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In contrast to MAS and its focus on a few significant markers, GS examines together all markers in a population. Since the initial proposal of GS<ref name="de_Koning_2018" /> for application in breeding populations, it has been emerging as a solution to the deficiencies of MAS.<ref name="Heffner_2019" />
In contrast to MAS and its focus on a few significant markers, GS examines together all markers in a population. Since the initial proposal of GS<ref name="de_Koning_2018" /> for application in breeding populations, it has been emerging as a solution to the deficiencies of MAS.<ref name="Heffner_2019" />


The MAS has presented two main limitations in breeding applications. First, the bi-parental mapping populations are used for most [[Quantitative trait locus|QTL analyses]], limiting their accuracy.<ref>{{cite journal | vauthors = Dekkers JC, Hospital F | title = The use of molecular genetics in the improvement of agricultural populations | journal = Nature Reviews. Genetics | volume = 3 | issue = 1 | pages = 22–32 | date = January 2002 | pmid = 11823788 | doi = 10.1038/nrg701 | s2cid = 32216266 }}</ref><ref>{{cite journal | vauthors = Schön CC, Utz HF, Groh S, Truberg B, Openshaw S, Melchinger AE | title = Quantitative trait locus mapping based on resampling in a vast maize testcross experiment and its relevance to quantitative genetics for complex traits | journal = Genetics | volume = 167 | issue = 1 | pages = 485–498 | date = May 2004 | pmid = 15166171 | doi = 10.1534/genetics.167.1.485 | pmc = 1470842 }}</ref> This represents a problem because a single bi-parental population cannot represent allelic diversity and genetic background effects in a breeding population.
The MAS has presented two main limitations in breeding applications. First, the bi-parental mapping populations are used for most [[Quantitative trait locus|QTL analyses]], limiting their accuracy.<ref>{{cite journal | vauthors = Dekkers JC, Hospital F | title = The use of molecular genetics in the improvement of agricultural populations | journal = Nature Reviews. Genetics | volume = 3 | issue = 1 | pages = 22–32 | date = January 2002 | pmid = 11823788 | doi = 10.1038/nrg701 | s2cid = 32216266 }}</ref><ref name="Schon">
{{Unbulleted list citebundle
|{{*}} {{cite journal | vauthors = Schön CC, Utz HF, Groh S, Truberg B, Openshaw S, Melchinger AE | title = Quantitative trait locus mapping based on resampling in a vast maize testcross experiment and its relevance to quantitative genetics for complex traits | journal = Genetics | volume = 167 | issue = 1 | pages = 485–498 | date = May 2004 | pmid = 15166171 | doi = 10.1534/genetics.167.1.485 | pmc = 1470842 }}
|{{*}} {{ Cite journal
| year = 2004
| publisher = [[Faculty Opinions Ltd]]
| s2cid = 224103363
| doi = 10.3410/f.1020376.234444
| title = Faculty Opinions recommendation of Quantitative trait locus mapping based on resampling in a vast maize testcross experiment and its relevance to quantitative genetics for complex traits
| last = Maloof
| first = Julin
}}
|{{*}} {{ Cite journal
| year = 2010
| issue = 2
| volume = 9
| publisher = [[Oxford University Press]] (OUP)
| issn = 2041-2649
| journal = [[Briefings in Functional Genomics]]
| last3 = Iwata
| last2 = Lorenz
| last1 = Jannink
| first3 = H.
| first2 = A. J.
| first1 = Jean-Luc
| pages = 166–177
| s2cid = 3379276
| pmid = 20156985
| doi = 10.1093/bfgp/elq001
| title = Genomic selection in plant breeding: from theory to practice
}}
|{{*}} {{ Cite book
| year = 2017
| publication-place = [[Cham, Switzerland]]
| publisher = [[Springer International Publishing AG]]
| edition = 1
| last3 = Sorrells
| last2 = Roorkiwal
| last1 = Varshney
| first3 = Mark E.
| first2 = Manish
| first1 = R. K.
| pages = xii+258
| id = {{ ISBN | 978-3-319-63168-4 }}. {{ ISBN | 978-3-319-87489-0 }}
| doi = 10.1007/978-3-319-63170-7
| isbn = 978-3-319-63170-7
| s2cid = 6537864
| oclc = 1015215250
| title = Genomic Selection for Crop Improvement : New Molecular Breeding Strategies for Crop Improvement
}}{{ RP | page = 16 }}
}}
</ref> This represents a problem because a single bi-parental population cannot represent allelic diversity and genetic background effects in a breeding population.


Furthermore, [[Complex traits|polygenic traits]] (or complex traits) controlled by several small-effects markers have been an incredible hassle for MAS. The statistical methods applied for identifying target markers and implementing MAS for improvement of polygenic traits have been unsuccessful.<ref name="Heffner_2019" />
Furthermore, [[Complex traits|polygenic traits]] (or complex traits) controlled by several small-effects markers have been an incredible hassle for MAS. The statistical methods applied for identifying target markers and implementing MAS for improvement of polygenic traits have been unsuccessful.<ref name="Heffner_2019" />

Revision as of 19:08, 30 October 2022

Genomic Selection (GS) predicts the breeding values of an offspring in a population by associating their traits (i.e. resistance to pests) with their high-density genetic marker scores.[1] GS is a method proposed to address deficiencies of marker-assisted selection (MAS) in breeding programs. However, GS is a form of MAS that differs from it by estimating, at the same time, all genetic markers, haplotypes or marker effects along the entire genome to calculate the values of genomic estimated breeding values (GEBV).[1] The potentiality of GS is to explain the genetic diversity of a breeding program through a high coverage of genome-wide markers and to assess the effects of those markers to predict breeding values.[2]

MAS limitations

In contrast to MAS and its focus on a few significant markers, GS examines together all markers in a population. Since the initial proposal of GS[1] for application in breeding populations, it has been emerging as a solution to the deficiencies of MAS.[2]

The MAS has presented two main limitations in breeding applications. First, the bi-parental mapping populations are used for most QTL analyses, limiting their accuracy.[3][4] This represents a problem because a single bi-parental population cannot represent allelic diversity and genetic background effects in a breeding population.

Furthermore, polygenic traits (or complex traits) controlled by several small-effects markers have been an incredible hassle for MAS. The statistical methods applied for identifying target markers and implementing MAS for improvement of polygenic traits have been unsuccessful.[2]

References

  1. ^ a b c de Koning DJ (May 2016). "Meuwissen et al. on Genomic Selection". Genetics. 203 (1): 5–7. doi:10.1534/genetics.116.189795. PMC 4858795. PMID 27183561.
  2. ^ a b c Heffner EL, Sorrells ME, Jannink JL (January 2009). "Genomic Selection for Crop Improvement". Crop Science. 49 (1): 1–12. doi:10.2135/cropsci2008.08.0512.{{cite journal}}: CS1 maint: date and year (link)
  3. ^ Dekkers JC, Hospital F (January 2002). "The use of molecular genetics in the improvement of agricultural populations". Nature Reviews. Genetics. 3 (1): 22–32. doi:10.1038/nrg701. PMID 11823788. S2CID 32216266.
  4. ^
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