Comparision of GS Models: Difference between revisions

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[[Category:Readings]] [[Category:Rajkumar]] [[Category:Biology]]
[[Category:Readings]] [[Category:Rajkumar]] [[Category:Biology]]
== The scope ==  
== The scope ==  
* Various GS models such as Bayes B, SPLS, BL, RR_BLUP, PLS have been compared
* Various GS models such as Bayes B, SPLS, BL, RR_BLUP, PLS have been compared<ref>[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4592981 Comparison of Genomic Selection Models to Predict Flowering Time and Spike Grain Number in Two Hexaploid Wheat Doubled Haploid Populations]</ref>
<ref>[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4592981 Comparison of Genomic Selection Models to Predict Flowering Time and Spike Grain Number in Two Hexaploid Wheat Doubled Haploid Populations]</ref>
* Prediction accuracy in Time to young microspore: '''0.5 to 0.84'''
 
* Prediction accuracy of grain number under Osmatic stress conditions : '''0.27 - 0.46''' 
* Prediction accuracy of grain number under controlled conditions    : '''0.1 - 0.42'''
* Accuracies were generally lower in independent validation than cross validation (?!)
* BayesB and SPLS were turned out to be the best models
* Hence BayesB and SPLS capture the LD between the markers and traits => higher accuracy.
* Excluding markers from QTL mapping reduces the accuracies (?!)
==
==

Latest revision as of 11:46, 27 May 2016

The scope

  • Various GS models such as Bayes B, SPLS, BL, RR_BLUP, PLS have been compared[1]
  • Prediction accuracy in Time to young microspore: 0.5 to 0.84
  • Prediction accuracy of grain number under Osmatic stress conditions : 0.27 - 0.46
  • Prediction accuracy of grain number under controlled conditions  : 0.1 - 0.42
  • Accuracies were generally lower in independent validation than cross validation (?!)
  • BayesB and SPLS were turned out to be the best models
  • Hence BayesB and SPLS capture the LD between the markers and traits => higher accuracy.
  • Excluding markers from QTL mapping reduces the accuracies (?!)

==