Ascertainment bias from imputation methods evaluation in wheat

BMC Genomics

Research Article

Marcos Malosetti¹  Antonio Augusto Franco Garcia²  Ariel J. Castro³  Alejandro del Pozo⁴  Iván Matus⁵  Martín Quincke⁶  Marina Castro⁶  Jarislav von Zitzewitz⁷  Sofía P. Brandariz⁸  Agustín González Reymúndez⁸  Bettina Lado⁸  Lucía Gutiérrez⁹ 

[1] Biometris - Applied Statistics, Department of Plant Science, Wageningen University and Research Center, P.O. Box 16, 6700 AA, Wageningen, Netherlands;Departamento de Ciências Exatas, Escola Superior de Agricultura “Luiz de Queiroz” (ESALQ), Universidade de São Paulo (USP), CP 9, CEP 13400-970, Piracicaba, SP, Brazil;Department of Plant Production, Facultad de Agronomía, Universidad de la República, Ruta 3, Km.363, 60000, Paysandú, Uruguay;Facultad de Ciencias Agrarias, Universidad de Talca, Casilla 747, Talca, Chile;Instituto de Investigaciones Agropecuarias, Centro Regional de Investigación Quilamapu, Casilla 426, Chillán, Chile;Programa Nacional de Investigación Cultivos de Secano, Instituto Nacional de investigación Agropecuaria, Est. Exp. La Estanzuela, 70000, Colonia, Uruguay;Secobra Saatzucht GmbH, Feldkirchen 3, 85368, Moosburg, Germany;Statistics Department, Facultad de Agronomía, Universidad de la República, Garzón 780, 12900, Montevideo, Uruguay;Statistics Department, Facultad de Agronomía, Universidad de la República, Garzón 780, 12900, Montevideo, Uruguay;Department of Agronomy, University of Wisconsin-Madison, 1575 Linden Dr, 53706, Madison, WI, USA;

关键词: GBS;    QTL;    GWAS;    Power;    False positive;   

DOI  :  10.1186/s12864-016-3120-5

 received in 2016-02-24, accepted in 2016-09-23,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundWhole-genome genotyping techniques like Genotyping-by-sequencing (GBS) are being used for genetic studies such as Genome-Wide Association (GWAS) and Genomewide Selection (GS), where different strategies for imputation have been developed. Nevertheless, imputation error may lead to poor performance (i.e. smaller power or higher false positive rate) when complete data is not required as it is for GWAS, and each marker is taken at a time. The aim of this study was to compare the performance of GWAS analysis for Quantitative Trait Loci (QTL) of major and minor effect using different imputation methods when no reference panel is available in a wheat GBS panel.ResultsIn this study, we compared the power and false positive rate of dissecting quantitative traits for imputed and not-imputed marker score matrices in: (1) a complete molecular marker barley panel array, and (2) a GBS wheat panel with missing data. We found that there is an ascertainment bias in imputation method comparisons. Simulating over a complete matrix and creating missing data at random proved that imputation methods have a poorer performance. Furthermore, we found that when QTL were simulated with imputed data, the imputation methods performed better than the not-imputed ones. On the other hand, when QTL were simulated with not-imputed data, the not-imputed method and one of the imputation methods performed better for dissecting quantitative traits. Moreover, larger differences between imputation methods were detected for QTL of major effect than QTL of minor effect. We also compared the different marker score matrices for GWAS analysis in a real wheat phenotype dataset, and we found minimal differences indicating that imputation did not improve the GWAS performance when a reference panel was not available.ConclusionsPoorer performance was found in GWAS analysis when an imputed marker score matrix was used, no reference panel is available, in a wheat GBS panel.

【 授权许可】

CC BY   
© The Author(s). 2016