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 == | |||
* 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> | |||
* 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 (?!)
==