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Inference for set-based effects in genetic association studies with interval-censored outcomes.

Ryan Sun1, Liang Zhu2, Yimei Li3

  • 1Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

Biometrics
|February 15, 2022
PubMed
Summary
This summary is machine-generated.

New statistical tests enable set-based genetic analysis for interval-censored outcomes, improving accuracy and power in genetic studies. These methods address a critical gap in analyzing complex disease data, such as bone density and fracture risk.

Keywords:
burden testinterval censoringoptimal combinationset-based inferencevariance components test

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Area of Science:

  • Genetics and Biostatistics
  • Computational Biology

Background:

  • Biomedical research generates vast genetic datasets with longitudinal disease information.
  • Interval-censored outcomes are common in these datasets, but analytical tools are limited.
  • Set-based inference, a popular genetics strategy, lacks methods for interval-censored data.

Purpose of the Study:

  • To develop novel statistical tests for set-based genetic association analysis with interval-censored outcomes.
  • To provide tools analogous to existing popular methods for continuous and binary data.
  • To enable robust genetic analysis of complex traits using longitudinal data.

Main Methods:

  • Developed the interval-censored sequence kernel association test (ICSKAT), a variance components test.
  • Created an interval-censored version of the Burden test.
  • Integrated ICSKAT and Burden into a combined test, ICSKATO.

Main Results:

  • Simulation studies demonstrated that the new methods offer improved Type I error rate control and increased statistical power compared to alternative approaches.
  • The developed tests effectively handle interval-censored data, a common challenge in genetic epidemiology.
  • The methods showed advantages over ad hoc alternatives in simulation settings.

Conclusions:

  • The new suite of tests (ICSKAT, Burden, ICSKATO) successfully extends set-based inference to interval-censored genetic data.
  • These tools facilitate the analysis of genetic compendiums with longitudinal disease data.
  • The methods were applied to study genes associated with bone mineral density deficiency and fracture risk.