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OSCA: a tool for omic-data-based complex trait analysis.

Futao Zhang1, Wenhan Chen1, Zhihong Zhu1

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We developed MOMENT, a new method to analyze DNA methylation (DNAm) and complex traits. MOMENT improves accuracy by accounting for unobserved factors, offering a more robust approach for omic data analysis.

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Area of Science:

  • Genetics and Genomics
  • Epigenetics
  • Bioinformatics

Background:

  • The increasing volume of omic data, including DNA methylation (DNAm), enables large-scale studies of complex traits.
  • Investigating associations between DNAm and complex traits requires methods that can handle unobserved confounders.

Purpose of the Study:

  • To introduce MOMENT, a mixed-linear-model-based method for testing associations between DNA methylation probes and traits.
  • To account for unobserved confounders by incorporating distal probes as random effects.

Main Methods:

  • Utilizing a mixed-linear-model framework.
  • Fitting distal probes within multiple random-effect components.
  • Simulations to evaluate performance against existing methods.

Main Results:

  • MOMENT demonstrates a lower false positive rate compared to existing methods.
  • The method shows increased robustness in association analyses.
  • MOMENT is implemented in the OSCA software package.

Conclusions:

  • MOMENT provides a more accurate and robust approach for DNA methylation association studies.
  • The OSCA package offers a versatile tool for omic data analysis, including the MOMENT method.