Developmental Genes Have Pleiotropic Effects on Plant Morphology and Source Capacity, Eventually Impacting on Seed Protein Content and Productivity in Pea

Abstract

Increasing pea (Pisum sativum) seed nutritional value and particularly seed protein content, while maintaining yield, is an important challenge for further development of this crop. Seed protein content and yield are complex and unstable traits, integrating all the processes occurring during the plant life cycle. During filling, seeds are the main sink to which assimilates are preferentially allocated at the expense of vegetative organs. Nitrogen seed demand is satisfied partly by nitrogen acquired by the roots, but also by nitrogen remobilized from vegetative organs. In this study, we evaluated the respective roles of nitrogen source capacity and sink strength in the genetic variability of seed protein content and yield. We showed in eight genotypes of diverse origins that both the maximal rate of nitrogen accumulation in the seeds and nitrogen source capacity varied among genotypes. Then, to identify the genetic factors responsible for seed protein content and yield variation, we searched for quantitative trait loci (QTL) for seed traits and for indicators of sink strength and source nitrogen capacity. We detected 261 QTL across five environments for all traits measured. Most QTL for seed and plant traits mapped in clusters, raising the possibility of common underlying processes and candidate genes. In most environments, the genes Le and Afila, which control internode length and the switch between leaflets and tendrils, respectively, determined plant nitrogen status. Depending on the environment, these genes were linked to QTL of seed protein content and yield, suggesting that source-sink adjustments depend on growing conditions. The last two decades have seen an exponential increase in the number of plant sequences in databases and the explosion of investigations on the molecular functions and physiological roles of these genes. At the same time, new concepts, such as quantitative trait loci (QTL) mapping followed by the development of statistical tools, have emerged in quantitative genetics to identify the genes involved in the genetic variability of complex traits (Lander and Botstein, 1989). The functions of thousands of genes have been identified mainly through knockout mutant analysis (Østergaard and Yanovsky, 2004), but also through QTL identification (for review, see Paran and Zamir, 2003). These tools can now be used to address the question of phenotypic plasticity-which genes control plant functioning in which environments-and to provide some clues about which forces shaped natural variation and the strategies that should be used to breed more adapted cultivars (Paran and Zamir, 2003;Reymond et al., 2003;Koornneef et al., 2004;Mitchell-Olds and Schmitt, 2006). In this respect, Tonsor et al. (2005) proposed to analyze natural genetic variation in the model species Arabidopsis (Arabidopsis thaliana) to identify the role of genes having subtle, partially redundant, and/or environment-dependent effects on phenotypes, and to better understand gene interactions and pleiotropy. The study of natural genetic ...

Unraveling the Complex Trait of Crop Yield With Quantitative Trait Loci Mapping in Brassica napus

“…In other words, QTL for yield may result from pleiotropic QTL: genomic regions that affect multiple traits by containing multiple, tightly linked, trait-specific genes or genes that affect multiple traits (Hall et al 2006). Most published fine-mapped QTL and the genes identified as affecting yield exhibit pleiotropic effects on at least one trait (Peng et al 1999;Brown 2002;Yuan et al 2002;Li et al 2004;Ashikari et al 2005;Waller et al 2005;Clark et al 2006;Tian et al 2006a;Cockram et al 2007) or multiple yieldassociated traits (Quarrie et al 2006;Xie et al 2006Xie et al , 2008Burstin et al 2007;Song et al 2007;Shomura et al 2008;Xue et al 2008). Even a short region with a confidence interval of 0.4 cM and a physical distance of 37.4 kb has revealed pleiotropic effects between seven QTL of different yield-associated traits and the QTL of grain yield in rice (Xie et al 2008).…”

“…Yield-related traits (such as biomass, harvest index, plant architecture, adaptation, resistance to biotic and abiotic constraints) may also indirectly affect yield by affecting the yield-component traits or by other, unknown mechanisms. Increasing evidence suggests that ''fine-mapped'' quantitative trait loci (QTL) or genes identified as affecting crop yield involve diverse pathways, such as seed number (Ashikari et al 2005;Tian et al 2006b;Burstin et al 2007;Xie et al 2008;Xing et al 2008;Xue et al 2008), seed weight (Ishimaru 2003;Song et al 2005;Shomura et al 2008;Wang et al 2008;Xie et al 2006Xie et al , 2008Xing et al 2008;Xue et al 2008), flowering time (Cockram et al 2007;Song et al 2007;Xie et al 2008;Xue et al 2008), plant height (Salamini 2003;Ashikari et al 2005;Xie et al 2008;Xue et al 2008), branching (Clark et al 2006;Burstin et al 2007;Xing et al 2008), biomass yield (Quarrie et al 2006;Burstin et al 2007), resistance and tolerance to biotic and abiotic stresses (Khush 2001;Brown 2002;Yuan et al 2002;Waller et al 2005;Zhang 2007;Warrington et al 2008), and root architecture (Hochholdinger et al 2008).…”

Association of SNP markers with agronomic and quality traits of field pea in Italy

“…Single nucleotide polymorphisms (SNP) have now become the preferred markers due to their abundance and uniform distribution throughout genomes (Gupta et al 2008), as confirmed by molecular linkage maps produced by Aubert et al (2006), Deulvot et al (2010), Duarte et al (2014) and Sindhu et al (2014). However, investigations on the linkage of SNP markers with pea production, phenology or grain quality traits are relatively few (Timmerman-Vaughan et al 2005;Burstin et al 2007;Klein et al 2014;Cheng et al 2015;Jha et al 2015), and the QTL ability to explain sufficient phenotypic variation for use in MAS is controversial. One reason for that is the complex and polygenic control of most of these traits, which results in only moderate cumulative QTL effect (Tar'an et al 2004;Burstin et al 2007;Klein et al 2014).…”

“…However, investigations on the linkage of SNP markers with pea production, phenology or grain quality traits are relatively few (Timmerman-Vaughan et al 2005;Burstin et al 2007;Klein et al 2014;Cheng et al 2015;Jha et al 2015), and the QTL ability to explain sufficient phenotypic variation for use in MAS is controversial. One reason for that is the complex and polygenic control of most of these traits, which results in only moderate cumulative QTL effect (Tar'an et al 2004;Burstin et al 2007;Klein et al 2014).…”

QTLs for oil yield components in an elite oil palm (Elaeis guineensis) cross

“…In rice (Oryza sativa), three yield-related traits [spikelet fertility, 1000 grain weight (TGW), and number of spikelets per panicle (SPP)] were identified in one yield-enhancing QTL cluster, to the tune of 4.6 g grain per plant (Liu et al 2013). In pea, Burstin et al (2007) reported that most of the QTLs for seed traits mapped into a cluster possibly corresponding to genes controlling vegetative and flowering development as well as having pleiotropic effects on plant morphology, i.e., the source capacity contributing to final seed protein content and seed yield. In wheat (Triticum aestivum), the dwarfism gene Rht-B1 is associated with numerous QTLs, including those related to root development and grain yield (Laperche et al 2006).…”