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      Mapping a Quantitative Trait Locus (QTL) conferring pyrethroid resistance in the African malaria vector Anopheles funestus

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          Pyrethroid resistance in Anopheles funestus populations has led to an increase in malaria transmission in southern Africa. Resistance has been attributed to elevated activities of cytochrome P450s but the molecular basis underlying this metabolic resistance is unknown. Microsatellite and SNP markers were used to construct a linkage map and to detect a quantitative trait locus (QTL) associated with pyrethroid resistance in the FUMOZ-R strain of An. funestus from Mozambique.


          By genotyping 349 F 2 individuals from 11 independent families, a single major QTL, rp1, at the telomeric end of chromosome 2R was identified. The rp1 QTL appears to present a major effect since it accounts for more than 60% of the variance in susceptibility to permethrin. This QTL has a strong additive genetic effect with respect to susceptibility. Candidate genes associated with pyrethroid resistance in other species were physically mapped to An. funestus polytene chromosomes. This showed that rp1 is genetically linked to a cluster of CYP6 cytochrome P450 genes located on division 9 of chromosome 2R and confirmed earlier reports that pyrethroid resistance in this strain is not associated with target site mutations ( knockdown resistance).


          We hypothesize that one or more of these CYP6 P450s clustered on chromosome 2R confers pyrethroid resistance in the FUMOZ-R strain of An. funestus.

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          Most cited references 39

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          Mapping mendelian factors underlying quantitative traits using RFLP linkage maps.

          The advent of complete genetic linkage maps consisting of codominant DNA markers [typically restriction fragment length polymorphisms (RFLPs)] has stimulated interest in the systematic genetic dissection of discrete Mendelian factors underlying quantitative traits in experimental organisms. We describe here a set of analytical methods that modify and extend the classical theory for mapping such quantitative trait loci (QTLs). These include: (i) a method of identifying promising crosses for QTL mapping by exploiting a classical formula of SEWALL WRIGHT; (ii) a method (interval mapping) for exploiting the full power of RFLP linkage maps by adapting the approach of LOD score analysis used in human genetics, to obtain accurate estimates of the genetic location and phenotypic effect of QTLs; and (iii) a method (selective genotyping) that allows a substantial reduction in the number of progeny that need to be scored with the DNA markers. In addition to the exposition of the methods, explicit graphs are provided that allow experimental geneticists to estimate, in any particular case, the number of progeny required to map QTLs underlying a quantitative trait.
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            Precision mapping of quantitative trait loci.

             Z Zeng (1994)
            Adequate separation of effects of possible multiple linked quantitative trait loci (QTLs) on mapping QTLs is the key to increasing the precision of QTL mapping. A new method of QTL mapping is proposed and analyzed in this paper by combining interval mapping with multiple regression. The basis of the proposed method is an interval test in which the test statistic on a marker interval is made to be unaffected by QTLs located outside a defined interval. This is achieved by fitting other genetic markers in the statistical model as a control when performing interval mapping. Compared with the current QTL mapping method (i.e., the interval mapping method which uses a pair or two pairs of markers for mapping QTLs), this method has several advantages. (1) By confining the test to one region at a time, it reduces a multiple dimensional search problem (for multiple QTLs) to a one dimensional search problem. (2) By conditioning linked markers in the test, the sensitivity of the test statistic to the position of individual QTLs is increased, and the precision of QTL mapping can be improved. (3) By selectively and simultaneously using other markers in the analysis, the efficiency of QTL mapping can be also improved. The behavior of the test statistic under the null hypothesis and appropriate critical value of the test statistic for an overall test in a genome are discussed and analyzed. A simulation study of QTL mapping is also presented which illustrates the utility, properties, advantages and disadvantages of the method.
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              Theoretical basis for separation of multiple linked gene effects in mapping quantitative trait loci.

               Z Zeng (1993)
              It is now possible to use complete genetic linkage maps to locate major quantitative trait loci (QTLs) on chromosome regions. The current methods of QTL mapping (e.g., interval mapping, which uses a pair or two pairs of flanking markers at a time for mapping) can be subject to the effects of other linked QTLs on a chromosome because the genetic background is not controlled. As a result, mapping of QTLs can be biased, and the resolution of mapping is not very high. Ideally when we test a marker interval for a QTL, we would like our test statistic to be independent of the effects of possible QTLs at other regions of the chromosome so that the effects of QTLs can be separated. This test statistic can be constructed by using a pair of markers to locate the testing position and at the same time using other markers to control the genetic background through a multiple regression analysis. Theory is developed in this paper to explore the idea of a conditional test via multiple regression analysis. Various properties of multiple regression analysis in relation to QTL mapping are examined. Theoretical analysis indicates that it is advantageous to construct such a testing procedure for mapping QTLs and that such a test can potentially increase the precision of QTL mapping substantially.

                Author and article information

                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                29 January 2007
                : 8
                : 34
                [1 ]Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA, UK
                [2 ]Vector Control Reference Unit, National Institute for Communicable Diseases, NHLS, 1 Modderfontein Road, Sandringham 2131, Johannesburg, South Africa
                [3 ]Medical Entomology, Division of Virology & Communicable Diseases Surveillance, School of Pathology of the National Health Laboratory Service and the University of the Witwatersrand, Johannesburg, South Africa
                [4 ]School of Animal, Plant & Environmental Sciences, University of the Witwatersrand, Johannesburg, South Africa
                [5 ]Arthropod-Borne and Infectious Diseases Laboratory, Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, Colorado 80523, US
                Copyright © 2007 Wondji et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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