Genomic prediction with epistasis models: on the marker-coding-dependent performance of the extended GBLUP and properties of the categorical epistasis model (CE)
作者: Johannes W. R. MartiniNing GaoDiercles F. CardosoValentin WimmerMalena ErbeRodolfo J. C. CantetHenner Simianer
作者单位: 1Georg-August University
2South China Agricultural University
3São Paulo State University
5Bavarian State Research Centre for Agriculture
6University of Buenos Aires
刊名: BMC Bioinformatics, 2017, Vol.18 (1)
来源数据库: Springer Nature Journal
DOI: 10.1186/s12859-016-1439-1
关键词: Genomic predictionEpistasis modelInteraction
原始语种摘要: Epistasis marker effect models incorporating products of marker values as predictor variables in a linear regression approach (extended GBLUP, EGBLUP) have been assessed as potentially beneficial for genomic prediction, but their performance depends on marker coding. Although this fact has been recognized in literature, the nature of the problem has not been thoroughly investigated so far.
全文获取路径: Springer Nature  (合作)
影响因子:3.024 (2012)

  • epistasis 上位
  • CE Call Entry
  • prediction 预报
  • coding 编码
  • marker 标试器
  • genomic 染色体组的
  • potentially 可能地
  • predictor 预示
  • categorical 范畴
  • extended 扩展