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Comparative analysis of four disease prediction models of Parkinson’s disease

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Abstract

Parkinson’s disease (PD) is a multi-factorial disorder with high-penetrant mutations accounting for small percentage of PD. Our previous studies demonstrated individual association of genetic variants in folate, xenobiotic, and dopamine metabolic pathways with PD risk. The rational of the study was to develop a risk prediction model for PD using these genetic polymorphisms along with synuclein (SNCA) polymorphism. We have generated additive, multifactor dimensionality reduction (MDR), recursive partitioning (RP), and artificial neural network (ANN) models using 21 SNPs as inputs and disease outcome as output. The clinical utility of all these models was assessed by plotting receiver operating characteristics curves where in area under the curve (AUC) was used as an index of diagnostic utility of the model. The additive model was the simplest and exhibited an AUC of 0.72. The MDR model showed significant gene–gene interactions between SNCA, DRD4VNTR, and DRD2A polymorphisms. The RP model showed SHMT C1420T as important determinant of PD risk. This variant allele was found to be protective and this protection was nullified by MTRR A66G. Inheritance of SHMT wild allele and SNCA intronic polymorphism was shown to increase the risk of PD. The ANN model showed higher diagnostic utility (AUC = 0.86) compared to all the models and was able to explain 56.6 % cases of sporadic PD. To conclude, the ANN model developed using SNPs in folate, xenobiotic, and dopamine pathways along with SNCA has higher clinical utility in predicting PD risk compared to other models.

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Acknowledgments

We thank all the subjects who participated in this study.

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Authors and Affiliations

  1. Department of Biotechnology, Jawaharlal Nehru Technological University-Hyderabad, Hyderabad, India

    Nadella Kumudini

  2. School of Chemical and Biotechnology, SASTRA University, Tirumalaisamudram, Thanjavur, India

    Shaik Mohammad Naushad, Balraj Alex Stanley, Manoharan Niveditha & Gunasekaran Sharmila

  3. Department of Clinical Pharmacology and Therapeutics, Nizam’s Institute of Medical Sciences, Panjagutta, Hyderabad, India

    Konda Kumaraswami & Vijay Kumar Kutala

  4. Department of Neurology, Nizam’s Institute of Medical Sciences, Panjagutta, Hyderabad, India

    Rupam Borghain & Rukmini Mridula

Authors
  1. Nadella Kumudini
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  2. Shaik Mohammad Naushad
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  3. Balraj Alex Stanley
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  4. Manoharan Niveditha
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  7. Rupam Borghain
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  9. Vijay Kumar Kutala
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Corresponding author

Correspondence to Vijay Kumar Kutala.

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Kumudini, N., Naushad, S.M., Alex Stanley, B. et al. Comparative analysis of four disease prediction models of Parkinson’s disease. Mol Cell Biochem 411, 127–134 (2016). https://doi.org/10.1007/s11010-015-2574-0

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  • DOI: https://doi.org/10.1007/s11010-015-2574-0

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