Abstract
Increasing understanding of human genome variability allows for better use of the predictive potential of DNA. An obvious direct application is the prediction of the physical phenotypes. Significant success has been achieved, especially in predicting pigmentation characteristics, but the inference of some phenotypes is still challenging. In search of further improvements in predicting human eye colour, we conducted whole-exome (enriched in regulome) sequencing of 150 Polish samples to discover new markers. For this, we adopted quantitative characterization of eye colour phenotypes using high-resolution photographic images of the iris in combination with DIAT software analysis. An independent set of 849 samples was used for subsequent predictive modelling. Newly identified candidates and 114 additional literature-based selected SNPs, previously associated with pigmentation, and advanced machine learning algorithms were used. Whole-exome sequencing analysis found 27 previously unreported candidate SNP markers for eye colour. The highest overall prediction accuracies were achieved with LASSO-regularized and BIC-based selected regression models. A new candidate variant, rs2253104, located in the ARFIP2 gene and identified with the HyperLasso method, revealed predictive potential and was included in the best-performing regression models. Advanced machine learning approaches showed a significant increase in sensitivity of intermediate eye colour prediction (up to 39%) compared to 0% obtained for the original IrisPlex model. We identified a new potential predictor of eye colour and evaluated several widely used advanced machine learning algorithms in predictive analysis of this trait. Our results provide useful hints for developing future predictive models for eye colour in forensic and anthropological studies.
Highlights
Increasing understanding of human genome variability is enabling better use of DNA’s predictive potential [1]
There are many machine learning (ML) methods available for developing predictive models, and their effectiveness may depend on the type and amount of data used; some of them may be more suitable than others for taking into account diverse genetic phenomena, including epistasis
It has been proved that ensemble methods such as random forest (RF) or extreme gradient boosting (XGB) are among the most powerful classification models; they usually achieve significantly higher accuracy when compared to simple models
Summary
Increasing understanding of human genome variability is enabling better use of DNA’s predictive potential [1]. It has been proved that ensemble methods such as random forest (RF) or extreme gradient boosting (XGB) are among the most powerful classification models; they usually achieve significantly higher accuracy when compared to simple models The price for this is the higher computational cost and more complicated interpretation. In the case of some classification methods, feature selection is an integral element of learning the model; for example, in tree-based methods, relevant attributes are chosen during the building of the tree Another solution is using regularization techniques [18], such as least absolute shrinkage and selection operator (LASSO) regularization, which ensure sparsity in the parameter vector and allow one to find attributes influencing the class variable
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
