Pea (Pisum sativum L.) is one of the oldest domesticated legume crops (Zohary and Hopf, 1973). The global pea production in 2020 was ~14.7 million tons, of which Canada produced ~4.6 million tons (FAOSTAT, 2023). The pea crop is valued for its rich content of seed protein, fiber, vitamins, and minerals (Shanthakumar et al., 2022). Pea protein has a well-balanced amino acid profile with high content of essential amino acids lysine and threonine, high digestibility, and low allergenicity (Lu et al., 2020). However, pea seeds are low in sulfur-containing amino acids methionine and cysteine (Stone et al., 2015). The physicochemical properties of pea protein combined with its availability, affordability, and sustainable production practices make it an attractive ingredient in various food and feed applications (Shanthakumar et al., 2022; Wu et al., 2023). The use of pea protein in food products has gained immense popularity in recent years, especially among consumers looking for plant-based protein sources. Improving functionality of plant proteins will increase their usefulness as food ingredients (Akharume et al., 2021). According to a report by MarketsandMarkets Blog (2022), the global pea protein market value was estimated at USD 1.7 billion in 2022 and is projected to reach USD 2.9 billion by 2027. Increasing the SPC of grain legumes also contributes to the increasing demand for protein-rich human diets and to minimizing greenhouse gas emissions (Jha et al., 2022).

Western Canada is a major producer of peas accounting for nearly 30% of global pea production in 2020 (FAOSTAT, 2023). Pea protein processing is a growing industry in this region and provides a means of utilizing the pea crop for additional markets beyond human and animal consumption. The industry growth in this region is driven by the abundance of pea production in western Canada, increasing demand for plant-based protein ingredients, and sustainable agriculture, and adds value to the western Canadian pea crops. Peas are a low-input crop, requiring less fertilizer and pesticides than most other crops, and are known to improve soil health by fixing nitrogen for subsequent crops (Pelzer et al., 2012). This aligns with the goals of sustainable agriculture, which seeks to minimize environmental impact while maximizing production efficiency.

The average seed protein concentration (SPC) of pea is 20%–25% on a dry weight basis (Shanthakumar et al., 2022). The major constituents of seed protein in pea are albumin (10%–20%), globulin (65%–80%), prolamin, and glutelin. Pea cultivars with higher SPC are valuable for processing companies to produce a higher yield of protein per unit of raw material. The estimated commercial value of pea seeds, with high protein content based on the average retail price of pea protein isolate in the range of 25–30 USD/kg of isolate, has led to an increased focus on plant breeding programs that aim to develop pea cultivars with higher SPC. In the last decade, the pea breeding program at the Crop Development Centre (CDC) at the University of Saskatchewan has developed cultivars such as CDC Amarillo (Warkentin et al., 2014a), CDC Limerick (Warkentin et al., 2014b), and CDC Inca (Warkentin et al., 2018) with improved SPC of up to 25%. It is well known in field peas that SPC is negatively correlated with grain yield (GY) (Tar’an et al., 2004). Although the SPC and yield have been improved in pea through different breeding strategies, the underlying molecular mechanisms controlling these complex traits are relatively unknown.

QTLs associated with SPC in pea will enable marker-assisted selection (MAS) to accelerate development of pea cultivars with improved protein content. A few studies have reported QTLs for SPC in pea. Krajewski et al. (2012) reported two QTLs with LOD values of 5.6 and 5.2 located on linkage group (LG) 5. Klein et al. (2020) used nine inter-connected pea RIL populations and identified 21 QTLs for SPC explaining the phenotypic variance from 4% to 22%. Meta-analysis of these QTLs identified six meta-QTLs for SPC in two to four environments. Gali et al. (2019) conducted a genome-wide association study (GWAS) and identified one locus on LG3 and two loci on LG5 associated with SPC. All of these studies indicated that SPC in pea is a complex quantitative trait. Most of these QTLs captured only small to moderate variability for SPC, and combined with variability across environments and negative correlation with yield, the SPC QTLs have not been used effectively in pea breeding programs. Seed protein QTLs have been identified in many crop species, including soybean, maize, wheat, and rice (Wang et al., 2021; Saini et al., 2022). In soybean, for example, QTLs associated with SPC have been mapped to specific chromosomes and were used to develop soybean varieties with higher SPC (Prenger et al., 2019). Similarly, in wheat, QTLs associated with gluten protein content have been identified (Li et al., 2023), which can be used to develop wheat varieties with improved bread-making properties.

Linkage analysis is a useful approach to dissect the genetic basis of complex traits in crop plants. With an objective of identifying and comparing the QTLs of SPC, diverse recombinant inbred line (RIL) populations were derived from crosses made between high protein and medium protein lines. Previously, we evaluated a bi-parental RIL population PR-25 under field trials in Saskatchewan, Canada from 2019 to 2021. PR-25 is a RIL population derived from the cross of two elite cultivars, CDC Amarillo and CDC Limerick. Three QTLs for SPC were reported from this population (Zhou et al., 2022). The genetic architecture of complex traits such as SPC is known to differ between the mapping populations based on their genetic background (Park et al., 2023). In the current study, we attempted to identify SPC QTLs in RIL populations PR-30 and PR-31 derived from different high SPC parents that differed significantly in their agronomic performance. The overall goal was to provide information that breeders can use for MAS to efficiently improve the nutritional quality of the pea crop.

In the current study, we attempted to understand the genetic basis of SPC in pea using diverse RIL populations using crosses made between high and moderate SPC cultivars. Advances in genomics and the availability of genome sequences have supported the identification of QTLs and candidate genes associated with many complex traits including SPC in grain legumes (Jha et al., 2022). The genetic basis of SPC in many different crop plants is known to be governed by multiple major and minor genes. For example, 241 QTLs associated with SPC have been reported in soybean (soybase.org, accessed 17 November 2023). The complex interaction between these different genes and the environment affects the heritability of SPC. In pea, SPC was demonstrated to have low to moderate heritability (Jermyn, 1977) and was largely influenced by environmental factors such as soil moisture (Tao et al., 2017) and temperature during flowering and pod developmental stages (Karjalainen and Kortet, 1987). The effect of genetic variation and environment and their interaction on the protein content of pea are well known (Daba and Morris, 2021). In the current study, we identified highly significant effects of genetic variation and environment on SPC and GY in two diverse RIL populations. Thus, it is difficult to completely rely on conventional breeding for selection of low heritability traits such as SPC. Like many other crops, in pea as well, a negative correlation between SPC and GY has been reported (Jermyn, 1977; Tar’an et al., 2004). Simultaneously, significant cultivar × environment effects on SPC in pea is also known (Mohammed et al., 2018). We observed a negative correlation between SPC and GY in PR-30 and PR-31 populations, which adds additional challenges for breeding yield and SPC simultaneously. Thus, MAS is desirable to select for high SPC among high yielding lines in a breeding program. The current study was useful to identify the potential targets for MAS of SPC in pea, and also facilitates the exploration and introgression of advantageous natural genetic variability for SPC, which ranges up to ~31% in pea core germplasm (Coyne et al., 2005).

Like many published studies (Daba and Morris, 2021), we observed that the G × E interaction for SPC and GY in PR-30 and PR-31 RIL populations was significant. The correlation between SPC and yield in PR-30 and PR-31 was negative, which is consistent with several previous studies in pea (Jermyn, 1977) and other legume crops (e.g., Obala et al., 2020). The G × E interaction on SPC at the molecular level has been reported in soybean. Hooker et al. (2023) studied the differential gene expression in soybean genotypes with varying levels of SPC grown in different environments and identified that seed protein-related genes, mainly asparaginase and asparagine synthetase, were influenced by the environment.

In the current study, major and minor QTLs associated with SPC, distinguished by their LOD scores, were identified in PR-30 and PR-31. These QTLs are positioned on different linkage groups. Based on sequence-based comparisons of their positions on the reference pea genome sequence (Kreplak et al., 2019), none of these eight QTLs were co-localized. These QTLs were also compared with the three QTLs earlier identified in PR-25 (Zhou et al., 2022), which was also a RIL population derived from a cross between a high SPC and moderate SPC cultivar. The peak of PC-QTL-3 in the PR-25 population overlapped with SPC-Ps-5.1 in the PR-31 population based on the position of flanking markers on the pea reference genome, which indicates that SPC-Ps-5.1 is valuable for MAS of SPC. Overall, the diversity of SPC QTLs in mapping populations derived from different cultivars further indicates the complex genetic basis of this trait. The eight QTLs reported are contributed by four moderate or high SPC pea accessions and adds to the list of potential QTLs for MAS of SPC.

Several SPC-associated QTLs have been reported in pea in earlier studies. Gali et al. (2018) identified SPC QTLs in two related RIL populations, PR-02 (Orb × CDC Striker) and PR-07 (Carrera × CDC Striker). Two QTLs positioned on LG1b and LG4a were identified in the PR-02 population. The flanking marker of the QTL on LG4a, Chr4LG4_28114041 (PsC16121p109), is within the range of SPC-PS-4.1 identified in the PR-30 population. The QTL identified on LG3 and LG7 in the PR-07 population did not match those identified in PR-30 and PR-31. Several SPC QTLs were also detected in other studies involving PR-31 evaluated in French environments (Bourgeois et al., 2011; Klein et al., 2020). These QTLs showed co-locations with SPC-Ps-3.1, 5.1, 6.1, and 7.2.

In a GWAS conducted based on representatives of pea accessions from global pea breeding programs, Gali et al. (2019) identified significant marker–trait associations for SPC. The important markers identified, Chr5LG3_145264443, Chr3LG5_138253621, and Chr3LG5_194530376, did not co-localize with the SPC QTL identified in this study. It must be noted that PR-30 and PR-31 were derived from accessions known for high SPC and are ideal populations for QTL mapping of SPC. Klein et al. (2020) identified several SPC meta-QTLs across the linkage groups. The LOD values of QTLs identified in the current study and the percent phenotypic variance explained by these QTLs are higher than known QTLs, and thus are potential candidates for MAS of SPC.

Eight QTLs associated with GY in PR-30 and PR-31 mapping populations were also identified in this study. These QTLs explained a significant phenotypic variance of GY ranging from 5.0% to 15.4% and are positioned on five chromosomes. The genomic positions of the SPC-associated QTL SPC-PS-5.1 and the yield-associated QTL GY-Ps-5.3 in the PR-31 population were co-localized. These QTLs also shared their peak positions and differed by the alleles contributed by the parents in this region. The contribution of the same QTL for either SPC or GY with positive or negative additive effect provides a further validation of the negative correlation between SPC and GY. In addition, co-localization of SPC QTLs with those of PH, DTM, and SSC, with opposite additive effects for SPC and other traits, indicates that simultaneous selection of SPC and other characteristics needs a careful consideration of the trade-offs in breeding for high SPC and high yielding cultivars. It is of notable consideration that four of the five SPC QTLs identified in PR-31 are co-localized with SSC QTLs with opposite additive effects, which is in synchronization with the negative correlation between SSC and SSC. The co-localization of QTLs for SPC and other traits indicate that these traits are controlled by either closely linked genes or the same genes with pleiotropic effects. Obala et al. (2020) made similar observations in pigeonpea that co-localized QTLs of SPC and other yield traits varied in their additive effect values from positive to negative or vice versa. Klein et al. (2020) identified co-localized QTLs for SPC and TSW in pea. The QTLs SPC-Ps-5.1 and TSW-Ps-5.2 identified in this study co-localized and the additive effect of both these QTLs was a positive value. The summary of previous and current findings on co-localized QTLs varying in their additive effects substantiate the need for fine mapping of SPC QTLs to breed for SPC in a high-yielding and/or a good agronomic background. We have developed three new mapping populations derived from crosses between CDC Lewochko (Warkentin et al., 2022) and the high SPC parents of PR-25, PR-30, and PR-31, which are CDC Limerick, P0540-91, and Cameor, respectively. Identification of QTL associated with SPC in these new mapping populations is in progress to validate the current QTLs in a common, high yielding genetic background.

The SPC QTLs identified in this study identified the complex genetic architecture of SPC in two different RIL populations. These QTLs, in addition to MAS towards breeding for high SPC, can also provide insight into the genetic basis of SPC in pea at the gene level, helping to elucidate the molecular mechanisms underlying this important trait. Such information through fine mapping of these QTLs facilitates future research on seed protein biosynthesis and develops new approaches to improve the nutritional quality of plant-based protein sources. Overall, the identification of SPC QTLs in PR-30 and PR-31 contributes to improve the nutritional quality of the pea crop and, in that way, contributes to the development of more sustainable and environmentally friendly sources of plant-based protein.

In conclusion, the SPC QTLs identified in this study were contributed for by four pea accessions with high or moderate SPC. These QTLs are potentially important for improving the seed nutritional quality of pea through MAS in breeding programs. The co-localization of two QTLs cautions the careful deployment of MAS for simultaneous selection of high SPC and high yield. Three QTLs SPC-Ps-4.2, SPC-Ps-5.1, and SPC-Ps-7.2 contributed by P0540-91, Ballet, and Cameor, respectively, can be used by plant breeders to select the corresponding alleles and develop crop varieties with higher protein content.

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. Any additional raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

KG: Conceptualization, Formal analysis, Methodology, Writing – original draft. AJ: Methodology, Writing – review & editing. BT: Conceptualization, Funding acquisition, Resources, Writing – review & editing. JB: Methodology, Writing – review & editing. GAu: Methodology, Writing – review & editing. DB: Methodology, Writing – review & editing. GAr: Methodology, Writing – review & editing. TW: Conceptualization, Funding acquisition, Resources, Supervision, Writing – review & editing.