Blott, S. | Search bioRxiv

bioRxiv2023

A functional single nucleotide polymorphism upstream of the collagen type III gene may contribute to catastrophic fracture risk in Thoroughbred horses.

Fractures caused by bone overloading are a leading cause of euthanasia in Thoroughbred racehorses. The risk of fatal fracture has been shown to be influenced by both environmental and genetic factors but, to date, no specific genetic mechanisms underpinning fracture have been identified. The aim of this study was to utilise a genome-wide polygenic risk score to establish an in vitro cell system to study bone gene regulation in horses at high and low genetic risk of fracture. Candidate gene expression analysis revealed differential expression of COL3A1 and STAT1 genes in osteoblasts derived from high and low risk horses. Whole genome sequencing of fracture case and control horses revealed a single nucleotide polymorphism (SNP) upstream of COL3A1 that was confirmed in a larger cohort to be significantly associated with fracture. Bioinformatics tools predicted that this SNP may impact the binding of the transcription factor SOX11. Gene modulation demonstrated SOX11 is upstream of COL3A1 and the region binds to nuclear proteins. Furthermore, luciferase assays demonstrated that the region containing the SNP has promoter activity. However, the specific effect of the SNP depends on the broader genetic background of the cells and suggest other factors may also be involved in regulating COL3A1 expression. In conclusion, this approach provides a powerful means to identify novel DNA variants and understand their mechanism of action to enable the development of new ways to identify and treat horses at high risk of a catastrophic fracture.

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↗