Biology subjects

Kyratzi, P.

Publications and source records attributed to Kyratzi, P..

3 recordsLinked to original sources

bioRxiv2025

Genomic Informational Field Theory (GIFT) to map complex traits in small sample sizes

Genome-wide association studies (GWAS) are commonly used to investigate the genetic basis of complex traits. However, to be adequately powered they typically require large sample sizes to provide precise inferences. To address this challenge, this paper introduces GIFT, a novel data analytic method that enhances the power of genetic analyses, enabling the use of smaller datasets without compromising precision. In a small cohort of 157 ponies, GIFT was applied to examine the complex trait of "height at withers", comparing its performance to traditional GWAS. GIFT enabled the identification genetic loci linked to insulin physiology validating, in turn, a long-standing hypothesis that "height at withers" is associated with insulin physiology in equids, potentially promoting equine metabolic syndrome (EMS). By redefining correlations between single nucleotide polymorphisms (SNPs), GIFT provides new insights into linkage disequilibrium and reveals underlying gene network structures. This, in turn, enables the distinction between core and peripheral genes within these networks. By reducing the time and cost associated with large-scale genotype-phenotype mapping studies without sacrificing statistical robustness, GIFT broadens access to quantitative genetic research, allowing smaller-scale studies to investigate the genetic architecture of complex traits with greater resolution. NEW & NOTEWORTHYInferring genotype-phenotype associations typically require large sample sizes, limiting many genetic studies. GIFT, a novel data analytics tool, overcomes this by enabling accurate association mapping in small datasets. We further show how GIFTs extended framework infers linkage disequilibrium and gene networks, distinguishing core from peripheral genes involved in complex traits. This advancement enhances understanding of biological architecture and enables high-resolution genetic research in limited cohorts, offering a powerful, cost-effective alternative to traditional large-scale approaches.

genetics↗

bioRxiv2024

Investigative power of Genomic Informational Field Theory (GIFT) relative to GWAS for genotype-phenotype mapping

Identifying associations between phenotype and genotype is the fundamental basis of genetic analyses. Inspired by frequentist probability and the work of R.A. Fisher, genome-wide association studies (GWAS) extract information using averages and variances from genotype-phenotype datasets. Averages and variances are legitimated upon creating distribution density functions obtained through the grouping of data into categories. However, as data from within a given category cannot be differentiated, the investigative power of such methodologies is limited. Genomic Informational Field Theory (GIFT) is a method specifically designed to circumvent this issue. The way GIFT proceeds is opposite to that of GWAS. Whilst GWAS determines the extent to which genes are involved in phenotype formation (bottom-up approach), GIFT determines the degree to which the phenotype can select microstates (genes) for its subsistence (top-down approach). Doing so requires dealing with new genetic concepts, a.k.a. genetic paths, upon which significance levels for genotype-phenotype associations can be determined. By using different datasets obtained in ovis aries related to bone growth (Dataset-1) and to a series of linked metabolic and epigenetic pathways (Dataset-2), we demonstrate that removing the informational barrier linked to categories enhances the investigative and discriminative powers of GIFT, namely that GIFT extracts more information than GWAS. We conclude by suggesting that GIFT is an adequate tool to study how phenotypic plasticity and genetic assimilation are linked. NEW & NOTEWORTHYThe genetic basis of complex traits remains challenging to investigate using classic GWASs. Given the success of gene editing technologies this point needs to be addressed urgently since there can only be useful editing technologies if precise genotype-phenotype mapping information is available initially. GIFT is a new mapping method designed to increase the investigative power of biological/medical datasets suggesting, in turn, the need to rethink the conceptual bases of quantitative genetics.

genetics↗

bioRxiv2022

FIT-GWA: A new method for the genetic analysis of small gene effects, high precision in phenotype measurements and small sample sizes.

Small gene effects involved in complex/omnigenic traits remain costly to analyse using current genome-wide association methods (GWAS) because of the number of individuals required to return meaningful association(s), a.k.a. study power. Inspired by field theory in physics, we provide a different method called Genomic Informational Field Theory (GIFT). In contrast to GWAS, GIFT assumes that the phenotype is measured precisely enough and/or the number of individuals in the population is too small to permit the creation of categories. To extract information, GIFT uses the information contained in the cumulative sums difference of gene microstates between two configurations: (i) when the individuals are taken at random without information on phenotype values, and (ii) when individuals are ranked as a function of their phenotypic value. The difference in the cumulative sum is then attributed to the emergence of phenotypic fields. We demonstrate that GIFT recovers GWAS, that is, Fishers theory, when the phenotypic fields are linear (first order). However, unlike GWAS, GIFT demonstrates how the variance of microstate distribution density functions can also be involved in genotype-phenotype associations when the phenotypic fields are quadratic (second order). Using genotype-phenotype simulations based on Fishers theory as a toy model, we illustrate the application of the method with a small sample size of 1000 individuals.

genetics↗