Molecular Breeding - Genetics, Genomics and Breeding of Crop Plants - page 240

populations. As mentioned in previous sections, association mapping
approaches are currently being explored in sunflower.
7.5.2 Assay Optimization
After the development of molecular markers and the validation of their
selection efficiency, it is often necessary to optimize the marker assay. This
is driven by the need to reduce laboratory costs and turn around times,
whilst increasing throughput and minimizing errors. Technologies, which
speed up the implementation process, reduce laboratory requirements or
errors, and lower the costs associated with scaling-up, are therefore crucial
to the success of MAS. In fact, one of the main priorities [included in the
“White paper: Priorities for research, education and extension in genomics,
genetics and breeding of the Compositae” (The Compositae Genome project,
http://compgenomics.ucdavis.edu / 2007 )] for translating sunflower genomics
into practical breeding programs was the reduction in total marker costs.
Several advances in sunflower marker technologies have been made in
recent years. For SSR markers, PCR multiplexes for a genome-wide framework
of SSR marker loci developed by Tang et al. (2003) increased genotyping
throughput and reduced reagent costs, which is ideal for genotyping
applications that always use the same set of markers. In addition, multi-
color assays, SSR primer design to facilitate “pooled amplicon multiplexing”
by length in SSR development, and SSR analyses in semi-automated, high-
throughput genotyping systems (Tang et al. 2002; Yu et al. 2002) have all
resulted in time-saving and reduced costs in routine microsatellite analysis.
Currently, different techniques for SNP detection are being used in sunflower
to type SNPs in a high-throughput, time-saving and cost-effective fashion.
These include denaturing high-performance liquid chromatography
(dHPLC) (Lai et al. 2005), single-base extension (SBE) and allele-specific
primer extension assays (ASPE) for flow cytometric platforms (Knapp et al.
2007), and high-resolution melting curve analyses (MCA) for real-time PCR
platforms (Tang and Knapp 2008).
Improved QTL detection methods that reduce genotyping have also
been proposed. Micic et al. (2005b) used selective genotyping (Lebowitz et
al. 1987) for detecting QTL for Sclerotinia resistance in sunflower. This method
exploits the fact that most of the information for QTL effects is in the “tails”
of the quantitative trait distribution, allowing the reduction of population
sizes to those individuals found in these “tails”. It was concluded that
selective genotyping could be efficiently used for major QTL detection and
analysis of congruency for resistance genes across populations, but the
limited sample size and the non-random sampling may lead to biased
estimations of the QTL effects.