Biology subjects

Piche, L.

Publications and source records attributed to Piche, L..

6 recordsLinked to original sources

bioRxiv2026

Genomic and Transcriptomic Basis of Salinity Tolerance in Dry Pea

Salinity is a major crop production constraint in dry pea (Pisum sativum L.), making the development of salt-tolerant varieties essential to improve crop productivity and land-use efficiency. The genetic mechanisms of salt tolerance in dry pea is largely unknown, and research on salt-tolerant genes is limited. In this study, we established comprehensive genomic and transcriptomic resources, along with a robust screening protocol, to dissect the genetic basis of salinity tolerance using two germplasm sets: the USDA pea diversity panel, consisting of approximately 200 globally sourced accessions, and a set of 300 modern elite lines from the NDSU Pulse Crops Breeding Program. Genetic variation for the salinity response was assessed based on ten phenotypic traits, with root dry weight, shoot dry weight, and specific root length identified as key indicators based on their heritability. Genome-wide association mapping uncovered significant genomic regions and several candidate genes linked to salt stress, with the strongest association found on chromosome 6. Overlapping QTL signals across traits suggest a shared genetic architecture underlying salinity tolerance. Field-based transcriptomic analysis further identified five putative genes involved in salinity response conserved across multiple crop species. Notably, Psat5g000800, encoding a glycosyl hydrolase gene, was markedly upregulated under salinity stress. These findings highlight the complex, multi-gene regulatory nature of salinity tolerance in dry pea and underscore the importance of functional validation of candidate genes. This study provides key insights and practical tools to support breeding efforts aimed at improving salt tolerance in dry pea.

genetics↗

bioRxiv2024

High-Throughput Phenotyping of Seed Quality Traits Using Imaging and Deep Learning in Dry Pea

Seed traits, such as seed color and seed size, directly impact seed quality, affecting the marketability and value of dry peas [1]. Assessing seed quality is integral to a plant breeding programs to ensure optimal seed standards. This research introduced a phenotyping tool to assess seed quality traits specifically tailored for pulse crops, which integrates image processing with cutting-edge deep learning models. The proposed method is designed for automation, seamlessly processing a sequence of images while minimizing human intervention. The pipeline standardized red-green-blue (RGB) images captured from a color light box and used deep learning models to segment and detect seed features. Our method extracted up to 86 distinct seed characteristics, ranging from basic size metrics to intricate texture details and color nuances. Compared to traditional methods, our pipeline demonstrated a 95 percent similarity in seed quality assessment and increased time efficiency (from 2 weeks to 30 minutes for processing time). Specifically, we observed an improvement in the accuracy of seed trait identification by simply using an RGB value instead of a categorical, non-standard description, which allowed for an increase in the range of detectable seed quality characteristics. By integrating conventional image processing techniques with foundational deep learning models, this approach emerges as a pivotal instrument in pulse breeding programs, guaranteeing the maintenance of superior seed quality standards.

plant biology↗

bioRxiv2024

Multi-trait multi-environment genomic prediction of preliminary yield trials in pulse crops

Phenotypic selection in preliminary yield trials (PYT) is challenged by limited seeds, resulting in trials with few replications and environments. The emergence of multi-trait multi-environment enabled genomic prediction (MTME-GP) offers opportunity for enhancing prediction accuracy and genetic gain across multiple traits and diverse environments. Using a set of 300 advanced breeding lines in the North Dakota State University (NDSU) pulse crop breeding program, we assessed the efficiency of a MTME-GP model for improving seed yield and protein content in field peas in stress and non-stress environments. MTME-GP significantly improved predictive ability, improving up to 2.5-fold, particularly when a significant number of genotypes overlapped across environments. Heritability of the training environments contributed significantly to the overall prediction of the model. Average predictive ability ranged from 3 to 7-folds when environments with low heritability were excluded from the training set. Overall, the Reproducing Kernel Hilbert Spaces (RKHS) model consistently resulted in improved predictive ability across all breeding scenarios considered in our study. Our results lay the groundwork for further exploration, including integration of diverse traits, incorporation of deep learning techniques, and the utilization of multi-omics data in predictive modeling. Core ideasO_LIPhenotypic selection in PYT is challenged by limited seeds, resulting to few replications and environments. C_LIO_LIMTME-GP offers opportunity for enhancing prediction accuracy of multi-trait and diverse environments in PYT. C_LIO_LIMTME-GP enhances prediction by up to 2.5-fold, especially with numerous overlapping genotypes in various tested environments. C_LIO_LIRKHS MTME-GP models, excels in low-heritability, negatively correlated traits, like drought-affected conditions. C_LI

genomics↗

bioRxiv2024

Effective Population Size in Field Pea

BackgroundEffective population size (Ne) is a pivotal parameter in population genetics as it can provide information on the rate of inbreeding and the contemporary status of genetic diversity in breeding populations. The population with smaller Ne can lead to faster inbreeding, with little potential for genetic gain making selections ineffective. The importance of Ne has become increasingly recognized in plant breeding, which can help breeders monitor and enhance the genetic variability or redesign their selection protocols. Here, we present the first Ne estimates based on linkage disequilibrium (LD) in the pea genome. ResultsWe calculated and compared Ne using SNP markers from North Dakota State University (NDSU) modern breeding lines and United States Department of Agriculture (USDA) diversity panel. The extent of LD was highly variable not only between populations but also among different regions and chromosomes of the genome. Overall, NDSU had a higher and longer-range LD than the USDA that could extend up to 500Kb, with a genome-wide average r2 of 0.57 (vs 0.34), likely due to its lower recombination rates and the selection background. The estimated Ne for the USDA was nearly three-fold higher (Ne = 174) than NDSU (Ne = 64), which can be confounded by a high degree of population structure due to the selfing nature of pea. ConclusionsOur results provided insights into the genetic diversity of the germplasm studied, which can guide plant breeders to actively monitor Ne in successive cycles of breeding to sustain viability of the breeding efforts in the long term.

genetics↗

bioRxiv2022

A greenhouse-based high-throughput phenotyping platform for identification and genetic dissection of resistance to Aphanomyces root rot in field pea

Aphanomyces root rot (ARR) is a devastating disease in field pea (Pisum sativum L.) that can cause up to 100% crop failure. Assessment of ARR resistance can be a rigorous, costly, time-demanding activity that is relatively low-throughput and prone to human errors. These limits the ability to effectively and efficiently phenotype the disease symptoms arising from ARR infection, which remains a perennial bottleneck to the successful evaluation and incorporation of disease resistance into new cultivars. In this study, we developed a greenhouse-based high throughput phenotyping (HTP) platform that moves along the rails above the greenhouse benches and captures the visual symptoms caused by Aphanomyces euteiches in field pea. We pilot tested this platform alongside with conventional visual scoring in five experimental trials under greenhouse conditions, assaying over 12,600 single plants. Precision estimated through broad-sense heritability (H2) was consistently higher for the HTP-indices (H2 Exg =0.86) than the traditional visual scores (H2 DSI=0.59), potentially increasing the power of genetic mapping. We genetically dissected variation for ARR resistance using the HTP-indices, and identified a total of 260 associated single nucleotide polymorphism (SNP) through genome-wide association (GWA) mapping. The number of associated SNP for HTP-indices was consistently higher with some SNP overlapped to the associated SNP identified using the visual scores. We identified numerous small-effect QTLs, with the most significant SNP explaining about 5 to 9% of the phenotypic variance per index, and identified previously mapped genes known to be involved in the biological pathways that trigger immunity against ARR, including Psat5g280480, Psat5g282800, Psat5g282880, and Psat2g167800. We also identified a few novel QTLs with small-effect sizes that may be worthy of validation in the future. The newly identified QTLs and underlying genes, along with genotypes with promising resistance identified in this study, can be useful for improving a long-term, durable resistance to ARR.

pathology↗

bioRxiv2022

Multi-trait genomic prediction improves selection accuracy for enhancing seed mineral concentrations in pea (Pisum sativum L.)

The superiority of multi-trait genomic selection (MT-GS) over univariate genomic selection (UNI-GS) can be improved by redesigning the phenotyping strategy. In this study, we used about 300 advanced breeding lines from North Dakota State University (NDSU) pulse breeding program and about 200 USDA accessions evaluated for ten nutritional traits to assess the efficiency of sparse testing in MT-GS. Our results showed that sparse phenotyping using MT-GS consistently outperformed UNI-GS when compared to partially balanced phenotyping using MT-GS. This strategy can be further extended to multi-environment multi-trait GS to improve prediction performance and reduce the cost of phenotyping and time-consuming data collection process. Given that MT-GS relies on borrowing information from genetically correlated traits and relatives, consideration should be given to trait combinations in the training and prediction sets to improve model parameters estimate and ultimately prediction performance. Our results point to heritability and genetic correlation between traits as possible parameters to achieve this objective.

genetics↗