RWGAIM: An efficient high-dimensional random whole genome average (QTL) interval mapping approach

Genetics Research

Volumn 94, Issue 6, 2012, Pages 291-306

  Verbyla, Arunas P ^a,b   Taylor, Julian D ^a,b   Verbyla, Klara L ^b  

^a UNIVERSITY OF ADELAIDE   (Australia)

^b CSIRO   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CONTROLLED STUDY; GENETIC LINE; GENETIC LINKAGE; HERITABILITY; POPULATION SIZE; PREDICTION; QUANTITATIVE TRAIT LOCUS; QUANTITATIVE TRAIT LOCUS MAPPING; RANDOM WHOLE GENOME AVERAGE INTERVAL MAPPING;

CHROMOSOME MAPPING; CHROMOSOMES, PLANT; MODELS, GENETIC; QUANTITATIVE TRAIT LOCI; QUANTITATIVE TRAIT, HERITABLE; TRITICUM;

EID: 84873481524     PISSN: 00166723     EISSN: 14695073     Source Type: Journal    
DOI: 10.1017/S0016672312000493     Document Type: Article

References (44)

1. Beavis, W. D. (1994). The power and deceit of QTL experiments: Lessons from comparative QTL studies. In Proceedings of the 49th Annual Corn and Sorghum Industry Research Conference, pp. 250-266.
2. American Seed Trade Association, Washington, DC. Beavis, W. D. (1998). QTL analyses: power, precision and accuracy. In Molecular Dissection of Complex Traits, (ed. A. H. Patterson), pp. 145-162.
3. Broman, K. W., Wu, H., Churchill, G., Sen, S., Arends, D., Jansen, R., Prins, P., Yandell, B. (2012). qtl : Tools for Analyzing QTL Experiments. R package version 1.23-16. http://cran.r-project.org/web/packages/qtl/index.html
4. Broman, K. W., Wu, H., Sen, S. &Churchill, G. A. (2003). R/qtl: QTL mapping in experimental crosses. Bioinformatics 19, 889-890.
5. Butler, D. G., Cullis, B. R., Gilmour, A. R. &Gogel, B. J. (2011). Mixed models for S language environments: ASReml-R reference manual. Technical report, Queensland Department of Primary Industries.
6. Che, X. &Xu, S. (2010). Significance test and genome selection in Bayesian shrinkage analysis. International Journal of Plant Genomics 2010, Article ID 893206, 11 pages
7. Genc, Y., Oldach, K., Verbyla, A. P., Lott, G., Hassan, M., Tester, M., Wallwork, H. &McDonald, G. K. (2010). Sodium exclusion QTL associated with improved seedling growth in bread wheat under salinity stress. Theoretical and Applied Genetics 121, 877-894.
8. Golub, G. &van Loan, C. (1996). Matrix Computations, 3rd edn. Baltimore, MD: The Johns Hopkins University Press
9. Griffin, J. E. &Brown, P. J. (2010). Inference with normalgamma prior distributions in regression problems. Bayesian Analysis 5, 171-188.
10. Haley, C. S. &Knott, S. A. (1992). A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69, 315-324.
11. Heath, S. C. (1997). Markov chain Monte Carlo segregation and linkage analysis of oligogenic models. American Journal of Human Genetics 61, 748-760.
12. Jansen, R. C. (1994). Controlling the Type I and Type II errors in mapping quantitative trait loci. Genetics 138, 871-881.
13. Kao, C.-H., Zeng, Z.-B. &Teasdale, R. D. (1999). Multiple interval mapping for quantitative trait loci. Genetics 152, 1203-1216.
14. Lander, E. S. &Botstein, D. (1989). Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121, 185-199.
15. Lander, E. S. &Green, P. (1987). Construction of multilocus genetic linkage maps in humans. Proceedings of the National Academy of Sciences, USA 84, 2363-2367.
16. Manly, K. F., Cudmore, Jr R. H. &Meer, J. M. (2001). Map Manager QTX, cross platform software for genetic mapping. Mammalian Genome 12, 930-932.
17. Martinez, O. &Curnow, R. N. (1992). Estimating the locations and the sizes of the effects of quantitative trait loci using flanking markers. Theoretical and Applied Genetics 85, 480-488.
18. Melchinger, A. E., Utz, H. F. &Schön, C. C. (1998). Quantitative trait loci (QTL) mapping using different testers and independent population samples in maize reveals low power of QTL detection and large bias in estimates of QTL effects. Genetics 149, 383-403.
19. Meuwissen, T. H., Hayes, B. J. &Goddard, M. E. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819V1829.
20. Oakey, H., Verbyla, A., Pitchford, W., Cullis, B. &Kuchel, H. (2006). Joint modelling of additive and non-additive genetic line effects in single field trials. Theoretical and Applied Genetics 113, 809-819.
21. Piepho, H.-P. (2001). A quick method for computing approximate thresholds for quantitative trait loci detection. Genetics 157, 425-432.
22. Robinson, G. K. (1991). That BLUP is a good thing: The estimation of random effects. Statistical Science 6, 15-51.
23. R Development Core Team (2012). R:A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. ISBN 3-900051-07-0.
24. Satagopan, J., Yandell, B. S., Newton, M. A. &Osborn, T. C. (1996). A Bayesian approach to detect quantitative trait loci using Markov chain Monte Carlo. Genetics 144, 805-816.
25. Sillanpaa, M. J. &Arjas, E. (1998). Bayesian mapping of multiple quantitative trait loci from incomplete inbred line cross data. Genetics 148, 1373-1388.
26. Sillanpaa, M. J. &Arjas, E. (1999). Bayesian mapping of multiple quantitative trait loci from incomplete outbred offspring data. Genetics 151, 1605-1619.
27. Smith, A., Cullis, B. &Thompson, R. (2005). The analysis of crop cultivar breeding and evaluation trials : An overview of current mixed model approaches. Journal of Agricultral Science (Cambridge) 143, 449-462.
28. Smith, A. B., Lim, P. &Cullis, B. R. (2006). The design and analysis of multi-phase quality trait experiments. Journal of Agicultural Science (Cambridge) 144, 393-409.
29. Stram, D. O. &Lee, J. W. (1994). Variance components testing in the longitudinal mixed effects model. Biometrics 50, 1171-1177.
30. Stranden, I. &Garrick, D. J. (2009). Derivation of equivalent computing algorithms for genomic predictions and reliabilities of animal merit. Journal of Dairy Science 92, 2971-2975.
31. Taylor, J. D. &Verbyla, A. P. (2011). R package wgaim: QTL analysis in bi-parental populations using linear mixed models. Journal of Statistical Software 40, 1-18.
32. Taylor, J. D., Diffey, S., Verbyla, A. P. &Cullis, B. R. (2011). wgaim: Whole Genome Average Interval Mapping for QTL detection using mixed models. R package version 1.1. http://cran.r-project.org/web/packages/wgaim/ index. html
33. Taylor, J. D., Verbyla, A. P., Cavanagh, C. C. &Newberry, M. (2012). Variable selection in linear mixed models using an extended class of penalities. Australian and New Zealand Journal of Statistics in Press
34. Van Os, H., Stam, P., Visser, R. &Van Eck, H. J. (2005). RECORD:A novel method for ordering loci on a genetic linkage map. Theoretical and Applied Genetics 112, 30-40.
35. Varshney, R. K., Nayak, S. N., May, G. D. &Jackson, S. A. (2009). Next generation sequencing technologies and their implications for crop genetics and breeding. Trends in Biotechnology 27, 522-530.
36. Verbyla, A. P. (1990). A conditional derivation of residual maximum likelihood. Australian Journal of Statistics 32, 227-230.
37. Verbyla, A. P., Cullis, B. R. &Thompson, R. (2007). The analysis of QTL by simultaneous use of the full linkage map. Theoretical and Applied Genetics 116, 95-111.
38. Wang, H., Zhang, Y., Li, X., Masinde, G. L., Mohan, S., Baylink, D. J. &Xu, S. (2005). Bayesian shrinkage estimation of quantitative trait loci parameters. Genetics 170, 465-480.
39. Weller, J. I. (1986). Maximum likelihood techniques for the mapping and analysis of quantitative trait loci with the aid of genetic markers. Biometrics 42, 627-640.
40. Xu, S. (2003). Theoretical basis of the Beavis effect. Genetics 165, 2259-2268.
41. Yi, N. (2004). A unified Markov chain Monte Carlo framework for mapping multiple quantitative trait loci. Genetics 167, 967-975.
42. Yi, N. &Shriner, D. (2008). Advances in Bayesian multiple QTL mapping in experimental designs. Heredity 100, 240-252.
43. Yi, N., George, V. &Allison, D. B. (2003). Stochastic search variable selection for identifying multiple quantitative trait loci. Genetics 164, 1129-1138.
44. Zeng, Z.-B. (1994). Precision mapping of quantitative trait loci. Genetics 136, 1457-1468.