Wageningen, Netherlands
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Wientjes Y.C.J.,Animal Breeding and Genomics Center | Wientjes Y.C.J.,Wageningen University | Veerkamp R.F.,Animal Breeding and Genomics Center | Calus M.P.L.,Animal Breeding and Genomics Center
Genetics | Year: 2013

Although the concept of genomic selection relies on linkage disequilibrium (LD) between quantitative trait loci and markers, reliability of genomic predictions is strongly influenced by family relationships. In this study, we investigated the effects of LD and family relationships on reliability of genomic predictions and the potential of deterministic formulas to predict reliability using population parameters in populations with complex family structures. Five groups of selection candidates were simulated by taking different information sources from the reference population into account: (1) allele frequencies, (2) LD pattern, (3) haplotypes, (4) haploid chromosomes, and (5) individuals from the reference population, thereby having real family relationships with reference individuals. Reliabilities were predicted using genomic relationships among 529 reference individuals and their relationships with selection candidates and with a deterministic formula where the number of effective chromosome segments (Me) was estimated based on genomic and additive relationship matrices for each scenario. At a heritability of 0.6, reliabilities based on genomic relationships were 0.002 ±0.0001 (allele frequencies), 0.022 ± 0.001 (LD pattern), 0.018 ±0.001 (haplotypes), 0.100 ± 0.008 (haploid chromosomes), and 0.318 ± 0.077 (family relationships). At a heritability of 0.1, relative differences among groups were similar. For all scenarios, reliabilities were similar to predictions with a deterministic formula using estimated Me. So, reliabilities can be predicted accurately using empirically estimated Me and level of relationship with reference individuals has a much higher effect on the reliability than linkage disequilibrium per se. Furthermore, accumulated length of shared haplotypes is more important in determining the reliability of genomic prediction than the individual shared haplotype length. © 2013 by the Genetics Society of America.


Hill W.G.,University of Edinburgh | Mulder H.A.,Animal Breeding and Genomics Center
Genetics Research | Year: 2010

Environmental variation (VE) in a quantitative trait-variation in phenotype that cannot be explained by genetic variation or identifiable genetic differences-can be regarded as being under some degree of genetic control. Such variation may be either between repeated expressions of the same trait within individuals (e.g. for bilateral traits), in the phenotype of different individuals, where variation within families may differ, or in both components. We consider alternative models for defining the distribution of phenotypes to include a component due to heterogeneity of VE. We review evidence for the presence of genetic variation in VE and estimates of its magnitude. Typically the heritability of VE is under 10%, but its genetic coefficient of variation is typically 20% or more. We consider experimental designs appropriate for estimating genetic variance in VE and review alternative methods of estimation. We consider the effects of stabilizing and directional selection on VE and review both the forces that might be maintaining levels of VE and heritability found in populations. We also evaluate the opportunities for reducing VE in breeding programmes. Although empirical and theoretical studies have increased our understanding of genetic control of environmental variance, many issues remain unresolved. © 2011 Cambridge University Press.


Calus M.P.L.,Animal Breeding and Genomics Center | Veerkamp R.F.,Animal Breeding and Genomics Center
Genetics Selection Evolution | Year: 2011

Background: Genomic selection has become a very important tool in animal genetics and is rapidly emerging in plant genetics. It holds the promise to be particularly beneficial to select for traits that are difficult or expensive to measure, such as traits that are measured in one environment and selected for in another environment. The objective of this paper was to develop three models that would permit multi-trait genomic selection by combining scarcely recorded traits with genetically correlated indicator traits, and to compare their performance to single-trait models, using simulated datasets. Methods. Three (SNP) Single Nucleotide Polymorphism based models were used. Model G and BC0 assumed that contributed (co)variances of all SNP are equal. Model BSSVS sampled SNP effects from a distribution with large (or small) effects to model SNP that are (or not) associated with a quantitative trait locus. For reasons of comparison, model A including pedigree but not SNP information was fitted as well. Results: In terms of accuracies for animals without phenotypes, the models generally ranked as follows: BSSVS > BC0 > G > > A. Using multi-trait SNP-based models, the accuracy for juvenile animals without any phenotypes increased up to 0.10. For animals with phenotypes on an indicator trait only, accuracy increased up to 0.03 and 0.14, for genetic correlations with the evaluated trait of 0.25 and 0.75, respectively. Conclusions: When the indicator trait had a genetic correlation lower than 0.5 with the trait of interest in our simulated data, the accuracy was higher if genotypes rather than phenotypes were obtained for the indicator trait. However, when genetic correlations were higher than 0.5, using an indicator trait led to higher accuracies for selection candidates. For different combinations of traits, the level of genetic correlation below which genotyping selection candidates is more effective than obtaining phenotypes for an indicator trait, needs to be derived considering at least the heritabilities and the numbers of animals recorded for the traits involved. © 2011 Calus and Veerkamp; licensee BioMed Central Ltd.


Windig J.J.,Animal Breeding and Genomics Center | Hoving-Bolink R.A.,Animal Breeding and Genomics Center | Veerkamp R.F.,Animal Breeding and Genomics Center
Livestock Science | Year: 2015

Currently almost all dairy cattle are dehorned as calf to avoid injuries later in life. A welfare friendly alternative to dehorning is to breed polled cattle. This paper explores the potential to breed for polledness in the Holstein breed, and investigates the genetic merit and relatedness of both polled and horned bulls. In 2009 there were 33 polled bulls available for artificial insemination (AI), two of them being homozygous for polledness. In 2014 more than 150 bulls were available, 31 of them being homozygous. Breeding values for the total merit index (NVI-Dutch Flemish Index) have increased considerably for polled bulls. In 2009 the difference in average Estimated Breeding Value (EBV) for NVI between polled bulls (available for AI) and the top 100 horned AI bulls was 180 points, equivalent to about 18 years of selection at that time. In 2014 the difference was reduced to 149 points, equivalent to about 5 years of (genomic) selection. Genomic selection has made an important contribution to this reduced difference between polled and horned bulls. Polled bulls in 2009 were more inbred (F=0.045) than horned bulls (F=0.037) but less related to cows born at Dutch farms in that year (r=0.070 vs. 0.089). Using optimal contributions and a combination of polled and horned bulls, a next generation of animals can be bred that combines a high genetic merit with a relatively low relatedness and higher frequency of polledness. However, homozygous polled bulls born in 2012-2014 had a relatively high average inbreeding level (F=0.079) and almost all originated from the same two polled founder bulls. This may form a potential risk for lethal alleles showing up with inbreeding. Overall, breeding high genetic merit polled Holstein cows has become a realistic perspective, but care must be taken to avoid high relatedness and inbreeding levels. © 2015 Elsevier B.V..


Bouwman A.C.,Animal Breeding and Genomics Center | Veerkamp R.F.,Animal Breeding and Genomics Center
BMC Genetics | Year: 2014

Background: The aim of this study was to determine the consequences of splitting sequencing effort over multiple breeds for imputation accuracy from a high-density SNP chip towards whole-genome sequence. Such information would assist for instance numerical smaller cattle breeds, but also pig and chicken breeders, who have to choose wisely how to spend their sequencing efforts over all the breeds or lines they evaluate. Sequence data from cattle breeds was used, because there are currently relatively many individuals from several breeds sequenced within the 1,000 Bull Genomes project. The advantage of whole-genome sequence data is that it carries the causal mutations, but the question is whether it is possible to impute the causal variants accurately. This study therefore focussed on imputation accuracy of variants with low minor allele frequency and breed specific variants. Results: Imputation accuracy was assessed for chromosome 1 and 29 as the correlation between observed and imputed genotypes. For chromosome 1, the average imputation accuracy was 0.70 with a reference population of 20 Holstein, and increased to 0.83 when the reference population was increased by including 3 other dairy breeds with 20 animals each. When the same amount of animals from the Holstein breed were added the accuracy improved to 0.88, while adding the 3 other breeds to the reference population of 80 Holstein improved the average imputation accuracy marginally to 0.89. For chromosome 29, the average imputation accuracy was lower. Some variants benefitted from the inclusion of other breeds in the reference population, initially determined by the MAF of the variant in each breed, but even Holstein specific variants did gain imputation accuracy from the multi-breed reference population. Conclusions: This study shows that splitting sequencing effort over multiple breeds and combining the reference populations is a good strategy for imputation from high-density SNP panels towards whole-genome sequence when reference populations are small and sequencing effort is limiting. When sequencing effort is limiting and interest lays in multiple breeds or lines this provides imputation of each breed. © 2014 Bouwman and Veerkamp; licensee BioMed Central Ltd.


Aslam M.A.,Animal Breeding and Genomics Center | Groothuis T.G.,University of Groningen | Smits M.A.,Animal Breeding and Genomics Center | Woelders H.,Animal Breeding and Genomics Center
Biology of Reproduction | Year: 2014

In various studies, chronic elevation of corticosterone levels in female birds under natural or experimental conditions resulted in female biased offspring sex ratios. In chicken, one study with injected corticosterone resulted in a male sex ratio bias. In the current study, we chronically elevated blood plasma corticosterone levels through corticosterone feeding (20 mg/kg feed) for 14 days using 30 chicken hens in each of treatment and control groups and studied the primary offspring sex ratio (here defined as the proportion of male fertile eggs determined in freshly laid eggs, i.e., without egg incubation). Mean plasma corticosterone concentrations were significantly higher in the treatment group but were not associated with sex ratio, laying rate, and fertility rate. Corticosterone treatment by itself did not affect egg sex but affected sex ratio as well as laying rate and fertility rate in interaction with hen body mass. Body mass had a negative association with sex ratio, laying rate, and fertility rate per hen in the corticosterone group, but a positive association with sex ratio in untreated hens. These interactions were already seen when taking the body mass at the beginning of the experiment, indicating intrinsic differences between light and heavy hens with regard to their reaction to corticosterone treatment. The effects on laying rate, fertility rate, and sex ratio suggest that some factor related to body mass act together with corticosterone to modulate ovarian functions. We propose that corticosterone treatment in conjunction with hen body mass can interfere with meiosis, which can lead to meiotic drive and to chromosomal aberrations resulting in postponed ovulation or infertile ova. © 2014 by the Society for the Study of Reproduction, Inc.


Calus M.P.,Animal Breeding and Genomics Center
Genetics Selection Evolution | Year: 2014

Background: Since both the number of SNPs (single nucleotide polymorphisms) used in genomic prediction and the number of individuals used in training datasets are rapidly increasing, there is an increasing need to improve the efficiency of genomic prediction models in terms of computing time and memory (RAM) required. Methods. In this paper, two alternative algorithms for genomic prediction are presented that replace the originally suggested residual updating algorithm, without affecting the estimates. The first alternative algorithm continues to use residual updating, but takes advantage of the characteristic that the predictor variables in the model (i.e. the SNP genotypes) take only three different values, and is therefore termed "improved residual updating". The second alternative algorithm, here termed "right-hand-side updating" (RHS-updating), extends the idea of improved residual updating across multiple SNPs. The alternative algorithms can be implemented for a range of different genomic predictions models, including random regression BLUP (best linear unbiased prediction) and most Bayesian genomic prediction models. To test the required computing time and RAM, both alternative algorithms were implemented in a Bayesian stochastic search variable selection model. Results: Compared to the original algorithm, the improved residual updating algorithm reduced CPU time by 35.3 to 43.3%, without changing memory requirements. The RHS-updating algorithm reduced CPU time by 74.5 to 93.0% and memory requirements by 13.1 to 66.4% compared to the original algorithm. Conclusions: The presented RHS-updating algorithm provides an interesting alternative to reduce both computing time and memory requirements for a range of genomic prediction models. © 2014Calus; licensee BioMed Central Ltd.


Mulder H.A.,Animal Breeding and Genomics Center | Calus M.P.L.,Animal Breeding and Genomics Center | Druet T.,University of Liège | Schrooten C.,Crv Inc.
Journal of Dairy Science | Year: 2012

Genomic selection using 50,000 single nucleotide polymorphism (50k SNP) chips has been implemented in many dairy cattle breeding programs. Cheap, low-density chips make genotyping of a larger number of animals cost effective. A commonly proposed strategy is to impute low-density genotypes up to 50,000 genotypes before predicting direct genomic values (DGV). The objectives of this study were to investigate the accuracy of imputation for animals genotyped with a low-density chip and to investigate the effect of imputation on reliability of DGV. Low-density chips contained 384, 3,000, or 6,000 SNP. The SNP were selected based either on the highest minor allele frequency in a bin or the middle SNP in a bin, and DAGPHASE, CHROMIBD, and multivariate BLUP were used for imputation. Genotypes of 9,378 animals were used, from which approximately 2,350 animals had deregressed proofs. Bayesian stochastic search variable selection was used for estimating SNP effects of the 50k chip. Imputation accuracies and imputation error rates were poor for low-density chips with 384 SNP. Imputation accuracies were higher with 3,000 and 6,000 SNP. Performance of DAGPHASE and CHROMIBD was very similar and much better than that of multivariate BLUP for both imputation accuracy and reliability of DGV. With 3,000 SNP and using CHROMIBD or DAGPHASE for imputation, 84 to 90% of the increase in DGV reliability using the 50k chip, compared with a pedigree index, was obtained. With multivariate BLUP, the increase in reliability was only 40%. With 384 SNP, the reliability of DGV was lower than for a pedigree index, whereas with 6,000 SNP, about 93% of the increase in reliability of DGV based on the 50k chip was obtained when using DAGPHASE for imputation. Using genotype probabilities to predict gene content increased imputation accuracy and the reliability of DGV and is therefore recommended for applications of imputation for genomic prediction. A deterministic equation was derived to predict accuracy of DGV based on imputation accuracy, which fitted closely with the observed relationship. The deterministic equation can be used to evaluate the effect of differences in imputation accuracy on accuracy and reliability of DGV. © 2012 American Dairy Science Association.


de los Campos G.,University of Alabama at Birmingham | Hickey J.M.,University of New England of Australia | Pong-Wong R.,Roslin Institute | Daetwyler H.D.,Australian Department of Primary Industries and Fisheries | Calus M.P.L.,Animal Breeding and Genomics Center
Genetics | Year: 2013

Genomic-enabled prediction is becoming increasingly important in animal and plant breeding and is also receiving attention in human genetics. Deriving accurate predictions of complex traits requires implementing whole-genome regression (WGR) models where phenotypes are regressed on thousands of markers concurrently. Methods exist that allow implementing these large-p with small-n regressions, and genome-enabled selection (GS) is being implemented in several plant and animal breeding programs. The list of available methods is long, and the relationships between them have not been fully addressed. In this article we provide an overview of available methods for implementing parametric WGR models, discuss selected topics that emerge in applications, and present a general discussion of lessons learned from simulation and empirical data analysis in the last decade. © 2013 by the Genetics Society of America.


Calus M.P.L.,Animal Breeding and Genomics Center | Mulder H.A.,Animal Breeding and Genomics Center | Bastiaansen J.W.M.,Wageningen University
Genetics Selection Evolution | Year: 2011

Background: Using SNP genotypes to apply genomic selection in breeding programs is becoming common practice. Tools to edit and check the quality of genotype data are required. Checking for Mendelian inconsistencies makes it possible to identify animals for which pedigree information and genotype information are not in agreement. Methods. Straightforward tests to detect Mendelian inconsistencies exist that count the number of opposing homozygous marker (e.g. SNP) genotypes between parent and offspring (PAR-OFF). Here, we develop two tests to identify Mendelian inconsistencies between sibs. The first test counts SNP with opposing homozygous genotypes between sib pairs (SIBCOUNT). The second test compares pedigree and SNP-based relationships (SIBREL). All tests iteratively remove animals based on decreasing numbers of inconsistent parents and offspring or sibs. The PAR-OFF test, followed by either SIB test, was applied to a dataset comprising 2,078 genotyped cows and 211 genotyped sires. Theoretical expectations for distributions of test statistics of all three tests were calculated and compared to empirically derived values. Type I and II error rates were calculated after applying the tests to the edited data, while Mendelian inconsistencies were introduced by permuting pedigree against genotype data for various proportions of animals. Results: Both SIB tests identified animal pairs for which pedigree and genomic relationships could be considered as inconsistent by visual inspection of a scatter plot of pairwise pedigree and SNP-based relationships. After removal of 235 animals with the PAR-OFF test, SIBCOUNT (SIBREL) identified 18 (22) additional inconsistent animals. Seventeen animals were identified by both methods. The numbers of incorrectly deleted animals (Type I error), were equally low for both methods, while the numbers of incorrectly non-deleted animals (Type II error), were considerably higher for SIBREL compared to SIBCOUNT. Conclusions: Tests to remove Mendelian inconsistencies between sibs should be preceded by a test for parent-offspring inconsistencies. This parent-offspring test should not only consider parent-offspring pairs based on pedigree data, but also those based on SNP information. Both SIB tests could identify pairs of sibs with Mendelian inconsistencies. Based on type I and II error rates, counting opposing homozygotes between sibs (SIBCOUNT) appears slightly more precise than comparing genomic and pedigree relationships (SIBREL) to detect Mendelian inconsistencies between sibs. © 2011 Calus et al; licensee BioMed Central Ltd.

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