MTHFR C677T is a risk factor, whereas MTRR A66G and SHMT C1420T polymorphisms reduce risk for autism. MTHFR A1298C acts additively in increasing the risk for autism.

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The objective of the current study was to explore the role of ABCB1 and CYP3A5 genetic polymorphisms in predicting the bioavailability of tacrolimus and the risk for post-transplant diabetes. Artificial neural network (ANN) and logistic regression (LR) models were used to predict the bioavailability of tacrolimus and risk for post-transplant diabetes, respectively. The five-fold cross-validation of ANN model showed good correlation with the experimental data of bioavailability (r2 = 0.93–0.96). Younger age, male gender, optimal body mass index were shown to exhibit lower bioavailability of tacrolimus. ABCB1 1236 C>T and 2677G>T/A showed inverse association while CYP3A5*3 showed a positive association with the bioavailability of tacrolimus. Gender bias was observed in the association with ABCB1 3435 C>T polymorphism. CYP3A5*3 was shown to interact synergistically in increasing the bioavailability in combination with ABCB1 1236 TT or 2677GG genotypes. LR model showed an independent association of ABCB1 2677 G>T/A with post transplant diabetes (OR: 4.83, 95% CI: 1.22–19.03). Multifactor dimensionality reduction analysis (MDR) revealed that synergistic interactions between CYP3A5*3 and ABCB1 2677 G>T/A as the determinants of risk for post-transplant diabetes. To conclude, the ANN and MDR models explore both individual and synergistic effects of variables in modulating the bioavailability of tacrolimus and risk for post-transplant diabetes.

In view of growing body of evidence substantiating the role of aberrations in one-carbon metabolism in the pathophysiology of breast cancer and lack of studies on gene-gene interactions, we investigated the role of dietary micronutrients and eight functional polymorphisms of one-carbon metabolism in modulating the breast cancer risk in 244 case-control pairs of Indian women and explored possible gene-gene interactions using Multifactor dimensionality reduction analysis (MDR). Dietary micronutrient status was assessed using the validated Food Frequency Questionnaire. Genotyping was done for glutamate carboxypeptidase II (GCPII) C1561T, reduced folate carrier (RFC)1 G80A, cytosolic serine hydroxymethyltransferase (cSHMT) C1420T, thymidylate synthase (TYMS) 5'-UTR tandem repeat, TYMS 3'-UTR ins6/del6, methylenetetrahydrofolate reductase (MTHFR) C677T, methyltetrahydrofolate-homocysteine methyltransferase (MTR) A2756G, methyltetrahydrofolate-homocysteine methyltransferase reductase (MTRR) A66G polymorphisms by using the PCR-RFLP/AFLP methods. Low dietary folate intake (P < 0.001), RFC1 G80A (OR: 1.38, 95% CI 1.06-1.81) and MTHFR C677T (OR: 1.74 (1.11-2.73) were independently associated with the breast cancer risk whereas cSHMT C1420T conferred protection (OR: 0.72, 95% CI 0.55-0.94). MDR analysis demonstrated a significant tri-variate interaction among RFC1 80, MTHFR 677 and TYMS 5'-UTR loci (P (trend) < 0.02) with high-risk genotype combination showing inflated risk for breast cancer (OR 4.65, 95% CI 1.77-12.24). To conclude, dietary as well as genetic factors were found to influence susceptibility to breast cancer. Further, the current study highlighted the importance of multi-loci analyses over the single-locus analysis towards establishing the epistatic interactions between loci of one-carbon metabolism modulate susceptibility to the breast cancer.

To optimize the warfarin dose, a population-specific pharmacogenomic algorithm was developed using multiple linear regression model with vitamin K intake and cytochrome P450 IIC polypeptide9 (CYP2C9(*)2 and (*)3), vitamin K epoxide reductase complex 1 (VKORC1(*)3, (*)4, D36Y and -1639 G>A) polymorphism profile of subjects who attained therapeutic international normalized ratio as predictors. New algorithm was validated by correlating with Wadelius, International Warfarin Pharmacogenetics Consortium and Gage algorithms; and with the therapeutic dose (r=0.64, P<0.0001). New algorithm was more accurate (Overall: 0.89 vs 0.51, warfarin resistant: 0.96 vs 0.77 and warfarin sensitive: 0.80 vs 0.24), more sensitive (0.87 vs 0.52) and specific (0.93 vs 0.50) compared with clinical data. It has significantly reduced the rate of overestimation (0.06 vs 0.50) and underestimation (0.13 vs 0.48). To conclude, this population-specific algorithm has greater clinical utility in optimizing the warfarin dose, thereby decreasing the adverse effects of suboptimal dose.