Biology subjects

Garin, V.

Publications and source records attributed to Garin, V..

7 recordsLinked to original sources

bioRxiv2025

Factors Influencing Phenomic Prediction: A Case Study on a Large Sorghum BCNAM Population

Plant breeding efficiency is crucial to develop varieties able to cope with climate change and support food and feed value chains. Genomic prediction (GP) has been a major step in increasing this efficiency and is now routinely used in breeding programs. Recently, phenomic prediction (PP) has gained attention as a promising complementary approach to GP, further increasing the breeding programs efficiency. Factors impacting the predictive ability (PA) of PP have been studied on many species but are not fully clarified. In this context, we studied the impacts of spectra pre-processing, prediction methods, population structure, training set size, NIRS acquisition environment and wavelength selection on a large multi-parental sorghum population including 2498 genotypes. Our results show that PP can compete with GP, that it is less affected by population structure, and can reach its maximal PA with smaller training sets than GP, but its performances are trait dependant. We also show that NIRS can be acquired in a reference environment to perform prediction in other environments and that it is possible to randomly select as little as 10 wavelengths to perform predictions. Finally, we show that spectra pre-processing, and statistical methods have a limited and unclear impact on PA. Our study confirms that PP is a relevant trait prediction method that deserves attention to optimize breeding schemes. The main challenges for the future will be to better understand the information contained in the spectra and disentangle their genetic and proxy components to optimize the use of PP in breeding programs. Key messagePhenomic prediction is promising for sorghum breeding. Geneticists methods may not be suited to optimally extract spectral information.

genomics↗

bioRxiv2024

Performance of phenomic selection in rice: effects of population size and genotype-environment interactions on predictive ability

Phenomic prediction (PP), a novel approach utilizing Near Infrared Spectroscopy (NIRS) data, offers an alternative to genomic prediction (GP) for breeding applications. In PP, a hyperspectral relationship matrix replaces the genomic relationship matrix, potentially capturing both additive and non-additive genetic effects. While PP boasts advantages in cost and throughput compared to GP, the factors influencing its accuracy remain unclear and need to be defined. This study investigated the impact of various factors, namely the training population size, the multi-environment information integration, and the incorporations of genotype x environment (GxE) effects, on PP compared to GP. We evaluated the prediction accuracies for several agronomically important traits (days to flowering, plant height, yield, harvest index, thousand-grain weight, and grain nitrogen content) in a rice diversity panel grown in four distinct environments. Training population size and GxE effects inclusion had minimal influence on PP accuracy. The key factor impacting the accuracy of PP was the number of environments included. Using data from a single environment, GP generally outperformed PP. However, with data from multiple environments, using genotypic random effect and relationship matrix per environment, PP achieved comparable accuracies to GP. Combining PP and GP information did not significantly improve predictions compared to the best model using a single source of information (e.g., average predictive ability of GP, PP, and combined GP and PP for grain yield were of 0.44, 0.42, and 0.44, respectively). Our findings suggest that PP can be as accurate as GP when all genotypes have at least one NIRS measurement, potentially offering significant advantages for rice breeding programs. Authors SummaryThis study explores the interest of phenomic selection within the context of rice breeding. Unlike genomic selection, phenomic selection utilizes near-infrared spectroscopic (NIRS) technology to predict genotypes performance. The importance of this methodology lies in its capacity to reduce the costs and enhance the genetic gains of breeding programs, particularly in developing countries where genomic information is not always easily accessible (cost, availability, ease of use). Also, NIRS technology is often already available, even in resource-constrained breeding programs. By focusing the study on rice, a staple food for billions, our research aims to demonstrate the applicability of phenomic selection compared to genomic selection. By investigating the influence of various factors on phenomic prediction accuracy (training population size, incorporation of multiple environment information, consideration of genotype x environment effects in the prediction models), we are contributing to the optimization of this novel breeding method, which could potentially lead to significant improvements in agricultural productivity and food security.

plant biology↗

bioRxiv2023

Characterization of adaptation mechanisms in sorghum using a multi-reference back-cross nested association mapping design and envirotyping

The identification of haplotypes influencing traits of agronomic interest, with well-defined effects across environments, is of key importance to develop varieties adapted to their context of use. It requires advanced crossing schemes, multi-environment characterization and relevant statistical tools. Here we present a sorghum multi-reference back-cross nested association mapping (BCNAM) population composed of 3901 lines produced by crossing 24 diverse parents to three elite parents from West and Central Africa (WCA-BCNAM). The population was characterized in environments contrasting for photoperiod, rainfall, temperature, and soil fertility. To analyse this multi-parental and multi-environment design, we developed a new methodology for QTL detection and parental effect estimation. In addition, envirotyping data were mobilized to determine the influence of specific environmental covariables on the genetic effects, which allowed spatial projections of the QTL effects. We mobilized this strategy to analyse the genetic architecture of flowering time and plant height, which represent key adaptation mechanisms in environments like West Africa. Our results allowed a better characterisation of well-known genomic regions influencing flowering time concerning their response to photoperiod with Ma6 and Ma1 being photoperiod sensitive and candidate gene Elf3 being insensitive. We also accessed a better understanding of plant height genetic determinism with the combined effects of phenology dependent (Ma6) and independent (qHT7.1 and Dw3) genomic regions. Therefore, we argue that the WCA-BCNAM constitutes a key genetic resource to feed breeding programs in relevant elite parental lines and develop climate-smart varieties.

genetics↗

bioRxiv2023

Characterization of the pearl millet cultivation environments in India: status and perspectives enabled by expanded data analytics and digital tools

The cultivation of pearl millet in India is experiencing important transformations due to changes in weather, socio-economic trends, and technological progress. In this scope, we propose a new characterization of the pearl millet production environment in India using the latest available data and methodology. For that, we constructed a database incorporating data on various aspects of pearl millet cultivation at the district level from 1998 to 2017. We complemented this analysis using extensive pearl millet agri-system simulations to evaluate crop models abilities to reconstruct and analyse the system at an unprecedented scale. We also proposed a new method to infer system parameters from crop model data. Our results show important differences compared to the characterization currently used. The East part of the pearl millet tract (East Rajasthan, Haryana, Uttar Pradesh, and Madhya Pradesh) emerges as the only region where pearl millet cultivation has grown with potential surplus that is likely exported. Important reductions of pearl millet cultivated area in Gujarat, Maharashtra and Karnataka are potentially due to economy-driven transition to other more pro table crops like cotton, maize, or castor bean. The data used also point toward a constant increase of the rain during the growing season which could have major consequences on the future of this crop, with potential positive effects like extra yield but also negative like extra pressure due to more intense and erratic rainfall or transition to more pro table crops requiring more water. Despite difficulties to predict pearl millet yield in rapidly changing environments, the tested crop models reflected reasonably well the pearl millet production system, thus, setting the base for effective system design in future climatic scenarios. Our data and results have been gathered in an open-source interactive online application.

plant biology↗

bioRxiv2023

New algorithm for pearl millet modelling in APSIM allowing a mechanistic simulation of tillers

We present a new algorithm for pearl millet simulation in APSIM. Compared to the actual released model, this new model increases the ability to simulate dynamic tillers by integrating recent progresses about biological understanding of the tillering mechanism. The new algorithm also offers the possibility to have an increased genetic control over key functions like canopy development and tillering through additional genotype related parameters. Next to model description, we also present the parametrization of 9444 and HHB 67-2, two genotypes broadly used in India. Overall, we could show that the new algorithm is able to reconstruct the main plant function like biomass accumulation and tillering. Some margin of improvement remains concerning the simulation of tiller cessation.

plant biology↗

bioRxiv2020

The influence of QTL allelic diversity on QTL detection in multi-parent populations: a simulation study in sugar beet

Multi-parent populations (MPPs) are important resources for studying plant genetic architecture and detecting quantitative trait loci (QTLs). In MPPs, the QTL effects can show various levels of allelic diversity, which is an important factor influencing the detection of QTLs. In MPPs, the allelic effects can be more or less specific. They can depend on an ancestor, a parent or the combination of parents in a cross. In this paper, we evaluated the effect of QTL allelic diversity on the QTL detection power in MPPs. We simulated: a) cross-specific QTLs; b) parental and ancestral QTLs; and c) bi-allelic QTLs. Inspired by a real application, we tested different MPP designs (diallel, chessboard, factorial, and NAM) derived from five or nine parents to explore the ability to sample genetic diversity and detect QTLs. Using a fixed total population size, the QTL detection power was larger in MPPs with fewer but larger crosses derived from a reduced number of parents. The use of a larger set of parents was useful to detect rare alleles with a large phenotypic effect. The benefit of using a larger set of parents was however conditioned on an increase of the total population size. We also determined empirical confidence intervals for QTL location to compare the resolution of different designs. For QTLs representing 6% of the phenotypic variation, using 1600 offspring individuals, we found 95% empirical confidence intervals of 50 and 26 cM for cross-specific and bi-allelic QTLs, respectively. MPPs derived from less parents with few but large crosses generally increased the QTL detection power. Using a larger set of parents to cover a wider genetic diversity can be useful to detect QTLs with a reduced minor allele frequency when the QTL effect is large and when the total population size is increased.

genetics↗

bioRxiv2020

The usefulness of multi-parent multi-environment QTL analysis: an illustration in different NAM populations

Commonly QTL detection in multi-parent population (MPPs) data measured in multiple environments (ME) is done by a single environment analysis on phenotypic values averaged across environments. This method can be useful to detect QTLs with a consistent effect across environments but it does not allow to estimate environment-specific QTL (QTLxE) effects. Running separate single environment analyses is a possibility to measure QTLxE effects but those analyses do not model the genetic covariance due to the use of the same genotype in different environments. In this paper, we propose methods to analyze MPP-ME QTL experiments using simultaneously the data from several environments and modelling the genotypic covariances. Using data from the EU-NAM and the US-NAM populations, we show that these methods allow to estimate the QTLxE effects and that they give a more precise description of the trait genetic architecture than separate within environment analyses. The MPP-ME models we propose can also be extended to integrate environmental indices (e.g. temperature, precipitation, etc.) to understand better the mechanisms behind the QTLxE effects. Therefore, our methodology allows to exploit the full potential of MPP-ME data: to estimate QTL effect variations a) within the MPP between sub-populations due to different genetic backgrounds; and b) between environments.

genetics↗