Evidence for epistasis between SLC6A4 and a chromosome 4 gene as risk factors in major depression

Reprogrammed Epigenetic Landscape-Prophesied Functions of Bioactive Polysaccharides in Alleviating Diseases: A Pilot Study of DNA Methylome Remodeling in Astragalus Polysaccharide (APS)-Improved Osteoporosis in a Rat Model

DNA methylation is an epigenetic event that plays critical roles in the pathogenesis, progression, and treatment of human diseases. In this study, we investigated the epigenetic mechanisms for Astragalus polysaccharide (APS)-improved osteoporosis in a rat model. The results showed that APS significantly changed the DNA methylome in colonic epithelia with great efficiency. Gene set enrichment analysis (GSEA) based on differentially methylated sites (DMSs) revealed that APS caused promoter DNA methylation changes of genes associated with calcium homeostasis, osteoclast/osteoblast balance, Wnt signaling, and hormone-related processes. Further analysis showed high consistency of APS-induced gene methylomic changes in colonic epithelia and its effects on diabetes, virus infection, and wound healing, which had been reported already. Moreover, we suggested new functions and the involved mechanisms of APS in heart disease, neurological disorder, reproductive problem, and olfactory dysfunction. In this study, we offered epigenetic mechanisms for APS-improved osteoporosis. More importantly, we proposed and proved a reliable method to explore the beneficial effects of bioactive polysaccharides by studying DNA methylation changes at nonfocal sites. We firmly believed the promising prospects of this method for its great efficiency, rapidness, and economy in exploring possible beneficial or therapeutic effects of functional macromolecules with one single experiment.

Assessment of genetic and nongenetic interactions for the prediction of depressive symptomatology: an analysis of the Wisconsin Longitudinal Study using machine learning algorithms

OBJECTIVES

We examined depression within a multidimensional framework consisting of genetic, environmental, and sociobehavioral factors and, using machine learning algorithms, explored interactions among these factors that might better explain the etiology of depressive symptoms.

METHODS

We measured current depressive symptoms using the Center for Epidemiologic Studies Depression Scale (n = 6378 participants in the Wisconsin Longitudinal Study). Genetic factors were 78 single nucleotide polymorphisms (SNPs); environmental factors-13 stressful life events (SLEs), plus a composite proportion of SLEs index; and sociobehavioral factors-18 personality, intelligence, and other health or behavioral measures. We performed traditional SNP associations via logistic regression likelihood ratio testing and explored interactions with support vector machines and Bayesian networks.

RESULTS

After correction for multiple testing, we found no significant single genotypic associations with depressive symptoms. Machine learning algorithms showed no evidence of interactions. Naïve Bayes produced the best models in both subsets and included only environmental and sociobehavioral factors.

CONCLUSIONS

We found no single or interactive associations with genetic factors and depressive symptoms. Various environmental and sociobehavioral factors were more predictive of depressive symptoms, yet their impacts were independent of one another. A genome-wide analysis of genetic alterations using machine learning methodologies will provide a framework for identifying genetic-environmental-sociobehavioral interactions in depressive symptoms.

Multigene interactions and the prediction of depression in the Wisconsin Longitudinal Study

OBJECTIVES

Single genetic loci offer little predictive power for the identification of depression. This study examined whether an analysis of gene-gene (G × G) interactions of 78 single nucleotide polymorphisms (SNPs) in genes associated with depression and age-related diseases would identify significant interactions with increased predictive power for depression.

DESIGN

A retrospective cohort study.

SETTING

A survey of participants in the Wisconsin Longitudinal Study.

PARTICIPANTS

A total of 4811 persons (2464 women and 2347 men) who provided saliva for genotyping; the group comes from a randomly selected sample of Wisconsin high school graduates from the class of 1957 as well as a randomly selected sibling, almost all of whom are non-Hispanic white.

PRIMARY OUTCOME MEASURE

Depression as determine by the Composite International Diagnostic Interview-Short-Form.

RESULTS

Using a classification tree approach (recursive partitioning (RP)), the authors identified a number of candidate G × G interactions associated with depression. The primary SNP splits revealed by RP (ANKK1 rs1800497 (also known as DRD2 Taq1A) in men and DRD2 rs224592 in women) were found to be significant as single factors by logistic regression (LR) after controlling for multiple testing (p=0.001 for both). Without considering interaction effects, only one of the five subsequent RP splits reached nominal significance in LR (FTO rs1421085 in women, p=0.008). However, after controlling for G × G interactions by running LR on RP-specific subsets, every split became significant and grew larger in magnitude (OR (before) → (after): men: GNRH1 novel SNP: (1.43 → 1.57); women: APOC3 rs2854116: (1.28 → 1.55), ACVR2B rs3749386: (1.11 → 2.17), FTO rs1421085: (1.32 → 1.65), IL6 rs1800795: (1.12 → 1.85)).

CONCLUSIONS

The results suggest that examining G × G interactions improves the identification of genetic associations predictive of depression. 4 of the SNPs identified in these interactions were located in two pathways well known to impact depression: neurotransmitter (ANKK1 and DRD2) and neuroendocrine (GNRH1 and ACVR2B) signalling. This study demonstrates the utility of RP analysis as an efficient and powerful exploratory analysis technique for uncovering genetic and molecular pathway interactions associated with disease aetiology.