Biallelic GINS2 variant p.(Arg114Leu) causes Meier-Gorlin syndrome with craniosynostosis

Contains fulltext : 284813.pdf (Publisher’s version ) (Open Access)INTRODUCTION: Replication of the nuclear genome is an essential step for cell division. Pathogenic variants in genes coding for highly conserved components of the DNA replication machinery cause Meier-Gorlin syndrome (MGORS). OBJECTIVE: Identification of novel genes associated with MGORS. METHODS: Exome sequencing was performed to investigate the genotype of an individual presenting with prenatal and postnatal growth restriction, a craniofacial gestalt of MGORS and coronal craniosynostosis. The analysis of the candidate variants employed bioinformatic tools, in silico structural protein analysis and modelling in budding yeast. RESULTS: A novel homozygous missense variant NM_016095.2:c.341G>T, p.(Arg114Leu), in GINS2 was identified. Both non-consanguineous healthy parents carried this variant. Bioinformatic analysis supports its classification as pathogenic. Functional analyses using yeast showed that this variant increases sensitivity to nicotinamide, a compound that interferes with DNA replication processes. The phylogenetically highly conserved residue p.Arg114 localises at the docking site of CDC45 and MCM5 at GINS2. Moreover, the missense change possibly disrupts the effective interaction between the GINS complex and CDC45, which is necessary for the CMG helicase complex (Cdc45/MCM2-7/GINS) to accurately operate. Interestingly, our patient's phenotype is strikingly similar to the phenotype of patients with CDC45-related MGORS, particularly those with craniosynostosis, mild short stature and patellar hypoplasia. CONCLUSION: GINS2 is a new disease-associated gene, expanding the genetic aetiology of MGORS

Quantitative Genetics and Functional-Structural Plant Growth Models: Simulation of Quantitative Trait Loci Detection for Model Parameters and Application to Potential Yield Optimization

Background and Aims: Prediction of phenotypic traits from new genotypes under untested environmental conditions is crucial to build simulations of breeding strategies to improve target traits. Although the plant response to environmental stresses is characterized by both architectural and functional plasticity, recent attempts to integrate biological knowledge into genetics models have mainly concerned specific physiological processes or crop models without architecture, and thus may prove limited when studying genotype x environment interactions. Consequently, this paper presents a simulation study introducing genetics into a functional-structural growth model, which gives access to more fundamental traits for quantitative trait loci (QTL) detection and thus to promising tools for yield optimization. Methods: The GreenLab model was selected as a reasonable choice to link growth model parameters to QTL. Virtual genes and virtual chromosomes were defined to build a simple genetic model that drove the settings of the species-specific parameters of the model. The QTL Cartographer software was used to study QTL detection of simulated plant traits. A genetic algorithm was implemented to define the ideotype for yield maximization based on the model parameters and the associated allelic combination. Key Results and Conclusions: By keeping the environmental factors constant and using a virtual population with a large number of individuals generated by a Mendelian genetic model, results for an ideal case could be simulated. Virtual QTL detection was compared in the case of phenotypic traits - such as cob weight - and when traits were model parameters, and was found to be more accurate in the latter case. The practical interest of this approach is illustrated by calculating the parameters (and the corresponding genotype) associated with yield optimization of a GreenLab maize model. The paper discusses the potentials of GreenLab to represent environment x genotype interactions, in particular through its main state variable, the ratio of biomass supply over demand