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Inferring Evolutionary Processes of Humans
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Ecology and Genetics, Evolutionary Biology. (Jakobsson Lab)
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.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 997
Keyword [en]
Approximate Bayesian Computation, TMRCA, demography, human evolution, Bantu-Expansion, Out-of-Africa
National Category
Evolutionary Biology
Identifiers
URN: urn:nbn:se:uu:diva-183517ISBN: 978-91-554-8538-2 (print)OAI: oai:DiVA.org:uu-183517DiVA: diva2:564027
Public defence
2012-12-17, Lindhalsalen, Norbyvägen 18A, Uppsala, 10:00 (English)
Opponent
Supervisors
Available from: 2012-11-26 Created: 2012-10-28 Last updated: 2013-07-22
List of papers
1. Estimating demographic parameters from large-scale population genomic data using Approximate Bayesian Computation
Open this publication in new window or tab >>Estimating demographic parameters from large-scale population genomic data using Approximate Bayesian Computation
2012 (English)In: BMC Genetics, ISSN 1471-2156, E-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.

National Category
Natural Sciences
Identifiers
urn:nbn:se:uu:diva-176993 (URN)10.1186/1471-2156-13-22 (DOI)000304916700001 ()22453034 (PubMedID)
Available from: 2012-06-29 Created: 2012-06-29 Last updated: 2017-12-07Bibliographically approved
2. On the migration process during the Bantu-expansion of peoples from West Africa
Open this publication in new window or tab >>On the migration process during the Bantu-expansion of peoples from West Africa
(English)Manuscript (preprint) (Other academic)
National Category
Evolutionary Biology
Identifiers
urn:nbn:se:uu:diva-183512 (URN)
Available from: 2012-10-28 Created: 2012-10-28 Last updated: 2012-11-26
3. Genome-wide distribution of the Time to the Most Recent Common Ancestor in various human populations
Open this publication in new window or tab >>Genome-wide distribution of the Time to the Most Recent Common Ancestor in various human populations
(English)Manuscript (preprint) (Other academic)
National Category
Evolutionary Biology
Identifiers
urn:nbn:se:uu:diva-183513 (URN)
Available from: 2012-10-28 Created: 2012-10-28 Last updated: 2012-11-26
4. Joint analysis of demography and selection in population genetics: where do we stand and where could we go?
Open this publication in new window or tab >>Joint analysis of demography and selection in population genetics: where do we stand and where could we go?
Show others...
2012 (English)In: Molecular Ecology, ISSN 0962-1083, E-ISSN 1365-294X, Vol. 21, no 1, 28-44 p.Article, review/survey (Refereed) Published
Abstract [en]

Teasing apart the effects of selection and demography on genetic polymorphism remains one of the major challenges in the analysis of population genomic data. The traditional approach has been to assume that demography would leave a genome-wide signature, whereas the effect of selection would be local. In the light of recent genomic surveys of sequence polymorphism, several authors have argued that this approach is questionable based on the evidence of the pervasive role of positive selection and that new approaches are needed. In the first part of this review, we give a few empirical and theoretical examples illustrating the difficulty in teasing apart the effects of selection and demography on genomic polymorphism patterns. In the second part, we review recent efforts to detect recent positive selection. Most available methods still rely on an a priori classification of sites in the genome but there are many promising new approaches. These new methods make use of the latest developments in statistics, explore aspects of the data that had been neglected hitherto or take advantage of the emerging population genomic data. A current and promising approach is based on first estimating demographic and genetic parameters, using, e.g., a likelihood or approximate Bayesian computation framework, focusing on extreme outlier regions, and then using an independent method to confirm these. Finally, especially for species where evidence of natural selection has been limited, more experimental and versatile approaches that contrast populations under varied environmental constraints might be more successful compared with species-wide genome scans in search of specific signatures.

Keyword
contemporary evolution, ecological genetics, population geneticsutheoretical, population geneticsuempirical
National Category
Biological Sciences
Identifiers
urn:nbn:se:uu:diva-169316 (URN)10.1111/j.1365-294X.2011.05308.x (DOI)000298582700004 ()
Available from: 2012-02-28 Created: 2012-02-28 Last updated: 2017-12-07Bibliographically approved

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