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Genetics. 2015 Oct;201(2):779-93. doi: 10.1534/genetics.115.177204. Epub 2015 Aug 6.

Prediction of Plant Height in Arabidopsis thaliana Using DNA Methylation Data.

Author information

1
Department of Animal Sciences, University of Wisconsin, Madison, Wisconsin 53706 yhu32@wisc.edu.
2
Department of Animal Science, University of Nebraska, Lincoln, Nebraska 68583.
3
Department of Animal Sciences, University of Wisconsin, Madison, Wisconsin 53706 Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin 53706.
4
Department of Animal Sciences, University of Wisconsin, Madison, Wisconsin 53706 Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin 53706 Department of Dairy Science, University of Wisconsin, Madison, Wisconsin 53706.

Abstract

Prediction of complex traits using molecular genetic information is an active area in quantitative genetics research. In the postgenomic era, many types of -omic (e.g., transcriptomic, epigenomic, methylomic, and proteomic) data are becoming increasingly available. Therefore, evaluating the utility of this massive amount of information in prediction of complex traits is of interest. DNA methylation, the covalent change of a DNA molecule without affecting its underlying sequence, is one quantifiable form of epigenetic modification. We used methylation information for predicting plant height (PH) in Arabidopsis thaliana nonparametrically, using reproducing kernel Hilbert spaces (RKHS) regression. Also, we used different criteria for selecting smaller sets of probes, to assess how representative probes could be used in prediction instead of using all probes, which may lessen computational burden and lower experimental costs. Methylation information was used for describing epigenetic similarities between individuals through a kernel matrix, and the performance of predicting PH using this similarity matrix was reasonably good. The predictive correlation reached 0.53 and the same value was attained when only preselected probes were used for prediction. We created a kernel that mimics the genomic relationship matrix in genomic best linear unbiased prediction (G-BLUP) and estimated that, in this particular data set, epigenetic variation accounted for 65% of the phenotypic variance. Our results suggest that methylation information can be useful in whole-genome prediction of complex traits and that it may help to enhance understanding of complex traits when epigenetics is under examination.

KEYWORDS:

DNA methylation; MeDIP-Chip; RKHS regression; GenPred; shared data resource; epigenetics; phenotypic prediction

PMID:
26253546
PMCID:
PMC4596684
DOI:
10.1534/genetics.115.177204
[Indexed for MEDLINE]
Free PMC Article

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