Pea Grain Protein Content Across Italian Environments: Genetic Relationship With Grain Yield, and Opportunities for Genome-Enabled Selection for Protein Yield

Abstract

Wider pea (Pisum sativum L.) cultivation has great interest for European agriculture, owing to its favorable environmental impact and provision of high-protein feedstuff. This work aimed to investigate the extent of genotype × environment interaction (GEI), genetically based trade-offs and polygenic control for crude protein content and grain yield of pea targeted to Italian environments, and to assess the efficiency of genomic selection (GS) as an alternative to phenotypic selection (PS) to increase protein yield per unit area. Some 306 genotypes belonging to three connected recombinant inbred line (RIL) populations derived from paired crosses between elite cultivars were genotyped through genotyping-by-sequencing and phenotyped for grain yield and protein content on a dry matter basis in three autumn-sown environments of northern or central Italy. Line variation for mean protein content ranged from 21.7 to 26.6%. Purely genetic effects, compared with GEI effects, were over two-fold larger for protein content, and over 2-fold smaller for grain and protein yield per unit area. Grain yield and protein content exhibited no inverse genetic correlation. A genome-wide association study revealed a definite polygenic control not only for grain yield but also for protein content, with small amounts of trait variation accounted for by individual loci. On average, the GS predictive ability for individual RIL populations based on the rrBLUP model (which was selected out of four tested models) using by turns two environments for selection and one for validation was moderately high for protein content (0.53) and moderate for grain yield (0.40) and protein yield (0.41). These values were about halved for inter-environment, inter-population predictions using one RIL population for model construction to predict data of the other populations. The comparison between GS and PS for protein yield based on predicted gains per unit time and similar evaluation costs indicated an advantage of GS for model construction including the target RIL population and, in case of multi-year PS, even for model training based on data of a non-target population. In conclusion, protein content is less challenging than grain yield for phenotypic or genome-enabled improvement, and GS is promising for the simultaneous improvement of both traits.

Ensuring Global Food Security by Improving Protein Content in Major Grain Legumes Using Breeding and ‘Omics’ Tools

“…Notable instances of using GS as a substitute for phenotypic selection for complex traits include grain yield under moisture stress in chickpea [ 245 ], common bean [ 246 ], cowpea (Ravelombola et al, 2021) [ 247 ], and pea [ 248 , 249 ] and cooking time in common bean [ 250 ]. However, GS has limited application for selecting high SPC genotypes in legumes [ 251 ]. A rrBLUP model was used to predict SPC in 306 pea genotypes derived from three RILs, tested in three autumn seasons in northern and central Italy, to determine any advantage of GS over phenotypic selection for SPC [ 251 ].…”

“…However, GS has limited application for selecting high SPC genotypes in legumes [ 251 ]. A rrBLUP model was used to predict SPC in 306 pea genotypes derived from three RILs, tested in three autumn seasons in northern and central Italy, to determine any advantage of GS over phenotypic selection for SPC [ 251 ]. The mean predictive ability of GS for SPC was 0.53.…”

Optimized methods for random and targeted mutagenesis in field pea (Pisum sativum L.)

“…Several natural pea germplasm collections have been established, including the USDA and European pea germplasm collections ( Jing et al, 2012 ; Al Bari et al, 2021 ). The analysis of genetic polymorphisms in these diverse germplasm pools is currently underway ( Burstin et al, 2015 ; Pandey et al, 2021 ; Shirasawa et al, 2021 ; Crosta et al, 2022 ) to facilitate the association of key phenotypes with the identified genetic polymorphisms. However, continuous selection of only yield-related traits among crosses of genetically related pea cultivars has led to narrowing of the genetic base of the crop, particularly affecting the historically neglected protein-related traits.…”

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

“…However, due to advancement in the genotyping platform, the cost of genotyping is becoming relatively less expensive compared to the cost of phenotyping; thus, genomic selection (GS) that uses whole-genome information to predict genomic estimated breeding value (GEBV) of unobserved genotypes is gaining traction as breeders’ choice of selection method (Poland et al 2012; Zhao et al 2021; Bassi et al 2016; Santantonio et al 2020; Atanda, et al 2021a). Though GS research in pea is scanty, the available studies (Annicchiarico et al 2019; Crosta et al 2021; Bari et al 2021) show GS potential to predict the genetic merit of pea lines and germplasm accessions. Following Bari et al (2021), the North Dakota State University (NDSU) pulse breeding program is prioritizing the use of GS particularly in the preliminary yield trial (PYT or stage 1) where effectiveness of phenotypic selection is limited by phenotyping in one/two locations due to seed multiplication challenges for hundreds of lines for multi-location trials.…”