Accounting for Population Stratification in DNA Methylation Studies Público
Barfield, Richard Thomas (2012)
Published
Abstract
DNA methylation is an important epigenetic mechanism that helps
regulate gene
expression and can be influenced by both the environment and the
genome. DNA
methylation has also been linked to some cancers, complex diseases,
and
transgenerational effects, and is thus of great interest to public
health researchers as a
potential link between genome, environment, and disease. In recent
years there has been
an increase in the number of genome-wide DNA methylation
association studies due to a
decrease in prices and improved technology. We can now perform DNA
methylation
association studies at the scale that genome wide association
studies (GWAS) were
performed a few years back. As with GWAS, problems such as
population stratification
will also need to be addressed in these DNA methylation studies.
Failure to adjust for
population stratification in genetic association studies can lead
to potential false positives
and erroneous results, but population stratification has yet to be
accounted for in DNA
methylation studies. To address this, we analyzed DNA methylation
for association with
race in two separate datasets, and identified widespread
associations with race across the
genome in both cases. We then performed principal components
analysis on different
forms of the data and included these principal components in the
model to determine
whether this approach would reduce the number of sites
significantly associated with
race. We examined principal components computed from data pruned
based on
correlation and principal components based on CpG sites within a
certain distance of a
SNP ("informed pruning"). We found that the principal components
from the informed
pruning performed the best in reducing the number of sites
significantly associated with
race (90.55- 97.82% reductions in the number of FDR-significant and
84.07-94.38%
reductions in the number of Holm-significant sites); this approach
was also less
computationally intensive than approaches requiring
correlation-based pruning. We have
therefore developed an effective method to account for population
stratification in DNA
methylation studies that does not require the collection of data on
genetic variants.
Table of Contents
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About this Master's Thesis
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