TrioMDR: Detecting SNP interactions in trio families with model-based multifactor dimensionality reduction

GADGETS: a genetic algorithm for detecting epistasis using nuclear families

MOTIVATION

Epistasis may play an etiologic role in complex diseases, but research has been hindered because identification of interactions among sets of single nucleotide polymorphisms (SNPs) requires exploration of immense search spaces. Current approaches using nuclear families accommodate at most several hundred candidate SNPs.

RESULTS

GADGETS detects epistatic SNP-sets by applying a genetic algorithm to case-parent or case-sibling data. To allow for multiple epistatic sets, island subpopulations of SNP-sets evolve separately under selection for evident joint relevance to disease risk. The software evaluates the identified SNP-sets via permutation testing and provides graphical visualization. GADGETS correctly identified epistatic SNP-sets in realistically simulated case-parent triads with 10 000 candidate SNPs, far more SNPs than competitors can handle, and it outperformed competitors in simulations with many fewer SNPs. Applying GADGETS to family-based oral-clefting data from dbGaP identified SNP-sets with possible epistatic effects on risk.

AVAILABILITY AND IMPLEMENTATION

GADGETS is part of the epistasisGA package at https://github.com/mnodzenski/epistasisGA.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

CHDH-PNPLA3 Gene-Gene Interactions Predict Insulin Resistance in Children with Obesity

INTRODUCTION

Insulin resistance plays a major role in metabolic syndrome and is recognized as the most common risk factor for non-alcoholic fatty liver disease (NAFLD). Identifying predictors for insulin resistance could optimize screening and prevention.

PURPOSE

To evaluate the contribution of multiple single nucleotide polymorphisms across genes related to NAFLD and choline metabolism, in predicting insulin resistance in children with obesity.

METHODS

One hundred fifty-three children with obesity (73 girls), aged 7-18 years, were evaluated within the NutriGen Study (ClinicalTrials.gov-NCT02837367). Insulin resistance was defined by Homeostatic Model Assessment for insulin-resistance cut-offs that accommodated pubertal and gender differences. Anthropometric, metabolic, intake-related variables, and 55 single nucleotide polymorphisms related to NAFLD and choline metabolism were evaluated. Gene-gene interaction effects were assessed using Multiple Data Reduction Software.

RESULTS

Sixty percent (93/153) of participants showed insulin resistance (58.7% of boys, 63% of girls). Children with insulin resistance presented significantly higher values for standardized body mass index, triglycerides, transaminases and plasma choline when compared to those without insulin resistance. Out of 52 single nucleotide polymorphisms analysed, the interaction between genotypes CHDH(rs12676) and PNPLA3(rs738409) predicted insulin resistance. The model presented a 6/10 cross-validation consistency and 0.58 testing accuracy. Plasma choline levels and alanine aminotransferase modulated the gene interaction effect, significantly improving the model.

CONCLUSION

The interaction between genotypes in CHDH and PNPLA3 genes, modulated by choline and alanine aminotransferase levels, predicted insulin-resistance status in children with obesity. If replicated in larger cohorts, these findings could help identify metabolic risk in children with obesity.