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Incorporating parent-of-origin effects in whole-genome prediction of complex traits.

Hu Y, Rosa GJ, Gianola D - Genet. Sel. Evol. (2016)

Bottom Line: The simulation and the negative result obtained in the real data analysis indicated that, in order to gain benefit from the POE model in terms of prediction, a sizable contribution of parent-of-origin effects to variation is needed and such variation must be captured by the genetic markers fitted.Recent studies, however, suggest that most parent-of-origin effects stem from epigenetic regulation but not from a change in DNA sequence.Therefore, integrating epigenetic information with genetic markers may help to account for parent-of-origin effects in whole-genome prediction.

View Article: PubMed Central - PubMed

Affiliation: Department of Animal Sciences, University of Wisconsin-Madison, 1675 Observatory Dr., Madison, WI, 53706, USA. yhu32@wisc.edu.

ABSTRACT

Background: Parent-of-origin effects are due to differential contributions of paternal and maternal lineages to offspring phenotypes. Such effects include, for example, maternal effects in several species. However, epigenetically induced parent-of-origin effects have recently attracted attention due to their potential impact on variation of complex traits. Given that prediction of genetic merit or phenotypic performance is of interest in the study of complex traits, it is relevant to consider parent-of-origin effects in such predictions. We built a whole-genome prediction model that incorporates parent-of-origin effects by considering parental allele substitution effects of single nucleotide polymorphisms and gametic relationships derived from a pedigree (the POE model). We used this model to predict body mass index in a mouse population, a trait that is presumably affected by parent-of-origin effects, and also compared the prediction performance to that of a standard additive model that ignores parent-of-origin effects (the ADD model). We also used simulated data to assess the predictive performance of the POE model under various circumstances, in which parent-of-origin effects were generated by mimicking an imprinting mechanism.

Results: The POE model did not predict better than the ADD model in the real data analysis, probably due to overfitting, since the POE model had far more parameters than the ADD model. However, when applied to simulated data, the POE model outperformed the ADD model when the contribution of parent-of-origin effects to phenotypic variation increased. The superiority of the POE model over the ADD model was up to 8 % on predictive correlation and 5 % on predictive mean squared error.

Conclusions: The simulation and the negative result obtained in the real data analysis indicated that, in order to gain benefit from the POE model in terms of prediction, a sizable contribution of parent-of-origin effects to variation is needed and such variation must be captured by the genetic markers fitted. Recent studies, however, suggest that most parent-of-origin effects stem from epigenetic regulation but not from a change in DNA sequence. Therefore, integrating epigenetic information with genetic markers may help to account for parent-of-origin effects in whole-genome prediction.

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Averaged mean squared error (MSE) of two models between observed and predicted phenotype under different simulation settings. ADD additive model, POE parent-of-origin effects model.  proportion of imprinted QTL;  and  denote complete imprinting and no imprinting, respectively
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Fig2: Averaged mean squared error (MSE) of two models between observed and predicted phenotype under different simulation settings. ADD additive model, POE parent-of-origin effects model. proportion of imprinted QTL; and denote complete imprinting and no imprinting, respectively

Mentions: As described in “Simulated data” section, each replicate had 20 scenarios, each corresponding to a combination of (imprinting level) and s (proportion of imprinted QTL). When , however, three scenarios were redundant because in this case, all QTL were unimprinted such that different values of s made no difference (Eq. 8). Figure 1 displays the average prediction accuracy measured by Pearson’s correlation between observed and predicted phenotypes in different simulation scenarios, and Fig. 2 shows the MSE performance of the two models. Under both evaluation metrics, the ADD model performed better than the POE model when no imprinting was simulated (). When there were no parent-of-origin effects, the p extra parameters in the POE model led to overfitting of the training data, thus sacrificing predictive ability of future data. With parent-of-origin effects, the POE model outperformed the ADD model but in a manner that depended on the s and settings. Typically, the POE model was better than the ADD model when was small and s was large.Fig. 1


Incorporating parent-of-origin effects in whole-genome prediction of complex traits.

Hu Y, Rosa GJ, Gianola D - Genet. Sel. Evol. (2016)

Averaged mean squared error (MSE) of two models between observed and predicted phenotype under different simulation settings. ADD additive model, POE parent-of-origin effects model.  proportion of imprinted QTL;  and  denote complete imprinting and no imprinting, respectively
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4834899&req=5

Fig2: Averaged mean squared error (MSE) of two models between observed and predicted phenotype under different simulation settings. ADD additive model, POE parent-of-origin effects model. proportion of imprinted QTL; and denote complete imprinting and no imprinting, respectively
Mentions: As described in “Simulated data” section, each replicate had 20 scenarios, each corresponding to a combination of (imprinting level) and s (proportion of imprinted QTL). When , however, three scenarios were redundant because in this case, all QTL were unimprinted such that different values of s made no difference (Eq. 8). Figure 1 displays the average prediction accuracy measured by Pearson’s correlation between observed and predicted phenotypes in different simulation scenarios, and Fig. 2 shows the MSE performance of the two models. Under both evaluation metrics, the ADD model performed better than the POE model when no imprinting was simulated (). When there were no parent-of-origin effects, the p extra parameters in the POE model led to overfitting of the training data, thus sacrificing predictive ability of future data. With parent-of-origin effects, the POE model outperformed the ADD model but in a manner that depended on the s and settings. Typically, the POE model was better than the ADD model when was small and s was large.Fig. 1

Bottom Line: The simulation and the negative result obtained in the real data analysis indicated that, in order to gain benefit from the POE model in terms of prediction, a sizable contribution of parent-of-origin effects to variation is needed and such variation must be captured by the genetic markers fitted.Recent studies, however, suggest that most parent-of-origin effects stem from epigenetic regulation but not from a change in DNA sequence.Therefore, integrating epigenetic information with genetic markers may help to account for parent-of-origin effects in whole-genome prediction.

View Article: PubMed Central - PubMed

Affiliation: Department of Animal Sciences, University of Wisconsin-Madison, 1675 Observatory Dr., Madison, WI, 53706, USA. yhu32@wisc.edu.

ABSTRACT

Background: Parent-of-origin effects are due to differential contributions of paternal and maternal lineages to offspring phenotypes. Such effects include, for example, maternal effects in several species. However, epigenetically induced parent-of-origin effects have recently attracted attention due to their potential impact on variation of complex traits. Given that prediction of genetic merit or phenotypic performance is of interest in the study of complex traits, it is relevant to consider parent-of-origin effects in such predictions. We built a whole-genome prediction model that incorporates parent-of-origin effects by considering parental allele substitution effects of single nucleotide polymorphisms and gametic relationships derived from a pedigree (the POE model). We used this model to predict body mass index in a mouse population, a trait that is presumably affected by parent-of-origin effects, and also compared the prediction performance to that of a standard additive model that ignores parent-of-origin effects (the ADD model). We also used simulated data to assess the predictive performance of the POE model under various circumstances, in which parent-of-origin effects were generated by mimicking an imprinting mechanism.

Results: The POE model did not predict better than the ADD model in the real data analysis, probably due to overfitting, since the POE model had far more parameters than the ADD model. However, when applied to simulated data, the POE model outperformed the ADD model when the contribution of parent-of-origin effects to phenotypic variation increased. The superiority of the POE model over the ADD model was up to 8 % on predictive correlation and 5 % on predictive mean squared error.

Conclusions: The simulation and the negative result obtained in the real data analysis indicated that, in order to gain benefit from the POE model in terms of prediction, a sizable contribution of parent-of-origin effects to variation is needed and such variation must be captured by the genetic markers fitted. Recent studies, however, suggest that most parent-of-origin effects stem from epigenetic regulation but not from a change in DNA sequence. Therefore, integrating epigenetic information with genetic markers may help to account for parent-of-origin effects in whole-genome prediction.

Show MeSH