Abstract
L.), the third most widely cultivated cereal crop in the world, plays a critical role in global food security. To improve the efficiency of selecting superior genotypes in breeding programs, researchers have aimed to identify key genomic regions that impact agronomic traits. In this study, the performance of multi-trait, multi-environment deep learning models was compared to that of Bayesian models (Markov Chain Monte Carlo generalized linear mixed models (MCMCglmm), Bayesian Genomic Genotype-Environment Interaction (BGGE), and Bayesian Multi-Trait and Multi-Environment (BMTME)) in terms of the prediction accuracy of flowering-related traits (Anthesis-Silking Interval: ASI, Female Flowering: FF, and Male Flowering: MF). A tropical maize panel of 258 inbred lines from Brazil was evaluated in three sites (Cambira-2018, Sabaudia-2018, and Iguatemi-2020 and 2021) using approximately 290,000 single nucleotide polymorphisms (SNPs). The results demonstrated a 14.4% increase in prediction accuracy when employing multi-trait models compared to the use of a single trait in a single environment approach. The accuracy of predictions also improved by 6.4% when using a single trait in a multi-environment scheme compared to using multi-trait analysis. Additionally, deep learning models consistently outperformed Bayesian models in both single and multiple trait and environment approaches. A complementary genome-wide association study identified associations with 26 candidate genes related to flowering time traits, and 31 marker-trait associations were identified, accounting for 37%, 37%, and 22% of the phenotypic variation of ASI, FF and MF, respectively. In conclusion, our findings suggest that deep learning models have the potential to significantly improve the accuracy of predictions, regardless of the approach used and provide support for the efficacy of this method in genomic selection for flowering-related traits in tropical maize.
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Claims 8
Claim · model-written summary
Multi-trait models increased prediction accuracy 14.4% over a single-trait single-environment approach.
- Outcome
- prediction accuracye0
- Direction
- increasee0
- Value
- 14.4%e0
- Comparator
- single trait in a single environment approache0
Zea maysOrganisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“14.4% increase in prediction accuracy when employing multi-trait models compared to the use of a single trait in a single environment approach”
t0“flowering traits in maize”
Claim · model-written summary
A single trait in multiple environments improved accuracy 6.4% compared with multi-trait analysis.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“accuracy of predictions also improved by 6.4% when using a single trait in a multi-environment scheme compared to using multi-trait analysis”
t0“flowering traits in maize”
Claim · model-written summary
Deep learning models outperformed Bayesian models across single and multiple trait and environment schemes.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“deep learning models consistently outperformed Bayesian models in both single and multiple trait and environment approaches”
t0“flowering traits in maize”
Claim · model-written summary
GWAS identified 26 candidate genes related to flowering time.
- Outcome
- associations with 26 candidate genes related to flowering time traitse0
- Value
- 26 candidate genese0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“identified associations with 26 candidate genes related to flowering time traits”
t0“flowering traits in maize”
Claim · model-written summary
GWAS identified 31 marker-trait associations.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“31 marker-trait associations were identified”
t0“flowering traits in maize”
Claim · model-written summary
The identified associations accounted for 37% of ASI variation.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“accounting for 37%, 37%, and 22% of the phenotypic variation of ASI, FF and MF, respectively”
t0“flowering traits in maize”
Claim · model-written summary
The identified associations accounted for 37% of FF variation.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“accounting for 37%, 37%, and 22% of the phenotypic variation of ASI, FF and MF, respectively”
t0“flowering traits in maize”
Claim · model-written summary
The identified associations accounted for 22% of MF variation.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“accounting for 37%, 37%, and 22% of the phenotypic variation of ASI, FF and MF, respectively”
t0“flowering traits in maize”