ABSTRACT
Maize (Zea mays 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.
ABSTRACT
The analysis of main additive effects and multiplicative interaction is commonly used in the evaluation of the genotype x environment interaction, however, its application can be used for other purposes, as it is performed in the presentresearch, which uses this technique in the selection of inbred lines, testers and hybrids in maize topcrosses. Thisresearch determined the effect of the inbred lines x testers interaction through the analysis of main additive effects and multiplicative interaction, verifying their efficiency in the selection of inbred lines, testers and hybrid combinations in topcrosses. The trials were carried out in the 2015/16 and 2016/17 crop seasons, with a complete block design, with three replications. Thirty S3 maize inbred lines were evaluated in crosses with the AG8025, P30B39, MLP102, 60.H23.1 and 70.H26.1 testers forming 150 hybrids topcrosses. The trait evaluated was grain yield. The adaptability and stability of testers and inbred lines were evaluated by the methodology of analysis of main additive effects and multiplicative interaction directed to the interaction of testers x inbred lines. The 96.3 inbred line has the most homogeneous performance and the highest grain yield, considering the crossing with all testers in both environments. The 70.H26.1 tester is considered the most stable and the most recommended for topcrosses. The best specific combinations were 96.3 x 70.H26.1 and 96.3 x 60.H23.1.
A análise dos principais efeitos aditivos e interação multiplicativa é comumente utilizada na avaliação da interação genótipo x ambiente, porém, sua aplicação pode ser estendida para outros propósitos assim como realizado no presente trabalho em que utiliza esta técnica na seleção de linhagens, testadores e híbridos em topcrosses de milho. O objetivo deste trabalho foi determinar o efeito da interação linhagens x testadores por meio da análise dos principais efeitos aditivos e da interação multiplicativa, verificando sua eficiência na seleção de linhagens, testadoras e combinações híbridas em topcrosses. Os ensaios foram realizados nas safras 2015/16 e 2016/17, com delineamento em blocos completos, com três repetições. Trinta linhagens de milho S3 foram avaliadas em cruzamentos com os testadores AG8025, P30B39, MLP102, 60.H23.1 e 70.H26.1 formando 150 topcrosses híbridos. A característica avaliada foi a produtividade de grãos. A adaptabilidade e estabilidade de testadores e linhagens foram avaliadas pela metodologia de análise de efeitos aditivos principais e interação multiplicativa direcionada à interação testadores x linhagens. A linhagem 96.3 apresentou o desempenho mais homogêneo e o maior rendimento de grãos, considerando o cruzamento com todos os testadores em ambos os ambientes. O testador 70.H26.1 é considerado o mais estável e o mais recomendado para topcrosses. As melhores combinações específicas foram 96.3 x 70.H26.1 e 96.3 x 60.H23.1.