Sample Size Impact (SaSii): An R script for estimating optimal sample sizes in population genetics and population genomics studies.
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 o...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0316634 |
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| author | Matheus Scaketti Patricia Sanae Sujii Alessandro Alves-Pereira Kaiser Dias Schwarcz Ana Flávia Francisconi Matheus Sartori Moro Kauanne Karolline Moreno Martins Thiago Araujo de Jesus Guilherme Brener Ferreira de Souza Maria Imaculada Zucchi |
| author_facet | Matheus Scaketti Patricia Sanae Sujii Alessandro Alves-Pereira Kaiser Dias Schwarcz Ana Flávia Francisconi Matheus Sartori Moro Kauanne Karolline Moreno Martins Thiago Araujo de Jesus Guilherme Brener Ferreira de Souza Maria Imaculada Zucchi |
| author_sort | Matheus Scaketti |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d12a99f33b1b4cb49f5a5e8f1e7c6c5e |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-d12a99f33b1b4cb49f5a5e8f1e7c6c5e2025-08-20T02:56:03ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01202e031663410.1371/journal.pone.0316634Sample Size Impact (SaSii): An R script for estimating optimal sample sizes in population genetics and population genomics studies.Matheus ScakettiPatricia Sanae SujiiAlessandro Alves-PereiraKaiser Dias SchwarczAna Flávia FrancisconiMatheus Sartori MoroKauanne Karolline Moreno MartinsThiago Araujo de JesusGuilherme Brener Ferreira de SouzaMaria Imaculada ZucchiObtaining 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.https://doi.org/10.1371/journal.pone.0316634 |
| spellingShingle | Matheus Scaketti Patricia Sanae Sujii Alessandro Alves-Pereira Kaiser Dias Schwarcz Ana Flávia Francisconi Matheus Sartori Moro Kauanne Karolline Moreno Martins Thiago Araujo de Jesus Guilherme Brener Ferreira de Souza Maria Imaculada Zucchi Sample Size Impact (SaSii): An R script for estimating optimal sample sizes in population genetics and population genomics studies. PLoS ONE |
| title | Sample Size Impact (SaSii): An R script for estimating optimal sample sizes in population genetics and population genomics studies. |
| title_full | Sample Size Impact (SaSii): An R script for estimating optimal sample sizes in population genetics and population genomics studies. |
| title_fullStr | Sample Size Impact (SaSii): An R script for estimating optimal sample sizes in population genetics and population genomics studies. |
| title_full_unstemmed | Sample Size Impact (SaSii): An R script for estimating optimal sample sizes in population genetics and population genomics studies. |
| title_short | Sample Size Impact (SaSii): An R script for estimating optimal sample sizes in population genetics and population genomics studies. |
| title_sort | sample size impact sasii an r script for estimating optimal sample sizes in population genetics and population genomics studies |
| url | https://doi.org/10.1371/journal.pone.0316634 |
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