A review of statistical methods for expression quantitative trait loci mapping

Mammalian Genome

Volumn 17, Issue 6, 2006, Pages 509-517

  Kendziorski, Christina ^a   Wang, Ping ^b  

^a UNIVERSITY OF WISCONSIN SCHOOL OF MEDICINE AND PUBLIC HEALTH   (United States)

^b University of Wisconsin Madison   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CALCULATION; EXPERIMENTAL DESIGN; GENE EXPRESSION PROFILING; GENE MAPPING; GENETIC LINKAGE; GENETIC TRANSCRIPTION; GENOTYPE; HIGH THROUGHPUT SCREENING; HUMAN; LINKAGE ANALYSIS; MICROARRAY ANALYSIS; PHENOTYPE; QUANTITATIVE TRAIT LOCUS; REVIEW; STATISTICAL ANALYSIS;

ANIMALS; CHROMOSOME MAPPING; GENE EXPRESSION PROFILING; HUMANS; QUANTITATIVE TRAIT LOCI;

EID: 33745220961     PISSN: 09388990     EISSN: 14321777     Source Type: Journal    
DOI: 10.1007/s00335-005-0189-6     Document Type: Review

References (58)

1. Barry WT, Nobel AB, Wright FA (2005) Significance analysis of functional categories in gene expression studies: a structured permutation approach. Bioinformatics 21, 1943-1949
2. Bing N, Hoeschele I (2005) Genetical genomics analysis of a yeast segregant population for transcription network inference. Genetics 170, 533-542
3. Black MA, Doerge RW (2002) Calculation of the minimum number of replicate spots required for detection of significant gene expression fold change in microarray experiments. Bioinformatics 18, 1609-1616
4. Brem RB, Kruglyak L (2005) The landscape of genetic complexity across 5700 gene expression traits in yeast. Proceedings of the National Academy of Sciences 102, 1572-1577
5. Brem RB, Yvert G, Clinton R, Kruglyak L (2002) Genetic dissection of transcriptional regulation in budding yeast. Science 296, 752-755
6. Bystrykh L, Weersing E, Dontje B, Sutton S, Pletcher MT, et al. (2005) Uncovering regulatory pathways that affect hematopoietic stem cell function using "genetical genomics." Nat Genet 37, 225-232
7. Chesler EJ, Lu L, Shou S, Qu Y, Gu J, et al. (2005) Complex trait analysis of gene expression uncovers polygenic and pleiotropic networks that modulate nervous system function. Nat Genet 37, 233-242
8. Churchill GA (2002) Fundamentals of experimental design for cDNA microarrays. Nat Genet 32, 490-495
9. Cui X, Churchill GA (2003) How many mice and how many arrays? Replication in mouse cDNA microarray experiments In: Methods of Microarray Data Analysis III, Johnson KF, Lin SM (eds.) (Norwell MA: Kluwer Academic Publishers) pp 139-154
10. Dobbin K, Simon R (2005) Sample size determination in microarray experiments for class comparison and prognostic classification. Biostatistics 6(1), 27-38
11. Dobbin K, Shih JH, Simon R (2003a) Statistical design of reverse dye microarrays. Bioinformatics 19(7), 803-810
12. Dobbin K, Shih JH, Simon R (2003b) Questions and answers on design of dual-label microarrays for identifying differentially expressed genes. J Natl Cancer Inst 95(18), 1362-1369
13. Dombkowski AA, Thibodeau BJ, Starcevic SL, Novak RF (2004) Gene-specific dye bias in microarray reference designs. FEBS Lett 560, 120-124
14. Dupuis J, Siegmund D (1999) Statistical methods for mapping quantitative trait loci from a dense set of markers. Genetics 151, 373-386
15. Efron B (2005) Local False Discovery Rates. Available at http://www-stanford.edu/~brad/papers/. Last accessed April 21 2006
16. Eisen MB, Spellman PT, Brown PO, Botstein D (1998) Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci 95, 14863-14868
17. Gadbury GL, Page GP, Edwards JW, Kayo T, Prolla TA, et al. (2004) Power and sample size estimation in high dimensional biology. Stat Methods Med Res 13, 325-338
18. Gentleman R (2005) Using GO for Statistical Analyses, Bioconductor vignette http://www.bioconductor. org
19. Hubner N, Wallace CA, Zimdahl H, Petretto E, Schulz H, et al. (2005) Integrated transcriptional profiling and linkage analysis for identification of genes underlying disease. Nat Genet 37, 243-253
20. Hu J, Zou F, Wright FA (2005) Practical FDR-based sample size calculations in microarray experiments. Bioinformatics 21(15), 3264-3272
21. Irizarry RA, Warren D, Spencer F, Kim IF, Biswal S, et al. (2005) Multiple-laboratory comparison of microarray platforms. Nat Methods 2, 345-350
22. Jannink JL (2005) Selective phenotyping to accurately map quantitative trait loci. Crop Sci 45, 901-908
23. Jansen RC, Nap JP (2001) Genetical genomics: the added value from segregation. Trends Genet 17, 388-391
24. Jensen FV (2001) Bayesian Network and Decision Graphs. In Statistics for Engineering and Information Science (New York: Springer-Verlag)
25. Jeong H, Tombor B, Albert R, Oltvai ZN, Barabasi AL (2000) The large scale organization of metabolic networks. Nature 407, 651-653
26. Jin C, Lan H, Attie AD, Bulutuglo D, Churchill GA, et al. (2004) Selective phenotyping for increased efficiency in genetic mapping studies. Genetics 168, 2285-2293
27. Jung S-H, Bang H, Young S (2005a) Sample size calculation for multiple testing in microarray data analysis. Biostatistics 6(1), 157-169
28. Jung S-H (2005b) Sample size for FDR-control in microarray data analysis. Bioinformatics 21(14), 3097-3104
29. Kendziorski C, Zhang Y, Lan H, Attie AD (2003) The efficiency of mRNA pooling in microarray experiments. Biostatistics 4, 465-477
30. Kendziorski C, Irizarry RA, Chen K, Haag JD, Gould MN (2005) On the utility of pooling biological samples in microarray experiments. Proc Natl Acad Sci USA 102(12), 4252-4257
31. Kendziorski C, Chen M, Yuan M, Lan H, Attie AD (2006) Statistical methods for expression quantitative trait loci (eQTL) mapping. Biometrics 62, 19-27
32. Kerr K (2003) Design considerations for efficient and effective microarray studies. Biometrics 59(4), 822-828
33. Kerr K, Churchill GA (2001) Experimental design for gene expression microarrays. Biostatistics 2, 183-201
34. Lan H, Chen M, Flowers JB, Yandell BS, Stapleton DS, et al. (2006) Combined expression trait correlations and expression quantitative trait locus mapping. PLoS Genet 2, e6
35. Larget B, Simon D (1999) Markov chain Monte Carlo algorithms for the Bayesian analysis of phylogenetic trees. Mol Biol Evol 16, 750-759
36. Larkin JE, Frank BC, Gavras H, Sultana R, Quackenbush J (2005) Independence and reproducibility across microarray platforms. Nat Methods 2, 337-344
37. Lee MT, Whitmore GA (2002) Power and sample size for DNA microarray studies. Stat Med 21, 3543-3570
38. Li H, Lu L, Manly KF, Chesler EJ, Bao L, et al. (2005a) Inferring gene transcriptional modulatory relations: a genetical genomics approach. Hum Mol Genet 14(9), 1119-1125
39. Li L, Alderson D, Doyle JC, Willinger W (2005b) Towards a theory of scale-free graphs: definition, properties, and implications. Internet Mathematics 2(4), 431-523
40. Liu Y, Zeng ZB (2000) A general mixture model approach for mapping quantitative trait loci from diverse cross designs involving multiple inbred lines. Genet Res 75, 345-355
41. Mehrabian M, Allayee H, Stockton J, Lum PY, Drake TA, et al. (2005) Integrating genotypic and expression data in a segregating mouse population to identity 5-lipoxygenase as a susceptibility gene for obesity and bone traits. Nat Genet 37, 1224-1233
42. Morley M, Molony CM, Weber TM, Devlin JL, Ewens KG, et al. (2004) Genetic analysis of genome-wide variation in human gene expression. Nature 430, 743-747
43. Muller P, Parmigiani G, Robert C, Rousseau J (2004) Optimal sample size for multiple testing: the case of gene expression microarrays. J Am Stat Assoc 99, 990-1001
44. Pan W, Lin J, Le CT (2002) How many replicates of arrays are required to detect gene expression changes in microarray experiments? A mixture model approach. Genome Biol 3(5), research0022
45. Perez-Enciso M (2004) In silico study of transcriptome genetic variation in outbred populations. Genetics 166, 547-554
46. R Development Core Team (2004) R: A language and environment for statistical computing (Vienna, Austria: R Foundation for Statistical Computing)
47. Ruschhaupt M, Huber W, Poustka A, Mansmann U (2004) A compendium to ensure computational reproducibility in high-dimensional classification tasks. Statistical Applications in Genetics and Molecular Biology 3(1), article 37
48. Schadt EE, Monks SA, Drake TA, Lusis AJ, Che N, et al. (2003) Genetics of gene expression surveyed in maize, mouse and man. Nature 422, 297-302
49. Schadt EE, Lamb J, Yang X, Zhu J, Edwards S, et al. (2005) An integrative genomics approach to infer causal associations between gene expression and disease. Nat Genet 37, 710-717
50. Sen S, Satagopan J, Churchill GA (2005) QTL study design from an information perspective. Genetics 170, 447-464
51. Simon RM, Dobbin K (2003) Experimental design of DNA microarray experiments. BioTechniques Suppl, 16-21
52. Storey JD, Tibshirani R (2003) Statistical significance for genomewide studies. Proc Natl Acad Sci USA 100(16), 9440-9445
53. Storey JD, Akey JM, Kruglyak L (2005) Multiple locus linkage analysis of genomewide expression in yeast. PLoS Biol 3(8), e267
54. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, et al. (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci 102, 15545-15550
55. Weis BK, Members of the Toxicogenomics Research Consortium (2005) Standardizing global gene expression analysis between laboratories and across platforms. Nat Methods 2(5), 351-356
56. Yang YH, Speed TP (2002) Design issues for cDNA microarray experiments. Nat Rev Genet 3, 579-588
57. Yvert G, Brem RB, Whittle J, Akey JM, Foss E, et al. (2003) Trans-acting regulatory variation in Saccharomyces cerevisiae and the role of transcription factors. Nat Genet 35, 57-64
58. Zhu J, Lum PY, Lamb J, GuhaThakurta D, Edwards SW, et al. (2004) An integrative genomics approach to the reconstruction of gene networks in segregating populations. Cytogenet Genome Res 105, 363-374