Empirical and deterministic accuracies of across-population genomic prediction

Wientjes, Y.C.J.; Veerkamp, R.F.; Bijma, P.; Bovenhuis, H.; Schrooten, C.; Calus, M.P.L.

doi:10.1186/s12711-014-0086-0

Cited by 76 publications

(146 citation statements)

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“…In this study, the estimated M e within a population was 1350 for all three populations and the values for M e across populations were 20% higher. In a study using different closely related cattle breeds, the M e values across populations were reported to be 10 times larger than M e within a population (Wientjes et al 2015b). This indicates that when very closely related populations are investigated, the M e across populations can be expected to be 2 times the M e within a population.…”

Section: Multipopulation Genomic Prediction 811mentioning

confidence: 90%

“…In analogy with Wientjes et al (2015b) is the total phenotypic variance in population A. So, the total variance of x A can be written as…”

Section: Theorymentioning

confidence: 99%

“…The original derivation of this equation is rather complex and difficult to extend to multipopulation genomic prediction. As shown by Wientjes et al (2015b), the same equation can also be derived by partitioning the variance of the average phenotype of N individuals into a part explained by one locus ðs 2 a =MÞ and a part not explained by that locus ððs 2 p 2 ðs 2 a =MÞÞ=NÞ; in which s 2 a is the total genetic variance and s 2 p is the phenotypic variance. In general, the accuracy of predicting an effect is equal to the square root of the proportion of the total variance explained by that effect (Appendix A provides a formal proof that this result applies to estimation of gene effects).…”

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confidence: 99%

“…One type of deterministic equation is based on prediction error variance of the mixed-model equation and uses the genomic relationships within the training population and between training and predicted individuals (Vanraden 2008). This equation has been extended to enable the calculation of the accuracy when different populations are combined in one training population (Wientjes et al 2015b). A disadvantage of this equation is, however, that individuals have to be genotyped before the accuracy can be calculated.…”

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confidence: 99%

“…The increase in accuracy by adding individuals from another population to the training population is in most cases much lower than the increase in accuracy obtained by adding an equal number of individuals from the same population. This is a result of differences that exist between populations, like differences in allele frequencies, LD patterns (De Roos et al 2008;Zhong et al 2009;De Los Campos et al 2012), allele substitution effects of QTL (Spelman et al 2002;Thaller et al 2003;Wientjes et al 2015b), environments in combination with genotype-by-environment interactions (Lund et al 2011;Haile-Mariam et al 2015), the presence of QTL that are segregating only in one population (Kemper et al 2015), and the absence of close family relationships across populations. Different deterministic equations are available to calculate the accuracy of genomic prediction when the training population is a subset from the same population as the predicted individuals (Daetwyler et al 2008;Vanraden 2008;Goddard 2009).…”

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An Equation to Predict the Accuracy of Genomic Values by Combining Data from Multiple Traits, Populations, or Environments

et al. 2015

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Predicting the accuracy of estimated genomic values using genome-wide marker information is an important step in designing training populations. Currently, different deterministic equations are available to predict accuracy within populations, but not for multipopulation scenarios where data from multiple breeds, lines or environments are combined. Therefore, our objective was to develop and validate a deterministic equation to predict the accuracy of genomic values when different populations are combined in one training population. The input parameters of the derived prediction equation are the number of individuals and the heritability from each of the populations in the training population; the genetic correlations between the populations, i.e., the correlation between allele substitution effects of quantitative trait loci; the effective number of chromosome segments across predicted and training populations; and the proportion of the genetic variance in the predicted population captured by the markers in each of the training populations. Validation was performed based on real genotype information of 1033 Holstein-Friesian cows that were divided into three different populations by combining half-sib families in the same population. Phenotypes were simulated for multiple scenarios, differing in heritability within populations and in genetic correlations between the populations. Results showed that the derived equation can accurately predict the accuracy of estimating genomic values for different scenarios of multipopulation genomic prediction. Therefore, the derived equation can be used to investigate the potential accuracy of different multipopulation genomic prediction scenarios and to decide on the most optimal design of training populations.KEYWORDS genomic prediction; multipopulation; accuracy; prediction equation; genomic selection; GenPred; shared data resource G ENOMIC markers can be used to estimate genomic values of individuals, also known as additive genetic values or breeding values, that are used to select animals (e.g., Dekkers 2007; De Roos et al. 2011) and plants for breeding (e.g., Heffner et al. 2009;Jannink et al. 2010) and in humans to predict the genetic risk of diseases (e.g., Wray et al. 2007;De Los Campos et al. 2010). In genomic prediction, genome-wide singlenucleotide polymorphism (SNP) marker information is used to predict genomic values based on SNP effects estimated in a training population consisting of individuals with known SNP genotypes and phenotypes (Meuwissen et al. 2001). The accuracy of estimating genomic values is in general higher when the size of the training population is larger, when the level of linkage disequilibrium (LD) between the SNPs and the quantitative trait loci (QTL) underlying the trait is higher, and when the predicted individuals are more related to the individuals in the training population (e.g., Daetwyler et al. 2008;Zhong et al. 2009;De Los Campos et al. 2013;Wientjes et al. 2013).For numerically small populations, the size of the training p...

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Section: Multipopulation Genomic Prediction 811mentioning

confidence: 90%

“…In analogy with Wientjes et al (2015b) is the total phenotypic variance in population A. So, the total variance of x A can be written as…”

Section: Theorymentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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An Equation to Predict the Accuracy of Genomic Values by Combining Data from Multiple Traits, Populations, or Environments

et al. 2015

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Predicting genomic selection efficiency to optimize calibration set and to assess prediction accuracy in highly structured populations

2017

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Key message We propose a criterion to predict genomic selection efficiency for structured populations. This criterion is useful to define optimal calibration set and to estimate prediction reliability for multiparental populations. AbstractGenomic selection refers to the use of genotypic information for predicting the performance of selection candidates. It has been shown that prediction accuracy depends on various parameters including the composition of the calibration set (CS). Assessing the level of accuracy of a given prediction scenario is of highest importance because it can be used to optimize CS sampling before collecting phenotypes, and once the breeding values are predicted it informs the breeders about the reliability of these predictions. Different criteria were proposed to optimize CS sampling in highly diverse panels, which can be useful to screen collections of genotypes. But plant breeders often work on structured material such as biparental or multiparental populations, for which these criteria are less adapted. We derived from the generalized coefficient of determination (CD) theory different criteria to optimize CS sampling and to assess the reliability associated to predictions in structured populations. These criteria were evaluated on two nested association mapping (NAM) populations and two highly diverse panels of maize. They were efficient to sample optimized CS in most situations. They could also estimate at least partly the reliability associated to predictions between NAM families, but they could not estimate differences in the reliability associated to the predictions of NAM families using the highly diverse panels as calibration sets. We illustrated that the CD criteria could be adapted to various prediction scenarios including inter and intra-family predictions, resulting in higher prediction accuracies.Electronic supplementary materialThe online version of this article (doi:10.1007/s00122-017-2956-7) contains supplementary material, which is available to authorized users.

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Genomic selection efficiency and a priori estimation of accuracy in a structured dent maize panel

et al. 2018

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Empirical and deterministic accuracies of across-population genomic prediction

Cited by 76 publications

References 40 publications

An Equation to Predict the Accuracy of Genomic Values by Combining Data from Multiple Traits, Populations, or Environments

An Equation to Predict the Accuracy of Genomic Values by Combining Data from Multiple Traits, Populations, or Environments

Predicting genomic selection efficiency to optimize calibration set and to assess prediction accuracy in highly structured populations

Genomic selection efficiency and a priori estimation of accuracy in a structured dent maize panel

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