Phenotypic Associative Microsatellite (SSR) Marker Assisted Selection

L.J. Wright, D.B Cooper, BARI Ft. Collins CO; P.M. Hayes, I. Matus, K. Richardson, and J. Von Zitzewits, OSU, Corvallis, OR

Introduction

The breeding and selecting process for developing a new malting barley variety is an arduous process that may take up to 12 years. It takes that long to determine if an experimental barley line has the agronomic potential to be accepted by growers and the malting and brewing quality to be accepted by industry. A successful variety is a unique combination of genes (a genotype) that is able to perform in a range of production conditions (environments). Multi-environment testing will always be required to confirm that a phenotype will indeed have the desired agronomic and quality attributes (genotype). However, in current practice a significant amount of effort is devoted to testing selections that simply do not have the genetic potential to be varieties. Tools for determining the genotype of the experimental line increases the efficiency of the breeding process by increasing the likelihood that the experimental lines that are brought forward will have the desired quality to become a new malting variety. The challenge is to identify efficient genotype characterization tools and to determine what, and where, the genes are that determine agronomics and quality. Simple Sequence Repeats (SSRs) provide fairly comprehensive genomic coverage, they are amenable to automation, they have locus identity, and they are multi-allelic. Many agronomic and quality traits show quantitative inheritance, and the genes determining these traits have been quantified using Quantitative Trait Locus (QTL) tools. Our objective is to integrate allelic diversity and breeding value information on breeding lines, as revealed by SSRs and extensive phenotyping, with QTLs reported in a range of mapping populations. We are interested in determining if associations between specific alleles and QTLs could be identified, because these associations could then be used for designing crosses and for marker-assisted selection.

Material and Methods

In 2000 BARI and Oregon State University initiated a joint project to address this question. A sample of 196 six-rowed and two-rowed BARI experimental barley lines were genotyped with 72 SSR markers using either an ABI377 or ABI3700 Automated Sequencer. SSR markers were chosen to provide maximum genome coverage, and most are in the public domain.

The experimental barley lines were rated on a relative 1 9 scale (low to high measurement) for several agronomic and quality traits. Rankings were based on multi-environment agronomic data including heading, plant height, lodging, maturity, disease resistance and DON content. Quality data included grain protein, soluble protein, extract, diastatic power, alpha amylase, and beta glucan.

Genotype:phenotype associations were established by sorting the data by rating score for each trait. The allele sizes of lines with high and low scores were then determined for each SSR. Frequency differences greater than 20% between the two groups were considered significant. These trait-associated SSR markers were then compared with the QTL summary information posted at: http://www.barleyworld.org/NABGMP/qtllsum42401.htm. An example of how this process was done is shown in Table 1.

Results

Putative associations were identified between SSR markers and phenotypic traits for both six- and two-rowed malting barley. This presentation will focus on malting quality. Fifty associations were identified between SSR markers and phenotypic traits for the six-rowed barley lines (Table 2) and seventy-eight associations were found for the two-rowed barley lines (Table 3).

Of the thirty-four associations for malting quality traits for the six-rowed barley lines there were twenty-nine agreements with the known QTLs for malting quality. For the two-rowed barley lines, seventy-eight associations were identified with quality traits and fifty-seven were correlated with the known malting QTLs (Table 4). DON associated SSR markers and other associated traits also presented for six-rowed material (Table 5).

Conclusion

There was remarkably good agreement between trait-associated SSRs in the BARI germplasm and QTL detection in mapping populations. This result provides evidence that the QTL detection in mapping populations represent the effects of genes at which there is also allelic diversity in agronomically-relevant germplasm arrays. There was greater allelic diversity in the two-rowed that in the six-row germplasm, and accordingly more associations were identified. There were more trait:phenotype associations than published QTLs for the same trait, suggesting the presence of heretofore-undetected loci. Additional research will be necessary to validate these new QTL. We believe that information on allelic diversity at SSR loci may be useful for cross design and line selection in a malting barley variety development program. Further studies involving additional germplasm arrays, selection experiments, and formal association analysis are planned.

Table 1. Two-Row SSR Markers - Diastatic Power
Table 2. Six-Rowed Trait-Associated SSR Markers. PCR product size associated with low and high trait value.
Table 3. Two-Rowed Trait Associated SSR Markers. PCR product size associated with low and high trait value.
Table 4. Agreement between Associated SSR Markers and the Corresonding QTLs.
Table 5. Six-Rowed Trait-Associated SSR Markers PCR product size associated with low and high trait value.