Biology subjects

Bauchet, G.

Publications and source records attributed to Bauchet, G..

3 recordsLinked to original sources

bioRxiv2022

Validation of SNP markers for marker-assisted selection of genotypes with increased carotenoid and dry matter contents in cassava

Provitamin A biofortification and increased dry matter content are important breeding targets in cassava improvement programs worldwide. Biofortified varieties contribute to the alleviation of provitamin A deficiency, a leading cause of preventable blindness in developing countries. Dry matter content is a major component of dry yield and thus underlies overall variety performance and acceptability by growers, processors, and consumers. SNP markers linked to these traits have recently been discovered through several genome-wide association studies but have not been deployed for marker-assisted selection (MAS). Assessment of marker performance in diverse genetic backgrounds is an important step towards their deployment for routine MAS. In the present study, seven previously identified markers for these traits were converted to a robust set of uniplex allele-specific PCR assays and validated in two independent pre-breeding and breeding populations. These assays were efficient in discriminating marker genotypic classes and had an average call rate greater than 98%. A high correlation was observed between the predicted and observed carotenoid content as inferred by root yellowness intensity in the breeding (r = 0.92) and pre-breeding (r = 0.95) populations. On the other hand, dry matter content-markers had moderately low predictive accuracy in both populations (r < 0.40) due to the more quantitative nature of the trait. This work confirmed marker effectiveness in multiple backgrounds, therefore, further strengthening its value in cassava biofortification to ensure nutritional security as well as dry matter content productivity. Our study provides a framework to guide future marker development, thus leading to more routine use of markers in MAS in cassava improvement programs.

genomics↗

bioRxiv2020

Making WAVES in Breedbase: An Integrated Spectral Data Storage and Analysis Pipeline for Plant Breeding Programs

Visible and near-infrared (vis-NIRS) spectroscopy is a promising tool for increasing phenotyping throughput in plant breeding programs, but existing analysis software packages are not optimized for a breeding context. Additionally, commercial software options are often outside of budget constraints for some breeding and research programs. To that end, we developed an open-source R package, waves, for the streamlined analysis of spectral data with several cross-validation schemes to assess prediction accuracy. Waves is compatible with a wide range of spectrometer models and performs visualization, filtering, aggregation, cross-validation set formation, model training, and prediction functions for the association of vis-NIRS spectra with reference measurements. Furthermore, we have integrated this package into the Breedbase family of open-source databases, expanding the analysis capabilities of this growing digital ecosystem to a number of crop species. Taken together, the standalone and Breedbase versions of waves enhance the accessibility of tools for the analysis of spectral data during the plant breeding process. Core ideasO_LIwaves is an open-source R package for spectral data analysis in plant breeding C_LIO_LIBreeding relevant cross-validation schemes to evaluate predictive accuracy of models C_LIO_LIExtension of Breedbase--an open-source database--to support spectral data storage C_LIO_LIGraphical user interface developed for implementation of waves in Breedbase C_LI

bioinformatics↗

bioRxiv2020

Genome-wide association analysis reveals new insights into the genetic architecture of defensive, agro-morphological and quality-related traits in cassava

Cassava (Manihot esculenta) is one of the most important starchy root crops in the tropics due to its adaptation to marginal environments. Genetic progress in this clonally propagated crop can be accelerated through the discovery of markers and candidate genes that could be used in cassava breeding programs. We carried out a genome-wide association study (GWAS) using a panel of 5,310 clones developed at the International Institute of Tropical Agriculture - Nigeria. The population was genotyped at more than 100,000 SNP markers via genotyping-by-sequencing (GBS). Genomic regions underlying genetic variation for 14 traits classified broadly into four categories: biotic stress (cassava mosaic disease and cassava green mite severity); quality (dry matter content and carotenoid content) and plant agronomy (harvest index and plant type). We also included several agro-morphological traits related to leaves, stems and roots with high heritability. In total, 41 significant associations were uncovered. While some of the identified loci matched with those previously reported, we present additional association signals for the traits. We provide a catalogue of favourable alleles at the most significant SNP for each trait-locus combination and candidate genes occurring within the GWAS hits. These resources provide a foundation for the development of markers that could be used in cassava breeding programs and candidate genes for functional validation.

genetics↗