ARS | Publication request: Comparisons of Four Approximation Algorithms for Large-Scale Linkage Map Construction

Submitted to: Theoretical and Applied Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: May 8, 2011
Publication Date: June 1, 2011
Citation: Wu, J., Jenkins, J.N., McCarty Jr., J.C., Lou, X. 2011. Comparisons of four approximation algorithms for large-scale linkage map construction. Theoretical and Applied Genetics. 123:649-655.

Interpretive Summary: Many plant breeders are using linkage maps of DNA markers in their breeding program. We evaluated four published methods of developing linkage maps and compared their effectiveness and efficiencies. The methods compared were insertion, seriation, neighbor mapping, and unidirectional growth. Using simulation modeling for populations of various sizes and marker linkage distances, we showed that the insertion method outperformed or was comparable to the other three methods. When the four methods were applied to a real data set, the results showed that the accuracy and speed obtained by the insertion algorithm was superior to the other methods. This suggests that the insertion method is appropriate to use when constructing large-scale linkage maps.

Technical Abstract: Efficient construction of large-scale linkage maps is highly desired in current gene mapping projects. To evaluate the performance of available approaches in the literature, four published methods, the insertion, seriation (SER), neighbor mapping (NM), and unidirectional growth (UG) were compared on the basis of simulated F2 data with various population sizes, interferences, and missing genotype rates. Simulation results showed that the insertion method outperformed, or at least was comparable to, the other three methods. These algorithms were also applied to a real data set and results showed that the linkage order obtained by the insertion algorithm was superior to the other methods. Thus, this study suggests that the insertion method should be used when constructing large-scale linkage maps.