A powerful latent variable method for detecting and characterizing gene-based gene-gene interaction on multiple quantitative traits

Gene-gene interaction detection with deep learning

The extent to which genetic interactions affect observed phenotypes is generally unknown because current interaction detection approaches only consider simple interactions between top SNPs of genes. We introduce an open-source framework for increasing the power of interaction detection by considering all SNPs within a selected set of genes and complex interactions between them, beyond only the currently considered multiplicative relationships. In brief, the relation between SNPs and a phenotype is captured by a neural network, and the interactions are quantified by Shapley scores between hidden nodes, which are gene representations that optimally combine information from the corresponding SNPs. Additionally, we design a permutation procedure tailored for neural networks to assess the significance of interactions, which outperformed existing alternatives on simulated datasets with complex interactions, and in a cholesterol study on the UK Biobank it detected nine interactions which replicated on an independent FINRISK dataset.

Using Composite Phenotypes to Reveal Hidden Physiological Heterogeneity in High-Altitude Acclimatization in a Chinese Han Longitudinal Cohort

Altitude acclimatization is a human physiological process of adjusting to the decreased oxygen availability. Since several physiological processes are involved and their correlations are complicated, the analyses of single traits are insufficient in revealing the complex mechanism of high-altitude acclimatization. In this study, we examined these physiological responses as the composite phenotypes that are represented by a linear combination of physiological traits. We developed a strategy that combines both spectral clustering and partial least squares path modeling (PLSPM) to define composite phenotypes based on a cohort study of 883 Chinese Han males. In addition, we captured 14 composite phenotypes from 28 physiological traits of high-altitude acclimatization. Using these composite phenotypes, we applied k-means clustering to reveal hidden population physiological heterogeneity in high-altitude acclimatization. Furthermore, we employed multivariate linear regression to systematically model (Models 1 and 2) oxygen saturation (SpO₂) changes in high-altitude acclimatization and evaluated model fitness performance. Composite phenotypes based on Model 2 fit better than single trait-based Model 1 in all measurement indices. This new strategy of using composite phenotypes may be potentially employed as a general strategy for complex traits research such as genetic loci discovery and analyses of phenomics.

Assessing genetic and environmental influences on epicardial and abdominal adipose tissue quantities: a classical twin study

BACKGROUND/OBJECTIVES

Various adipose tissue compartments play an important role in the development of cardiometabolic diseases. The quantity of different fat compartments is influenced by genetic and environmental factors. The aim of our study was to evaluate the magnitude of genetic and environmental effects on epicardial, subcutaneous and visceral adipose tissue (EAT, SAT and VAT) quantities in a cohort of adult twin pairs.

SUBJECTS/METHODS

In this cross-sectional study we investigated adult twins (57 monozygotic (MZ) and 33 dizygotic (DZ) same-gender twin pairs; 180 twin subjects). We measured EAT volume using electrocardiogram-gated native computed tomography (CT) scan of the heart, and abdominal SAT and VAT areas were quantified between the third and fourth lumbar vertebra on native CT images. We calculated genetic and environmental impact on the size of various adipose tissue compartments by analyzing co-twin correlations in MZ and DZ pairs separately, and furthermore by using genetic structural equation models.

RESULTS

In co-twin analysis, MZ twins had stronger correlations than DZ twins for EAT (r_MZ=0.81, r_DZ=0.32), similar to SAT and VAT quantities (r_MZ=0.80, r_DZ=0.68 and r_MZ=0.79, r_DZ=0.48, respectively). In multi-trait model fitting analysis, the overall contribution of genetic factors to EAT, SAT and VAT volumes were 80%, 78% and 70%, whereas environmental factors were 20%, 22% and 30%, respectively. Common pathway model analyses indicated that none of the EAT, SAT and VAT phenotypes was independent of the other two.

CONCLUSIONS

Genetic factors have substantial influence, while environmental factors have only a modest impact on EAT volume, abdominal SAT and VAT quantities. There is a considerable amount of common genetic background influencing the quantities of all three adipose tissue compartments.

Discussing the putative role of obesity-associated genes in the etiopathogenesis of eating disorders

In addition to the identification of mutations clearly related to Mendelian forms of obesity; genome-wide association studies and follow-up studies have in the last years pinpointed several loci associated with BMI. These genetic alterations are located in or near genes expressed in the hypothalamus that are involved in the regulation of eating behavior. Accordingly, it seems plausible that these SNPs, or others located in related genes, could also help develop aberrant conduct patterns that favor the establishment of eating disorders should other susceptibility factors or personality dimensions be present. However, and somewhat surprisingly, with few exceptions such as BDNF, the great majority of the genes governing these pathways remain untested in patients with anorexia nervosa, bulimia nervosa or binge-eating disorder. In the present work, we review the few existing studies, but also indications and biological concepts that point to these genes in the CNS as good candidates for association studies with eating disorder patients.

A gene-based information gain method for detecting gene-gene interactions in case-control studies

Currently, most methods for detecting gene-gene interactions (GGIs) in genome-wide association studies are divided into SNP-based methods and gene-based methods. Generally, the gene-based methods can be more powerful than SNP-based methods. Some gene-based entropy methods can only capture the linear relationship between genes. We therefore proposed a nonparametric gene-based information gain method (GBIGM) that can capture both linear relationship and nonlinear correlation between genes. Through simulation with different odds ratio, sample size and prevalence rate, GBIGM was shown to be valid and more powerful than classic KCCU method and SNP-based entropy method. In the analysis of data from 17 genes on rheumatoid arthritis, GBIGM was more effective than the other two methods as it obtains fewer significant results, which was important for biological verification. Therefore, GBIGM is a suitable and powerful tool for detecting GGIs in case-control studies.

Detecting epistasis in human complex traits

Genome-wide association studies (GWASs) have become the focus of the statistical analysis of complex traits in humans, successfully shedding light on several aspects of genetic architecture and biological aetiology. Single-nucleotide polymorphisms (SNPs) are usually modelled as having additive, cumulative and independent effects on the phenotype. Although evidently a useful approach, it is often argued that this is not a realistic biological model and that epistasis (that is, the statistical interaction between SNPs) should be included. The purpose of this Review is to summarize recent directions in methodology for detecting epistasis and to discuss evidence of the role of epistasis in human complex trait variation. We also discuss the relevance of epistasis in the context of GWASs and potential hazards in the interpretation of statistical interaction terms.