Evolutionary aspects of the trade-off between seed size and number in crops

Empirical threshold values for quantitative trait mapping.

Pea (Pisum sativum L.) was the original model organism used in Mendel’s discovery (1866) of the laws of inheritance, making it the foundation of modern plant genetics. However, subsequent progress in pea genomics has lagged behind many other plant species. Although the size and repetitive nature of the pea genome has so far restricted its sequencing, comprehensive genomic and post genomic resources already exist. These include BAC libraries, several types of molecular marker sets, both transcriptome and proteome datasets and mutant populations for reverse genetics. The availability of the full genome sequences of three legume species has offered significant opportunities for genome wide comparison revealing synteny and co-linearity to pea. A combination of a candidate gene and colinearity approach has successfully led to the identification of genes underlying agronomically important traits including virus resistances and plant architecture. Some of this knowledge has already been applied to marker assisted selection (MAS) programs, increasing precision and shortening the breeding cycle. Yet, complete translation of marker discovery to pea breeding is still to be achieved. Molecular analysis of pea collections has shown that although substantial variation is present within the cultivated genepool, wild material offers the possibility to incorporate novel traits that may have been inadvertently eliminated. Association mapping analysis of diverse pea germplasm promises to identify genetic variation related to desirable agronomic traits, which are historically difficult to breed for in a traditional manner. The availability of high throughput ‘omics’ methodologies offers great promise for the development of novel, highly accurate selective breeding tools for improved pea genotypes that are sustainable under current and future climates and farming systems.

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Pea (Pisum sativum L.) in the Genomic Era

Field pea (Pisum sativum L.) and faba bean (Vicia faba L.) are cool-season grain legume species that provide rich sources of food for humans and fodder for livestock. To date, both species have been relative 'genomic orphans' due to limited availability of genetic and genomic information. A significant enrichment of genomic resources is consequently required in order to understand the genetic architecture of important agronomic traits, and to support germplasm enhancement, genetic diversity, population structure and demographic studies.

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Transcriptome sequencing of field pea and faba bean for discovery and validation of SSR genetic markers

Genetic Changes Accompanying the Domestication of Pisum sativum: Is there a Common Genetic Basis to the ‘Domestication Syndrome’ for Legumes?

driven by environmental influences (Charles-Edwards, 1982).Agenotype’sultimateperformanceisdetermined Seed yield in pea (Pisum sativum L.) is a physiologically complex by how it integrates genotype and environmental influtrait that is strongly influenced by both genotype and environment. Seed yield can be described in terms of its components, which include ences. The end result is seed yield, which has often been plant number per unit area, seed weight, and seed number. These described as the product of its components: number of yield components show interdependence and plasticity in response to plants per unit area, number of seeds per unit area environment; therefore, selection based on a single component is un- (number of pods per plant, number of seeds per pod), likely to succeed in increasing yield in breeding programs. To improve and mean seed weight (Moot and McNeil, 1995). These our understanding of the genetic basis of seed yield determination in yield components show interdependence or plasticity pea and to identify genetic loci involved, quantitative trait loci (QTL) (Wilson, 1987). For example, compensation is observed for yield per se, yield components (seed weight, seed number, and between the number of pods per plant and number of harvest index [HI]) and developmental traits (node of first flower [NFF], seeds per pod (Moot and McNeil, 1995), or between seed number of flowering nodes [NFN], total node number [TNN]) were number and seed weight (Sarawat et al., 1994). Since mapped. The QTL mapping was conducted using F2–derived families of a cross between Primo, a marrowfat cultivar, and OSU442-15, a selecting for a particular yield component may be inbluepea breedingline. Linkagemaps containing108 locion 11linkage effective as a means of increasing yield per se, alternate groups (LGs) were constructed for 227 families derived from this approaches have been proposed. Hedley and Ambrose cross. Traits were measured in three replicated field trials conducted (1985)suggestedselectingforplantsthatproduceahigh, in New Zealand during the summers of 1997–1998, 1998–1999, and stable plant HI, while Wilson (1987) suggested that se2002–2003.Thetrialsweremanagedtoensuremaximumexpressionof lecting weakly competitive plants may increase yield yield potential. The QTL affecting yield-related traits were associated stability, based on evidence that weak competitors may with 19 genomic regions on LGs I, II, III, IV, VI, and VII. The QTL be more successful within a pea crop than vigorous for different yield-related traits were colocalized, suggesting some genotypes that perform well as single plants. Bourion basis for understanding the reproductive plasticity that is observed et al. (2002) suggested that progress in seed yield and in pea at the genetic level. yield stability may be achieved using lines with different developmental and architectural features. For the genotypes that they examined, this would involve focusing P ea is grownthroughout the world for diverse uses primarily on NFF and on leaf appearance rate. as food and feed. Field pea is the world’s third most With the development of molecular linkage maps and significant grainlegume after soybean andcommonbean. statistical approaches for mapping QTL, it is possible Theaimswhendevelopingpeacultivarsincludeobtaining to identify and tag genetic loci controlling genetically high yields, disease resistance, quality attributes, and complex traits. Quantitative trait loci mapping provides adaptation to a range of environmental conditions. Re- information on the minimum number of genetic loci ducing yield variability is an important goal because pea that may be involved in determining a trait phenotype, exhibits poor stability of yield across years and environ- the genetic map locations of those loci and estimates of ments compared with other crops. the effects of the loci on trait values—information that Yield is a complex trait that, from a crop physiology ispotentially veryuseful tothe plantbreeder. Molecular perspective, is the culmination of a series of processes markers associated with QTL may also prove to be (phenological and canopy development, radiation inter- useful for marker-assisted plant breeding. In addition, ception, biomass production and partitioning) that are QTL mapping offers the opportunity to use map-based cloning approaches to identify the genes underlying the

Derek Wilson

Papers

Linkage Mapping of QTL for Seed Yield, Yield Components, and Developmental Traits in Pea