Analysis of phenotype-genotype associations using genomic informational field theory (GIFT)

Wattis, Jonathan A.D.; Bray, Sian M; Kyratzi, Panagiota; Rauch, Cyril

Abstract

We show how field- and information theory can be used to quantify the relationship between genotype and phenotype in cases where phenotype is a continuous variable. Given a sample population of phenotype measurements, from various known genotypes, we show how the ordering of phenotype data can lead to quantification of the effect of genotype. This method does not assume that the data has a Gaussian distribution, it is particularly effective at extracting weak and unusual dependencies of genotype on phenotype. However, in cases where data has a special form, (eg Gaussian), we observe that the effective phenotype field has a special form. We use asymptotic analysis to solve both the forward and reverse formulations of the problem. We show how p-values can be calculated so that the significance of correlation between phenotype and genotype can be quantified. This provides a significant generalisation of the traditional methods used in genome-wide association studies GWAS. We derive a field-strength which can be used to deduce how the correlations between genotype and phenotype, and their impact on the distribution of phenotypes.

Citation

Wattis, J. A., Bray, S. M., Kyratzi, P., & Rauch, C. (2022). Analysis of phenotype-genotype associations using genomic informational field theory (GIFT). Journal of Theoretical Biology, 548, Article 111198. https://doi.org/10.1016/j.jtbi.2022.111198

• Journal Article Type: Article
• Acceptance Date: Jun 8, 2022
• Online Publication Date: Jun 13, 2022
• Publication Date: Sep 7, 2022
• Deposit Date: Jun 9, 2022
• Publicly Available Date: Jun 14, 2023
• Journal: Journal of Theoretical Biology
• Print ISSN: 0022-5193
• Electronic ISSN: 1095-8541
• Publisher: Elsevier BV
• Peer Reviewed: Peer Reviewed
• Volume: 548
• Article Number: 111198
• DOI: https://doi.org/10.1016/j.jtbi.2022.111198
• Keywords: Applied Mathematics; General Agricultural and Biological Sciences; General Immunology and Microbiology; General Biochemistry, Genetics and Molecular Biology; Modeling and Simulation; General Medicine; Statistics and Probability
• Public URL: https://nottingham-repository.worktribe.com/output/8396515
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0022519322001965