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marker-assisted selection in wheat - ictsd

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Chapter 17 – Marker-<strong>assisted</strong> <strong>selection</strong> <strong>in</strong> fish and shellfish breed<strong>in</strong>g schemes 331IntroductionThe ma<strong>in</strong> goals of fish and shellfish breed<strong>in</strong>gprogrammes are to <strong>in</strong>crease the profitabilityand susta<strong>in</strong>ability of productionenterprises, while ma<strong>in</strong>ta<strong>in</strong><strong>in</strong>g genetic variability<strong>in</strong> the cultured stock. Traditionally,selective breed<strong>in</strong>g has targeted traits suchas body weight that can be easily improvedus<strong>in</strong>g mass <strong>selection</strong>. Relatively few studieshave analysed other traits that are economicallyimportant. However, disease resistanceand carcass quality are traits that are difficultto measure on candidates for <strong>selection</strong>,but have major effects on the productionefficiency and profitability of many species<strong>in</strong> aquaculture.When develop<strong>in</strong>g efficient breed<strong>in</strong>gprogrammes, pedigree <strong>in</strong>formation isrequired to maximize effective populationsizes and to use <strong>in</strong>formation from relativesto <strong>in</strong>crease the accuracy of predict<strong>in</strong>gbreed<strong>in</strong>g values for all traits <strong>in</strong>cluded <strong>in</strong>the breed<strong>in</strong>g objective. In most commercialapplications, pedigree <strong>in</strong>formation islack<strong>in</strong>g; therefore, <strong>marker</strong>s can be used to<strong>in</strong>fer relatedness between <strong>in</strong>dividuals, withor without parental <strong>in</strong>formation. Severalissues need to be considered on a case-bycasebasis when apply<strong>in</strong>g such molecular<strong>in</strong>formation for <strong>in</strong>creas<strong>in</strong>g the profitabilityof breed<strong>in</strong>g programmes <strong>in</strong> practice.For traits that are difficult to measureon candidates for <strong>selection</strong>, prediction ofbreed<strong>in</strong>g value has to rely on measurementson relatives. Under these circumstances,the accuracy of predicted breed<strong>in</strong>g values(and thus, response) is lower than whenrecords are obta<strong>in</strong>ed directly on candidatesfor <strong>selection</strong>. In addition, there is an<strong>in</strong>creased probability of co-select<strong>in</strong>g relatives.It is especially for these traits thatmolecular <strong>marker</strong>s that directly affect orare l<strong>in</strong>ked to quantitative trait loci (QTL)have been regarded as useful for <strong>marker</strong><strong>assisted</strong><strong>selection</strong> (MAS) or gene-<strong>assisted</strong><strong>selection</strong> (GAS) programmes.This chapter beg<strong>in</strong>s by discuss<strong>in</strong>g thestatus of “conventional” breed<strong>in</strong>g programmes,the challenges <strong>in</strong>volved whenstart<strong>in</strong>g such programmes for new speciesand the possibilities of <strong>in</strong>corporat<strong>in</strong>g<strong>marker</strong> <strong>in</strong>formation <strong>in</strong> “conventional” programmes.An outl<strong>in</strong>e is then provided ofthe molecular <strong>marker</strong>s developed for aquaculturespecies and of their use for geneticanalysis. The ma<strong>in</strong> features of designs forQTL mapp<strong>in</strong>g, <strong>in</strong>clud<strong>in</strong>g the use of chromosomalmanipulations, are described,followed by a discussion of the prospectsand challenges of GAS or MAS for diseaseor carcass traits. F<strong>in</strong>ally, new genomic toolsare considered briefly.Breed<strong>in</strong>g programmes andresponse to <strong>selection</strong>Management of modern breed<strong>in</strong>g programmes<strong>in</strong> aquaculture requires us<strong>in</strong>gpedigree <strong>in</strong>formation to carry out soundand efficient statistical evaluations (us<strong>in</strong>gbest l<strong>in</strong>ear unbiased prediction [BLUP]methodology). This approach enablesbreeders to maximize genetic ga<strong>in</strong> whilelimit<strong>in</strong>g rates of <strong>in</strong>breed<strong>in</strong>g to acceptablelevels (Meuwissen, 1997; Toro and Mäki-Tanila, 1999).Most of the genetic improvement <strong>in</strong>fish and shellfish species to date has beenmade through the use of traditional selectivebreed<strong>in</strong>g (reviewed by Hulata, 2001).Well-designed breed<strong>in</strong>g programmes haveshown substantial response to <strong>selection</strong>for body weight, e.g. Atlantic salmon, 10–14 percent (Gjøen and Bentsen, 1997). Inra<strong>in</strong>bow trout, rates of genetic ga<strong>in</strong> variedfrom 8 percent for <strong>in</strong>direct <strong>selection</strong> forbody weight at sea (Kause et al., 2005)to 13 percent for direct <strong>selection</strong> (Gjerde,1986). The response to <strong>selection</strong> was about

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