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The Impact of Genetic Relationship and Linkage Disequilibrium on Genomic Selection.

Liu H, Zhou H, Wu Y, Li X, Zhao J, Zuo T, Zhang X, Zhang Y, Liu S, Shen Y, Lin H, Zhang Z, Huang K, Lübberstedt T, Pan G - PLoS ONE (2015)

Bottom Line: This study is based on experimental data of two populations derived from the same two founder lines (B73, Mo17).The result showed that the closer the real genetic relationship between training and validation population, the fewer markers were required to reach a good prediction accuracy.Meanwhile, the result indicated that accuracies based on high LD between QTL and markers were more stable over generations, thus LD information would provide more robust prediction capacity in practical applications.

View Article: PubMed Central - PubMed

Affiliation: Maize Research Institute of Sichuan Agricultural University, Chengdu, China.

ABSTRACT
Genomic selection is a promising research area due to its practical application in breeding. In this study, impact of realized genetic relationship and linkage disequilibrium (LD) on marker density and training population size required was investigated and their impact on practical application was further discussed. This study is based on experimental data of two populations derived from the same two founder lines (B73, Mo17). Two populations were genotyped with different marker sets at different density: IBM Syn4 and IBM Syn10. A high-density marker set in Syn10 was imputed into the Syn4 population with low marker density. Seven different prediction scenarios were carried out with a random regression best linear unbiased prediction (RR-BLUP) model. The result showed that the closer the real genetic relationship between training and validation population, the fewer markers were required to reach a good prediction accuracy. Taken the short-term cost for consideration, relationship information is more valuable than LD information. Meanwhile, the result indicated that accuracies based on high LD between QTL and markers were more stable over generations, thus LD information would provide more robust prediction capacity in practical applications.

No MeSH data available.


Related in: MedlinePlus

Prediction accuracies depending on marker number (NM) and training population size (NP) for different scenarios and traits.
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pone.0132379.g002: Prediction accuracies depending on marker number (NM) and training population size (NP) for different scenarios and traits.

Mentions: A nonlinear increase in prediction accuracies with increasing size of NP and NM was observed for both traits within both populations (Fig 2, S1 Table). Generally, the highest rMG values were obtained for the highest NP, and an increase in NM generally led to increased accuracies. Increase in accuracies was smooth and did not reach an obvious plateau, while increasing NP using most fixed NM of all scenarios (S1 Fig). However, the effect of increasing NM was different under fixed NP in different scenarios.


The Impact of Genetic Relationship and Linkage Disequilibrium on Genomic Selection.

Liu H, Zhou H, Wu Y, Li X, Zhao J, Zuo T, Zhang X, Zhang Y, Liu S, Shen Y, Lin H, Zhang Z, Huang K, Lübberstedt T, Pan G - PLoS ONE (2015)

Prediction accuracies depending on marker number (NM) and training population size (NP) for different scenarios and traits.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4493124&req=5

pone.0132379.g002: Prediction accuracies depending on marker number (NM) and training population size (NP) for different scenarios and traits.
Mentions: A nonlinear increase in prediction accuracies with increasing size of NP and NM was observed for both traits within both populations (Fig 2, S1 Table). Generally, the highest rMG values were obtained for the highest NP, and an increase in NM generally led to increased accuracies. Increase in accuracies was smooth and did not reach an obvious plateau, while increasing NP using most fixed NM of all scenarios (S1 Fig). However, the effect of increasing NM was different under fixed NP in different scenarios.

Bottom Line: This study is based on experimental data of two populations derived from the same two founder lines (B73, Mo17).The result showed that the closer the real genetic relationship between training and validation population, the fewer markers were required to reach a good prediction accuracy.Meanwhile, the result indicated that accuracies based on high LD between QTL and markers were more stable over generations, thus LD information would provide more robust prediction capacity in practical applications.

View Article: PubMed Central - PubMed

Affiliation: Maize Research Institute of Sichuan Agricultural University, Chengdu, China.

ABSTRACT
Genomic selection is a promising research area due to its practical application in breeding. In this study, impact of realized genetic relationship and linkage disequilibrium (LD) on marker density and training population size required was investigated and their impact on practical application was further discussed. This study is based on experimental data of two populations derived from the same two founder lines (B73, Mo17). Two populations were genotyped with different marker sets at different density: IBM Syn4 and IBM Syn10. A high-density marker set in Syn10 was imputed into the Syn4 population with low marker density. Seven different prediction scenarios were carried out with a random regression best linear unbiased prediction (RR-BLUP) model. The result showed that the closer the real genetic relationship between training and validation population, the fewer markers were required to reach a good prediction accuracy. Taken the short-term cost for consideration, relationship information is more valuable than LD information. Meanwhile, the result indicated that accuracies based on high LD between QTL and markers were more stable over generations, thus LD information would provide more robust prediction capacity in practical applications.

No MeSH data available.


Related in: MedlinePlus