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Simple models of genomic variation in human SNP density.

Sainudiin R, Clark AG, Durrett RT - BMC Genomics (2007)

Bottom Line: 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.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.Accounting for mutational and recombinational heterogeneities can allow for empirically sound distributions in genome scans for "outliers", when the alternative hypotheses include fundamentally historical and unobserved phenomena.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Statistics, University of Oxford, Oxford, UK. sainudii@stats.ox.ac.uk

ABSTRACT

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.

Conclusion: Accounting for mutational and recombinational heterogeneities can allow for empirically sound distributions in genome scans for "outliers", when the alternative hypotheses include fundamentally historical and unobserved phenomena.

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The distribution of SNP density in 100 kb morphs from the geometric distribution (black dots) towards the Poisson distribution (gray dots) as the scaled recombination rate ρ increases from 0 to 1000 in decreasing shades of gray for θ = 10 (top) and θ = 100 (bottom).
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Figure 2: The distribution of SNP density in 100 kb morphs from the geometric distribution (black dots) towards the Poisson distribution (gray dots) as the scaled recombination rate ρ increases from 0 to 1000 in decreasing shades of gray for θ = 10 (top) and θ = 100 (bottom).

Mentions: However, when the recombination rate is some intermediate value between the above two extremes no explicit forms are known for the SNP density. We use empirical estimates of the SNP density from a large number of simulations (typically 100,000). Figure 2 shows how the distribution of SNP density under our assumptions morphs from the geometric distribution (black dots) towards the Poisson distribution (grey dots) as the scaled recombination rate ρ increases from 0 to 1000 in decreasing shades of grey. This behavior is identical for any fixed value of θ except for a scale change.


Simple models of genomic variation in human SNP density.

Sainudiin R, Clark AG, Durrett RT - BMC Genomics (2007)

The distribution of SNP density in 100 kb morphs from the geometric distribution (black dots) towards the Poisson distribution (gray dots) as the scaled recombination rate ρ increases from 0 to 1000 in decreasing shades of gray for θ = 10 (top) and θ = 100 (bottom).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1919371&req=5

Figure 2: The distribution of SNP density in 100 kb morphs from the geometric distribution (black dots) towards the Poisson distribution (gray dots) as the scaled recombination rate ρ increases from 0 to 1000 in decreasing shades of gray for θ = 10 (top) and θ = 100 (bottom).
Mentions: However, when the recombination rate is some intermediate value between the above two extremes no explicit forms are known for the SNP density. We use empirical estimates of the SNP density from a large number of simulations (typically 100,000). Figure 2 shows how the distribution of SNP density under our assumptions morphs from the geometric distribution (black dots) towards the Poisson distribution (grey dots) as the scaled recombination rate ρ increases from 0 to 1000 in decreasing shades of grey. This behavior is identical for any fixed value of θ except for a scale change.

Bottom Line: 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.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.Accounting for mutational and recombinational heterogeneities can allow for empirically sound distributions in genome scans for "outliers", when the alternative hypotheses include fundamentally historical and unobserved phenomena.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Statistics, University of Oxford, Oxford, UK. sainudii@stats.ox.ac.uk

ABSTRACT

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.

Conclusion: Accounting for mutational and recombinational heterogeneities can allow for empirically sound distributions in genome scans for "outliers", when the alternative hypotheses include fundamentally historical and unobserved phenomena.

Show MeSH