Sample Size Impact (SaSii): an R script for estimating optimal sample sizes in population genetics and population genomics studies

Abstract

AbstractObtaining large sample sizes for genetic studies can be challenging, time-consuming, and expensive. However, 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 aim to present SaSii (Sample Size Impact), a R script to help researchers to define minimum sample size and to indicate minimum sample size patterns for some taxa groups. The patterns were obtained by analyzing previously published 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 and does not require the scientist to take time to write any program code, extract and sequence samples or use population genetics programs, making it easier to gather this information. We also confirmed that sample sizes of five to twenty-five for SNP and fifteen to thirty for SSR can be used for most plant species, giving a better direction for new studies.