Limits...
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.

Show MeSH
Training accuracy of two models measured by Pearson’s correlation  between observed and fitted 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
getmorefigures.php?uid=PMC4834899&req=5

Fig4: Training accuracy of two models measured by Pearson’s correlation between observed and fitted 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 stated above, when , the value of s does not affect the simulated data. In this simpler case, the ADD model outperformed the POE model in terms of predictive correlation and MSE, since the extra parameters in the POE model captured noise only. This is because, if, instead of capturing signal in the data, the better fit is due to higher model complexity, a penalty would be given to such a model during the testing process [93]. In our Bayesian implementation, genome-wide incorporation of parent-of-origin effects approximately doubled the number of parameters relative to the ADD model. This higher complexity provided a better fit to the data, as shown in Fig. 4: the training correlation of the POE model was always higher than that of the ADD model by about 4 %. However, a lower predictive correlation of the POE model (, Fig. 1) indicated that the extra parameters in the POE model were not capturing model signal, at least when . For the same reason, the POE model was expected to have a higher prediction error than the ADD model when no parent-of-origin effects affected the trait (Fig. 2).Fig. 4


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

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

Training accuracy of two models measured by Pearson’s correlation  between observed and fitted 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

Fig4: Training accuracy of two models measured by Pearson’s correlation between observed and fitted 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 stated above, when , the value of s does not affect the simulated data. In this simpler case, the ADD model outperformed the POE model in terms of predictive correlation and MSE, since the extra parameters in the POE model captured noise only. This is because, if, instead of capturing signal in the data, the better fit is due to higher model complexity, a penalty would be given to such a model during the testing process [93]. In our Bayesian implementation, genome-wide incorporation of parent-of-origin effects approximately doubled the number of parameters relative to the ADD model. This higher complexity provided a better fit to the data, as shown in Fig. 4: the training correlation of the POE model was always higher than that of the ADD model by about 4 %. However, a lower predictive correlation of the POE model (, Fig. 1) indicated that the extra parameters in the POE model were not capturing model signal, at least when . For the same reason, the POE model was expected to have a higher prediction error than the ADD model when no parent-of-origin effects affected the trait (Fig. 2).Fig. 4

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