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Related Experiment Videos

Confounding from cryptic relatedness in haplotype-based association studies.

Feng Zhang1, Hong-Wen Deng

  • 1College of Medicine, Xi'an Jiaotong University, 710061 Xi'an, People's Republic of China. fzhxjtu@mail.xjtu.edu.cn

Genetica
|August 4, 2010
PubMed
Summary

Cryptic relatedness can slightly improve haplotype phase inference but negatively impacts population-based association studies (PBAS). Ignoring this relatedness in haplotype frequency-based association tests (HFAT) and haplotype similarity-based association tests (HSAT) may lead to false positive results.

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Area of Science:

  • Population Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Cryptic relatedness is a potential confounder in population-based association studies (PBAS).
  • The influence of cryptic relatedness on haplotype inference and association testing remains unclear.

Purpose of the Study:

  • To investigate the impact of cryptic relatedness on haplotype phase inference.
  • To evaluate the performance of haplotype frequency-based association tests (HFAT) and haplotype similarity-based association tests (HSAT) in the presence of cryptic relatedness.

Main Methods:

  • Utilized Hapmap genetic data to simulate related samples.
  • Assessed haplotype phase inference accuracy using PHASE 2.1.
  • Calculated power, type I error rates, accuracy, and positive prediction value (PPV) for HFAT and HSAT under varying relatedness levels, disease models, and sample sizes.

Main Results:

  • Cryptic relatedness showed a slight positive effect on haplotype phase inference accuracy.
  • A significant negative effect of cryptic relatedness was observed on the performance of both HFAT and HSAT.
  • Ignoring cryptic relatedness increased the likelihood of spurious association findings in haplotype-based PBAS.

Conclusions:

  • Cryptic relatedness has a complex impact on genetic association studies.
  • Failure to account for cryptic relatedness in haplotype-based analyses can compromise the reliability of results.
  • Future population-based association studies should consider methods to detect and correct for cryptic relatedness.