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Estimating demographic parameters from large-scale population genomic data using Approximate Bayesian Computation.

Li S, Jakobsson M - BMC Genet. (2012)

Bottom Line: We compared the ability of different summary statistics to infer demographic parameters, including haplotype and LD based statistics, and found that the accuracy of the parameter estimates can be improved by combining summary statistics that capture different parts of information in the data.Furthermore, our results suggest that poor choices of prior distributions can in some circumstances be detected using ABC.We conclude that the ABC approach can accommodate realistic genome-wide population genetic data, which may be difficult to analyze with full likelihood approaches, and that the ABC can provide accurate and precise inference of demographic parameters from these data, suggesting that the ABC approach will be a useful tool for analyzing large genome-wide datasets.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Evolutionary Biology, EBC, Uppsala University, Norbyvägen 18D, Uppsala SE-75236, Sweden.

ABSTRACT

Background: The Approximate Bayesian Computation (ABC) approach has been used to infer demographic parameters for numerous species, including humans. However, most applications of ABC still use limited amounts of data, from a small number of loci, compared to the large amount of genome-wide population-genetic data which have become available in the last few years.

Results: We evaluated the performance of the ABC approach for three 'population divergence' models - similar to the 'isolation with migration' model - when the data consists of several hundred thousand SNPs typed for multiple individuals by simulating data from known demographic models. The ABC approach was used to infer demographic parameters of interest and we compared the inferred values to the true parameter values that was used to generate hypothetical "observed" data. For all three case models, the ABC approach inferred most demographic parameters quite well with narrow credible intervals, for example, population divergence times and past population sizes, but some parameters were more difficult to infer, such as population sizes at present and migration rates. We compared the ability of different summary statistics to infer demographic parameters, including haplotype and LD based statistics, and found that the accuracy of the parameter estimates can be improved by combining summary statistics that capture different parts of information in the data. Furthermore, our results suggest that poor choices of prior distributions can in some circumstances be detected using ABC. Finally, increasing the amount of data beyond some hundred loci will substantially improve the accuracy of many parameter estimates using ABC.

Conclusions: We conclude that the ABC approach can accommodate realistic genome-wide population genetic data, which may be difficult to analyze with full likelihood approaches, and that the ABC can provide accurate and precise inference of demographic parameters from these data, suggesting that the ABC approach will be a useful tool for analyzing large genome-wide datasets.

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Estimated posterior distribution of each parameter in model 2. A) Past population size N1', B) past population size N2', C) present population size N1, and D) present population size N2. The vertical dashed red line indicates the true value of each parameter, the blue line shows the estimated posterior distribution of each parameter, and the green line shows the prior distribution of each parameter.
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Figure 3: Estimated posterior distribution of each parameter in model 2. A) Past population size N1', B) past population size N2', C) present population size N1, and D) present population size N2. The vertical dashed red line indicates the true value of each parameter, the blue line shows the estimated posterior distribution of each parameter, and the green line shows the prior distribution of each parameter.

Mentions: For model 2, the means of the posterior samples of past population sizes were slightly closer to the true values than the means for the present population sizes, but the 95% credible intervals of N1' and N2' were much more narrow compared to 95% credible intervals of N1 and N2, indicating that present population sizes are more difficult to estimate. Table 3 shows the mean and the 95% credible interval of the posterior samples of the past population sizes (N1' and N2'), and the present population sizes (N1 and N2). Figure 3 shows the prior and the estimated posterior distributions for the same parameters.


Estimating demographic parameters from large-scale population genomic data using Approximate Bayesian Computation.

Li S, Jakobsson M - BMC Genet. (2012)

Estimated posterior distribution of each parameter in model 2. A) Past population size N1', B) past population size N2', C) present population size N1, and D) present population size N2. The vertical dashed red line indicates the true value of each parameter, the blue line shows the estimated posterior distribution of each parameter, and the green line shows the prior distribution of each parameter.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3368717&req=5

Figure 3: Estimated posterior distribution of each parameter in model 2. A) Past population size N1', B) past population size N2', C) present population size N1, and D) present population size N2. The vertical dashed red line indicates the true value of each parameter, the blue line shows the estimated posterior distribution of each parameter, and the green line shows the prior distribution of each parameter.
Mentions: For model 2, the means of the posterior samples of past population sizes were slightly closer to the true values than the means for the present population sizes, but the 95% credible intervals of N1' and N2' were much more narrow compared to 95% credible intervals of N1 and N2, indicating that present population sizes are more difficult to estimate. Table 3 shows the mean and the 95% credible interval of the posterior samples of the past population sizes (N1' and N2'), and the present population sizes (N1 and N2). Figure 3 shows the prior and the estimated posterior distributions for the same parameters.

Bottom Line: We compared the ability of different summary statistics to infer demographic parameters, including haplotype and LD based statistics, and found that the accuracy of the parameter estimates can be improved by combining summary statistics that capture different parts of information in the data.Furthermore, our results suggest that poor choices of prior distributions can in some circumstances be detected using ABC.We conclude that the ABC approach can accommodate realistic genome-wide population genetic data, which may be difficult to analyze with full likelihood approaches, and that the ABC can provide accurate and precise inference of demographic parameters from these data, suggesting that the ABC approach will be a useful tool for analyzing large genome-wide datasets.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Evolutionary Biology, EBC, Uppsala University, Norbyvägen 18D, Uppsala SE-75236, Sweden.

ABSTRACT

Background: The Approximate Bayesian Computation (ABC) approach has been used to infer demographic parameters for numerous species, including humans. However, most applications of ABC still use limited amounts of data, from a small number of loci, compared to the large amount of genome-wide population-genetic data which have become available in the last few years.

Results: We evaluated the performance of the ABC approach for three 'population divergence' models - similar to the 'isolation with migration' model - when the data consists of several hundred thousand SNPs typed for multiple individuals by simulating data from known demographic models. The ABC approach was used to infer demographic parameters of interest and we compared the inferred values to the true parameter values that was used to generate hypothetical "observed" data. For all three case models, the ABC approach inferred most demographic parameters quite well with narrow credible intervals, for example, population divergence times and past population sizes, but some parameters were more difficult to infer, such as population sizes at present and migration rates. We compared the ability of different summary statistics to infer demographic parameters, including haplotype and LD based statistics, and found that the accuracy of the parameter estimates can be improved by combining summary statistics that capture different parts of information in the data. Furthermore, our results suggest that poor choices of prior distributions can in some circumstances be detected using ABC. Finally, increasing the amount of data beyond some hundred loci will substantially improve the accuracy of many parameter estimates using ABC.

Conclusions: We conclude that the ABC approach can accommodate realistic genome-wide population genetic data, which may be difficult to analyze with full likelihood approaches, and that the ABC can provide accurate and precise inference of demographic parameters from these data, suggesting that the ABC approach will be a useful tool for analyzing large genome-wide datasets.

Show MeSH