Exponential increase in QTL detection with increased sample size

Abstract

Power analyses are often used to determine the number of animals required for a genome wide association analysis (GWAS). These analyses are typically intended to estimate the sample size needed for at least one locus to exceed a genome-wide significance threshold. A related question that is less commonly considered is the number of significant loci that will be discovered with a given sample size. We used simulations based on a real dataset that consisted of 3,173 male and female adult N/NIH heterogeneous stock (HS) rats to explore the relationship between sample size and the number of significant loci discovered. Our simulations examined the number of loci identified in sub-samples of the full dataset. The sub-sampling analysis was conducted for four traits with low (0.15 0.03), medium (0.31 0.03 and 0.36 0.03) and high (0.46 0.03) SNP-based heritabilities. For each trait, we sub-sampled the data 100 times at different sample sizes (500, 1,000, 1,500, 2,000, and 2,500). We observed an exponential increase in the number of significant loci with larger sample sizes. Our results are consistent with similar observations in human GWAS and imply that future rodent GWAS should use sample sizes that are significantly larger than those needed to obtain a single significant result.

Distinct Network Patterns Emerge from Cartesian and XOR Epistasis Models: A Comparative Network Science Analysis

“…Genetic and phenotypic data used in this analysis come from an openly available dataset of an outbred, related rat (Rattus norvegicus) population, consisting of both males and females, that is derived from eight inbred founders (Heterogenous Stock [45]). Specifically, the genotype and phenotype values we use and used by Batista and colleagues, come from SNPs and phenotype scores utilized in a previously published GWAS [46,47] investigating obesity-related traits in this population of (R. norvegicus).…”

“…The methodology of Batista et al 2023 [12] exhaustively tests the interaction terms of every possible pairwise interaction (n choose k ). To test the methodology's capabilities, Batista and colleagues used 10,000 SNPs with the largest main effects (lowest genome-wide corrected p-value) from the (R. norvegicus) GWAS [46,47]. The phenotype of interest used in the epistasis analysis was body mass index (BMI) measured from the whole body, including the animal's tail (BMI TAIL).…”

“…The comparison for edges, illustrated in Table 2, elaborates that the two networks share 99 common edges (range equals to 0 Mb), encompassing 51 SNPs. The majority of the SNPs connected by these common edges are situated within the chromosomal range of chr1.280924773 to chr1.282730801 which includes the putative quantitative trait locus (QTL) with the largest main effect for BMI TAIL from the original GWAS (chr1.281788173) [46,47].…”

“…This range overlaps strongly with the position of the SNPs linked by the common edges (with Range equals to 0 Mb) between the two epistatic networks (chr1.280924773 to chr1.282730801). These closely located triangles could be attributed to cis-regulatory epistatic interactions in association with the putative univariate QTL at chr1.281788173 [46,47], or false positives.…”

Computational framework for statistical epistasis supports XOR penetrance function in a living system

“…As specific examples, we apply our algorithms to detect statistical evidence for two-way and three-way interactions using the Cartesian and XOR penetrance functions on the phenotype of body mass index (BMI) in real data sets from rats (Rattus norvegicus) from a GWAS study [21][22][23] investigating obesity-related traits and from mice (Mus musculus) from Wellcome Trust [24,25]. For both data sets we use approximately 10,000 SNPs for two-way epistasis detection using both penetrance functions.…”