Batte, Michael

Brown, A.

Christelova, Pavla

Doležel, Jaroslav

Hribova, E.

Lorenzen, Jim

Nyine, Moses

Swennen, Rony L.

Uwimana, Brigitte

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www.iita.org   I   www.cgiar.org  Black Sigatoka-related traits were not affected by crop cycle. These could be measured in the first cycle thus reducing on phenotyping burden. Growth traits such as plant height and girth were the least affected by field input management. Conversely, yield-related traits such as bunch weight, number of hands and number of fingers were significantly affected by both crop cycle and field input management. The variation in traits observed suggest that different genomic selection models should be tested. For traits affected by cycle and field management, models that account for non-additive genetic effect are likely to have better predictive ability on them. Integration of genomic selection in crossbreeding allows simultaneous prediction and selection of best hybrids. This is likely to reduce the selection cycle and increase genetic gain per unit time.  References 1.Meuwissen THE, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001;157: 1819-1829.2. Nakaya A, Isobe SN. Will genomic selection be a practical method for plant breeding? Ann Bot. 2012;110: 1303-1316.  Trait Variation in a Banana Training Population for Genomic Selection Moses Nyine1,3, Brigitte Uwimana1, Rony Swennen2, Michael Batte1, Allan Brown2, Pavla Christelová3, Eva Hřibová3  Jim Lorenzen1, and Jaroslav Doležel3  1.  International Institute of Tropical Agriculture (IITA), Kampala, Uganda  2.  International Institute of Tropical Agriculture (IITA), Arusha, Tanzania  3.  Institute of Experimental Botany, Palacký University, Czech Republic  Introduction  Conventional crossbreeding is the main approach used in banana improvement. However, the method requires up to two decades of crossing and field evaluation to develop a new hybrid. This is because selection is carried out at different levels (Fig 1). At every level, plants are evaluated after three crop cycles, each taking about a year. Yield traits can only be scored at harvest while organoleptic traits are recorded after harvesting, making the selection process slow, expensive and labour intensive. Molecular tools with the potential to improve banana breeding efficiency are being investigated. These include genomic selection (GS), which will benefit breeding through increased genetic gain per unit time (Meuwissen et al. 2001; Nakaya and Isobe 2009). Understanding trait variation and the correlation among economically important traits is an essential first step in the development of GS models. In this study we tested the hypothesis that trait variations in bananas are not affected by cross combination, cycle, field management and their interaction with genotype.  B JUICE VARIETY   OR   COOKING VARIETY Genotyping  at tissue culture or nursery level Phenotype the best Predicted Genotypes Genomic Selection Prediction On-Farm Trial/ Farmer Participatory Selection Advanced/ Multilocation Evaluation Trial Early Evaluation Trial Cross pollination Embryo rescue In-vitro multiplication of genotypes Preliminary Yield Trial 1-1.5 years 3-4 years <10% selected 3-4 years < 5% selected 3-4 years < 1% selected 1-1.5 years 3-6 months ~2-3 years Confirm phenotype  Sensory Evaluation A HYBRID Fig 1: Approaches to hybrid selection in banana breeding program. (A) the classical phenotypic selection of banana hybrids and (B) integrated genomic selection and phenotypic selection approach being investigated. Materials and Methods The training population consists of 307 genotypes that include parents and the resulting hybrids. The population was phenotyped under low (no mulch and NPK fertilizer) and high (mulch + NPK) field input management at Namulonge research station. Data collected on two crop cycles were analysed using R statistical software. The correlations and significance of correlations were determined using R package Hmisc.  Analysis of variance was performed to understand the effect of genotype and the interaction between genotype and cycle, and genotype and  field management on trait variation.   Pant height Plant girth Index of non-spotted leaf Bunch weight Number of hands Number of fruits Fruit length Fruit circumference Fruit diameter Plant girth 0.77*                 Index of non-spotted leaf 0.21 0.27               Bunch weight 0.37* 0.62* −0.13             Number of hands 0.22 0.42* 0.10 0.52*           Number of fingers 0.37* 0.58* 0.19 0.57* 0.84*         Fruit length 0.20 0.44* −0.15 0.83* 0.28* 0.27*       Fruit circumference 0.33* 0.45* −0.15 0.81* 0.15 0.15 0.85*     Fruit diameter 0.39* 0.48* −0.16 0.79* 0.16 0.18 0.80* 0.97*   Pulp diameter 0.39* 0.45* −0.16 0.74* 0.11 0.13 0.76* 0.94* 0.99* Table 1: Pearson’s correlation coefficients of traits under high input field management  Trait Indep. variable Sum Sq Df F value P value Plant height Genotype 2222889 306 3.77 <0.0001   Genotype x Field 432297 284 0.79 0.995   Genotype x Cycle 332846 299 1.05 0.266 Plant girth Genotype 73176 306 4.30 <0.0001 Genotype x Field 12061 284 0.76 0.998 Genotype x Cycle 13057 299 1.51 <0.0001 Index of non-spotted leaf     Genotype 116602 306 2.44 <0.0001 Genotype x Field 58584 284 1.32 0.0005 Genotype x Cycle 51026 299 0.95 0.695 Bunch weight* Genotype 1214 303 12.55 <0.0001   Genotype x Field 127 269 1.48 <0.0001   Genotype x Cycle 109 276 1.49 <0.0001 Number of hands     Genotype 3334 303 8.67 <0.0001 Genotype x Field 570 269 1.67 <0.0001 Genotype x Cycle 429 276 1.26 0.005 Number of fruits     Genotype 1380509 303 5.46 <0.0001 Genotype x Field 333081 269 1.49 <0.0001 Genotype x Cycle 262981 276 1.23 0.009 Conclusions and Recommendations Genomic selection as a form of marker assisted selection is a non-stand alone approach but if integrated into conventional crossbreeding it has the potential to accelerate the breeding process. The effectiveness of genomic selection in banana will greatly depend on the prediction accuracy of the genomic selection models. Table 2: Effect of genotype and genotype interaction with cycle and field management on the traits  * Original data square root transformed Results and Discussion A high level of correlation among vegetative and yield related traits was observed (Table 1). This could mean that the predictive ability of traits that are difficult to phenotype will be similar to less difficult traits they are highly correlated with. Therefore, genomic selection models could be developed for traits that are easily measured. Table 2 summarizes the genotypic effects and the interaction between genotype and cycle and genotype and field management on the traits.  * Significant correlation with P-value < 0.05 * Currently with the Bill and Melinda Gates Foundation M.Nyine@cgiar.org|B. Uwimana@cgiar.org 

Trait variation in a banana training population for genomic selection.

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Trait variation in a banana training population for genomic selection.

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