Population size in QTL detection using quantile regression in genome-wide association studies
Abstract The aim of this study was to evaluate the performance of Quantile Regression (QR) in Genome-Wide Association Studies (GWAS) regarding the ability to detect QTLs (Quantitative Trait Locus) associated with phenotypic traits of interest, considering different population sizes. For this, simula...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-06-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-36730-z |
| _version_ | 1797801465473925120 |
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| author | Gabriela França Oliveira Ana Carolina Campana Nascimento Camila Ferreira Azevedo Maurício de Oliveira Celeri Laís Mayara Azevedo Barroso Isabela de Castro Sant’Anna José Marcelo Soriano Viana Marcos Deon Vilela de Resende Moysés Nascimento |
| author_facet | Gabriela França Oliveira Ana Carolina Campana Nascimento Camila Ferreira Azevedo Maurício de Oliveira Celeri Laís Mayara Azevedo Barroso Isabela de Castro Sant’Anna José Marcelo Soriano Viana Marcos Deon Vilela de Resende Moysés Nascimento |
| author_sort | Gabriela França Oliveira |
| collection | DOAJ |
| description | Abstract The aim of this study was to evaluate the performance of Quantile Regression (QR) in Genome-Wide Association Studies (GWAS) regarding the ability to detect QTLs (Quantitative Trait Locus) associated with phenotypic traits of interest, considering different population sizes. For this, simulated data was used, with traits of different levels of heritability (0.30 and 0.50), and controlled by 3 and 100 QTLs. Populations of 1,000 to 200 individuals were defined, with a random reduction of 100 individuals for each population. The power of detection of QTLs and the false positive rate were obtained by means of QR considering three different quantiles (0.10, 0.50 and 0.90) and also by means of the General Linear Model (GLM). In general, it was observed that the QR models showed greater power of detection of QTLs in all scenarios evaluated and a relatively low false positive rate in scenarios with a greater number of individuals. The models with the highest detection power of true QTLs at the extreme quantils (0.10 and 0.90) were the ones with the highest detection power of true QTLs. In contrast, the analysis based on the GLM detected few (scenarios with larger population size) or no QTLs in the evaluated scenarios. In the scenarios with low heritability, QR obtained a high detection power. Thus, it was verified that the use of QR in GWAS is effective, allowing the detection of QTLs associated with traits of interest even in scenarios with few genotyped and phenotyped individuals. |
| first_indexed | 2024-03-13T04:50:52Z |
| format | Article |
| id | doaj.art-c8f503afa7e047199e44ff82de5f7c2c |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-13T04:50:52Z |
| publishDate | 2023-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-c8f503afa7e047199e44ff82de5f7c2c2023-06-18T11:14:46ZengNature PortfolioScientific Reports2045-23222023-06-0113111010.1038/s41598-023-36730-zPopulation size in QTL detection using quantile regression in genome-wide association studiesGabriela França Oliveira0Ana Carolina Campana Nascimento1Camila Ferreira Azevedo2Maurício de Oliveira Celeri3Laís Mayara Azevedo Barroso4Isabela de Castro Sant’Anna5José Marcelo Soriano Viana6Marcos Deon Vilela de Resende7Moysés Nascimento8Department of Statistics, Federal University of ViçosaDepartment of Statistics, Federal University of ViçosaDepartment of Statistics, Federal University of ViçosaDepartment of Statistics, Federal University of ViçosaFederal Institute of Education, Science and Technology of Mato GrossoRubber Tree and Agroforestry Systems Research Center, Campinas Agronomy Institute (IAC)Department of General Biology, Federal University of ViçosaBrazilian Agricultural Research Corporation, Embrapa CoffeeDepartment of Statistics, Federal University of ViçosaAbstract The aim of this study was to evaluate the performance of Quantile Regression (QR) in Genome-Wide Association Studies (GWAS) regarding the ability to detect QTLs (Quantitative Trait Locus) associated with phenotypic traits of interest, considering different population sizes. For this, simulated data was used, with traits of different levels of heritability (0.30 and 0.50), and controlled by 3 and 100 QTLs. Populations of 1,000 to 200 individuals were defined, with a random reduction of 100 individuals for each population. The power of detection of QTLs and the false positive rate were obtained by means of QR considering three different quantiles (0.10, 0.50 and 0.90) and also by means of the General Linear Model (GLM). In general, it was observed that the QR models showed greater power of detection of QTLs in all scenarios evaluated and a relatively low false positive rate in scenarios with a greater number of individuals. The models with the highest detection power of true QTLs at the extreme quantils (0.10 and 0.90) were the ones with the highest detection power of true QTLs. In contrast, the analysis based on the GLM detected few (scenarios with larger population size) or no QTLs in the evaluated scenarios. In the scenarios with low heritability, QR obtained a high detection power. Thus, it was verified that the use of QR in GWAS is effective, allowing the detection of QTLs associated with traits of interest even in scenarios with few genotyped and phenotyped individuals.https://doi.org/10.1038/s41598-023-36730-z |
| spellingShingle | Gabriela França Oliveira Ana Carolina Campana Nascimento Camila Ferreira Azevedo Maurício de Oliveira Celeri Laís Mayara Azevedo Barroso Isabela de Castro Sant’Anna José Marcelo Soriano Viana Marcos Deon Vilela de Resende Moysés Nascimento Population size in QTL detection using quantile regression in genome-wide association studies Scientific Reports |
| title | Population size in QTL detection using quantile regression in genome-wide association studies |
| title_full | Population size in QTL detection using quantile regression in genome-wide association studies |
| title_fullStr | Population size in QTL detection using quantile regression in genome-wide association studies |
| title_full_unstemmed | Population size in QTL detection using quantile regression in genome-wide association studies |
| title_short | Population size in QTL detection using quantile regression in genome-wide association studies |
| title_sort | population size in qtl detection using quantile regression in genome wide association studies |
| url | https://doi.org/10.1038/s41598-023-36730-z |
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