Spatial rank-based multifactor dimensionality reduction to detect gene-gene interactions for multivariate phenotypes

Overview of frequent pattern mining

Various methods of frequent pattern mining have been applied to genetic problems, specifically, to the combined association of two genotypes (a genotype pattern, or diplotype) at different DNA variants with disease. These methods have the ability to come up with a selection of genotype patterns that are more common in affected than unaffected individuals, and the assessment of statistical significance for these selected patterns poses some unique problems, which are briefly outlined here.

Melatonin Receptor 1B Genetic Variants on Susceptibility to Gestational Diabetes Mellitus: A Hospital-Based Case-Control Study in Wuhan, Central China

PURPOSE

The aim of the study was to find out the associations of Melatonin receptor 1B (MTNR1B) genetic variants with gestational diabetes mellitus (GDM) in Wuhan of central China.

PATIENTS AND METHODS

A hospital-based case-control study that included 1679 women was carried out to explore the associations of MTNR1B single nucleotide polymorphisms (SNPs) with GDM risk, which were analyzed through logistic regression analysis by adjusting age, pre-pregnancy BMI and family history of diabetes. Multifactor dimensionality reduction was applied to determine gene-gene interactions between SNPs.

RESULTS

MTNR1B SNPs rs10830962, rs10830963, rs1387153, rs7936247 and rs4753426 were significantly associated with GDM risk (P<0.05). The rs10830962/G, rs10830963/G, rs1387153/T, and rs7936247/T were risk variants, whereas rs4753426/T was protective variant for GDM development. Fasting plasma glucose (FPG) and 1h-plasma glucose (PG) were significantly different among genotypes at rs10830962 and rs10830963, whereas 2h-PG levels were not. Gene-gene interactions were not found among the five SNPs on GDM risk.

CONCLUSION

MTNR1B genetic variants have significant associations but no gene-gene interactions with GDM risk in central Chinese population. Furthermore, MTNR1B SNPs have significant relationships with glycemic traits.