Unravelling Rubber Tree Growth by Integrating GWAS and Biological Network-Based Approaches

Abstract

Hevea brasiliensis (rubber tree) is a large tree species of the Euphorbiaceae family with inestimable economic importance. Rubber tree breeding programs currently aim to improve growth and production, and the use of early genotype selection technologies can accelerate such processes, mainly with the incorporation of genomic tools, such as marker-assisted selection (MAS). However, few quantitative trait loci (QTLs) have been used successfully in MAS for complex characteristics. Recent research shows the efficiency of genome-wide association studies (GWAS) for locating QTL regions in different populations. In this way, the integration of GWAS, RNA-sequencing (RNA-Seq) methodologies, coexpression networks and enzyme networks can provide a better understanding of the molecular relationships involved in the definition of the phenotypes of interest, supplying research support for the development of appropriate genomic based strategies for breeding. In this context, this work presents the potential of using combined multiomics to decipher the mechanisms of genotype and phenotype associations involved in the growth of rubber trees. Using GWAS from a genotyping-by-sequencing (GBS) Hevea population, we were able to identify molecular markers in QTL regions with a main effect on rubber tree plant growth under constant water stress. The underlying genes were evaluated and incorporated into a gene coexpression network modelled with an assembled RNA-Seq-based transcriptome of the species, where novel gene relationships were estimated and evaluated through in silico methodologies, including an estimated enzymatic network. From all these analyses, we were able to estimate not only the main genes involved in defining the phenotype but also the interactions between a core of genes related to rubber tree growth at the transcriptional and translational levels. This work was the first to integrate multiomics analysis into the in-depth investigation of rubber tree plant growth, producing useful data for future genetic studies in the species and enhancing the efficiency of the species improvement programs.

A divide-and-conquer approach for genomic prediction in rubber tree using machine learning

“…Subset selection showed improvements for EG2 (Supplementary Figs. S20 , S21 ); however, there were no sizable improvements because of the genetic complexity of SC 120 and the low density of SSR markers 121 . In EG1, although an overall improvement in prediction accuracy was observed (Supplementary Fig.…”

“…Additionally, base-excision repair was found in both Pop1 and Pop3, which represents a very important defense pathway for maintaining genomic integrity 137 and is clearly essential for rubber tree growth and development 138 . Due to the increased quantity of individuals in Pop2 and Pop3, more GO categories were found, including important processes for plant growth, such as response to different types of stress and several metabolic processes 120 .…”

A divide-and-conquer approach for genomic prediction in rubber tree using machine learning

“…Subset selection showed improvements for EG2 (Supplementary Figs. 20-21); however, there were no sizable improvements because of the genetic complexity of SC (Francisco et al, 2021) and the low density of SSR markers (Nadeem et al, 2018). In EG1, although an overall improvement in prediction accuracy was observed (Supplementary Fig.…”

“…Additionally, base-excision repair was found in both Pop1 and Pop3, which represents a very important defense pathway for maintaining genomic integrity (Roldán-Arjona et al, 2019) and is clearly essential for rubber tree growth and development (Murphy, 2005). Due to the increased quantity of individuals in Pop2 and Pop3, more GO categories were found, including important processes for plant growth, such as response to different types of stress and several metabolic processes (Francisco et al, 2021).…”

Genome-wide association reveals novel insights into the molecular mechanisms regulating stem volume inPinus taeda

“…Nonparametric models for GP represent an alternative for modeling genotype-phenotype associations, and the role of machine learning (ML) is still not fully understood (Wang et al, 2018). Additionally, multiomics joint analyses have demonstrated a high potential to dissect and better understand the architecture of complex traits, mapping genetic variants with unknown biological roles to molecular mechanisms through integrative methodologies, such as gene coexpression networks (Schaefer et al, 2018; Francisco et al, 2021). Loci detected under association with a trait can be linked to genes within coexpressed modules, providing a deeper comprehension of the mechanisms underlying the complex traits (Francisco et al, 2021).…”