Logistic regression protects against population structure in genetic association studies.

We conduct an extensive simulation study to compare the merits of several methods for using null (unlinked) markers to protect against false positives due to cryptic substructure in population-based genetic association studies. The more sophisticated "structured association" methods perform well but are computationally demanding and rely on estimating the correct number of subpopulations. The simple and fast "genomic control" approach can lose power in certain scenarios. We find that procedures based on logistic regression that are flexible, computationally fast, and easy to implement also provide good protection against the effects of cryptic substructure, even though they do not explicitly model the population structure.

• Publication year

  2005

• Venue

  Genome Research

• Publication date

  2005-12-14

• Fields of study

  Biology, Medicine

• Identifiers
  DOI 10.1101/GR.4346306PMID 16354752PMCID PMC1361725
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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