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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 the empirical estimates of the sex-averaged recombination rate in 100 kb segments of the human genome from the Genethon map (joined black dots) and , the maximum simulated likelihood estimate of the weights on θi ∈ Θ (gray line) for the coalescent mixture model.
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Figure 3: The distribution of the empirical estimates of the sex-averaged recombination rate in 100 kb segments of the human genome from the Genethon map (joined black dots) and , the maximum simulated likelihood estimate of the weights on θi ∈ Θ (gray line) for the coalescent mixture model.

Mentions: The empirical estimates of the sex-averaged human recombination rates in 1 Mbp intervals based on Genethon [9], Marshfield [10] and deCODE [11] maps were downloaded from [12]. We intrapolated to obtain the estimates over 100 kb segments by assuming rate constancy over the 10 consecutive 100 kb segments that constitute the 1 Mbp segment for which an empirical estimate of the recombination rate were available. The empirical distribution of the sex-averaged human recombination rate in 100 kb intervals, based on Genethon map, as shown in Figure 3, is denoted by . The strategy described in Methods was used to obtain a simulation-based empirical estimate of the SNP density distribution for each scaled mutation rate


Simple models of genomic variation in human SNP density.

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

The distribution of the empirical estimates of the sex-averaged recombination rate in 100 kb segments of the human genome from the Genethon map (joined black dots) and , the maximum simulated likelihood estimate of the weights on θi ∈ Θ (gray line) for the coalescent mixture model.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: The distribution of the empirical estimates of the sex-averaged recombination rate in 100 kb segments of the human genome from the Genethon map (joined black dots) and , the maximum simulated likelihood estimate of the weights on θi ∈ Θ (gray line) for the coalescent mixture model.
Mentions: The empirical estimates of the sex-averaged human recombination rates in 1 Mbp intervals based on Genethon [9], Marshfield [10] and deCODE [11] maps were downloaded from [12]. We intrapolated to obtain the estimates over 100 kb segments by assuming rate constancy over the 10 consecutive 100 kb segments that constitute the 1 Mbp segment for which an empirical estimate of the recombination rate were available. The empirical distribution of the sex-averaged human recombination rate in 100 kb intervals, based on Genethon map, as shown in Figure 3, is denoted by . The strategy described in Methods was used to obtain a simulation-based empirical estimate of the SNP density distribution for each scaled mutation rate

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