Agriculture Reference
In-Depth Information
A tension has developed, or rather continued perhaps, since the days of
molecular versus traditional breeding, between
pure
geneticists and
plant breeders. Of course, most scientists involved in crop
improvement are a hybrid of breeders and geneticists since genetics
is the scienti
pure
of breeding. Tension develops in the
investment devoted to genetics compared with the investment in breed-
ing, with breeders sometimes feeling like they are left out of the genetics
funding and that the high cost of markers reduces their own budgets. On
the other hand, geneticists see a lot of resources put into breeding that,
without guidance from geneticists, can be wasteful in that genetic
advance is limited in breeding programs that do not experiment beyond
the boundaries of typical crosses or do not avail themselves of modern
technology.
Involvement of actual plant breeding populations in marker analysis,
such as those from multiple cross pedigree or recurrent selection pro-
grams, is one way to use breeding material more directly in genetic
studies. One example of this type of breeding
c basis for the
art
genetic fusion includes
marker-assisted recurrent selection (MARS), which shows the promise
of this technique. In addition, a wide network of scientists in multiple
crops have used advanced backcross (AB-QTL) approaches for genetic
study and introgression of exotic germplasm into commercial back-
grounds inspired by the initial work in tomato and rice that kicked
off this approach. Recently, however, breeders, especially those
involved in animal breeding, combined with geneticists and statisticians
have started an approach that uses other types of breeding populations
and breeding line genotypes and develops models for whole-genome
selection that are improved upon each successive generation, season,
and phenotyping exercise based on whole-genome haplotypes rather
than individual gene evaluation (Meuwissen et al. 2001). This method is
called GS.
The potential of GS to revolutionize MAS is driven mostly by the
availability of higher-throughput marker systems and statistical modeling
(Habier et al. 2007). Marker technologies of interest include affymetrix
SNP platforms with over 10,000markers, whole-genome resequencing, or
variants of this such as genotyping by sequencing to evaluate populations
of interest. Statistical advances are based on the implementation of
accurate genome-assisted breeding value s (GEBVs) for each individual
in a population (Meuwissen et al. 2001). EstimatingGEBVs typically relies
on regression-based approaches that basically associate markers with
phenotypic traits in a similar manner as in AM studies.
The
-
first use of GS was in livestock breeding, as would be expected
given the high value, long life span, and multiple progeny of each animal
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