A number of different coalescent simulators that generate simulated DNA fragments for different evolutionary models are discussed in the literature that differ from the approach we describe in the main text. These applications simulate different standard neutral evolutionary models with recombination, variable population size, and migration. They also allow for spatial and temporal environmental heterogeneity. Carvajal-Rodríguez provide a comprehensive description of many of these models.32 The most recent and
one of the most versatile models is not included in his analysis. GWAsimulator is a program that simulates genotype data for SNP chips that are used in GWAS.33 It creates whole genome case-control or population samples. It also
can simulate specific genomic regions. For case-control data, the program can be linked to sample cases and controls according to a user-specified multilocus disease model. The program requires phased data as input, and the simulated data will have similar LD patterns as the input data.
140 Chapter 8
A second program worth mentioning is GENOME.34 It simulates a wider
range of scenarios including recombination hotspots. As well as whole genome data. In addition to features of standard coalescent simulators, the program allows for recombination rates to vary along the genome and for flexible population histories. The program and C++ source code are available online at http://www.sph.umich.edu/csg/liang/genome/.
Chapter References
1. Visscher PM, Brown MA, McCarthy MI, et al. Five years of GWAS discovery. Am J Hum Genet. 2012;90(1):7-24.
2. Chatterjee N, Wheeler B, Sampson J, et al. Projecting the performance of risk prediction based on polygenic analyses of genome-wide association studies. Nat Genet. 2013;45(4):400-5, 405e1-3.
3. Wang Y, Liu G, Feng M, et al. An empirical comparison of several recent epistatic interaction detection methods. Bioinformatics. 2011;27(21):2936- 43.
4. Zhang X, Huang S, Zou F, et al. TEAM: efficient two-locus epistasis tests in human genome-wide association study. Bioinformatics. 2010;26(12):i217- 27.
5. Cooley P, Clark R, Folsom R, et al. Genetic inheritance and genome wide association statistical test performance. J Proteomics Bioinform. 2010;3(12):321-325.
6. Schymick JC, Scholz SW, Fung HC, et al. Genome-wide genotyping in amyotrophic lateral sclerosis and neurologically normal controls: first stage analysis and public release of data. Lancet Neurol. 2007;6(4):322-8. 7. Plomin R, Simpson MA. The future of genomics for developmentalists.
Dev Psychopathol. 2013;25(4 Pt 2):1263-78.
8. Benyamin B, Pourcain B, Davis OS, et al. Childhood intelligence is heritable, highly polygenic and associated with FNBP1L. Mol Psychiatry. 2014;19(2):253-8.
9. Stranger BE, Stahl EA, Raj T. Progress and promise of genome- wide association studies for human complex trait genetics. Genetics. 2011;187(2):367-83.
10. International Schizophrenia Consortium, Purcell SM, Wray NR, et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature. 2009;460(7256):748-52.
11. Yang J, Benyamin B, McEvoy BP, et al. Common SNPs explain a large proportion of the heritability for human height. Nat Genet. 2010;42(7):565-9.
12. Gibson G. Hints of hidden heritability in GWAS. Nat Genet. 2010;42(7):558-60.
13. Mackay TF, Stone EA, Ayroles JF. The genetics of quantitative traits: challenges and prospects. Nat Rev Genet. 2009;10(8):565-77.
14. Dudbridge F. Power and predictive accuracy of polygenic risk scores. PLoS Genet. 2013;9(3):e1003348.
15. Plomin R, Haworth CM, Meaburn EL, et al. Common DNA markers can account for more than half of the genetic influence on cognitive abilities. Psychol Sci. 2013;24(4):562-8.
16. Cooley PC, Clark RF, Folsom RE. Statistical methods that identify
genotype-phenotype associations in the presence of environmental effects. RTI Press Publication No. RR-0022-1405. Research Triangle Park, NC: RTI Press; 2014.
17. Culverhouse R, Suarez BK, Lin J, et al. A perspective on epistasis: limits of models displaying no main effect. Am J Hum Genet. 2002;70(2):461-71. 18. Hoh J, Wille A, Zee R, et al. Selecting SNPs in two-stage analysis of disease
association data: a model-free approach. Ann Hum Genet. 2000;64(Pt 5):413-7.
19. Li J, Horstman B, Chen Y. Detecting epistatic effects in association studies at a genomic level based on an ensemble approach. Bioinformatics. 2011;27(13):i222-9.
20. Sha Q, Zhang Z, Schymick JC, et al. Genome-wide association reveals three SNPs associated with sporadic amyotrophic lateral sclerosis through a two-locus analysis. BMC Med Genet. 2009;10:86.
21. Moore JH, Ritchie MD. STUDENTJAMA. The challenges of whole- genome approaches to common diseases. JAMA. 2004;291(13):1642-3. 22. Bush WS, Moore JH. Chapter 11: Genome-wide association studies. PLoS
142 Chapter 8
23. Bush WS, Dudek SM, Ritchie MD. Biofilter: a knowledge-integration system for the multi-locus analysis of genome-wide association studies. Pac Symp Biocomput. 2009:368-79.
24. Herold C, Steffens M, Brockschmidt FF, et al. INTERSNP: genome-wide interaction analysis guided by a priori information. Bioinformatics. 2009;25(24):3275-81.
25. Cooley P, Gaddis N, Folsom R, et al. Conducting genome-wide association studies: epistasis scenarios. J Proteomics Bioinform. 2012;5(10):245-251. 26. Wu X, Dong H, Luo L, et al. A novel statistic for genome-wide interaction
analysis. PLoS Genet. 2010;6(9):e1001131.
27. Ueki M, Cordell HJ. Improved statistics for genome-wide interaction analysis. PLoS Genet. 2012;8(4):e1002625.
28. McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine. Online Medelian Inheritance in Man (OMIM). 2016 [cited 2016 Feb 11]; Available from: http://www.ncbi.nlm. nih.gov/omim
29. Lin CY, Xing G, Xing C. Measuring linkage disequilibrium by the partial correlation coefficient. Heredity (Edinb). 2012;109(6):401-2.
30. Abecasis GR, Noguchi E, Heinzmann A, et al. Extent and distribution of linkage disequilibrium in three genomic regions. Am J Hum Genet. 2001;68(1):191-197.
31. Eunice Kennedy Shriver National Institute of Child Health and Human
Development. Add Health: The National Longitudinal Study of Adolescent ot Adult Health. 2015 [cited 2015 July 20]; Available from: http://www.cpc. unc.edu/projects/addhealth
32. Carvajal-Rodriguez A. Simulation of genomes: a review. Curr Genomics. 2008;9(3):155-9.
33. Li C, Li M. GWAsimulator: a rapid whole-genome simulation program. Bioinformatics. 2008;24(1):140-2.
34. Liang L, Zollner S, Abecasis GR. GENOME: a rapid coalescent-based whole genome simulator. Bioinformatics. 2007;23(12):1565-7.