Eye color prediction using single nucleotide polymorphisms in Saudi population

Alghamdi, Jahad; Amoudi, Manal; Kassab, Ahmad Ch.; Mufarrej, Mansour Al; Ghamdi, Saleh Al

doi:10.1016/j.sjbs.2018.09.011

Cited by 9 publications

(16 citation statements)

References 20 publications

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“…These deviations provide an indication of the association with Shapturenko et al, (2019) 9 . A study in Saudi population 10 observed the SNP rs1800407 was not polymorphic in the Saudi population with the presence of the allele G. In this study, concluded the rs1800407 indicating of eye color with the rs12913832 because of the linkage disequilibrium coefficient between alleles of these SNPs strong associated with eye color.…”

Section: Discussionmentioning

confidence: 56%

The Impact of OCA2 (rs1800407) and HERC2 (rs12913832) Gene Polymorphisms on Iris Color in a Sample of Iraqi People

Noaman,

A. Alkazaz

2021

Indian Journal of Forensic Medicine & Toxicology

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The study was directed in order to determine Single Nucleotide Polymorphisms (SNPs) (rs1800407 G>A) of OCA2 and (rs12913832 A>G) HERC2 genes and their association as a DNA marker with eye color in 60 samples of Iraqi people. Samples were collected from College of Science / University of Baghdad and also people from different areas of Baghdad, Iraq; during the two months period from September to November 2020. These SNPs determination was carried out by using quantitative real-time polymerase chain reaction (qPCR) of blood samples. The results of rs12913832 genotype showed significantly differences (p<0.05) in the green eye color group. A Survey of HERC2 gene SNP genotypes and allele frequencies in the black and green iris groups showed that there was a significant variation in the wild type (AA) and homozygous mutant (GG) genotype frequencies in (rs 12913832), while the SNP (rs1800407) of OCA2 concluded that gene may have a non-significant effect among a sample of this study comparing with green eye to black eye color.

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Section: Discussionmentioning

confidence: 56%

The Impact of OCA2 (rs1800407) and HERC2 (rs12913832) Gene Polymorphisms on Iris Color in a Sample of Iraqi People

Noaman,

A. Alkazaz

2021

Indian Journal of Forensic Medicine & Toxicology

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“…A t-statistic is determined in this method to compare the averages of the two marker genotype groups. The F-statistic is used for more than two potential genotypes [ 11 ]. There are various approaches, including score checks, logistic regression, and Bayesian methods, for Haplotype association with a phenotype.…”

Section: Introductionmentioning

confidence: 99%

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

Muneeb

Henschel

2021

BMC Bioinformatics

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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.

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“…LowerT hreshold = (1 − T hreshold/100) * maxF ullM utation (11) selectedSN P s = minF ullM utation <= LowerT hreshold…”

Section: Snps Preselectionmentioning

confidence: 99%

“…We also tested the proposed approach onType-2 diabetes phenotype. We considered different linear thresholds and the results for the optimal number of SNPs are summarized in table 11. Figure 12 and 13 shows the Confusion matrix and AUC of the best classification results.…”

Section: Type-2 Diabetes Predictionmentioning

confidence: 99%

Eye-Color and Type-2 Diabetes Phenotype Prediction From Genotype Data Using Deep Learning Methods

Muneeb

Henschel

2020

Preprint

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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 percent 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 percent. 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.

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Eye color prediction using single nucleotide polymorphisms in Saudi population

Cited by 9 publications

References 20 publications

The Impact of OCA2 (rs1800407) and HERC2 (rs12913832) Gene Polymorphisms on Iris Color in a Sample of Iraqi People

The Impact of OCA2 (rs1800407) and HERC2 (rs12913832) Gene Polymorphisms on Iris Color in a Sample of Iraqi People

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

Eye-Color and Type-2 Diabetes Phenotype Prediction From Genotype Data Using Deep Learning Methods

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