Effects of the number of markers per haplotype and clustering of haplotypes on the accuracy of QTL mapping and prediction of genomic breeding values

Calus, M.P.L.; Meuwissen, Theo H. E.; Windig, J.J.; Knol, E.F.; Schrooten, C.; Vereijken, Addie; Veerkamp, R.F.

doi:10.1186/1297-9686-41-11

Cited by 56 publications

(59 citation statements)

References 25 publications

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“…An additional difference between marker AIS and IBD haplotypes is that the IBD status of haplotypes is evaluated at one position in the genome (a so-called putative QTL position), whereas AIS haplotypes are considered to be AIS across the whole haplotype. When IBD probabilities between two haplotypes are close to one, those haplotypes are usually considered to be the same to reduce the number of effects that needs to be estimated and thereby increasing power to estimate effects (Yu et al, 2005) and speeding up convergence (Calus et al, 2009). …”

Section: Genome -Wide Breeding Value Estimationmentioning

confidence: 99%

“…Apart from a few methods that assume equal contribution to the genetic variance by all loci, a common feature of these methods is to reduce the dimension of the SNP data. Reported alternatives include non-parametric kernel methods (Gianola et al, 2006;Bennewitz and Meuwissen, 2008;Gianola and van Kaam, 2008), partial least squares (PLS) regression Solberg, 2008), principal component analysis (PCA; Solberg, 2008), genetic algorithms, and Bayesian LASSO (de los Campos et al, 2009). Kernel methods were shown to yield similar results as the Bayesian methods for purely additive models (Bennewitz and Meuwissen, 2008), but may outperform the Bayesian models when considering non-additive effects (Gonzá lez- Recio et al, 2008).…”

Section: Genome -Wide Breeding Value Estimationmentioning

confidence: 99%

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Genomic breeding value prediction: methods and procedures

Calus

2010

Animal

163

142

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Animal breeding faces one of the most significant changes of the past decades -the implementation of genomic selection. Genomic selection uses dense marker maps to predict the breeding value of animals with reported accuracies that are up to 0.31 higher than those of pedigree indexes, without the need to phenotype the animals themselves, or close relatives thereof. The basic principle is that because of the high marker density, each quantitative trait loci (QTL) is in linkage disequilibrium (LD) with at least one nearby marker. The process involves putting a reference population together of animals with known phenotypes and genotypes to estimate the marker effects. Marker effects have been estimated with several different methods that generally aim at reducing the dimensions of the marker data. Nearly all reported models only included additive effects. Once the marker effects are estimated, breeding values of young selection candidates can be predicted with reported accuracies up to 0.85. Although results from simulation studies suggest that different models may yield more accurate genomic estimated breeding values (GEBVs) for different traits, depending on the underlying QTL distribution of the trait, there is so far only little evidence from studies based on real data to support this. The accuracy of genomic predictions strongly depends on characteristics of the reference populations, such as number of animals, number of markers, and the heritability of the recorded phenotype. Another important factor is the relationship between animals in the reference population and the evaluated animals. The breakup of LD between markers and QTL across generations advocates frequent re-estimation of marker effects to maintain the accuracy of GEBVs at an acceptable level. Therefore, at low frequencies of re-estimating marker effects, it becomes more important that the model that estimates the marker effects capitalizes on LD information that is persistent across generations.

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Section: Genome -Wide Breeding Value Estimationmentioning

confidence: 99%

Section: Genome -Wide Breeding Value Estimationmentioning

confidence: 99%

Genomic breeding value prediction: methods and procedures

Calus

2010

Animal

163

142

View full text Add to dashboard Cite

show abstract

“…Examples are estimation of breeding values in livestock to enable genomic selection (Meuwissen et al, 2001), or prediction of the (genetic) susceptibility of an individual for a disorder or disease (Wray et al, 2007). Generally, the applied models for both types of GWA studies may be the same, although fine-tuning for one of both objectives may result in subtle differences in the applied models (Calus et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Including copy number variation in association studies to predict genotypic values

2010

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SummaryThe objective of this study was to investigate, both empirically and deterministically, the ability to explain genetic variation resulting from a copy number polymorphism (CNP) by including the CNP, either by its genotype or by a continuous derivation thereof, alone or together with a nearby single nucleotide polymorphism (SNP) in the model. This continuous measure of a CNP genotype could be a raw hybridization measurement, or a predicted CNP genotype. Results from simulations showed that the linkage disequilibrium (LD) between an SNP and CNP was lower than LD between two SNPs, due to the higher mutation rate at the CNP loci. The model R 2 values from analysing the simulated data were very similar to the R 2 values predicted with the deterministic formulae. Under the assumption that x copies at a CNP locus lead to the effect of x times the effect of 1 copy, including a continuous measure of a CNP locus in the model together with the genotype of a nearby SNP increased power to explain variation at the CNP locus, even when the continuous measure explained only 15% of the variation at the CNP locus.

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“…An alternative to the RHM would be to cluster the haplotypes on the basis of their origin. The clustering could be based on IBD probabilities where haplotypes with very high IBD probability are clustered together (Blott et al, 2003;Sahana et al, 2008;Calus et al, 2009) or it could be based on genealogy (tree-based) (Pan et al, 2009). Such clustering may also be used in combination with the RHM.…”

Section: Discussionmentioning

confidence: 99%

“…Several studies (e.g. Zhao et al, 2007;Becker and Herold, 2009;Calus et al, 2009) compared power of haplotype-based models for QTL mapping, but did not compare type I errors. Sahana et al (2010) observed a very high type I error rate using haplotype as a fixed effect in the model.…”

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confidence: 99%

Strategies for haplotype-based association mapping in a complex pedigreed population

Boleckova¹,

Christensen²,

Sørensen³

et al. 2012

CZECH J ANIM SCI

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AbStrAct:In association mapping, haplotype-based methods are generally regarded to provide higher power and increased precision than methods based on single markers. For haplotype-based association mapping most studies use a fixed haplotype effect in the model. However, an increase in haplotype length raises the number of parameters in the model, resulting in low accuracy of the estimates especially for the low-frequency haplotypes. Modeling of haplotype effects can be improved if they are assumed to be random effects, as only one parameter, i.e. haplotype variance, needs to be estimated compared to estimating the effects of all different haplotypes in a fixed haplotype model. Using simulated data, we investigated statistical models where haplotypes were fitted either as a fixed or random effect and we compared them for the power, precision, and type I error. We investigated five haplotype lengths of 2, 4, 6, 10 and 20. The simulated data resembled the Danish Holstein cattle pedigree representing a complex relationship structure and QTL effects of different sizes were simulated. We observed that the random haplotype models had high power and very low type I error rates (after the Bonferroni correction), while the fixed haplotype models had lower power and excessively high type I errors. Haplotype length of 4 to 6 gave the best results for random model in the present study. Though the present study was conducted on data structure more frequent in livestock, our findings on random vs. fixed haplotype effects in association mapping models are applicable to data from other species with a similar pedigree structure.

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Effects of the number of markers per haplotype and clustering of haplotypes on the accuracy of QTL mapping and prediction of genomic breeding values

Cited by 56 publications

References 25 publications

Genomic breeding value prediction: methods and procedures

Genomic breeding value prediction: methods and procedures

Including copy number variation in association studies to predict genotypic values

Strategies for haplotype-based association mapping in a complex pedigreed population

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