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Tuesday, August 29, 2017 at 12:00pm to 1:00pm
Genomic selection (GS) has become a mature technology for genetic improvement of crops and livestock making use of phenotypic and genomic information. At the core of GS is linear mixed models, a proven machinery for prediction of breeding values. We explore herein the use of multi-trait (multi-response) and single-trait multikernel linear mixed models for breeding value estimation, extracting inheritable portions of GxE, mapping (co)inheritance patterns in complex traits and assessing estimated gains from selection. We used data from the cassava breeding population of the International Institute for Tropical Agriculture (IITA) for this exploration. Our studies provide evidence that multi-trait mixed models will be beneficial for a cassava genetic improvement program based on GS. We also provide evidence of good potential for breeding high starch white cassava and a limited prospect for provitamin-A cassava.
Uche Okeke
Plant Breeding and Genetics, Cornell University
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