Principal component of explained variance: An efficient and optimal data dimension reduction framework for association studies

Whole-genome bisulfite sequencing in systemic sclerosis provides novel targets to understand disease pathogenesis

Background

Systemic sclerosis (SSc) is a rare autoimmune connective tissue disease whose pathogenesis remains incompletely understood. Increasing evidence suggests that both genetic susceptibilities and changes in DNA methylation influence pivotal biological pathways and thereby contribute to the disease. The role of DNA methylation in SSc has not been fully elucidated, because existing investigations of DNA methylation predominantly focused on nucleotide CpGs within restricted genic regions, and were performed on samples containing mixed cell types.

Methods

We performed whole-genome bisulfite sequencing on purified CD4+ T lymphocytes from nine SSc patients and nine controls in a pilot study, and then profiled genome-wide cytosine methylation as well as genetic variations. We adopted robust statistical methods to identify differentially methylated genomic regions (DMRs). We then examined pathway enrichment associated with genes located in these DMRs. We also tested whether changes in CpG methylation were associated with adjacent genetic variation.

Results

We profiled DNA methylation at more than three million CpG dinucleotides genome-wide. We identified 599 DMRs associated with 340 genes, among which 54 genes exhibited further associations with adjacent genetic variation. We also found these genes were associated with pathways and functions that are known to be abnormal in SSc, including Wnt/β-catenin signaling pathway, skin lesion formation and progression, and angiogenesis.

Conclusion

The CD4+ T cell DNA cytosine methylation landscape in SSc involves crucial genes in disease pathogenesis. Some of the methylation patterns are also associated with genetic variation. These findings provide essential foundations for future studies of epigenetic regulation and genome-epigenome interaction in SSc.

The pleiotropic effect of rs7903146 on type 2 diabetes and ischemic stroke: a family-based study in a Chinese population

The rs7903146, an established genetic variant susceptible to type 2 diabetes (T2D), is also reported to be related to ischemic stroke (IS), though conflicting. Furthermore, it remained unknown whether the genetic association with stroke is independent of T2D. In the current study, 1603 individuals across 986 families were included. The genetic pleiotropic effect on each outcome diseases (T2D, overall IS, or each subtype) was assessed using multilevel logistic regression after adjustment for multiple covariates. Principal component of heritability (PCH) was also used to assess the pleiotropy by combining T2D and IS into one outcome for analysis. To identify the T2D-independent path out of the pleiotropic effect on IS, T2D status was additionally adjusted for the risk of IS or each subtype. The analyses of putative molecular pathways (dyslipidemia, hypertension, obesity and inflammation) and gene-lifestyle interactions were also performed. We found that rs7903146_T allele was associated with a 77% higher risk of T2D, 55% of IS, and 70% of large artery atherosclerosis (LAA) subtype respectively. Particularly, a T2D-independent genetic effect was identified to increase the risk of overall IS and LAA. No evidence on the molecular mechanisms and gene-lifestyle interaction behind the pleiotropic genetic effect was observed. In conclusion, our study provided evidence that a T2D-independent path was identified out of the pleiotropic effect of rs7903146 on IS. However, further studies were needed to validate the biological mechanisms behind the pleiotropic effect and the modification by lifestyle intervention.

Application of novel and existing methods to identify genes with evidence of epigenetic association: results from GAW20

BACKGROUND

The rise in popularity and accessibility of DNA methylation data to evaluate epigenetic associations with disease has led to numerous methodological questions. As part of GAW20, our working group of 8 research groups focused on gene searching methods.

RESULTS

Although the methods were varied, we identified 3 main themes within our group. First, many groups tackled the question of how best to use pedigree information in downstream analyses, finding that (a) the use of kinship matrices is common practice, (b) ascertainment corrections may be necessary, and (c) pedigree information may be useful for identifying parent-of-origin effects. Second, many groups also considered multimarker versus single-marker tests. Multimarker tests had modestly improved power versus single-marker methods on simulated data, and on real data identified additional associations that were not identified with single-marker methods, including identification of a gene with a strong biological interpretation. Finally, some of the groups explored methods to combine single-nucleotide polymorphism (SNP) and DNA methylation into a single association analysis.

CONCLUSIONS

A causal inference method showed promise at discovering new mechanisms of SNP activity; gene-based methods of summarizing SNP and DNA methylation data also showed promise. Even though numerous questions still remain in the analysis of DNA methylation data, our discussions at GAW20 suggest some emerging best practices.

CpG-set association assessment of lipid concentration changes and DNA methylation

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 LGALS9C as significantly associated with changes in triglycerides. However, univariate tests of individual CpG sites yielded no valid statistically significant results.