|Wu, Jixiang - MISS STATE UNIVERSITY|
|Zhu, Jun - ZHEJIANG UNIVERSITY|
|Watson, Clarence - MISS STATE UNIVERSITY|
Submitted to: Euphytica
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: January 16, 2003
Publication Date: August 1, 2003
Citation: Wu, J., Jenkins, J.N., Zhu, J., McCarty, J.C., Watson, C.E. 2003. Monte carlo simulations on marker grouping and ordering. Theoretical Applied Genetics. 107:568-573. Interpretive Summary: Double haploid populations of plants are very useful for genetic and plant breeding studies. The manner in which genes are located on chromosomes (grouping) and the order of gene arrangement are important in understanding marker relationships with quantitative trait loci for important plant traits. We used several computer simulation algorithms to compare gene grouping and gene ordering patterns among multiple linkage groups, population sizes, linkage cut off criterion, marker spacing patterns, and marker spacing distances. Simulation results indicated that grouping power was related to population size, marker spacing distance, and cut off criterion. Generally larger populations could provide higher grouping power than small populations, and closely linked markers could provide higher grouping power than loosely linked markers. When all the simulations of various scenarios were considered a cut off criterion between 50 and 60 cM achieved acceptable grouping and ordering power for population sizes as small as 100 and increasing population size to 200 did not markedly increase grouping or ordering power.
Technical Abstract: Four global algorithms, maximum likelihood (ML), sum of adjacent LOD score (SALOD), sum of adjacent recombinant fractions (SARF) and product of adjacent recombinant fraction (PARF), and one approximation algorithm, seriation (SER), were used to compare the marker ordering efficiencies for correctly given linkage groups based on doubled haploid (DH) populations. The Monte Carlo simulation results indicated the marker ordering powers for the five methods were almost identical. High correlation coefficients of greater than 0.99 between grouping power and ordering power indicated that all these methods for marker ordering were reliable. Therefore, the main problem for linkage analysis was how to improve the grouping power. Since the SER approach provided the advantages of fast speed without losing ordering power, this approach was used for more detailed simulations. For more generality, multiple linkage groups were employed, and population size, linkage cut off criterion, marker spacing pattern (even or uneven), and marker spacing distance (close or loose) were considered for obtaining acceptable grouping powers. Simulation results indicated that the grouping power was related to population size, marker spacing distance, and cut off criterion. Generally, a large population size could provide higher grouping power than small population size, and closely linked markers could provide higher grouping power than loosely linked markers. Cut off criterion range for achieving acceptable grouping power and ordering power differed for varying cases; however, combining all situations in this study, cut off criterion ranging from 50-60 cM was recommended for achieving acceptable grouping power and ordering power for different cases.