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Linkage effects and analysis of finite sample errors in the HapMap.

Noah Zaitlen1, Hyun Min Kang, Eleazar Eskin

  • 1Bioinformatics Program, University of California, San Diego, Calif., USA.

Human Heredity
|April 15, 2009
PubMed
Summary

The HapMap

Area of Science:

  • Genetics
  • Population Genetics
  • Bioinformatics

Background:

  • The HapMap project provides crucial data for identifying genetic variants associated with complex traits.
  • Understanding linkage disequilibrium (LD) patterns in human populations is essential for genetic studies.
  • Current HapMap data has limitations due to a finite number of individuals per population.

Purpose of the Study:

  • To develop an analytical framework to assess the impact of finite sample sizes in the HapMap.
  • To evaluate the accuracy of current HapMap-based estimates for genetic analysis.
  • To determine the benefits of increasing sample size for future genomic projects.

Main Methods:

  • Developed an analytical framework for finite sample HapMap analysis.

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  • Derived approximations for estimating statistics like r(2) (squared correlation coefficient) between SNPs.
  • Assessed the impact of sample size on LD estimates and tag set coverage.

    • Main Results:

    • HapMap-based estimates of r(2) and study power contain significant errors.
    • Current SNP tag sets overestimate their coverage due to finite sample limitations.
    • Increasing sample size, as in the 1000 Genomes Project, substantially reduces errors for many SNPs.

    Conclusions:

    • Finite sample sizes in the HapMap introduce considerable errors in genetic analysis.
    • Larger sample sizes are critical for improving the accuracy of genomic studies and variant discovery.
    • The 1000 Genomes Project approach significantly mitigates finite sample size issues.