How to implement genomic selection?
Using genome-assisted prediction to increase accuracy, manage diversity, intensity or reduce cycle time is normally referred as genomic selection (GS).
The following simulation reports provide practical recommendations for a successful implementation of these methodologies.
Contact: g.covarrubias@cgiar.org
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Harnessing dominance and heterosis in IITA-RTB yam by genomic prediction of cross performance
Selection on genomic predicted cross performance increased the additive value while maintaining the dominance value.
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Increasing accuracy with GBLUP in CIAT East Africa Beans program
Using GBLUP to estimate the surrogate of genetic value for selection can increase the accuracy dramatically at low h2 EVEN for small family sizes.
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Increasing accuracy using GBLUP and PBLUP in IITA-NextGen-Cassava program
The use of GBLUP at early stages is the best approach to apply GBLUP for accuracy purposes.
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Reducing cycle time with GBLUP in the CIAT East Africa Beans program
Genomic prediction is highly recommended to reduce cycle time, if all proper steps have been adopted.
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Optimizing the use of GBLUP for recycling in IITA-NextGen-Cassava program
Genomic prediction of cross performance is a promising method to ensure maintenance of dominance and guard against rapid inbreeding depression.
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Early recycling in IITA-RTB yam with genomic prediction of cross performance
Selection of parents by genomic predicted cross performance allowed earlier recycling.
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Increasing intensity with GBLUP in CIAT East Africa Beans program
Using GBLUP to adjust intensity can reduce resources needed while maintaining accuracy and response to selection, even for multi-trait scenarios.