In this proof-of-concept study, we demonstrate that incorporation of multiple genetic variants, conventional risk factors and their two-way interactions into a logistic model enhances our ability to predict diabetic nephropathy.
In conclusion, the present study indicates that a panel of multiple genetic biomarkers, consisting of the DPYD, TYMS, TYMP, TK1, and TK2 genes, could be a potential aid in clinical predictions to obtain better CCRT response prediction models.
Few prospective studies have investigated the value of multiple genetic variants in type 2 diabetes prediction (23, 55, 68, 79, 89, 92, 94).
Currently, genome-wide association studies increasingly identify the contribution of multiple genetic loci to phenotypes by providing clues as to the biological pathways and interactions involved.
However, molecular biological studies have demonstrated the existence of multiple genetic and epigenetic alterations of oncogenes and tumour suppressor genes in the process of lung carcinogenesis, providing useful information for new therapeutic strategies.
In this study, we tested whether the combination of multiple genetic risk variants known to predict T2DM can be used to predict cardiovascular events in individuals with chronic coronary artery disease.
This is the first study that systematically evaluates the influence of multiple genetic variants in the CYP1B1 gene on prostate cancer risk using a haplotype approach.
Genes are the unit of analysis or interpretation of multiple genetic association studies.
The purpose of this study was to estimate the combinatorial contribution of multiple genetic variants to disease risk.
The methodological aim of the present study was to enable an integrated analysis of multiple genetic interaction datasets using a common scoring framework.
Few studies have systematically investigated the joint effect of multiple genetic variants.
