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Estimating demographic parameters from large-scale population genomic data using Approximate Bayesian Computation
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Ecology and Genetics, Evolutionary Biology.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Ecology and Genetics, Evolutionary Biology. Uppsala University, Science for Life Laboratory, SciLifeLab.
2012 (English)In: BMC Genetics, ISSN 1471-2156, Vol. 13, 22- p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2012. Vol. 13, 22- p.
National Category
Natural Sciences
URN: urn:nbn:se:uu:diva-176993DOI: 10.1186/1471-2156-13-22ISI: 000304916700001PubMedID: 22453034OAI: diva2:538482
Available from: 2012-06-29 Created: 2012-06-29 Last updated: 2012-12-13Bibliographically approved
In thesis
1. Inferring Evolutionary Processes of Humans
Open this publication in new window or tab >>Inferring Evolutionary Processes of Humans
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

More and more human genomic data has become available in recent years by the improvement of DNA sequencing technologies. These data provide abundant genetic variation information which is an important resource to help us to understand the evolutionary history of humans. In this thesis I evaluated the performance of the Approximate Bayesian Computation (ABC) approach for inferring demographic parameters for large-scale population genomic data. According to simulation results, I can conclude that the ABC approach will continue to be a useful tool for analysing realistic genome-wide population-genetic data in the post-genomic era. Secondly, I implemented the ABC approach to estimate the pre-historic events connected with the “Bantu-expansion”, the spread of peoples from West Africa. The analysis based on genetic data with a large number of loci support a rapid population growth in west Africans, which lead to their concomitant spread to southern and eastern Africa. Contrary to hypotheses based on language studies, I found that Bantu-speakers in south Africa likely migrated directly from west Africa, and not from east Africa. Thirdly, I evaluated Thomson's estimator of the time to most recent common ancestor (TMRCA). It is robust to different recombination rates and the least-biased compared to other commonly used approaches. I used the Thomson estimator to infer the genome-wide distribution of TMRCA for complete human genome sequence data in various populations from across the world and compare the result to simulated data. Finally, I investigated and analysed the effects of selection and demography on genetic polymorphism patterns. In particular, we could detect a clear signal in the distribution of TMRCA caused by selection for a constant-size population. However, if the population was growing, the signal of selection will be difficult to detect under some circumstances. I also discussed and gave a few suggestions that might lead to a more realistic path of successful identification of genes targeted by selection in large-scale genomic data.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2012. 58 p.
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 997
Approximate Bayesian Computation, TMRCA, demography, human evolution, Bantu-Expansion, Out-of-Africa
National Category
Evolutionary Biology
urn:nbn:se:uu:diva-183517 (URN)978-91-554-8538-2 (ISBN)
Public defence
2012-12-17, Lindhalsalen, Norbyvägen 18A, Uppsala, 10:00 (English)
Available from: 2012-11-26 Created: 2012-10-28 Last updated: 2013-07-22

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