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An imputation platform to enhance integration of rice genetic resources.
Wang, Diane R; Agosto-Pérez, Francisco J; Chebotarov, Dmytro; Shi, Yuxin; Marchini, Jonathan; Fitzgerald, Melissa; McNally, Kenneth L; Alexandrov, Nickolai; McCouch, Susan R.
Afiliação
  • Wang DR; Section of Plant Breeding and Genetics, School of Integrated Plant Sciences, Cornell University, Ithaca, NY, 14853-1901, USA.
  • Agosto-Pérez FJ; Department of Geography, University at Buffalo, Buffalo, NY, 14261, USA.
  • Chebotarov D; Section of Plant Breeding and Genetics, School of Integrated Plant Sciences, Cornell University, Ithaca, NY, 14853-1901, USA.
  • Shi Y; International Rice Research Institute, DAPO Box 7777,, 1301, Metro Manila, Philippines.
  • Marchini J; Section of Plant Breeding and Genetics, School of Integrated Plant Sciences, Cornell University, Ithaca, NY, 14853-1901, USA.
  • Fitzgerald M; Department of Statistics, University of Oxford, Oxford, OX1 3LB, UK.
  • McNally KL; School of Agriculture and Food Science, University of Queensland, 4072, QLD, Brisbane, Australia.
  • Alexandrov N; International Rice Research Institute, DAPO Box 7777,, 1301, Metro Manila, Philippines.
  • McCouch SR; International Rice Research Institute, DAPO Box 7777,, 1301, Metro Manila, Philippines.
Nat Commun ; 9(1): 3519, 2018 08 29.
Article em En | MEDLINE | ID: mdl-30158584
ABSTRACT
As sequencing and genotyping technologies evolve, crop genetics researchers accumulate increasing numbers of genomic data sets from various genotyping platforms on different germplasm panels. Imputation is an effective approach to increase marker density of existing data sets toward the goal of integrating resources for downstream applications. While a number of imputation software packages are available, the limitations to utilization for the rice community include high computational demand and lack of a reference panel. To address these challenges, we develop the Rice Imputation Server, a publicly available web application leveraging genetic information from a globally diverse rice reference panel assembled here. This resource allows researchers to benefit from increased marker density without needing to perform imputation on their own machines. We demonstrate improvements that imputed data provide to rice genome-wide association (GWA) results of grain amylose content and show that the major functional nucleotide polymorphism is tagged only in the imputed data set.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Oryza Idioma: En Revista: Nat Commun Assunto da revista: BIOLOGIA / CIENCIA Ano de publicação: 2018 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Oryza Idioma: En Revista: Nat Commun Assunto da revista: BIOLOGIA / CIENCIA Ano de publicação: 2018 Tipo de documento: Article País de afiliação: Estados Unidos