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A reduced representation approach to population genetic analyses and applications to human evolution
Francesca Luca1, Richard R Hudson, David B Witonsky
1Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
Genome Research
|June 2, 2011
Summary
This study introduces a cost-effective method for large-scale population resequencing, enabling population genetic inferences. The approach aids in understanding human dispersal and migration history using genomic data.
Area of Science:
- Genomics
- Population Genetics
- Bioinformatics
Background:
- Second-generation sequencing enables large-scale sequence variation surveys.
- Whole-genome sequencing at a population scale remains cost-prohibitive for many researchers.
Purpose of the Study:
- To develop an inexpensive, reduced representation protocol for resequencing targets.
- To create analytical tools for population genetic inferences from reduced representation data.
- To enable population-scale genomic studies for any species with a reference genome.
Main Methods:
- Implemented a reduced representation protocol for preparing resequencing targets.
- Developed analytical tools for read alignment, genotype calling, and error rate incorporation.
- Applied the protocol and tools to 19 individuals from 18 human populations.
Main Results:
- Successfully sampled largely overlapping, genome-representative regions across individuals.
- Tested the serial founder model of human dispersal.
- Estimated the timing of the Out of Africa migration.
- Provided a time frame for Australia's colonization using large-scale resequencing data.
Conclusions:
- The developed protocol and tools offer an affordable approach to population-scale genomics.
- This method facilitates population genetic inferences and evolutionary parameter estimation.
- The study provides novel insights into human migration history and colonization events.
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