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. Only GIFT identified 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). Additionally, by redefining correlations between single nucleotide polymorphisms (SNPs), GIFT offers new insights into linkage disequilibrium and epistatic interactions that, through the development of a network centrality measure, distinguishes core from peripheral genes within gene networks involved in height at withers. By reducing the time and cost associated with large-scale genotype phenotype mapping studies, GIFT expands access to quantitative genetic research, enabling smaller-scale investigations to explore the genetic architecture of complex traits with greater resolution.