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Incorporation of Discrete Genotype Effects for Multiple Genes into Animal Model Evaluations when only a Small Fraction of the Population Has Been Genotyped

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Abstract

An efficient algorithm is described for marker-assisted selection appropriate for large populations, even though only a small fraction of the population is genotyped. Genotype probabilities for specific loci are computed for all animals without genotypes. Effects of the quantitative trait loci (QTL) are then estimated by a "cow model" and the appropriate effects are subtracted from the cows' records. Selection is based on genetic evaluations computed from the adjusted records after addition of each animal's QTL genotype effect. The proposed scheme was applied to 10 simulated populations of 37,000 cows generated over 30 yr and compared with a selection scheme based on a standard animal model. Two diallelic QTL with substitution effects of 0.5 and 0.32 phenotypic standard deviations were simulated with initial frequencies of 0.5 for both alleles. Means and standard errors of estimates of the QTL effects at yr 30 were 0.498 +/- 0.011 and 0.347 +/- 0.008. Thus, estimation of the larger QTL was nearly exact, whereas the smaller QTL was slightly overestimated. At yr 9 through 12 after the beginning of the breeding program, genetic gain in the marker-assisted selection scheme was 0.17 standard deviations greater than the standard scheme. This corresponds to nearly 2 yr of genetic progress relative to the standard scheme, or more than 40% of the total genetic gain obtained by the standard scheme at yr 9. Although genetic gain of the 2 schemes was nearly equal by yr 30, the Gibson effect of eventual greater progress by trait-based selection was not observed. Extension of the methods proposed in the current study could be applied to rank sires accurately including both marker and pedigree information for the large number of segregating QTL that will be detected by whole-genome single nucleotide polymorphism scans.

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... Finally, the QTL effects of each animal were added to the AM evaluation, and these evaluations were used to rank animals for selection. On simulated data with one or two segregating QTL, this method was able to derive unbiased genetic evaluations and genetic progress was increased relative to a standard AM Weller, 2008 and. ...
... The objectives of this study were to apply the method of Weller (2008 and to the actual genotype data of the Israeli Holstein population for the BovineSNP50 BeadChip (Matukumalli et al., 2009), using the 400 markers with the largest effects, in order to derive GEBVs for production and nonproduction economic traits, and to evaluate the GEBV based on R 2 and bias. ...
... For animals with unknown parents, genotype probabilities were assumed to be equal to the mean probabilities in the entire sample of genotyped bulls. 2. The effects of these 400 markers were estimated jointly by the 'cow model' of Baruch and Weller (2008). 3. The sum of the 400 marker effects as estimated by the cow model were subtracted from the production records of the cows based on each cow's genotype probabilities, and the marker effects as estimated by the cow model. ...
Article
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An efficient algorithm for genomic selection of moderately sized populations based on single nucleotide polymorphism chip technology is described. A total of 995 Israeli Holstein bulls with genetic evaluations based on daughter records were genotyped for either the BovineSNP50 BeadChip or the BovineSNP50 v2 BeadChip. Milk, fat, protein, somatic cell score, female fertility, milk production persistency and herd-life were analyzed. The 400 markers with the greatest effects on each trait were first selected based on individual analysis of each marker with the genetic evaluations of the bulls as the dependent variable. The effects of all 400 markers were estimated jointly using a 'cow model,' estimated from the data truncated to exclude lactations with freshening dates after September 2006. Genotype probabilities for each locus were computed for all animals with missing genotypes. In Method I, genetic evaluations were computed by analysis of the truncated data set with the sum of the marker effects subtracted from each record. Genomic estimated breeding values for the young bulls with genotypes, but without daughter records, were then computed as their parent averages combined with the sum of each animal's marker effects. Method II genomic breeding values were computed based on regressions of estimated breeding values of bulls with daughter record on their parent averages, sum of marker effects and birth year. Method II correlations of the current breeding values of young bulls without daughter records in the truncated data set were higher than the correlations of the current breeding values with the parent averages for fat and protein production, persistency and herd-life. Bias of evaluations, estimated as a difference between the mean of current breeding values of the young bulls and their genomic evaluations, was reduced for milk production traits, persistency and herd-life. Bias for milk production traits was slightly negative, as opposed to the positive bias of parent averages. Correlations of Method II with the means of daughter records adjusted for fixed effects were higher than parent averages for fat, protein, fertility, persistency and herd-life. Reducing the number of markers included in the analysis from 400 to 300 did not reduce correlations of genomic breeding values for protein with current breeding values, but did slightly reduce correlations with means of daughter records. Method II has the advantages as compared with the method of VanRaden in that genotypes of cows can be readily incorporated into the Method II analysis, and it is more effective for moderately sized populations.
... Thus, if the allelic frequency of the less-frequent allele is ,0.3, a QTL of this magnitude will account for no more than 10% of the phenotypic variance. New results demonstrate that the 'cow model' was also able to accurately estimate the effect of a QTL with a substitution effect of 0.32 standard deviations (Baruch and Weller, 2008). ...
Article
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The method of Israel and Weller (Estimation of candidate gene effects in dairy cattle populations. Journal of Dairy Science 1998, 81, 1653-1662) to estimate quantitative trait locus (QTL) effects when only a small fraction of the population was genotyped was investigated by simulation. The QTL effect was underestimated in all cases, but bias was greater for extreme allelic frequencies, and increased with the number of generations included in the simulations. Apparently, as the fraction of animals with inferred genotypes increases, the genotype probabilities tend to 'mimic' the effect of relationships. Unbiased estimates of QTL effects were derived by a modified 'cow model' without the inclusion of the relationship matrix on simulated data, even though only a small fraction of the population was genotyped. This method yielded empirically unbiased estimates for the effects of the genes DGAT1 and ABCG2 on milk production traits in the Israeli Holstein population. Based on these results, an efficient algorithm for marker-assisted selection in dairy cattle was proposed. Quantitative trait loci effects are estimated and subtracted from the cows' records. Genetic evaluations are then computed for the adjusted records. Animals are then selected based on the sum of their polygenic genetic evaluations and QTL effects. This scheme differs from a traditional dairy cattle breeding scheme in that all bull calves were considered candidates for selection. At year 10, total genetic gain was 20% greater by the proposed algorithm as compared to the selection based on a standard animal model for a locus with a substitution effect of 0.5 phenotypic standard deviations. The proposed method is easy to apply, and all required software are 'on the shelf.' It is only necessary to genotype breeding males, which are a very small fraction of the entire population. The method is flexible with respect to the model used for routine genetic evaluation. Any number of genetic markers can be easily incorporated into the algorithm, and the reduction in genetic gain due to incorrect QTL determination is minimal.
... Should QTN be treated differently than LD markers in genetic evaluation programs? The problem that only a small fraction of the population will be genotyped will still apply, but it would seem that once a QTN is detected, bias is no longer a factor, and the QTN can be treated as a fixed rather than a random effect (e.g., Baruch and Weller, 2008). ...
Article
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Abstract The efficiency of the French marker-assisted selection (MAS) was estimated by a simulation study. The data files of two different time periods were used: April 2004 and 2006. The simulation method used the structure of the existing French MAS: same pedigree, same marker genotypes and same animals with records. The program simulated breeding values and new records based on this existing structure and knowledge on the QTL used in MAS (variance and frequency). Reliabilities of genetic values of young animals (less than one year old) obtained with and without marker information were compared to assess the efficiency of MAS for evaluation of milk, fat and protein yields and fat and protein contents. Mean gains of reliability ranged from 0.015 to 0.094 and from 0.038 to 0.114 in 2004 and 2006, respectively. The larger number of animals genotyped and the use of a new set of genetic markers can explain the improvement of MAS reliability from 2004 to 2006. This improvement was also observed by analysis of information content for young candidates. The gain of MAS reliability with respect to classical selection was larger for sons of sires with genotyped progeny daughters with records. Finally, it was shown that when superiority of MAS over classical selection was estimated with daughter yield deviations obtained after progeny test instead of true breeding values, the gain was underestimated.
Article
Simulation. In order to test the effectiveness of the analysis to detect and position QTL, we simulated phenotypic data with a QTL effect for all genotyped animals. This was done by first simulating polygenic effects for founder animals in the extended pedigree. The polygenic effect for animals in subsequent generations was generated from half of the sire plus half the dam polygenic effect, and included a mendelian sampling term to maintain a polygenic variance (σ2g) of 0.25. A QTL effect was simulated over one of the 8204 existing polymorphic SNPs in the genotype data. A simulated environmental effect (σ 2 e=0.7), together with the polygenic and QTL effect were then summed to provide the phenotypic effect for all genotyped animals (average σ2p=1.0). Simulated data were generated for a total of 25 different QTL positions which were chosen from SNPs genome wide, representing allele frequencies from 0.1 to 0.9. The QTL effect was varied to maintain a constant QTL variance of 0.05 (ie. 5% of the phenotypic variance). At each of the QTL positions, 5 replicates were simulated, making a total of 125 phenotypic data sets. In addition, a further 20 replicates were generated with no QTL effect (polygenic σ 2 g=0.3).
Article
A theoretical analysis of the potential benefits of marker-assisted selection (MAS) of candidate bulls prior to entry into a young sire progeny testing programme was carried out. It is assumed that quantitative trait loci (QTL) affecting milk production have been mapped with respect to known genetic markers, and MAS is based on evaluation of elite sires in order to identify marker alleles in coupling to favourable or unfavourable QTL alleles. Candidate bulls, descendants of the elite sire will then be selected, prior to conventional progeny testing, on the basis of the marker alleles derived from the elite-sire ancestor. The analysis considers recombination between marker and QTL, the difficulty of tracing specific marker alleles from sire to progeny, and the expectation that MAS, in practice, will be implemented in the grandsons, rather than in the sons of elite sires. It is shown that MAS of candidate bulls, based on the use of a single diallelic marker in linkage to a QTL will have only a negligible effect on the rate of genetic progress. Increases of 15 to 20% in the rate of genetic gain, however, can be obtained by the use of single polyallelic markers, and increases of 20 to 30% can be obtained by utilizing haplotypes of diallelic or polyallelic markers.
Article
Top down preselection of young bulls before entering progeny testing has been proposed as a practicable form of marker-assisted selection (MAS), especially in dairy cattle populations with large male paternal half-sib families. Linkage phase between the superior (Q) and the inferior (q) QTL alleles of heterozygous sires (Qq at the QTL) with informative markers is established within each paternal half-sib family and may be used for selection among grand-progeny. If, additionally to sires, bulldams are also genotyped and data from consecutive generations are used, then a marker-assisted best linear unbiased prediction (MA-BLUP) model can be employed to connect the information of all generations and families of a top down design, and to select across all families. A customized 'augmented' sire model (with sires and dams of sires as random effects) is introduced for this purpose. Adapted formulae for the mixed model equations are given and their equivalence to a corresponding animal model and to a certain variant of previously proposed reduced animal models is shown. The application of the augmented sire model in MA-BLUP estimation from daughter-yield deviations and effective daughter contributions is presented.
Article
This study investigates the value of a `bottom-up' approach to marker-assisted selection in a conventional progeny-testing dairy-breeding programme. By marker genotyping the daughters in the progeny test for markers known to be closely linked to a quantitative trait locus (QTL), it can be decided whether their sire is heterozygous for the QTL. If the sire is heterozygous with allelic contrast greater than some threshold, c, then only those bull-sons which inherited the favourable QTL allele are retained for subsequent progeny testing. In this way, posterior information on a sire's genotype from his daughters is used to preselect his sons and thereby increase the selection differential in the new generation of bulls. Simulations were used to predict the genetic gains and costs of using the bottom-up approach in a national dairy breeding scheme in which 500 young bulls were progeny-tested each generation. It was found that rates of genetic gains could be increased by 8%, 14% and 23% compared with conventional progeny testing if selection was based on 1, 2 and 5 QTL, respectively, and that this would cost less than US$100,000 per locus. A `top-down' approach selecting QTL alleles inherited from the grandsires was also evaluated and shown to be highly profitable, though less so than for the bottom-up scheme.
Article
In a (grand)daughter design, maternal information is often neglected because the number of progeny per dam is limited. The number of dams per maternal grandsire (MGS), however, could be large enough to contribute to QTL detection. But dams and MGS usually are not genotyped, there are two recombination opportunities between the MGS and the progeny, and at a given location, only half the progeny receive a MGS chromosomal segment. A 3-step procedure was developed to estimate: (1) the marker phenotypes probabilities of the MGS; (2) the probability of each possible MGS haplotype; (3) the probabilities that the progeny receives either the first, or second MGS segment, or a maternal grandam segment. These probabilities were used for QTL detection in a linear model including the effects of sire, MGS, paternal QTL, MGS QTL and maternal grandam QTL. Including the grandam QTL effect makes it possible to detect QTL in the grandam population, even when MGS are not informative. The detection power, studied by simulation, was rather high, provided that MGS family size was greater than 50. Using maternal information in the French dairy cattle granddaughter design made it possible to detect 23 additional QTL genomewise significant.
Article
Standard animal model programs can be modified to include the effect of a quantitative gene, even if only a fraction of the population is genotyped. Five methods to estimate the effect of a diallelic quantitative gene affecting a quantitative trait were compared to a standard animal model (model I) on simulated populations, based on mean squared errors and bias. In models II, III, and IV complete linkage between a single genetic marker and the quantitative trait gene was assumed. In models II and III the elements of the incidence matrix for the gene effect were 0 or 1 for genotyped individuals, and the probabilities of the possible candidate gene genotypes for individuals that were not genotyped. In model III segregation analysis was used to compute these probabilities. If only some of the cows were genotyped, the model III estimates were nearly unbiased, while model II underestimated the simulated effects. When only sires were genotyped, model II overestimated the simulated effect. In models V and VI two markers bracketing the quantitative gene with recombination frequencies of 0.1 and 0.2 with the quantitative gene were simulated, and the algorithm of Whittaker et al. (1996) was used to derive estimates of gene effect and location. In model V marker allele effects were included in the animal model analysis. In model VI, the model I genetic evaluations were analyzed. Model V estimates for both effect and location of the quantitative gene were unbiased, while model VI estimates were only 0.25 of the simulated effect.
Article
Genomic selection is a form of marker-assisted selection in which genetic markers covering the whole genome are used so that all quantitative trait loci (QTL) are in linkage disequilibrium with at least one marker. This approach has become feasible thanks to the large number of single nucleotide polymorphisms (SNP) discovered by genome sequencing and new methods to efficiently genotype large number of SNP. Simulation results and limited experimental results suggest that breeding values can be predicted with high accuracy using genetic markers alone but more validation is required especially in samples of the population different from that in which the effect of the markers was estimated. The ideal method to estimate the breeding value from genomic data is to calculate the conditional mean of the breeding value given the genotype of the animal at each QTL. This conditional mean can only be calculated by using a prior distribution of QTL effects so this should be part of the research carried out to implement genomic selection. In practice, this method of estimating breeding values is approximated by using the marker genotypes instead of the QTL genotypes but the ideal method is likely to be approached more closely as more sequence and SNP data is obtained. Implementation of genomic selection is likely to have major implications for genetic evaluation systems and for genetic improvement programmes generally and these are discussed.
Strategies for the improvement of animal production using marker assisted selection. Pages 305– 328 in Gene mapping: Strategies, Techniques and Applications
  • J I Weller
  • R L Fernando
Weller, J. I., and R. L. Fernando. 1991. Strategies for the improvement of animal production using marker assisted selection. Pages 305– 328 in Gene mapping: Strategies, Techniques and Applications. L. B. Schook, H. A. Lewin, and D. G. McLaren, ed. Marcel Dekker Inc., New York, NY.
Application of the BovineSNP50 assay for QTL
  • T P Sonstegard
  • S S Smith
  • C T Moore
  • J F Lawley
  • Taylor
Sonstegard, T. P. Smith, S. S. Moore, C. T. Lawley, and J. F. Taylor. 2008. Application of the BovineSNP50 assay for QTL Journal of Dairy Science Vol. 91 No. 11, 2008 BARUCH AND WELLER mapping and prediction of genetic merit in Holstein cattle. Plant & Animal Genomes XVI Conference, San Diego, CA.