Eye-color and Type-2 diabetes phenotype prediction from genotype data using deep learning methods

Abstract

Background Genotype–phenotype predictions are of great importance in genetics. These predictions can help to find genetic mutations causing variations in human beings. There are many approaches for finding the association which can be broadly categorized into two classes, statistical techniques, and machine learning. Statistical techniques are good for finding the actual SNPs causing variation where Machine Learning techniques are good where we just want to classify the people into different categories. In this article, we examined the Eye-color and Type-2 diabetes phenotype. The proposed technique is a hybrid approach consisting of some parts from statistical techniques and remaining from Machine learning. Results The main dataset for Eye-color phenotype consists of 806 people. 404 people have Blue-Green eyes where 402 people have Brown eyes. After preprocessing we generated 8 different datasets, containing different numbers of SNPs, using the mutation difference and thresholding at individual SNP. We calculated three types of mutation at each SNP no mutation, partial mutation, and full mutation. After that data is transformed for machine learning algorithms. We used about 9 classifiers, RandomForest, Extreme Gradient boosting, ANN, LSTM, GRU, BILSTM, 1DCNN, ensembles of ANN, and ensembles of LSTM which gave the best accuracy of 0.91, 0.9286, 0.945, 0.94, 0.94, 0.92, 0.95, and 0.96% respectively. Stacked ensembles of LSTM outperformed other algorithms for 1560 SNPs with an overall accuracy of 0.96, AUC = 0.98 for brown eyes, and AUC = 0.97 for Blue-Green eyes. The main dataset for Type-2 diabetes consists of 107 people where 30 people are classified as cases and 74 people as controls. We used different linear threshold to find the optimal number of SNPs for classification. The final model gave an accuracy of 0.97%. Conclusion Genotype–phenotype predictions are very useful especially in forensic. These predictions can help to identify SNP variant association with traits and diseases. Given more datasets, machine learning model predictions can be increased. Moreover, the non-linearity in the Machine learning model and the combination of SNPs Mutations while training the model increases the prediction. We considered binary classification problems but the proposed approach can be extended to multi-class classification.

Development and validation of immune-based biomarkers and deep learning models for Alzheimer’s disease

“…The random forest (RF) algorithm is an emerging and high precision machine learning algorithm that has been widely used in numerous fields, and of course, its role in the medical field is also exact. RF algorithms have been used for clinical diseases, such as using random forests to identify biomarkers for glioblastoma to find potential targets for treatment ( Li et al, 2021 ), building COPD risk prediction models ( Perret et al, 2021 ), and detecting and predicting type 2 diabetes ( Muneeb and Henschel, 2021 ), all with good results. An artificial neural network is a new type of algorithm derived from imitating the structure and function of the human brain, which has the characteristics of self-learning ability and high efficiency compared with the traditional machine learning algorithm.…”

Correction to: Eye‑color and Type‑2 diabetes phenotype prediction from genotype data using deep learning methods

“…Following the publication of the original article [ 1 ], errors were identified in the affiliation and funding note.…”

An empirical comparison between polygenic risk scores and machine learning for case/control classification

“…PRS is a more substantial quantity than classification in machine learning because PRS predicts a particular person's tendency to have a specific disease or trait [6,7]. In contrast, machine learning classifies people into traits or categories [8].…”

“…In this article [8], researchers benchmarked 9 deep/machine learning algorithms for eye-color and type-2 diabetes prediction. In this article [12], researchers benchmarked PRS calculation using 4 tools: PRScise, Plink, LDpred-2, and Lassosum, which we also followed when comparing the machine learning with PRS.…”