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Characterization of multilocus linkage disequilibrium.

Alessandro Rinaldo1, Silviu-Alin Bacanu, B Devlin

  • 1Department of Statistics, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.

Genetic Epidemiology
|January 8, 2005
PubMed
Summary

This study introduces a new computational method to identify distinct haplotype blocks in the human genome. The approach accurately partitions genomic regions and efficiently selects tag single nucleotide polymorphisms (SNPs) for association studies.

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Area of Science:

  • Genomics
  • Population Genetics
  • Bioinformatics

Background:

  • Linkage disequilibrium (LD) exhibits spatial heterogeneity across the human genome.
  • Certain genomic regions display limited haplotype diversity, termed 'haplotype blocks'.
  • Existing methods for block identification have limitations in handling adjacent or multiple distinct blocks.

Purpose of the Study:

  • To develop a novel computational method for distinguishing between blocked and unblocked genomic regions.
  • To introduce an efficient strategy for selecting tag single nucleotide polymorphisms (SNPs) for association analyses.
  • To compare the performance of the proposed blocking and tagging methods against existing approaches.

Main Methods:

  • Analysis of haplotype diversity using an approximate likelihood model.

Related Experiment Videos

  • Application of a parsimony criterion to penalize model complexity for efficient partitioning.
  • Development of a new method for selecting tag SNPs based on blocking strategies.

    • Main Results:

    • The proposed method accurately partitions genomic regions into blocks, even with adjacent or multiple distinct blocks.
    • Simulations indicate high accuracy in the identified genomic partitions.
    • The tag SNP selection method is efficient and performs favorably compared to existing methods.

    Conclusions:

    • The new method effectively identifies haplotype blocks and facilitates accurate genomic region partitioning.
    • The proposed tag SNP selection strategy is efficient and suitable for association studies.
    • This approach offers an improvement over existing methods for analyzing genomic LD patterns and selecting informative SNPs.