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Related Experiment Videos

Testing for linkage disequilibrium in genotypic data using the Expectation-Maximization algorithm

M Slatkin1, L Excoffier

  • 1Department of Integrative Biology, University of California, Berkeley 94720-3140, USA.

Heredity
|April 1, 1996
PubMed
Summary
This summary is machine-generated.

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This study introduces a new method using the Expectation-Maximization (EM) algorithm to test for linkage disequilibrium between genetic loci when only genotype data is available. The empirical distribution method is crucial for accurate significance testing with highly polymorphic loci.

Area of Science:

  • Population Genetics
  • Statistical Genetics

Background:

  • Haplotype frequencies are often unavailable, hindering linkage disequilibrium (LD) testing.
  • Existing methods for LD testing require haplotype data, limiting their applicability.

Purpose of the Study:

  • To generalize an approach for testing association between alleles at two loci using only genotype data.
  • To develop a statistically robust method for detecting significant linkage disequilibrium.

Main Methods:

  • Utilized the Expectation-Maximization (EM) algorithm to resolve double heterozygotes into haplotypes.
  • Employed a likelihood ratio test to assess the nonrandomness of haplotype resolutions.
  • Compared three significance testing methods: empirical distribution, Chi-squared (X2) approximation, and Z2 test.

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Main Results:

  • The EM algorithm-based method successfully tests for LD with genotype data.
  • All three significance testing methods performed well for loci with 2-3 alleles.
  • Only the empirical distribution method proved adequate for highly polymorphic loci (5-8 alleles), such as microsatellites.

Conclusions:

  • The EM algorithm provides a valuable tool for LD testing with highly polymorphic loci.
  • Accurate significance testing necessitates using the empirical distribution of likelihood ratios.
  • The method is applicable to X-linked loci and samples from haplodiploid species.