Biology subjects

Brault, C.

Publications and source records attributed to Brault, C..

6 recordsLinked to original sources

bioRxiv2025

A century of breeding has preserved genetic variation, accumulated favorable alleles, and shaped the Rht gene portfolio in North American spring wheat

Hard red spring wheat (HRSW) is an important market class of wheat in North America with a rich history of breeding. We genotyped 1,013 HRSW lines representing a century of breeding material using a SNP array and targeted KASP genotyping to assess the changes in genetic diversity and Rht genes in HRSW from North America. Results show that early breeding efforts broadened an initially narrow gene pool derived from a few founders and subsequently maintained the genetic diversity. Analysis of Rht genes revealed that Rht-D1b was the predominant semi-dwarf allele from introduction of the Green Revolution genes (Rht-B1 and Rht-D1) in 1960s until the FHB epidemics of 1990s, when most breeding programs shifted to Rht-B1b. However, adoption of Rht-B1b and Rht-D1b remains low in some regions of the Great Plains. The dwarf alleles at GA-sensitive Rht24 and Rht25 have persisted in the region at low frequencies before the introduction of Green Revolution genes and have increased in frequency in recent decades. We also analyzed the effect of Rht genes on plant height (PH) in the HRSW growing region and found that Rht-B1b or Rht-D1b results in [~]12% reduction in PH, which decreases to [~]9% in dry environments. Notably, combining dwarfing alleles at Rht24 and Rht25 reduced height by [~]6% in dry environments, suggesting an alternative approach for reducing PH. Our analysis also revealed significant genetic interactions between Rht-B1 and Rht25, as well as between Rht-D1 and Rht24, which could help fine-tune PH for different environments by employing appropriate Rht combinations. Furthermore, our results demonstrate positive selection for photoperiod sensitivity in recent decades, particularly in high-latitude regions. This study provides a valuable genomic resource, and the findings enhance our understanding of past breeding efforts while offering insights to guide future plant breeding strategies.

plant biology↗

bioRxiv2025

Genetic Gains from Sixty Years of Spring Wheat Breeding in the Northern Plains of the US

Evaluating genetic gains over time is essential for assessing the success of breeding programs and refining strategies for ongoing improvement. Hard red spring (HRS) wheat is an important wheat class in the US and is primarily grown in the Northern Great Plains. Despite a long history of breeding efforts in this region, long-term quantification of genetic gains for key traits has remained limited. This study analyzes over sixty years of data from the USDA-coordinated Hard Red Spring Wheat Uniform Regional Nursery (HRSWURN) to evaluate genetic advancements in agronomic traits across multiple phases. A significant positive genetic gain of 0.61% per annum was observed for grain yield in HRS wheat released in the Northern US region, which is lower than the expected gains needed to meet future wheat demand. The change was 0.07% for test weight, -0.04% for days to heading, and -0.16% for plant height. Notably, sustained yield improvements have not affected grain protein levels since they were first measured in 1995, indicating that ongoing selection has effectively balanced grain yield and protein despite their negative correlation (r = -0.31). Assessment of genetic gains over 20-year phases suggested slowing rates of genetic gains for grain yield but did not indicate any plateaus. The realized genetic gains were generally higher for individual breeding programs when breeding for target environments, with the public breeding program in Minnesota observing gains of approximately 1% per annum. These findings highlight the significant impact of long-term breeding efforts and offer valuable insights for refining future breeding strategies.

plant biology↗

bioRxiv2023

Enhancing grapevine breeding efficiency through genomic prediction and selection index

Grapevine (Vitis vinifera) breeding reaches a critical point. New cultivars are released every year with resistance to powdery and downy mildews. However, the traditional process remains time-consuming, taking 20 to 25 years, and demands the evaluation of new traits to enhance grapevine adaptation to climate change. Until now, the selection process has relied on phenotypic data and a limited number of molecular markers for simple genetic traits such as resistance to pathogens, without a clearly defined ideotype and was carried out on a large scale. To accelerate the breeding process and address these challenges, we investigated the use of genomic prediction, a methodology using molecular markers to predict genotypic values. In our study, we focused on two existing grapevine breeding programs: Rose wine and Cognac production. In these programs, several families were created through crosses of emblematic and inter-specific resistant varieties to powdery and downy mildews. 30 traits were evaluated for each program, using two genomic prediction methods: GBLUP (Genomic Best Linear Unbiased Predictor) and LASSO (Least Absolute Shrinkage Selection Operator). The results revealed substantial variability in predictive abilities across traits, ranging from 0 to 0.9. These discrepancies could be attributed to factors such as trait heritability and trait characteristics. Moreover, we explored the potential of across-population genomic prediction by leveraging other grapevine populations as training sets. Integrating genomic prediction allowed us to identify superior individuals for each program, using multivariate selection index method. The ideotype for each breeding program was defined collaboratively with representatives from the wine-growing sector.

genetics↗

bioRxiv2021

Interest of phenomic prediction as an alternative to genomic prediction in grapevine

Phenomic prediction has been defined as an alternative to genomic prediction by using spectra instead of molecular markers. A reflectance spectrum reflects the biochemical composition within a tissue, under genetic determinism. Thus, a relationship matrix built from spectra could potentially capture genetic signal. This new methodology has been successfully applied in several cereal species but little is known so far about its interest in perennial species. Besides, phenomic prediction has only been tested for a restricted set of traits, mainly related to yield or phenology. This study aims at applying phenomic prediction for the first time in grapevine, using spectra collected on two tissues and over two consecutive years, on two populations and for 15 traits. First, we characterized the genetic signal in spectra and under which condition it could be maximized, then phenomic predictive ability was compared to genomic predictive ability. We found that the co-inertia between spectra and genomic data was stable across tissues or years, but variable across populations, with co-inertia around 0.3 and 0.6 for diversity panel and half-diallel populations, respectively. Differences between populations were also observed for predictive ability of phenomic prediction, with an average of 0.27 for the diversity panel and 0.35 for the half-diallel. For both populations, there was a correlation across traits between predictive ability of genomic and phenomic prediction, with a slope around 1 and an intercept of -0.2, thus suggesting that phenomic prediction could be applied for any trait.

genetics↗

bioRxiv2021

Across-population genomic prediction in grapevine opens up promising prospects for breeding

Crop breeding involves two selection steps: choosing progenitors and selecting offspring within progenies. Genomic prediction, based on genome-wide marker estimation of genetic values, could facilitate these steps. However, its potential usefulness in grapevine (Vitis vinifera L.) has only been evaluated in non-breeding contexts mainly through cross-validation within a single population. We tested across-population genomic prediction in a more realistic breeding configuration, from a diversity panel to ten bi-parental crosses connected within a half-diallel mating design. Prediction quality was evaluated over 15 traits of interest (related to yield, berry composition, phenology and vigour), for both the average genetic value of each cross (cross mean) and the genetic values of individuals within each cross (individual values). Genomic prediction in these conditions was found useful: for cross mean, average per-trait predictive ability was 0.6, while per-cross predictive ability was halved on average, but reached a maximum of 0.7. Mean predictive ability for individual values within crosses was 0.26, about half the within-half-diallel value taken as a reference. For some traits and/or crosses, these across-population predictive ability values are promising for implementing genomic selection in grapevine breeding. This study also provided key insights on variables affecting predictive ability. Per-cross predictive ability was well predicted by genetic distance between parents and when this predictive ability was below 0.6, it was improved by training set optimization. For individual values, predictive ability mostly depended on trait-related variables (magnitude of the cross effect and heritability). These results will greatly help designing grapevine breeding programs assisted by genomic prediction.

genetics↗

bioRxiv2020

Harnessing multivariate, penalized regression methods for genomic prediction and QTL detection to cope with climate change affecting grapevine

Viticulture has to cope with climate change and decrease pesticide inputs, while maintaining yield and wine quality. Breeding is a potential key to meet this challenge, and genomic prediction is a promising tool to accelerate breeding programs, multivariate methods being potentially more accurate than univariate ones. Moreover, some prediction methods also provide marker selection, thus allowing quantitative trait loci (QTLs) detection and allowing the identification of positional candidate genes. We applied several methods, interval mapping as well as univariate and multivariate penalized regression, in a bi-parental grapevine progeny, in order to compare their ability to predict genotypic values and detect QTLs. We used a new denser genetic map, simulated two traits under four QTL configurations, and re-analyzed 14 traits measured in semi-controlled conditions under different watering conditions. Using simulations, we recommend the penalized regression method Elastic Net (EN) as a default for genomic prediction, and controlling the marginal False Discovery Rate on EN selected markers to prioritize the QTLs. Indeed, penalized methods were more powerful than interval mapping for QTL detection across various genetic architectures. Multivariate prediction did not perform better than its univariate counterpart, despite strong genetic correlation between traits. Using experimental data, penalized regression methods proved as very efficient for intra-population prediction whatever the genetic architecture of the trait, with accuracies reaching 0.68. These methods applied on the denser map found new QTLs controlling traits linked to drought tolerance and provided relevant candidate genes. These methods can be applied to other traits and species.

genetics↗