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Related Experiment Video

Updated: Jun 19, 2026

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Published on: August 21, 2016

[Advances in detecting positive selection on genome].

Kao Lin¹, Hai-Peng Li

¹CAS-MPG Partner Institute for Computational Biology, Chinese Academy of Sciences, Shanghai, China. linkao@picb.ac.cn

Yi Chuan = Hereditas

|October 13, 2009

Summary

Researchers review methods for detecting natural selection in populations. They discuss how DNA data helps identify adaptive evolutionary history, while acknowledging challenges from population demographics and other evolutionary forces.

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Area of Science:

Evolutionary biology
Population genetics
Molecular evolution

Context:

Darwin's theory of natural selection and Kimura's neutral theory of molecular evolution provide frameworks for understanding evolutionary forces.
Advancements in DNA sequencing have generated extensive polymorphism data, enabling empirical testing of evolutionary theories.
Distinguishing the genomic footprints of natural selection from population demographic history and other evolutionary forces is a key challenge.

Purpose:

To summarize fundamental concepts of neutral tests in evolutionary biology.
To provide an overview of classical methods used to detect natural selection.
To highlight and discuss recently developed methodologies for analyzing genomic data.

Summary:

This paper reviews the theoretical underpinnings and practical applications of neutral tests for inferring natural selection.
It examines how DNA sequence polymorphism data can reveal adaptive evolutionary trajectories.
The review addresses the complexities introduced by population demographic factors and other evolutionary processes that can mimic selection signals.

Impact:

Provides a consolidated resource for researchers studying evolutionary processes using genomic data.
Facilitates a deeper understanding of the interplay between natural selection, neutral evolution, and population dynamics.
Aims to improve the accuracy and reliability of detecting natural selection in diverse populations.