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Construction and analysis of high-density linkage map using high-throughput sequencing data.

Liu D, Ma C, Hong W, Huang L, Liu M, Liu H, Zeng H, Deng D, Xin H, Song J, Xu C, Sun X, Hou X, Wang X, Zheng H - PLoS ONE (2014)

Bottom Line: HighMap employs an iterative ordering and error correction strategy based on a k-nearest neighbor algorithm and a Monte Carlo multipoint maximum likelihood algorithm.The singleton rate was less than one-ninth of that generated by JoinMap4.1.It will facilitate genome assembling, comparative genomic analysis, and QTL studies.

View Article: PubMed Central - PubMed

Affiliation: Biomarker Technologies Corporation, Beijing, China.

ABSTRACT
Linkage maps enable the study of important biological questions. The construction of high-density linkage maps appears more feasible since the advent of next-generation sequencing (NGS), which eases SNP discovery and high-throughput genotyping of large population. However, the marker number explosion and genotyping errors from NGS data challenge the computational efficiency and linkage map quality of linkage study methods. Here we report the HighMap method for constructing high-density linkage maps from NGS data. HighMap employs an iterative ordering and error correction strategy based on a k-nearest neighbor algorithm and a Monte Carlo multipoint maximum likelihood algorithm. Simulation study shows HighMap can create a linkage map with three times as many markers as ordering-only methods while offering more accurate marker orders and stable genetic distances. Using HighMap, we constructed a common carp linkage map with 10,004 markers. The singleton rate was less than one-ninth of that generated by JoinMap4.1. Its total map distance was 5,908 cM, consistent with reports on low-density maps. HighMap is an efficient method for constructing high-density, high-quality linkage maps from high-throughput population NGS data. It will facilitate genome assembling, comparative genomic analysis, and QTL studies. HighMap is available at http://highmap.biomarker.com.cn/.

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Related in: MedlinePlus

NGS data utilization enhancement by HighMap.The X-axis represents marker numbers. The Y-axis represents Spearman rank correlation coefficient between estimated map marker order and true marker location for A, B and C, singleton rates for D, E and F, estimated genetic map distances for G, H and I, respectively.
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pone-0098855-g002: NGS data utilization enhancement by HighMap.The X-axis represents marker numbers. The Y-axis represents Spearman rank correlation coefficient between estimated map marker order and true marker location for A, B and C, singleton rates for D, E and F, estimated genetic map distances for G, H and I, respectively.

Mentions: Comparative analysis revealed that HighMap permitted the utilization of more markers than JoinMap4.1 (Figure 2A, 2B and 2C). HighMap could make use of 700 markers and create linkage maps with a Spearman rank order correlation coefficient greater than 0.9. In contrast, 300 markers led to the correlation coefficient smaller than 0.8 when the linkage map was constructed using JoinMap4.1. Based on a cutoff value of 0.8 [24], we estimated that HighMap could construct a linkage map with three times the number of markers as JoinMap4.1 could.


Construction and analysis of high-density linkage map using high-throughput sequencing data.

Liu D, Ma C, Hong W, Huang L, Liu M, Liu H, Zeng H, Deng D, Xin H, Song J, Xu C, Sun X, Hou X, Wang X, Zheng H - PLoS ONE (2014)

NGS data utilization enhancement by HighMap.The X-axis represents marker numbers. The Y-axis represents Spearman rank correlation coefficient between estimated map marker order and true marker location for A, B and C, singleton rates for D, E and F, estimated genetic map distances for G, H and I, respectively.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4048240&req=5

pone-0098855-g002: NGS data utilization enhancement by HighMap.The X-axis represents marker numbers. The Y-axis represents Spearman rank correlation coefficient between estimated map marker order and true marker location for A, B and C, singleton rates for D, E and F, estimated genetic map distances for G, H and I, respectively.
Mentions: Comparative analysis revealed that HighMap permitted the utilization of more markers than JoinMap4.1 (Figure 2A, 2B and 2C). HighMap could make use of 700 markers and create linkage maps with a Spearman rank order correlation coefficient greater than 0.9. In contrast, 300 markers led to the correlation coefficient smaller than 0.8 when the linkage map was constructed using JoinMap4.1. Based on a cutoff value of 0.8 [24], we estimated that HighMap could construct a linkage map with three times the number of markers as JoinMap4.1 could.

Bottom Line: HighMap employs an iterative ordering and error correction strategy based on a k-nearest neighbor algorithm and a Monte Carlo multipoint maximum likelihood algorithm.The singleton rate was less than one-ninth of that generated by JoinMap4.1.It will facilitate genome assembling, comparative genomic analysis, and QTL studies.

View Article: PubMed Central - PubMed

Affiliation: Biomarker Technologies Corporation, Beijing, China.

ABSTRACT
Linkage maps enable the study of important biological questions. The construction of high-density linkage maps appears more feasible since the advent of next-generation sequencing (NGS), which eases SNP discovery and high-throughput genotyping of large population. However, the marker number explosion and genotyping errors from NGS data challenge the computational efficiency and linkage map quality of linkage study methods. Here we report the HighMap method for constructing high-density linkage maps from NGS data. HighMap employs an iterative ordering and error correction strategy based on a k-nearest neighbor algorithm and a Monte Carlo multipoint maximum likelihood algorithm. Simulation study shows HighMap can create a linkage map with three times as many markers as ordering-only methods while offering more accurate marker orders and stable genetic distances. Using HighMap, we constructed a common carp linkage map with 10,004 markers. The singleton rate was less than one-ninth of that generated by JoinMap4.1. Its total map distance was 5,908 cM, consistent with reports on low-density maps. HighMap is an efficient method for constructing high-density, high-quality linkage maps from high-throughput population NGS data. It will facilitate genome assembling, comparative genomic analysis, and QTL studies. HighMap is available at http://highmap.biomarker.com.cn/.

Show MeSH
Related in: MedlinePlus