Genetic variation in LBL1 contributes to depth of leaf blades lobes between cotton subspecies, Gossypium barbadense and Gossypium hirsutum

Leaf is a essential part of the plants for photosynthetic activities which mainly economize the resources for boll heath. Significant variations of leaf shapes across the Gossypium sp. considerably influence the infiltration of sunlight for photosynthesis. To understand the genetic variants and molecular processes underlying for cotton leaf shape, we used F2 population derived from upland cotton genotype P30A （shallow-lobed leaf） and sea-island cotton genotype ISR （deep-lobed leaf） to map leaf deep lobed phenotype controlling genes LBL1 and LBL2. Genetic analysis and localization results have unmasked the position and interaction between both loci of LBL1 and LBL2, and revealed the co-dominance impact of the genes in regulating depth of leaf blades lobes in cotton. LBL1 had been described as a main gene and member of transcription factor family leucine zipper （HD-ZIPI） from a class I homologous domain factor Gorai.OO2G244000. The qRT-PCR results elaborated the continuous change in expression level of LBL1 at different growth stages and leaf parts of cotton. Higher expression level was observed in mature large leaves followed by medium and young leaves respectively. For further confirmation, plants were tested from hormonal induction treatments, which explained that LBL 1 expression was influenced by hormonal signaling. Moreover, the highest expression level was detected in brassinolides （BR） treatment as compared to other hormones, and this hormone plays an important role in the process of leaf blade lobed formation.

DNNGP, a deep neural network-based method for genomic prediction using multi-omics data in plants

Genomic prediction is an effective way to accelerate the rate of agronomic trait improvement in plants.Traditional methods typically use linear regression models with clear assumptions;such methods are unable to capture the complex relationships between genotypes and phenotypes.Non-linear models(e.g.,deep neural networks)have been proposed as a superior alternative to linear models because they can capture complex non-additive effects.Here we introduce a deep learning(DL)method,deep neural network genomic prediction(DNNGP),for integration of multi-omics data in plants.We trained DNNGP on four datasets and compared its performance with methods built with five classic models:genomic best linear unbiased prediction(GBLUP);two methods based on a machine learning(ML)framework,light gradient boosting machine(LightGBM)and support vector regression(SVR);and two methods based on a DL framework,deep learning genomic selection(DeepGS)and deep learning genome-wide association study(DLGWAS).DNNGP is novel in five ways.First,it can be applied to a variety of omics data to predict phenotypes.Second,the multilayered hierarchical structure of DNNGP dynamically learns features from raw data,avoiding overfitting and improving the convergence rate using a batch normalization layer and early stopping and rectified linear activation(rectified linear unit)functions.Third,when small datasets were used,DNNGP produced results that are competitive with results from the other five methods,showing greater prediction accuracy than the other methods when large-scale breeding data were used.Fourth,the computation time required by DNNGP was comparable with that of commonly used methods,up to 10 times faster than DeepGS.Fifth,hyperparameters can easily be batch tuned on a local machine.Compared with GBLUP,LightGBM,SVR,DeepGS and DLGWAS,DNNGP is superior to these existing widely used genomic selection(GS)methods.Moreover,DNNGP can generate robust assessments from diverse datasets,including omics data,and quickly incorporate complex and large datasets into usable models,making it a promising and practical approach for straightforward integration into existing GS platforms.