Soyagen: Improving yield and disease resistance in short-season soybean

Principal Investigator: Francois Belzile

Research Institution: Universite Laval

Timeline: September 2015 – December 2019

Objectives:

• Develop a novel platform for rapid and cost-effective genotyping in soybean.
• Develop new selection tools to enable breeders to more rapidly select lines with improved yield and disease resistance in early maturing soybeans.
• Develop diagnostic tools to rapidly and precisely screen for and identify races of Phytophthora root rot (PRR; Phytophthora sojae) and pathotypes of soybean cyst nematode (SCN; Heterodera glycines).
• Build a data-informed outreach strategy targeted toward accelerated producer adoption of soybean in Canada.

Impacts:

• The introduction of short-season soybeans in areas where they are not grown previously to diversify crop rotations.
• The development of on-farm diagnostic tools to allow a farmer to test for the presence of some diseases / pests.
• The improvements made to soybean varieties could allow for increased economic return by allowing farmers to maximize realized yield through a more optimal selection of the best-suited varieties.

Scientific Summary:

Soybean is a rapidly growing crop that offers many potential benefits to Canadian growers. It is a multipurpose crop whose seeds are an extremely valuable source of both protein and oil. From an environmental point of view, it is also highly attractive, as it does not need any chemical fertilizer to provide it with nitrogen. There are three important challenges for developing high yielding soybean varieties that are well-suited to Canadian conditions: short growing season, varieties resistant to pests and diseases, and farmer adoption. Genomics offers essential new tools to aid in these important challenges.

The spectacular progress in sequencing technologies has made it possible to characterize the genetic makeup of crops. By probing deep into the genetic code of soybeans, it is possible to identify DNA markers that control key aspects of plant growth, such as the time needed to reach maturity and resistance to diseases and pests. Once we have identified such DNA markers, breeders will be able to use them to develop improved varieties more rapidly and easily. Economic and social research will complement the genomics research by focusing on institutions and policies that will maximize the growth of the soybean industry. We assembled an exceptional team of research scientists to take on this task and have gained wide support from growers and key players in the seed industry.

Results

The Genomics and Bioinformatics Platforms at Université Laval are both offering genotyping services based on the tools and protocols derived from our work. The latest innovations, the use of a “skinny” reference genome to speed up single nucleotide polymorphism (SNP) calling from genotyping by sequencing (GBS) data and a new and improved version of SNP-calling pipeline (Fast-GBS v2.0) have been implemented. These innovations result in a marked reduction in the cost of genotyping large numbers of individuals.

The selection model developed in Activity 2 allowed us to test if it could help breeders make better choices when selecting soybean lines to use as parents when initiating new crosses. We found that practically none of the crosses our model had predicted to be below average produced superior advanced breeding lines or new varieties, while practically all superior progeny were derived from crosses that had been predicted to be promising. In more practical terms, this means that this predictive approach could allow breeders to cut by half the number of crosses made while still achieving the same output in terms of improved lines. The publication describing this work (Jean et al. 2021) was recognized as one of the “top papers” in Crop Science in 2022.

In this same activity, the expression quantitative trait loci (eQTL) work has brought us additional candidate maturity genes for which markers will be developed. These will be added to the growing list of trait-specific markers that are routinely used to enhance breeders’ ability to deliver earlier lines with improved disease resistance.

The efficacy and accuracy of the diagnostic tool for identifying pathotypes of Phytophthora sojae has been further confirmed by testing an additional 128 isolates and observing greater than 95% accuracy compared to the hydroponic test. Its usefulness is being expanded by characterizing two additional avirulence (Avr) genes.

The analysis of 127 new isolates from growers’ fields in both Manitoba and Quebec has helped anchor our mapping efforts in a way that complements the original collection of isolates collected mainly from Ontario. This has thus provided us with key data on the distribution of P. sojae pathotypes in the three largest soybean-growing provinces in the country. These results will help breeders target the most useful Rps (resistance to Phytophthora sojae) genes to introgress and guide growers in the choice of cultivars with the necessary Rps gene to protect their crop.

The work done on the research funding systems has been viewed as very timely and is also contributing to key policy discussions on crop royalty models.

External Funding Partners:

Agriculture and Agri-Food Canada

Canadian Field Crop Research Alliance

CEROM

Fédération des producteurs de grandes cultures du Québec

Genome Canada

Génome Québec

Industrial Partners (Sevita Genetics, La Coop Fédérée, Semences Prograin and Syngenta)

Manitoba Pulse & Soybean Growers

Ministère du Développement économique de l’Innovation et de l’Exportation du Québec

Saskatchewan Pulse Growers

University of Guelph

Université Laval

Western Grains Research Foundation

Project Related Publications:

Arsenault-Labrecque G., Sonah H., Lebreton A., Labbé C., Marchand G., Xue A., Belzile F., Knaus B.J., Grünwald N.J., and Bélanger R.R. 2018. Stable predictive markers of Phytophthora sojae avirulence genes that impair infection of soybean uncovered by whole genome sequencing of 31 isolates. BMC Biology. 16(1): 80.

Audette C., Bélanger R., Mimee B. 2020. Co-infection of soybean plants with Phytophthora sojae and soybean cyst nematode does not alter the efficacy of resistance genes. Plant Pathology. 69: 1437-1444.

Belzile F., Abed A., Torkamaneh D. 2019. Time for a paradigm shift in the use of plant genetic resources. Genome. 63(3).

Boucher St-Amour V.T., Mimee B., Torkamaneh D., Jean M., Belzile F., O’Donoughue L. 2020. Characterizing Resistance to soybean cyst nematode in PI 494182, an early-maturing soybean accession. Crop Science. 18(7): 1492-1494.

Boudhrioua C., Bastien M., Torkamaneh D., Belzile F. 2020. Genome-wide association mapping of Sclerotinia sclerotiorum resistance in soybean using whole-genome resequencing data. BMC Plant Biology. 20(195).

Bruce R., Torkamaneh D., Grainger C., Belzile F., Eskandari M., Rajcan I. 2019. Genome-wide genetic diversity is maintained through decades of soybean breeding. Theoretical and Applied Genetics. 132(11): 3089-3100.

Bruce R., Torkamaneh D., Grainger C., Belzile F., Eskandari M., and Rajcan I. 2020. Haplotype diversity underlying quantitative traits in Canadian soybean breeding germplasm. Theoretical and Applied Genetics. 133(2020): 1967-1976.

Copley T., Duceppe M., O’Donoughue L. 2018. Identification of novel loci associated with maturity and yield traits in early maturity soybean plant introduction lines. BMC Genomics. 19: 167.

Dussault-Benoit C., Arsenault-Labrecque G., Sonah H., Belzile F., Bélanger R. 2020. Discriminant haplotypes of avirulence genes of Phytophthora sojae lead to a molecular assay to predict phenotypes. Molecular plant pathology. 71(21): 6844-6855.

Gendron St-Marseille A., Lord E., Véronneau P., Brodeur J., and Mimee B. 2018. Genome scans reveal homogenization and local adaptations in populations of the soybean cyst nematode. Frontiers in Plant Science. 9 : 987.

Lebreton A., Labbe C., De Ronne M., Xue A., Marchand G., Bélanger R. 2018. Development of a simple hydroponic assay to study vertical and horizontal resistance of soybean and pathotypes of Phytophthora sojae. Plant Disease. 102(1): 114-123.

Lemay M.A., Torkamaneh D., Rigaill G., Boyle B., Stec A.O., Stupar R.M. and Belzile F. 2019. Screening populations for copy number variation using genotyping-by-sequencing: a proof of concept using soybean fast neutron mutants. BMC Genomics. 20(634).

Masonbrink R., Maier T.R., Muppirala U., Seetharam A.S., Lord E., Juvale P.S., Schmutz J., Johnson N.T., Korkin D., Mitchum M.G., Mimee B., Eves-van den Akker S., Hudson M., Severin A.J., and Baum T.J. 2019. The genome of the soybean cyst nematode (Heterodera glycines) reveals complex patterns of duplications involved in the evolution of parasitism genes. BMC Genomics. 20: 119.

Rasoolizadeh A., Labbé C., Sonah H., Ddeshmukh R., Belzile F., Menzies J.G., Bélanger R.R. 2018. Silicon protects soybean plants against Phytophthora sojae by interfering with effector-receptor expression. BMC Plant Biology. 18(1): 97.

Rasoolizadeh A., Santhanam P., Labbé C., Shivaraj S.M., Germain H., and Bélanger R. 2020. Silicon influences the localization and expression of Phytophthora sojae effectors in interaction with soybean. Journal of Experimental Botany. 71(21): 6844-6855.

Ronne M.D., Labbé C., Lebreton A., Sonah H., Deshmukh R., Jean M., Belzile F., O’Donoughue L., and Belanger R. 2019. Integrated QTL mapping, gene expression and nucleotide variation analyses to investigate complex quantitative traits: a case study with the soybean–Phytophthora sojae interaction. Plant Biotechnology Journal. 21(3) : 318–329.

Samanfar B., Cober E., Charette M., Tan L.H., Bekele W.A., Morrison M.J., Kilian A., Belzile F., and Molnar S.J. 2019. Genetic analysis of high protein content in ‘AC Proteus’ related soybean populations using SSR, SNP, DArT and DArTseq markers. Scientific Reports. 23;9(1): 19657.

Tardivel A., Torkamaneh D., Lemay M.A., Belzile F., O’Donoughue L.S. 2019. A Systematic gene-centric approach to define haplotypes and identify alleles on the basis of dense single nucleotide polymorphism datasets. The Plant Genome. 12(3).

Thiboutot Sainte-Croix D., Gendron St-Marseille A.F., Lord E., Bélanger R., Brodeur J., and Mimee B. 2020. Genomic profiling of virulence in the soybean cyst nematode, Heterodera glycines, using single-nematode sequencing. Phytopathology. 111(1).

Torkamaneh D., Boyle B., Belzile F. 2018. Efficient genome-wide genotyping strategies and data integration in crop plants. Theoretical and Applied Genetics. 131(3): 499-511.

Torkamaneh D., Boyle B., St-Cyr J, Légaré G, Pomerleau S, Belzile F. 2020. NanoGBS: a miniaturized procedure for GBS library preparation. Frontiers in Genetics. 11-2020.

Torkamaneh D., Laroche J., Belzile F. 2020. Fast-GBS v2.0: An analysis toolkit for genotyping-by-sequencing data. Genome. 63(11).

Torkamaneh D., Laroche J., Boyle B., Belzile F. 2019. Depth Finder: A tool to determine the optimal read depth for reduced-representation sequencing. Bioinformatics. 20: 634.

Torkamaneh D., Laroche J., Rajcan I., Belzile F. 2018. Identification of candidate domestication-related genes with a systematic survey of loss-of-function mutations. Plant Journal. 96(6): 1218-1227.

Torkamaneh D., Laroche J., Rajcan I., Belzile F. 2019. SRG Extractor: A skinny reference genome approach for reduced-representation sequencing. Bioinformatics. 35 (17): 3160-3162.

Torkamaneh D., Laroche J., Valliyodan B., O’Donoughue L., Cober E., Rajcan I., Abdelnoor R.V., Sreedasyam A., Schmutz J., Nguyen H.T., Belzile F. 2020. Soybean (Glycine max) haplotype map (GmHapMap): A universal resource for soybean translational and functional genomics. Plant Biotechnology Journal. 19(2): 324-334.

Tremblay V., McLaren D.L., Yong Min K., Strelkov S.E., Conner R.L., Wally O., and Bélanger R. 2021. Molecular assessment of pathotype diversity of Phytophthora sojae in Canada highlights declining sources of resistance in soybean. Plant Disease. 105(12): 4006-4013.