Study Finds Addition of Epigenetic Data Improves Predictions of Complex Traits
News Jun 26, 2015
Peter Visscher and his team at the University of Queensland compared how well genetic predictors, epigenetic predictors, and the two combined could explain variations in height and BMI in different cohorts representing a range of ages. They expected epigenetic effects to have a greater effect on BMI while height would mainly have a genetic influence.
True to expectations, the researchers found that the methylation profile scores they generated didn't contribute to variance in height, while they did for BMI. Further, the methylation profile scores and genetic predictors worked in an additive fashion to improve BMI prediction.
"The BMI results suggest that combining genetic and epigenetic information might have greater utility for complex-trait prediction," Visscher and his colleagues wrote in their paper.
Visscher and his team performed methylome-wide association studies for height and BMI in a discovery set of 1,366 people from two Scottish birth cohorts with average ages of 79 and 69, respectively. They then validated their findings in a cohort comprised of 750 Dutch adults with an average age of 45.
From this, they associated nine probes from the Scottish set with BMI and five in the Dutch cohort. Using these probes, the researchers developed methylation profile scores — a weighted sum of methylation levels at the associated CpG sites — in the validation cohort based on observed CpG associations in the discovery set for BMI.
For height, only one probe in the Dutch cohort associated with the phenotype, so to develop a height-profile score, they had to loosen the strictness of their significance threshold.
Based on these scores, Visscher and his team gauged how well DNA methylation could explain height and BMI. For height, the methylation profile scores explained only some 0.31 percent and 0.76 percent of variation in the Scottish and Dutch cohorts, while the genetic profile scores explained 18.5 percent and 19.8 percent of height variation in the respective cohorts.
BMI, on the other hand, was influenced more by epigenetics. According to the researchers, the methylation-profile scores could explain 6.9 percent and 4.9 percent of the variation in BMI in the Scottish and Dutch cohorts, respectively.
Genetic variation scores, meanwhile, could explain 8.0 percent and 9.4 percent of the variation in BMI in the Scottish and Dutch cohorts, respectively. The genetic variation scores — also weighted sums, though this time of the associated effect alleles of associated SNPs — was based on data from the most recent BMI and height meta-GWAS conducted by the Genetic Investigation of Anthropometric Traits (GIANT) consortium.
When the researchers combined the methylation and genetic scores in an additive model, it could explain 14.0 percent and 13.6 percent of the variation in BMI in the Scottish and Dutch cohorts, respectively.
How well the BMI methylation profiles scores could explain variation in BMI varied by the population used to generate those scores, with a noticeable influence by average age of the cohorts. For instance, BMI methylation profile scores generated in the Scottish cohort, which had an older average age, couldn't explain any variation in adolescents from the Brisbane Systems Genetics Study, a cohort of 403 individuals with a mean age of 14, even after BMI was adjusted for sex and age.
Similarly, the profile scores generated from the middle-aged subset of the Dutch cohort could explain 3.6 percent of the BMI variation in the Brisbane group, but a younger subset of the Dutch cohort could explain 5.4 percent of the BMI variation in the Brisbane group.
"Combined, the results suggest that these differences might be due to the direct effect of more prolonged exposure to environmental factors in older individuals, or the fact that older individuals are 'exposed' to the phenotype for longer, and therefore might show larger effects on methylation due to reverse causation," the researchers said.
They noted that their study had a number of limitations, including small sample sizes and a reliance on blood-based samples for DNA methylation study rather than of tissue-specific samples.
Still, "we have shown that inter-individual differences in environment or lifestyle are partly reflected in DNA-methylation data, and therefore DNA-methylation profiles have the potential to significantly improve complex-trait prediction over and above that of genetic predictors," the researchers said.
What’s in Your Wheat? Sequencing One of the Most Complex Genomes Known to ScienceNews
The wheat genome sequences may help biologists not only better understand the evolutionary history of wheat, but also advance the quest for hardier, more pest- and drought-resistant wheat types to help feed the world’s growing population.READ MORE
Revolutionary Imaging Technique Uses CRISPR to Map DNA MutationsNews
The new high-speed AFM method can map DNA to a resolution of tens of base pairs while creating images up to a million base pairs in size. And it does it using a fraction of the amount of specimen required for DNA sequencing.READ MORE
Comments | 0 ADD COMMENT
3rd Annual NGS Data Analysis and Informatics Conference
Feb 08 - Feb 09, 2018
3rd Annual Genome Editing & Engineering Conference
Feb 08 - Feb 09, 2018