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Evaluation of Cofactor Markers for Controlling Genetic Background Noise in QTL Mapping
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 Title & Authors
Evaluation of Cofactor Markers for Controlling Genetic Background Noise in QTL Mapping
Lee, Chaeyoung; Wu, Xiaolin;
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In order to control the genetic background noise in QTL mapping, cofactor markers were incorporated in single marker analysis (SMACO) and interval mapping (CIM). A simulation was performed to see how effective the cofactors were by the number of QTL, the number and the type of markers, and the marker spacing. The results of QTL mapping for the simulated data showed that the use of cofactors was slightly effective when detecting a single QTL. On the other hand, a considerable improvement was observed when dealing with more than one QTL. Genetic background noise was efficiently absorbed with linked markers rather than unlinked markers. Furthermore, the efficiency was different in QTL mapping depending on the type of linked markers. Well-chosen markers in both SMACO and CIM made the range of linkage position for a significant QTL narrow and the estimates of QTL effects accurate. Generally, 3 to 5 cofactors offered accurate results. Over-fitting was a problem with many regressor variables when the heritability was small. Various marker spacing from 4 to 20 cM did not change greatly the detection of multiple QTLs, but they were less efficient when the marker spacing exceeded 30 cM. Likelihood ratio increased with a large heritability, and the threshold heritability for QTL detection was between 0.30 and 0.05.
Heritability;Marker Spacing;QTL;Simulation;
 Cited by
Composite Interval Mapping of Quantitative Trait Loci with Granddaughter Design,이채영;이광전;

Genes and Genomics, 2003. vol.25. 1, pp.41-41
Basten, C. J., B. S. Weir and Z. B. Zeng. 1994. Zmap-a QTL cartographer. Proc. 5th World Cong. Genet. Appl. Livst. Prod. 22:65-66. Guelph, Ontario, Canada.

Belknap, J. K. 1998. Effect of within-strain sample size on QTL detection and mapping using recombinant inbred mouse strains. Behav. Genet. 28:29-38. crossref(new window)

Boyle, A. E. and K. Gill. 2001. Sensitivity of AXB/BXA recombinant inbred lines of mice to locomotor activating effects of cocaine: a quantitative trait analysis. Pharmacogenetics 11:255-264. crossref(new window)

Churchill, G. A. and R. W. Doerge. 1994. Empirical threshold values for quantitative trait mapping. Genetics 138:963-971.

Darvasi, A., A. Weinreb, V. Minke, J. I. Weller and M. Soller. 1993. Detecting marker-QTL linkage and estimating QTL gene effect and map location using a saturated genetic map. Genetics 134:943-951.

Drake, T. A., E. Schadt, K. Hannani, J. M. Kabo, K. Krass, V. Colinayo, L. E. Greaser, J. Goldin and A. J. Lusis. 2001. Genetic loci determining bone density in mice with dietinduced atherosclerosis. Physiol. Genomics 5:205-15.

Haley, C. S. and S. A. Knott. 1992. A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69:315-324.

Jansen, R. C. 1993. Interval mapping of multiple quantitative trait loci. Genetics 135:205-11.

Kearsey, M. J. and A. G. Farquhar. 1998. QTL analysis in plants; where are we now? Heredity 80:137-142. crossref(new window)

Lander, E. S. and D. Botstein. 1989. Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121:185-199.

Lee, C. 2002. What holds the future of quantitative genetics? Asian-Aust. J. Anim. Sci. 15:303-308.

Liu, B. H. 1998. Statistical Genomics: Linkage, Mapping and QTL Analysis. CRC Press LLC, Boca Raton, FL, USA.

Martinez, O. and R. N. Curnow. 1992. Estimating the locations and sizes of the effects of quantitative trait loci using flanking markers. Theor. Appl. Genet. 85:480-488.

Moreno-Gonzalez, J. 1993. Efficiency of generations for estimating marker-associated QTL effects by multiple regression. Genetics 135:223-231.

Piepho, H. P. 2000. Optimal marker density for interval mapping in a backcross population. Heredity 84:437-440. crossref(new window)

Piepho, H. P. and H. G. Gauch. 2001. Marker pair selection for mapping quantitative loci. Genetics 157: 433-444.

Robison, B. D., P. A. Wheeler, K. Sundin, P. Sikka and G. H. Thorgaard. 2001. Composite interval mapping reveals a major locus influencing embryonic development rate in rainbow trout (Oncorhynchus mykiss). J. Hered. 92:16-22. crossref(new window)

Wayne, M. L., J. B. Hackett, C. L. Dilda, S. V. Nuzhdin, E. G. Pasyukova and T. F. Mackay. 2001. Quantitative trait locus mapping of fitness-related traits in Drosophia melanogaster. Genet. Res. 77:107-116. crossref(new window)

Weller, J. I., Y. Kashi and M. Soller. 1990. Power of daughter and granddaughter designs for determining linkage between marker loci and quantitative trait loci in dairy cattle. J. Dairy Sci. 73:2525-2537.

Williams, J. T. and J. Blangero. 1999. Asymptotic power of likelihood-ratio tests for detecting quantitative trait loci using the COGA data. Genet. Epidemiol. 17(Suppl. 1):S397-402.

Wright, F. A. and A. Kong. 1997. Linkage mapping in experimental crosses: the robustness of single-gene models. Genetics 146:417-425.

Wu, R. L. 1999. Mapping quantitative trait loci by genotyping haploid tissues. Genetics 152:1741-1752.

Wu, X. L., C. Lee, J. Jiang, Y. L. Peng, S. L. Yang, B. N. Xiao, X. C. Liu and Q. S. Shi. 2001. Mapping a quantitative trait locus for growth and backfat on porcine chromosome 18. Asian-Aust. J. Anim. Sci. 14:1665-1669.

Wu, X. L., C. Lee, J. Jiang, Y. L. Peng, H. F. Yan, S. L. Yang, B. N. Xiao, X. C. Liu and Q. S. Shi. 2002. Quantitative trait loci mapping for porcine backfat thickness. Asian-Aust. J. Anim. Sci. 15:923-927.

Zeng, Z. B. 1993. Theoretical Basis for Separation of Multiple Linked Gene Effects in Mapping Quantitative Trait Loci. Proc. Natl. Acad. Sci. USA 90:10972-10976. crossref(new window)

Zeng, Z. B. 1994. Precision Mapping of Quantitative Trait Loci. Genetics 136:1457-1468.