Genetic prediction of type 2 diabetes using deep neural network

Genetic prediction of type 2 diabetes using deep neural network Type 2 diabetes (T2DM) has strong heritability but genetic models to explain heritability have been challenging. We tested deep neural network (DNN) to predict T2DM using the nested case‐control study of Nurses’ Health Study (3326 females, 45.6% T2DM) and Health Professionals Follow‐up Study (2502 males, 46.5% T2DM). We selected 96, 214, 399, and 678 single‐nucleotide polymorphism (SNPs) through Fisher’s exact test and L1‐penalized logistic regression. We split each dataset randomly in 4:1 to train prediction models and test their performance. DNN and logistic regressions showed better area under the curve (AUC) of ROC curves than the clinical model when 399 or more SNPs included. DNN was superior than logistic regressions in AUC with 399 or more SNPs in male and 678 SNPs in female. Addition of clinical factors consistently increased AUC of DNN but failed to improve logistic regressions with 214 or more SNPs. In conclusion, we show that DNN can be a versatile tool to predict T2DM incorporating large numbers of SNPs and clinical information. Limitations include a relatively small number of the subjects mostly of European ethnicity. Further studies are warranted to confirm and improve performance of genetic prediction models using DNN in different ethnic groups. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Clinical Genetics Wiley

Genetic prediction of type 2 diabetes using deep neural network

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Publisher
Wiley
Copyright
© 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd
ISSN
0009-9163
eISSN
1399-0004
D.O.I.
10.1111/cge.13175
Publisher site
See Article on Publisher Site

Abstract

Type 2 diabetes (T2DM) has strong heritability but genetic models to explain heritability have been challenging. We tested deep neural network (DNN) to predict T2DM using the nested case‐control study of Nurses’ Health Study (3326 females, 45.6% T2DM) and Health Professionals Follow‐up Study (2502 males, 46.5% T2DM). We selected 96, 214, 399, and 678 single‐nucleotide polymorphism (SNPs) through Fisher’s exact test and L1‐penalized logistic regression. We split each dataset randomly in 4:1 to train prediction models and test their performance. DNN and logistic regressions showed better area under the curve (AUC) of ROC curves than the clinical model when 399 or more SNPs included. DNN was superior than logistic regressions in AUC with 399 or more SNPs in male and 678 SNPs in female. Addition of clinical factors consistently increased AUC of DNN but failed to improve logistic regressions with 214 or more SNPs. In conclusion, we show that DNN can be a versatile tool to predict T2DM incorporating large numbers of SNPs and clinical information. Limitations include a relatively small number of the subjects mostly of European ethnicity. Further studies are warranted to confirm and improve performance of genetic prediction models using DNN in different ethnic groups.

Journal

Clinical GeneticsWiley

Published: Jan 1, 2018

Keywords: ; ; ;

References

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