Simple models of genomic variation in human SNP density.
BACKGROUND
Descriptive hierarchical Poisson models and population-genetic coalescent mixture models are used to describe the observed variation in single-nucleotide polymorphism (SNP) density from samples of size two across the human genome.


RESULTS
Using empirical estimates of recombination rate across the human genome and the observed SNP density distribution, we produce a maximum likelihood estimate of the genomic heterogeneity in the scaled mutation rate theta. Such models produce significantly better fits to the observed SNP density distribution than those that ignore the empirically observed recombinational heterogeneities.


CONCLUSIONS
Accounting for mutational and recombinational heterogeneities can allow for empirically sound null distributions in genome scans for "outliers", when the alternative hypotheses include fundamentally historical and unobserved phenomena.
Sainudiin
Raazesh
R
Department of Statistics, University of Oxford, Oxford, UK. sainudii@stats.ox.ac.uk
Clark
Andrew G
AG
Durrett
Richard T
RT
D000465:Algorithms; D004252:DNA Mutational Analysis; D030541:Databases, Genetic; D014644:Genetic Variation; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D009154:Mutation; D016012:Poisson Distribution; D020641:Polymorphism, Single Nucleotide; D011995:Recombination, Genetic