Forward-time simulations of human populations with complex diseases.
Due to the increasing power of personal computers, as well as the availability of flexible forward-time simulation programs like simuPOP, it is now possible to simulate the evolution of complex human diseases using a forward-time approach. This approach is potentially more powerful than the coalescent approach since it allows simulations of more than one disease susceptibility locus using almost arbitrary genetic and demographic models. However, the application of such simulations has been deterred by the lack of a suitable simulation framework. For example, it is not clear when and how to introduce disease mutants-especially those under purifying selection-to an evolving population, and how to control the disease allele frequencies at the last generation. In this paper, we introduce a forward-time simulation framework that allows us to generate large multi-generation populations with complex diseases caused by unlinked disease susceptibility loci, according to specified demographic and evolutionary properties. Unrelated individuals, small or large pedigrees can be drawn from the resulting population and provide samples for a wide range of study designs and ascertainment methods. We demonstrate our simulation framework using three examples that map genes associated with affection status, a quantitative trait, and the age of onset of a hypothetical cancer, respectively. Nonadditive fitness models, population structure, and gene-gene interactions are simulated. Case-control, sibpair, and large pedigree samples are drawn from the simulated populations and are examined by a variety of gene-mapping methods.
Peng
Bo
B
Department of Epidemiology, The University of Texas M. D. Anderson Cancer Center, Houston, Texas, United States of America. bpeng@mdanderson.org
Amos
Christopher I
CI
Kimmel
Marek
M
D017668:Age of Onset; D000465:Algorithms; D000483:Alleles; D005075:Biological Evolution; D016022:Case-Control Studies; D002874:Chromosome Mapping; D002877:Chromosomes, Human; D003198:Computer Simulation; D003710:Demography; D003920:Diabetes Mellitus / Q000235:genetics / Q000473:pathology; D004194:Disease; D004198:Disease Susceptibility; D004641:Emigration and Immigration; D005787:Gene Frequency; D040961:Genetic Drift; D005819:Genetic Markers; D005828:Genetics, Population; D005838:Genotype; D006801:Humans; D006973:Hypertension / Q000235:genetics / Q000473:pathology; D015810:Linkage Disequilibrium; D016015:Logistic Models; D018895:Microsatellite Repeats; D008957:Models, Genetic; D009369:Neoplasms / Q000235:genetics / Q000473:pathology; D010375:Pedigree; D017354:Point Mutation; D016012:Poisson Distribution; D020641:Polymorphism, Single Nucleotide; D016016:Proportional Hazards Models; D040641:Quantitative Trait Loci; D011995:Recombination, Genetic; D012641:Selection, Genetic; D013995:Time
D005819:Genetic Markers