The beavis effect in next-generation mapping panels in Drosophila melanogaster

G3: Genes, Genomes, Genetics

Volumn 7, Issue 6, 2017, Pages 1643-1652

  King, Elizabeth G ^a   Long, Anthony D ^b  

^a UNIVERSITY OF MISSOURI   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Beavis effect; Complex traits; DGRP; Drosophila melanogaster; DSPR; GWAS; MPP; Multiparental populations; QTL mapping

Indexed keywords

ALLELE; DROSOPHILA MELANOGASTER; FOLLOW UP; GENETIC MODEL; GENOME-WIDE ASSOCIATION STUDY; INBRED STRAIN; NONHUMAN; POPULATION MODEL; PRACTICE GUIDELINE; QUANTITATIVE TRAIT LOCUS MAPPING; SAMPLE SIZE; SIMULATION; STATISTICAL MODEL; ALGORITHM; ANIMAL; BIOLOGICAL MODEL; CHROMOSOMAL MAPPING; COMPUTER SIMULATION; GENETIC ASSOCIATION STUDY; GENETIC DATABASE; GENETIC VARIATION; GENETICS; PHENOTYPE; POPULATION GENETICS; QUANTITATIVE TRAIT; QUANTITATIVE TRAIT LOCUS; REPRODUCIBILITY; WEB BROWSER;

ALGORITHMS; ANIMALS; CHROMOSOME MAPPING; COMPUTER SIMULATION; DATABASES, GENETIC; DROSOPHILA MELANOGASTER; GENETIC ASSOCIATION STUDIES; GENETIC VARIATION; GENETICS, POPULATION; GENOME-WIDE ASSOCIATION STUDY; MODELS, GENETIC; PHENOTYPE; QUANTITATIVE TRAIT LOCI; QUANTITATIVE TRAIT, HERITABLE; REPRODUCIBILITY OF RESULTS; WEB BROWSER;

EID: 85020309404     PISSN: None     EISSN: 21601836     Source Type: Journal    
DOI: 10.1534/g3.117.041426     Document Type: Article

References (49)

1. Abdel-Azim, 2016 Gamal. gdmp: genomic data management. R package version 0.1.0. Available at: https://CRAN.R-project.org/package=gdmp. Accessed: February 1, 2017
2. Alonso-Blanco, C., J. Andrade, C. Becker, F. Bemm, J. Bergelson et al., 2016 1,135 Genomes reveal the global pattern of polymorphism in Arabidopsis thaliana. Cell 166: 481-491
3. Altshuler, D., J. Hirschhorn, M. Klannemark, C. Lindgren, M. Vohl et al., 2000 The common PPAR gamma Pro12Ala polymorphism is associated with decreased risk of type 2 diabetes. Nat. Genet. 26: 76-80
4. Aylor, D. L., W. Valdar, W. Foulds-Mathes, R. J. Buus, R. A. Verdugo et al., 2011 Genetic analysis of complex traits in the emerging collaborative cross. Genome Res. 21: 1213-1222
5. Beavis, W. D., 1994 The power and deceit of QTL experiments: lessons from comparative QTL studies, pp. 250-266 in Proceedings of the Forty-Ninth Annual Corn and Sorghum Industry Research Conference, American Seed Trade Association, Chicago, IL
6. Beavis, W. D., 1998 QTL analyses: power, precision, and accuracy. Mol. Dissection Complex Traits 1998: 145-162
7. Beavis, W. D., D. Grant, M. Albertsen, and R. Fincher, 1991 Quantitative trait loci for plant height in four maize populations and their associations with qualitative genetic loci. Theor. Appl. Genet. 83: 141-145
8. Broman, K. W., and S. Sen, 2009 A Guide to QTL Mapping with R/qtl. Springer, New York, pp. 1-399
9. Brzyski, D., C. Peterson, P. Sobczyk, E. Candes, M. Bogdan et al., 2017 Controlling the rate of GWAS false discoveries. Genetics 205: 61-75
10. Chen, L., and J. D. Storey, 2006 Relaxed significance criteria for linkage analysis. Genetics 173: 2371-2381
11. Cheng, R., M. Abney, A. A. Palmer, and A. D. Skol, 2011 QTLRel: an R package for genome-wide association studies in which relatedness is a concern. BMC Genet. 12: 66
12. Chesler, E. J., D. M. Gatti, A. P. Morgan, M. Strobel, L. Trepanier et al., 2016 Diversity outbred mice at 21: maintaining allelic variation in the face of selection. G3 (Bethesda) 6: 3893-3902
13. Churchill, G. A., and R. W. Doerge, 1994 Empirical threshold values for quantitative trait mapping. Genetics 138: 963-971
14. Clifford, D., and P. McCullagh, 2006 The regress function. R News 6: 6-10
15. Collaborative Cross Consortium, 2012 The genome architecture of the collaborative cross mouse genetic reference population. Genetics 190: 389-401
16. Cubillos, F. A., L. Parts, F. Salinas, A. Bergström, E. Scovacricchi et al., 2013 High-resolution mapping of complex traits with a four-parent advanced intercross yeast population. Genetics 195: 1141-1155
17. Endelman, J. B., 2011 Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Genome 4: 250-255
18. Falconer, D. S., and T. F. C. Mackay, 1996 Introduction to Quantitative Genetics. Pearson, Prentice Hall, Harlow
19. Flint-Garcia, S. A., A.-C. Thuillet, J. Yu, G. Pressoir, S. M. Romero et al., 2005 Maize association population: a high-resolution platform for quantitative trait locus dissection. Plant J. 44: 1054-1064
20. Gatti, D. M., K. L. Svenson, A. Shabalin, L.-Y. Wu, W. Valdar et al., 2014 Quantitative trait locus mapping methods for diversity outbred mice. G3 (Bethesda) 4: 1623-1633
21. Göring, H. H., J. D. Terwilliger, and J. Blangero, 2001 Large upward bias in estimation of locus-specific effects from genomewide scans. Am. J. Hum. Genet. 69: 1357-1369
22. Hirschhorn, J. N., and M. J. Daly, 2005 Genome-wide association studies for common diseases and complex traits. Nat. Rev. Genet. 6: 95-108
23. Huang, W., S. Richards, M. A. Carbone, D. Zhu, R. R. Anholt et al., 2012 Epistasis dominates the genetic architecture of Drosophila quantitative traits. Proc. Natl. Acad. Sci. USA 109: 15553-15559
24. Huang, W., A. Massouras, Y. Inoue, J. Peiffer, M. Ràmia et al., 2014 Natural variation in genome architecture among 205 Drosophila melanogaster genetic reference panel lines. Genome Res. 24: 1193-1208
25. Huang, X., M.-J. Paulo, M. Boer, S. Effgen, P. Keizer et al., 2011 Analysis of natural allelic variation in Arabidopsis using a multiparent recombinant inbred line population. Proc. Natl. Acad. Sci. USA 108: 4488-4493
26. King, E. G., C. M. Merkes, C. L. McNeil, S. R. Hoofer, S. Sen et al., 2012a Genetic dissection of a model complex trait using the Drosophila synthetic population resource. Genome Res. 22: 1558-1566
27. King, E. G., S. J. Macdonald, and A. D. Long, 2012b Properties and power of the Drosophila synthetic population resource for the routine dissection of complex traits. Genetics 191: 935-949
28. King, E. G., G. Kislukhin, K. N. Walters, and A. D. Long, 2014a Using Drosophila melanogaster to identify chemotherapy toxicity genes. Genetics 198: 31-43
29. King, E. G., B. J. Sanderson, C. L. McNeil, A. D. Long, and S. J. Macdonald, 2014b Genetic dissection of the Drosophila melanogaster female head transcriptome reveals widespread allelic heterogeneity. PLoS Genet. 10: e1004322
30. Kover, P. X., W. Valdar, J. Trakalo, N. Scarcelli, I. M. Ehrenreich et al., 2009 A multiparent advanced generation inter-cross to fine-map quantitative traits in Arabidopsis thaliana. PLoS Genet. 5: e1000551
31. Liu, C., Q. Zhou, L. Dong, H. Wang, F. Liu et al., 2016 Genetic architecture of the maize kernel row number revealed by combining QTL mapping using a high-density genetic map and bulked segregant RNA sequencing. BMC Genomics 17: 915
32. Long, A. D., S. J. Macdonald, and E. G. King, 2014 Dissecting complex traits using the Drosophila synthetic population resource. Trends Genet. 30: 488-495
33. Lynch, M., and B. Walsh, 1998 Genetics and Analysis of Quantitative Traits, Sinauer, Sunderland, MA
34. Mackay, T. F. C., S. Richards, E. A. Stone, A. Barbadilla, J. F. Ayroles et al., 2012 The Drosophila melanogaster genetic reference panel. Nature 482: 173-178
35. McClellan, J., and M.-C. King, 2010 Genetic heterogeneity in human disease. Cell 141: 210-217
36. McMullen, M. D., S. Kresovich, H. Sanchez Villeda, P. Bradbury, H. Li et al., 2009 Genetic properties of the maize nested association mapping population. Science 325: 737-740
37. Poland, J., J. Endelman, J. Dawson, J. Rutkoski, S. Wu et al., 2012 Genomic selection in wheat breeding using genotyping-by-sequencing. Plant Genome J. 5: 103
38. Pritchard, J. K., 2001 Are rare variants responsible for susceptibility to complex diseases? Am. J. Hum. Genet. 69: 124-137
39. R Core Team, 2016 R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/
40. Roff, D. A., 1997 Evolutionary Quantitative Genetics. Chapman & Hall, New York
41. Siegmund, D. O., N. R. Zhang, and B. Yakir, 2011 False discovery rate for scanning statistics. Biometrika 98: 979-985
42. Slager, S. l., J. Huang, and V. j. Vieland, 2000 Effect of allelic heterogeneity on the power of the transmission disequilibrium test. Genet. Epidemiol. 18: 143-156
43. Thornton, K. R., A. J. Foran, and A. D. Long, 2013 Properties and modeling of GWAS when complex disease risk is due to non-complementing, deleterious mutations in genes of large effect. PLoS Genet. 9: e1003258
44. Utz, H. F., and A. E. Melchinger, 1994 Comparison of different approaches to interval mapping of quantitative trait loci, pp. 195-203 in Biometrics in Plant Breeding: Applications of Molecular Markers, edited by van Ooijen, J. W., and J. Jansen European Association for Research on Plant Breeding. CPRO-DLO, Wageningen, The Netherlands
45. Utz, H. F., A. E. Melchinger, and C. C. Schön, 2000 Bias and sampling error of the estimated proportion of genotypic variance explained by quantitative trait loci determined from experimental data in maize using cross validation and validation with independent samples. Genetics 154: 1839-1849
46. Xavier, A., S. Xu, W. M. Muir, and K. M. Rainey, 2015 NAM: association studies in multiple populations. Bioinformatics 31: 3862-3864
47. Xu, S., 2003 Theoretical basis of the Beavis effect. Genetics 165: 2259-2268
48. Yang, J., B. Benyamin, B. P. McEvoy, S. Gordon, A. K. Henders et al., 2010 Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42: 565-569
49. Yang, J., N. A. Zaitlen, M. E. Goddard, P. M. Visscher, and A. L. Price, 2014 Advantages and pitfalls in the application of mixed-model association methods. Nat. Genet. 46: 100-106