Epigenome association studies that test a large number of methylation sites suffer from stringent multiple-testing corrections. This study's goals were to investigate region-based associations ...between DNA methylation sites and lipid-level changes in response to the treatment with fenofibrate in the GAW20 data and to investigate whether improvements in power could be obtained by taking into account correlations between DNA methylation at neighboring cytosine-phosphate-guanine (CpG) sites. To this end, we applied both a recently developed block-based data-dimension-reduction approach and a region-based variance-component (VC) linear mixed model to GAW20 data. We compared analyses of unrelated individuals with familial data. The region-based VC approach using unrelated (independent) individuals identified the gene
as significantly associated with changes in triglycerides. However, univariate tests of individual CpG sites yielded no valid statistically significant results.
GAW20 provided a platform for developing and evaluating statistical methods to analyze human lipid-related phenotypes, DNA methylation, and single-nucleotide markers in a study involving a ...pharmaceutical intervention. In this article, we present an overview of the data sets and the contributions analyzing these data. The data, donated by the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) investigators, included data from 188 families (
= 1105) which included genome-wide DNA methylation data before and after a 3-week treatment with fenofibrate, single-nucleotide polymorphisms, metabolic syndrome components before and after treatment, and a variety of covariates. The contributions from individual research groups were extensively discussed prior, during, and after the Workshop in groups based on discussion themes, before being submitted for publication.
Mixed models are a useful tool for evaluating the association between an outcome variable and genetic variables from a family-based genetic study, taking into account the kinship coefficients. When ...there are ultrahigh dimensional genetic variables (ie,
≫
), it is challenging to fit any mixed effect model.
We propose a two-stage strategy, screening genetic variables in the first stage and then fitting the mixed effect model in the second stage to those variables that survive the screening. For the screening stage, we can use the sure independence screening (SIS) procedure, which fits the mixed effect model to one genetic variable at a time. Because the SIS procedure may fail to identify those marginally unimportant but jointly important genetic variables, we propose a joint screening (JS) procedure that screens all the genetic variables simultaneously. We evaluate the performance of the proposed JS procedure via a simulation study and an application to the GAW20 data.
We perform the proposed JS procedure on the GAW20 representative simulated data set (
= 680 participant(s) and
= 463,995 CpG cytosine-phosphate-guanine CpG sites) and select the top
= ⌊
/ log(
)⌋ variables. Then we fit the mixed model using these top variables. Under significance level, 5%, 43 CpG sites are found to be significant. Some diagnostic analyses based on the residuals show the fitted mixed model is appropriate.
Although the GAW20 data set is ultrahigh dimensional and family-based having within group variances, we were successful in performing subset selection using a two-step strategy that is computationally simple and easy to understand.
Although methylation data continues to rise in popularity, much is still unknown about how to best analyze methylation data in genome-wide analysis contexts. Given continuing interest in gene-based ...tests for next-generation sequencing data, we evaluated the performance of novel gene-based test statistics on simulated data from GAW20. Our analysis suggests that most of the gene-based tests are detecting real signals and maintaining the Type I error rate. The minimum
value and threshold-based tests performed well compared to single-marker tests in many cases, especially when the number of variants was relatively large with few true causal variants in the set.
An increasing number of studies are focused on the epigenetic regulation of DNA to affect gene expression without modifications to the DNA sequence. Methylation plays an important role in shaping ...disease traits; however, previous studies were mainly experiment, based, resulting in few reports that measured gene-methylation interaction effects via statistical means. In this study, we applied the data set adaptive W-test to measure gene-methylation interactions. Performance was evaluated by the ability to detect a given set of causal markers in the data set obtained from the GAW20. Results from simulation data analyses showed that the W-test was able to detect most markers. The method was also applied to chromosome 11 of the experimental data set and identified clusters of genes with neuronal and retinal functions, including
,
, and
. Genes from the
family were also identified; these genes are potentially related to the regulation of triglyceride levels. Our results suggest that the W-test could be an efficient and effective method to detect gene-methylation interactions. Furthermore, the identified genes suggest an interesting relationship between lipid levels and the etiology of neurological disorders.
The heritability of a phenotype is an estimation of the percent of variance in that phenotype that is attributable to additive genetic factors. Heritability is optimally estimated in family-based ...sample populations. Traditionally, this involves use of a pedigree-based kinship coefficient generated from the collected genealogical relationships between family members. An alternative, when dense genotype data are available, is to directly measure the empirical kinship between samples. This study compares the use of pedigree and empirical kinships in the GAW20 data set. Two phenotypes were assessed: triglyceride levels and high-density lipoprotein cholesterol (HDL-C) levels pre- and postintervention with the cholesterol-reducing drug fenofibrate. Using SOLAR (Sequential Oligogenic Linkage Analysis Routines), pedigree-based kinships and empirically calculated kinships (using IBDLD and LDAK) were used to calculate phenotype heritability. In addition, a genome-wide association study was conducted using each kinship model for each phenotype to identify genetic variants significantly associated with phenotypic variation. The variant rs247617 was significantly associated with HDL-C levels both pre- and post-fenofibrate intervention. Overall, the phenotype heritabilities calculated using pedigree based kinships or either of the empirical kinships generated using IBDLD or LDAK were comparable. Phenotype heritabilities estimated from empirical kinships generated using IBDLD were closest to the pedigree-based estimations. Given that there was not an appreciable amount of unknown relatedness between the pedigrees in this data set, a large increase in heritability in using empirical kinship was not expected, and our calculations support this. Importantly, these results demonstrate that when sufficient genotypic data are available, empirical kinship estimation is a practical alternative to using pedigree-based kinships.
Epigenetic modification has an effect on gene expression under the environmental alteration, but it does not change corresponding genome sequence. DNA methylation (DNAm) is one of the important ...epigenetic mechanisms. DNAm variations could be used as epigenetic markers to predict and account for the change of many human phenotypic traits, such as cancer, diabetes, and high blood pressure. In this study, we built deep neural network (DNN) regression models to account for interindividual variation in triglyceride concentrations measured at different visits of peripheral blood samples using epigenome-wide DNAm profiles.
We used epigenome-wide DNAm profiles of before and after medication interventions (called
and
, respectively) to predict triglyceride concentrations for peripheral blood draws at visit 2 (using pretreatment data) and at visit 4 (using both pretreatment and posttreatment data). Our experimental results showed that DNN models can predict triglyceride concentrations for blood draws at visit 4 using pretreatment and posttreatment DNAm data more accurately than for blood draws at visit 2 using pretreatment DNAm data. Furthermore, we got the best prediction results when we used pretreatment DNAm data to predict triglyceride concentrations for blood draws at visit 4, which suggests a long-term epigenetic effect on phenotypic traits. We compared the prediction performances of our proposed DNN models with that of support vector machine (SVM). This comparison showed that our DNN models achieved better prediction performance than did SVM.
We demonstrated the superiority of our proposed DNN models over the SVM model for predicting triglyceride concentrations. This study also suggests that the DNN approach has advantages over other traditional machine-learning methods to model high-dimensional epigenome-wide DNAm data and other genomic data.
The Genetic Analysis Workshop (GAW) presents an opportunity to collaboratively evaluate methodology relevant to current issues in genetic epidemiology. The GAW20 data combine real clinical trial data ...with fictitious epigenetic drug response endpoints. Considering the evidence suggesting that networks of interactions between many genes underlie complex phenotypes, we utilize differential methylation status to identify a relevant gene set for enrichment analysis and use this to infer potential biological function underlying drug response. We highlight the pertinence of considering the potential for widespread epistatic interactions in the absence of main effects, and present evidence of epistasis between single-nucleotide polymorphisms (SNPs) on the two RNA demethylases
and
.
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