Sample Size Impact (SaSii): An R script for estimating optimal sample sizes in population genetics and population genomics studies
Year: 2025
Type: article
Abstract: Obtaining large sample sizes for genetic studies can be challenging, time-consuming, and expensive, and small sample sizes may generate biased or imprecise results. Many studies have suggested the minimum sample size necessary to obtain robust and reliable results, but it is not possible to define one ideal minimum sample size that fits all studies. Here, we present SaSii (Sample Size Impact), an R script to help researchers define the minimum sample size. Based on empirical and simulated data analysis using SaSii, we present patterns and suggest minimum sample sizes for experiment design. The patterns were obtained by analyzing previously published genotype datasets with SaSii and can be used as a starting point for the sample design of population genetics and genomic studies. Our results showed that it is possible to estimate an adequate sample size that accurately represents the real population without requiring the scientist to write any program code, extract and sequence samples, or use population genetics programs, thus simplifying the process. We also confirmed that the minimum sample sizes for SNP (single-nucleotide polymorphism) analysis are usually smaller than for SSR (simple sequence repeat) analysis and discussed other patterns observed from empirical plant and animal datasets.
Source: PLoS ONE
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,382
Awards: G1052725603, G1116453563, G181346330, G1844797778, G1987017828, G1996953296, G3359070137, G3539154131, G3571735135, G3572101131, Genetic diversity, genomics and phylogeography of manioc (Manihot esculenta Crantz): implications for the dispersion and diffusion of the crop along the main fluvial axes in Brazilian Amazon Basin, G3851452141, G3932444109, G4402799499, G5400960480, G6762759861, G6884494233, Conservation biology of Atlantic Forest’s medicinal species: a genetical approach to Forest restorations, G733191275, G8675574674
OpenAlex ID: W4407456024