Genetic prediction of quantitative traits: a machine learner's guide focused on height

• URL: http://arxiv.org/abs/2310.04028v1
• Date: Fri, 6 Oct 2023 05:43:50 GMT
• Title: Genetic prediction of quantitative traits: a machine learner's guide focused on height
• Authors: Lucie Bourguignon and Caroline Weis and Catherine R. Jutzeler and Michael Adamer
• Abstract summary: We provide an overview for the machine learning community on current state of the art models and associated subtleties. We use height as an example of a continuous-valued phenotype and provide an introduction to benchmark datasets, confounders, feature selection, and common metrics.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Machine learning and deep learning have been celebrating many successes in the application to biological problems, especially in the domain of protein folding. Another equally complex and important question has received relatively little attention by the machine learning community, namely the one of prediction of complex traits from genetics. Tackling this problem requires in-depth knowledge of the related genetics literature and awareness of various subtleties associated with genetic data. In this guide, we provide an overview for the machine learning community on current state of the art models and associated subtleties which need to be taken into consideration when developing new models for phenotype prediction. We use height as an example of a continuous-valued phenotype and provide an introduction to benchmark datasets, confounders, feature selection, and common metrics.

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