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Using propensity score adjustment method in genetic association studies.

Amrita Sengupta Chattopadhyay1, Ying-Chao Lin2, Ai-Ru Hsieh3

  • 1Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei, Taiwan; Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan; Institute of Information Science, Academia Sinica, Taipei, Taiwan.

Computational Biology and Chemistry
|March 19, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a new method, propensity score adjustment method (PSAM), to improve the power of genetic association studies. PSAM enhances the detection of causal single nucleotide polymorphisms (SNPs) by accounting for complex genetic influences, improving heritability estimates.

Keywords:
Gene–gene interactionLogistic regressionPropensity scoreRheumatoid arthritisSingle nucleotide polymorphismSingle-point association test

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Area of Science:

  • Genetics
  • Biostatistics
  • Computational Biology

Background:

  • Single nucleotide polymorphism (SNP) association studies often lack statistical power.
  • Complex genetic mechanisms involving multiple SNPs or environmental factors can obscure causal SNP effects.
  • Existing methods may miss causal variants due to unaddressed genetic influences.

Purpose of the Study:

  • To address the reduced power in single-point association studies.
  • To develop a method that accounts for genetic influences impacting causal variant detection.
  • To improve the power of single-locus association studies.

Main Methods:

  • Propensity score (PS) is used to adjust for the effects of SNPs influencing marginal association.
  • The propensity score adjustment method (PSAM) reduces dimensionality.
  • PSAM adjusts for the influence of linked or epistatic SNPs on disease status.

Main Results:

  • PSAM controls the type-I-error rate (<0.05) under the null hypothesis.
  • PSAM demonstrates >70% power, with a 15% improvement over logistic regression and PLINK.
  • PSAM identified 21 SNPs in the GAW16 dataset, with 6 linked to immunity and inflammation.

Conclusions:

  • PSAM enhances the significance of single-locus associations by adjusting for other SNPs.
  • The method aids in explaining missing heritability without complex models.
  • PSAM is a complementary exploratory tool, with a user-friendly SAS program available.