Reliability of Genomic Predictions Across Multiple Populations

Roos, A.P.W. de; Hayes, Ben J.; Goddard, Michael E.

doi:10.1534/genetics.109.104935

Cited by 289 publications

(335 citation statements)

References 30 publications

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Section: Influence Of Ts Compositionsupporting

confidence: 91%

“…Therefore it is recommended to exclude such families from the TS. This finding is also in agreement with a simulation study showing that increasing accuracies by combining different populations into a single TS requires persistence of marker-QTL LD across populations (de Roos et al 2009). The strong correlation (0.89 , r , 0.93) among s IBS , s GC , and s LP suggests that any of these measures can be used to estimate genetic similarity between populations to identify populations with negative predictive signal.…”

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confidence: 91%

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Genomic Predictability of Interconnected Biparental Maize Populations

et al. 2013

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Intense structuring of plant breeding populations challenges the design of the training set (TS) in genomic selection (GS). An important open question is how the TS should be constructed from multiple related or unrelated small biparental families to predict progeny from individual crosses. Here, we used a set of five interconnected maize (Zea mays L.) populations of doubled-haploid (DH) lines derived from four parents to systematically investigate how the composition of the TS affects the prediction accuracy for lines from individual crosses. A total of 635 DH lines genotyped with 16,741 polymorphic SNPs were evaluated for five traits including Gibberella ear rot severity and three kernel yield component traits. The populations showed a genomic similarity pattern, which reflects the crossing scheme with a clear separation of full sibs, half sibs, and unrelated groups. Prediction accuracies within full-sib families of DH lines followed closely theoretical expectations, accounting for the influence of sample size and heritability of the trait. Prediction accuracies declined by 42% if full-sib DH lines were replaced by half-sib DH lines, but statistically significantly better results could be achieved if half-sib DH lines were available from both instead of only one parent of the validation population. Once both parents of the validation population were represented in the TS, including more crosses with a constant TS size did not increase accuracies. Unrelated crosses showing opposite linkage phases with the validation population resulted in negative or reduced prediction accuracies, if used alone or in combination with related families, respectively. We suggest identifying and excluding such crosses from the TS. Moreover, the observed variability among populations and traits suggests that these uncertainties must be taken into account in models optimizing the allocation of resources in GS.G ENOMIC prediction or selection, initially proposed and rapidly implemented in animal breeding (Meuwissen et al. 2001), is increasingly applied in plant breeding (Bernardo and Yu 2009;Lorenz et al. 2011;Morrell et al. 2011). However, with more investigations in plant breeding applications, new challenges emerge, mainly as the result of the greater possibilities of genetic manipulation and reproduction modes in plants compared to animals. Genomic predictions within diverse populations (Crossa et al. 2010; Riedelsheimer et al. 2012a,b;Windhausen et al. 2012) largely overlap with the scenarios in animal breeding. Predicting crossbred performance in animals also has some similarities with the prediction of maize hybrids from fully homozygous inbred lines drawn from genetically distant heterotic pools (Technow et al. 2012). Apart from using mixtures of close and distant relatives, little overlap with the field of animal breeding however exists in the case of multiple related or unrelated biparental families of inbred lines with a size #200. The latter situation is investigated in this study, because it covers the most rele...

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Section: Influence Of Ts Compositionsupporting

confidence: 91%

supporting

confidence: 91%

Genomic Predictability of Interconnected Biparental Maize Populations

et al. 2013

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“…Since prediction of GEBVs is shown to be more reliable when juvenile animals share their recent pedigree with animals in the reference population (Habier et al, 2007), it seems straightforward that composing a reference population closely related to the juvenile selection candidates is an optimal strategy. De Roos et al (2008a), however, showed, that it is particularly important that at least some of the animals in the reference population originate from the same pedigree or line as the juvenile animals. This indicates that a reference population that needs to serve multiple lines may optimally contain animals from all lines, and yield nearly as accurate predictions for each of the lines as a line-specific reference population may do.…”

Section: Genomic Prediction -The Processmentioning

confidence: 99%

Genomic breeding value prediction: methods and procedures

Calus

2010

Animal

158

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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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“…Further, 20 000 females and 300 males from the last historical generation were used as founders. Similar strategies (shrinkage and expansion of the population) have been used in simulations of dairy cattle populations (De Roos et al, 2009;Habier et al, 2009).…”

Section: Simulationsmentioning

confidence: 99%

Genotyping strategies for genomic selection in small dairy cattle populations

Jiménez-Montero

González-Recio²,

Alenda

2012

Animal

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This study evaluated different female-selective genotyping strategies to increase the predictive accuracy of genomic breeding values (GBVs) in populations that have a limited number of sires with a large number of progeny. A simulated dairy population was utilized to address the aims of the study. The following selection strategies were used: random selection, two-tailed selection by yield deviations, two-tailed selection by breeding value, top yield deviation selection and top breeding value selection. For comparison, two other strategies, genotyping of sires and pedigree indexes from traditional genetic evaluation, were included in the analysis. Two scenarios were simulated, low heritability (h 2 5 0.10) and medium heritability (h 2 5 0.30). GBVs were estimated using the Bayesian Lasso. The accuracy of predicted GBVs using the two-tailed strategies was better than the accuracy obtained using other strategies (0.50 and 0.63 for the two-tailed selection by yield deviations strategy and 0.48 and 0.63 for the two-tailed selection by breeding values strategy in low-and medium-heritability scenarios, respectively, using 1000 genotyped cows). When 996 genotyped bulls were used as the training population, the sire' strategy led to accuracies of 0.48 and 0.55 for low-and medium-heritability traits, respectively. The Random strategies required larger training populations to outperform the accuracies of the pedigree index; however, selecting females from the top of the yield deviations or breeding values of the population did not improve accuracy relative to that of the pedigree index. Bias was found for all genotyping strategies considered, although the Top strategies produced the most biased predictions. Strategies that involve genotyping cows can be implemented in breeding programs that have a limited number of sires with a reliable progeny test. The results of this study showed that females that exhibited upper and lower extreme values within the distribution of yield deviations may be included as training population to increase reliability in small reference populations. The strategies that selected only the females that had high estimated breeding values or yield deviations produced suboptimal results.

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Reliability of Genomic Predictions Across Multiple Populations

Cited by 289 publications

References 30 publications

Genomic Predictability of Interconnected Biparental Maize Populations

Genomic Predictability of Interconnected Biparental Maize Populations

Genomic breeding value prediction: methods and procedures

Genotyping strategies for genomic selection in small dairy cattle populations

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