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Published on: June 23, 2012
Identification of regions of positive selection using Shared Genomic Segment analysis
Zheng Cai1, Nicola J Camp, Lisa Cannon-Albright
1Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT 84108, USA. z.cai@utah.edu
European Journal of Human Genetics : EJHG
|February 10, 2011
Summary
Shared genomic segment (SGS) analysis identified regions of positive selection in human genomes. This method highlights potential novel candidate regions for disease genes, advancing population genetics research.
Area of Science:
- Population Genetics
- Genomics
- Human Evolution
Background:
- Identifying genomic regions under selection is crucial for understanding human adaptation and disease.
- Previous methods have limitations in detecting complete or near-complete selective sweeps.
Purpose of the Study:
- To develop and apply a novel shared genomic segment (SGS) analysis with an error model.
- To identify complete or near-complete selective sweeps in human populations using HapMap data.
Main Methods:
- Applied shared genomic segment (SGS) analysis incorporating an error model.
- Detected heterozygous sharing across individuals to identify regions with common alleles.
- Utilized HapMap phase II data sets for analysis.
Main Results:
- Identified multiple genomic regions exhibiting selective sweeps.
- Many identified regions are concordant with previously detected positive selection signals.
- Several novel regions suggestive of selection were discovered.
Conclusions:
- Shared genomic segment (SGS) analysis is a valuable method for detecting genomic regions under selection.
- The identified regions, including novel ones, are potential candidates for harboring disease-related genes.
- This approach enhances the study of positive selection and its implications for human health.
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