Genetic Dissection of Sympatric Populations of Brown Planthopper, Nilaparvata lugens (Stål), Using DALP-PCR Molecular Markers. (19th April 2012)

• Record Type: Journal Article
• Authors: Latif, M. A.; Rafii, M. Y.; Mazid, M. S.; Ali, M. E.; Ahmed, F.; Omar, M. Y.; Tan, S. G.
• Other Names: Mastana S. Academic Editor.; Tanisaka T. Academic Editor.; Yu Y. Academic Editor.
• Is Part Of: TheScientificWorldjournal. Volume 2012(2012)
• Publication Date: 2012-04-19
• Subjects: Science -- Periodicals; Technology -- Periodicals; Medicine -- Periodicals; 505
• DOI: 10.1100/2012/586831
• Languages: English
• ISSNs: 2356-6140

Abstract

Abstract : Direct amplified length polymorphism (DALP) combines the advantages of a high-resolution fingerprint method and also characterizing the genetic polymorphisms. This molecular method was also found to be useful in brown planthopper, Nilaparvata lugens species complex for the analysis of genetic polymorphisms. A total of 11 populations of Nilaparvata spp. were collected from 6 locations from Malaysia. Two sympatric populations of brown planthopper, N. lugens, one from rice and the other from a weed grass ( Leersia hexandra ), were collected from each of five locations. N. bakeri was used as an out group. Three oligonucleotide primer pairs, DALP231/DALPR′5, DALP234/DALPR′5, and DALP235/DALPR′5 were applied in this study. The unweighted pair group method with arithmetic mean (UPGMA) dendrogram based on genetic distances for the 11 populations of Nilaparvata spp. revealed that populations belonging to the same species and the same host type clustered together irrespective of their geographical localities of capture. The populations of N. lugens formed into two distinct clusters, one was insects with high esterase activities usually captured from rice and the other was with low esterase activities usually captured from L. hexandra. N. bakeri, an out group, was the most isolated group. Analyses of principal components, molecular variance, and robustness also supported greatly to the findings of cluster analysis.