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| Registros recuperados : 872 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
08/12/2023 |
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Data da última atualização: |
08/12/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
OLIVEIRA, G. F.; NASCIMENTO, A. C. C.; AZEVEDO, C. F.; CELERI, M. de O.; BARROSO, L. M. A.; SANT’ANNA, I. de C.; VIANA, J. M. S.; RESENDE, M. D. V. de; NASCIMENTO, M. |
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Afiliação: |
GABRIELA FRANÇA OLIVEIRA, UNIVERSIDADE FEDERAL DE VIÇOSA; ANA CAROLINA CAMPANA NASCIMENTO, UNIVERSIDADE FEDERAL DE VIÇOSA; CAMILA FERREIRA AZEVEDO, UNIVERSIDADE FEDERAL DE VIÇOSA; MAURÍCIO DE OLIVEIRA CELERI, UNIVERSIDADE FEDERAL DE VIÇOSA; LAÍS MAYARA AZEVEDO BARROSO, INSTITUTO FEDERAL DE EDUCAÇÃO, CIÊNCIA E TECNOLOGIA DE MATO GROSSO; ISABELA DE CASTRO SANT’ANNA, INSTITUTO AGRONÔMICO DE CAMPINAS; JOSÉ MARCELO SORIANO VIANA, UNIVERSIDADE FEDERAL DE VIÇOSA; MARCOS DEON VILELA DE RESENDE, CNPCa; MOYSÉS NASCIMENTO, UNIVERSIDADE FEDERAL DE VIÇOSA. |
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Título: |
Population size in QTL detection using quantile regression in genome‑wide association studies. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Scientific Reports, v. 13, Article 9585, 2023. |
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Páginas: |
10 p. |
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DOI: |
https://doi.org/10.1038/s41598-023-36730-z |
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Idioma: |
Português |
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Conteúdo: |
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. |
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Thesaurus NAL: |
Genome-wide association study; Phenotypic variation; Regression analysis. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1159390/1/Population-size-in-QTL-detection.pdf
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Marc: |
LEADER 02367naa a2200277 a 4500
001 2159390
005 2023-12-08
008 2023 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1038/s41598-023-36730-z$2DOI
100 1 $aOLIVEIRA, G. F.
245 $aPopulation size in QTL detection using quantile regression in genome‑wide association studies.$h[electronic resource]
260 $c2023
300 $a10 p.
520 $aThe 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.
650 $aGenome-wide association study
650 $aPhenotypic variation
650 $aRegression analysis
700 1 $aNASCIMENTO, A. C. C.
700 1 $aAZEVEDO, C. F.
700 1 $aCELERI, M. de O.
700 1 $aBARROSO, L. M. A.
700 1 $aSANT’ANNA, I. de C.
700 1 $aVIANA, J. M. S.
700 1 $aRESENDE, M. D. V. de
700 1 $aNASCIMENTO, M.
773 $tScientific Reports$gv. 13, Article 9585, 2023.
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