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Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
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Related Experiment Video

Updated: Jun 18, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

A PCA-based method for ancestral informative markers selection in structured populations.

Feng Zhang1, Lei Zhang, Hong-Wen Deng

  • 1Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China.

Science in China. Series C, Life Sciences
|November 14, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for selecting ancestral informative markers (AIMs) using principle component analysis (PCA). PCA-based AIMs improve the accuracy of inferring individual ancestries without needing prior genetic information.

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Last Updated: Jun 18, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

  • Population Genetics
  • Genomic Data Analysis
  • Statistical Genetics

Background:

  • Population structure analysis is crucial for understanding human history and identifying disease-related genes.
  • Structured association (SA) methods are widely used but depend heavily on the quality and quantity of ancestral informative markers (AIMs).
  • Existing AIM selection methods often require unavailable or uncertain prior individual ancestry information.

Purpose of the Study:

  • To develop a novel, ancestry-information-free approach for selecting AIMs.
  • To enhance the accuracy of population structure identification and association mapping.
  • To provide a robust method for selecting informative AIMs from whole genome data.

Main Methods:

  • Developed a new AIM selection method utilizing principle component analysis (PCA).
  • The PCA-based approach does not require prior ancestry information from study subjects.
  • Applied the method to both simulated and real genetic data.

Main Results:

  • PCA-selected AIMs significantly improve the accuracy of inferred individual ancestries compared to random selection.
  • The method demonstrates effectiveness with equivalent numbers of AIMs.
  • Successfully applied to whole genome data for selecting highly informative AIMs.

Conclusions:

  • The novel PCA-based AIM selection method overcomes limitations of existing approaches.
  • This method enhances the accuracy of population structure inference and association studies.
  • It offers a valuable tool for correcting population stratification biases in genetic analyses.