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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

LD decay measured in different populations.
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pone.0132379.g001: LD decay measured in different populations.

Mentions: To understand the pattern of LD decay, estimates of pair-wise LD were averaged in increments of 100 kb distance between markers (Fig 1). The average physical distance between adjacent markers varied in different marker sets (Table 1), and average LD between adjacent markers (ALAM) in different marker sets was estimated according to the average distance. Results were listed in Table 3. The ALAM of full marker sets (NM = 6611) was 0.78, 0.87, and 0.81 in Syn10, Syn4, and mixed population (Table 3), respectively. A high degree of LD was observed even for extended distances between markers. For example, for the marker set of NM = 800 for Syn10, the average distance between adjacent markers was 2.57 Mb (which equates to 14 cM), but the corresponding LD was 0.32. For marker set of NM = 400 for Syn4, the average distance between adjacent markers was 5.13 Mb (equal to 15.6 cM), but the corresponding LD was 0.27. As expected, the LD in Syn4 was higher than in Syn10 and in the mixed population. The average realized genetic relationship (kinship) between any progeny pair was 0.63 and 0.44 for Syn4 and Syn10, respectively.


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)

LD decay measured in different populations.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0132379.g001: LD decay measured in different populations.
Mentions: To understand the pattern of LD decay, estimates of pair-wise LD were averaged in increments of 100 kb distance between markers (Fig 1). The average physical distance between adjacent markers varied in different marker sets (Table 1), and average LD between adjacent markers (ALAM) in different marker sets was estimated according to the average distance. Results were listed in Table 3. The ALAM of full marker sets (NM = 6611) was 0.78, 0.87, and 0.81 in Syn10, Syn4, and mixed population (Table 3), respectively. A high degree of LD was observed even for extended distances between markers. For example, for the marker set of NM = 800 for Syn10, the average distance between adjacent markers was 2.57 Mb (which equates to 14 cM), but the corresponding LD was 0.32. For marker set of NM = 400 for Syn4, the average distance between adjacent markers was 5.13 Mb (equal to 15.6 cM), but the corresponding LD was 0.27. As expected, the LD in Syn4 was higher than in Syn10 and in the mixed population. The average realized genetic relationship (kinship) between any progeny pair was 0.63 and 0.44 for Syn4 and Syn10, respectively.

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