Impact of imputation methods on the amount of genetic variation captured by a single-nucleotide polymorphism panel in soybeans

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• 作者：A. Xavier ; William M. Muir ; Katy M. Rainey
• 关键词：Empirical Bayes ; Heritability ; Genomic selection ; Association studies
• 刊名：BMC Bioinformatics
• 出版年：2016
• 出版时间：December 2016
• 年：2016
• 卷：17
• 期：1
• 全文大小：744 KB
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• 作者单位：A. Xavier (1)
  William M. Muir (2)
  Katy M. Rainey (1)
  
  1. Department of Agronomy, Purdue University, Lilly Hall of Life Sciences, 915 W. State St., West Lafayette, Indiana, 47907, USA
  2. Department of Animal Science, Purdue University, Lilly Hall of Life Sciences, 915 W. State St., West Lafayette, Indiana, 47907, USA
  
• 刊物主题：Bioinformatics; Microarrays; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Combinatorial Libraries; Algorithms;
• 出版者：BioMed Central
• ISSN：1471-2105

文摘

Background Success in genome-wide association studies and marker-assisted selection depends on good phenotypic and genotypic data. The more complete this data is, the more powerful will be the results of analysis. Nevertheless, there are next-generation technologies that seek to provide genotypic information in spite of great proportions of missing data. The procedures these technologies use to impute genetic data, therefore, greatly affect downstream analyses. This study aims to (1) compare the genetic variance in a single-nucleotide polymorphism panel of soybean with missing data imputed using various methods, (2) evaluate the imputation accuracy and post-imputation quality associated with these methods, and (3) evaluate the impact of imputation method on heritability and the accuracy of genome-wide prediction of soybean traits. The imputation methods we evaluated were as follows: multivariate mixed model, hidden Markov model, logical algorithm, k-nearest neighbor, single value decomposition, and random forest. We used raw genotypes from the SoyNAM project and the following phenotypes: plant height, days to maturity, grain yield, and seed protein composition.