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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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Stylized representation of the change of total additive variance across all 150 simulated QTL loci (, left panel) and proportion of total additive variance due to parent-of-origin effects (, right panel) at different values of  (imprinting level, changes from 0 to 1) and s (, proportion of imprinted QTL)
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Fig5: Stylized representation of the change of total additive variance across all 150 simulated QTL loci (, left panel) and proportion of total additive variance due to parent-of-origin effects (, right panel) at different values of (imprinting level, changes from 0 to 1) and s (, proportion of imprinted QTL)

Mentions: As imprinting changed from the highest (, complete imprinting) to the lowest level (, no imprinting), the predictive correlation of both models increased gradually for any value of s, since total additive variance (signal) increased during this course (Eq. 10; left panel of Fig. 5), so the predictive ability increased accordingly. Also, because the ADD model was better at but the POE model was better at , curves representing the two models crossed at some point, and it was interesting to note that the value of associated with the cross point increased (representing a lower level of imprinting) as s went up (Fig. 1). Intuitively, the POE model would outperform the ADD model when the proportion of signal due to parent-of-origin effects reaches some threshold. Here, the variance accounted for by parent-of-origin effects is expressed as:11\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} \sigma ^2_o = \frac{1}{2}pq(\theta _1-\theta _2)^2(1-\rho )^2 \end{aligned}$$\end{document}σo2=12pq(θ1-θ2)2(1-ρ)2according to the four genotypic values in Eq. 9 and the one-locus imprinting model of [59, 60], and [17]; the ratio between Eqs. 11 and 10 gives the proportion of additive variance accounted for by parent-of-origin effect at that locus. For a larger s, this threshold is reached much faster than at a smaller s as (Fig. 5, right panel), indicating that when fewer QTL are imprinted, a higher level of imprinting is needed for the POE model to gain advantage, as expected.Fig. 5


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

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

Stylized representation of the change of total additive variance across all 150 simulated QTL loci (, left panel) and proportion of total additive variance due to parent-of-origin effects (, right panel) at different values of  (imprinting level, changes from 0 to 1) and s (, proportion of imprinted QTL)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig5: Stylized representation of the change of total additive variance across all 150 simulated QTL loci (, left panel) and proportion of total additive variance due to parent-of-origin effects (, right panel) at different values of (imprinting level, changes from 0 to 1) and s (, proportion of imprinted QTL)
Mentions: As imprinting changed from the highest (, complete imprinting) to the lowest level (, no imprinting), the predictive correlation of both models increased gradually for any value of s, since total additive variance (signal) increased during this course (Eq. 10; left panel of Fig. 5), so the predictive ability increased accordingly. Also, because the ADD model was better at but the POE model was better at , curves representing the two models crossed at some point, and it was interesting to note that the value of associated with the cross point increased (representing a lower level of imprinting) as s went up (Fig. 1). Intuitively, the POE model would outperform the ADD model when the proportion of signal due to parent-of-origin effects reaches some threshold. Here, the variance accounted for by parent-of-origin effects is expressed as:11\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} \sigma ^2_o = \frac{1}{2}pq(\theta _1-\theta _2)^2(1-\rho )^2 \end{aligned}$$\end{document}σo2=12pq(θ1-θ2)2(1-ρ)2according to the four genotypic values in Eq. 9 and the one-locus imprinting model of [59, 60], and [17]; the ratio between Eqs. 11 and 10 gives the proportion of additive variance accounted for by parent-of-origin effect at that locus. For a larger s, this threshold is reached much faster than at a smaller s as (Fig. 5, right panel), indicating that when fewer QTL are imprinted, a higher level of imprinting is needed for the POE model to gain advantage, as expected.Fig. 5

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