROS-Cotton: a potential crop growth simulation model for cotton

A model for the development, growth and potential production of cotton (SUCROS-Cotton) was developed. Particular attention was given to the phenological development of the plant and the plasticity of fruit growth in response to temperature, radiation, daylength, variety traits, and management. The model is characterized by a comparatively simple code and transparent algorithms. The model was parameterized for Chinese cotton varieties and validated with extensive independent datasets on cotton growth and production from the Yellow River region and Xinjiang Province. The model validation showed that the phenology, growth and yield were simulated satisfactorily. The root mean square error (RMSE) for date of emergence, date of flowering, date of open boll stage and duration from sowing to boll opening was less than four calendar days, both for cotton grown in monoculture and cotton grown in a relay intercropping system with wheat. The RMSE of predicted total dry matter compared with observations was at most 6.6%, of lint yield 6.6%, and for number of harvestable bolls 10.0%. SUCROS-Cotton provides a tool to (1) assess production opportunities of cotton in various ecological zones in response to temperature, incoming radiation and management, (2) identify optimal cotton ideotypes for different agro-ecological conditions and for guiding breeding efforts, and (3) explore resource-use-efficient cropping systems, including intercropping options, and crop management practices such as plastic film mulching and sowing date

Accuracy of estimated genomic breeding values for wool and meat traits in a multi-breed sheep population

Estimated breeding values for the selection of more profitable sheep for the sheep meat and wool industries are currently based on pedigree and phenotypic records. With the advent of a medium-density DNA marker array, which genotypes ∼50000 ovine single nucleotide polymorphisms, a third source of information has become available. The aim of this paper was to determine whether this genomic information can be used to predict estimated breeding values for wool and meat traits. The effects of all single nucleotide polymorphism markers in a multi-breed sheep reference population of 7180 individuals with phenotypic records were estimated to derive prediction equations for genomic estimated breeding values (GEBV) for greasy fleece weight, fibre diameter, staple strength, breech wrinkle score, weight at ultrasound scanning, scanned eye muscle depth and scanned fat depth. Five hundred and forty industry sires with very accurate Australian sheep breeding values were used as a validation population and the accuracies of GEBV were assessed according to correlations between GEBV and Australian sheep breeding values . The accuracies of GEBV ranged from 0.15 to 0.79 for wool traits in Merino sheep and from 0.07 to 0.57 for meat traits in all breeds studied. Merino industry sires tended to have more accurate GEBV than terminal and maternal breeds because the reference population consisted mainly of Merino haplotypes. The lower accuracy for terminal and maternal breeds suggests that the density of genetic markers used was not high enough for accurate across-breed prediction of marker effects. Our results indicate that an increase in the size of the reference population will increase the accuracy of GEBV

Optimal breeding strategies for sheep should consider variation in feed availability

Large pasture growth variation across years requires changes in optimal management between years, making breeding objectives difficult to calculate. We modeled a farm with Merino sheep bred for wool and meat in a Mediterranean environment where feed availability and prices vary widely between years. We calculated profit and economic values for 6 traits by optimizing management across 5 years using dynamic recursive analysis, comparing varying to average pasture growth and prices. Profit decreased for the varying scenario but economic values increased. Economic values for yearling live weight and fibre diameter increased most and were least sensitive to uncertain pasture growth, having least effect on energy requirements. These changes shifted selection response from wool towards meat and reproduction, mostly because reproduction had a higher genetic correlation with yearling weight than wool traits. Therefore, variation in pasture growth should be considered when developing sheep breeding programs

Genetic correlations between body weight change and reproduction traits in Merino ewes depend on age

Merino sheep in Australia experience periods of variable feed supply. Merino sheep can be bred to be more resilient to this variation by losing less BW when grazing poor quality pasture and gaining more BW when grazing good quality pasture. Therefore, selection on BW change might be economically attractive but correlations with other traits in the breeding objective need to be known. The genetic correlations (rg) between BW, BW change, and reproduction were estimated using records from approximately 7,350 fully pedigreed Merino ewes managed at Katanning in Western Australia. Number of lambs and total weight of lambs born and weaned were measured on approximately 5,300 2-yr-old ewes, approximately 4,900 3-yrold ewes, and approximately 3,600 4-yr-old ewes. On a proportion of these ewes BW change was measured: approximately 1,950 2-yr-old ewes, approximately 1,500 3-yr-old ewes, and approximately 1,100 4-yr-old ewes. The BW measurements were for 3 periods. The first period was during mating period over 42 d on poor pasture. The second period was during pregnancy over 90 d for ewes that got pregnant on poor and medium quality pasture. The third period was during lactation over 130 d for ewes that weaned a lamb on good quality pasture. Genetic correlations between weight change and reproduction were estimated within age classes. Genetic correlations were tested to be significantly greater magnitude than 0 using likelihood ratio tests. Nearly all BW had significant positive genetic correlations with all reproduction traits. In 2-yr-old ewes, BW change during the mating period had a positive genetic correlation with number of lambs weaned (rg = 0.58); BW change during pregnancy had a positive genetic correlation with total weight of lambs born (rg = 0.33) and a negative genetic correlation with number of lambs weaned (rg = -0.49). All other genetic correlations were not significantly greater magnitude than 0 but estimates of genetic correlations for 3-yr-old ewes were generally consistent with these findings. The direction of the genetic correlations mostly coincided with the energy requirements of the ewes and the stage of maturity of the ewes. In conclusion, optimized selection strategies on BW changes to increase resilience will depend on the genetic correlations with reproduction and are dependent on age

An effective hyper-parameter can increase the prediction accuracy in a single-step genetic evaluation

The H-matrix best linear unbiased prediction (HBLUP) method has been widely used in livestock breeding programs. It can integrate all information, including pedigree, genotypes, and phenotypes on both genotyped and non-genotyped individuals into one single evaluation that can provide reliable predictions of breeding values. The existing HBLUP method requires hyper-parameters that should be adequately optimised as otherwise the genomic prediction accuracy may decrease. In this study, we assess the performance of HBLUP using various hyper-parameters such as blending, tuning, and scale factor in simulated and real data on Hanwoo cattle. In both simulated and cattle data, we show that blending is not necessary, indicating that the prediction accuracy decreases when using a blending hyper-parameter <1. The tuning process (adjusting genomic relationships accounting for base allele frequencies) improves prediction accuracy in the simulated data, confirming previous studies, although the improvement is not statistically significant in the Hanwoo cattle data. We also demonstrate that a scale factor, α, which determines the relationship between allele frequency and per-allele effect size, can improve the HBLUP accuracy in both simulated and real data. Our findings suggest that an optimal scale factor should be considered to increase prediction accuracy, in addition to blending and tuning processes, when using HBLUP.Mehdi Neshat, Soohyun Lee, Md. Moksedul Momin, Buu Truong, Julius H. J. van der Werf, and S. Hong Le