Dataset Information

Importance of nonsynonymous OCA2 variants in human eye color prediction.

ABSTRACT: The color of the eyes is one of the most prominent phenotypes in humans and it is often used to describe the appearance of an individual. The intensity of pigmentation in the iris is strongly associated with one single-nucleotide polymorphism (SNP), rs12913832:A>G that is located in the promotor region of OCA2 (OMIM #611409). Nevertheless, many eye colors cannot be explained by only considering rs12913832:A>G.In this study, we searched for additional variants in OCA2 to explain human eye color by sequencing a 500 kbp region, encompassing OCA2 and its promotor region.We identified three nonsynonymous OCA2 variants as important for eye color, including rs1800407:G>A (p.Arg419Gln) and two variants, rs74653330:A>T (p.Ala481Thr) and rs121918166:G>A (p.Val443Ile), not previously described as important for eye color variation. It was shown that estimated haplotypes consisting of four variants (rs12913832:A>G, rs1800407:G>A (p.Arg419Gln), rs74653330:A>T (p.Ala481Thr), and rs121918166:G>A (p.Val443Ile)) explained 75.6% (adjusted R (2) = 0.76) of normal eye color variation, whereas rs12913832:A>G alone explained 68.8% (adjusted R (2) = 0.69). Moreover, rs74653330:A>T (p.Ala481Thr) and rs121918166:G>A (p.Val443Ile) had a measurable effect on quantitative skin color (P = 0.008).Our data showed that rs74653330:A>T (p.Ala481Thr) and rs121918166:G>A (p.Val443Ile) have a measurable effect on normal pigmentation variation.

SUBMITTER: Andersen JD

PROVIDER: S-EPMC4947861 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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Importance of nonsynonymous OCA2 variants in human eye color prediction.

Andersen Jeppe D JD Pietroni Carlotta C Johansen Peter P Andersen Mikkel M MM Pereira Vania V Børsting Claus C Morling Niels N

Molecular genetics & genomic medicine 20160311 4

<h4>Background</h4>The color of the eyes is one of the most prominent phenotypes in humans and it is often used to describe the appearance of an individual. The intensity of pigmentation in the iris is strongly associated with one single-nucleotide polymorphism (SNP), rs12913832:A>G that is located in the promotor region of OCA2 (OMIM #611409). Nevertheless, many eye colors cannot be explained by only considering rs12913832:A>G.<h4>Methods</h4>In this study, we searched for additional variants i ...[more]

PMID: 27468418

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Project description:Background:DNA prediction of eye color represent one application of the externally visible characteristics (EVC), which attained growing interest in the field of DNA forensic phenotyping. This is mainly due to its ability to narrow the pool of suspects without the need to compare any retrieved DNA material from the crime scene to a reference DNA. Several methods and multiplex genetic panel were proposed with variable prediction accuracy between different populations. However, such panel was not previously tested in the Saudi population, nor any populations of the Middle East and North Africa origin. Method:A panel of eleven single nucleotide polymorphisms (SNPs) was tested for their association with three eye colors (brown, hazel, and intermediate) in 80 volunteer Saudi individuals. SNPs and haplotype association test with eye colors were performed to identify the top significant SNPs with the three eye colors. Also, multinomial logistic regression was used to construct the prediction model using a training set of 60 subjects, and a validation set of 20 subjects. The goodness of fit parameter of the model to correctly predicts each eye color as compared to the other was performed. Results:Eye color was significantly associated with rs12913832, rs7170852, and rs916977 that are located within HERC2. SNP rs12913832 was the top significant SNP (p-value?=?1.78E-15) that accounted for the association in this region, as the other SNPs were not significant after adjusting for rs12913832. A prediction model containing five SNPs showed high prediction accuracy with Area Under the receiver operating characteristic Curves (AUC) equals to 0.95 and 0.83 for brown and intermediate eye colors, respectively. However, the model's performance was very low for predicting the hazel eye color with AUC equals 0.75. Discussion:Despite the small sample size of our study, we reported very significant SNP associations with eye color. Our model to predict eye colors based on DNA material showed high accuracy for brown and intermediate eye colors. The eye color prediction-model underperformed for the hazel eye colors, suggesting that larger sample size, as well as more comprehensive set of SNPs, could improve the model-prediction accuracy.

Project description:BackgroundPredicting the eye and hair color from genotype became an established and widely used tool in forensic genetics, as well as in studies of ancient human populations. However, the accuracy of this tool has been verified on the West and Central Europeans only, while populations from border regions between Europe and Asia (like Caucasus and Ural) also carry the light pigmentation phenotypes.ResultsWe phenotyped 286 samples collected across North Eurasia, genotyped them by the standard HIrisPlex-S markers and found that predictive power in Caucasus/Ural/West Siberian populations is reasonable but lower than that in West Europeans. As these populations have genetic ancestries different from that of West Europeans, we hypothesized they may carry a somewhat different allele spectrum. Thus, for all samples we performed the exome sequencing additionally enriched with the 53 genes and intergenic regions known to be associated with the eye/hair color. Our association analysis replicated the importance of the key previously known SNPs but also identified five new markers whose eye color prediction power for the studied populations is compatible with the two major previously well-known SNPs. Four out of these five SNPs lie within the HERС2 gene and the fifth in the intergenic region. These SNPs are found at high frequencies in most studied populations. The released dataset of exomes from Russian populations can be further used for population genetic and medical genetic studies.ConclusionsThis study demonstrated that precision of the established systems for eye/hair color prediction from a genotype is slightly lower for the populations from the border regions between Europe and Asia that for the West Europeans. However, this precision can be improved if some newly revealed predictive SNPs are added into the panel. We discuss that the replication of these pigmentation-associated SNPs on the independent North Eurasian sample is needed in the future studies.

Project description:BackgroundGenotype-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.ResultsThe 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%.ConclusionGenotype-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.

Dataset Information

Importance of nonsynonymous OCA2 variants in human eye color prediction.

Publications

Importance of nonsynonymous OCA2 variants in human eye color prediction.

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