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Related Concept Videos

Correlations02:20

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Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
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A Distance-Based Kernel Association Test Based on the Generalized Linear Mixed Model for Correlated Microbiome

Hyunwook Koh1, Yutong Li2, Xiang Zhan3

  • 1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.

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|June 4, 2019
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Summary

Researchers developed a new statistical method, aGLMM-MiRKAT, to analyze microbiome data from family or longitudinal studies. This method effectively handles various host traits and avoids the need to pre-select distance measures, offering robust power and accurate results for microbiome association studies.

Keywords:
adaptive association analysiscommunity-level association analysiscorrelated microbiome studiesdistance-based association analysislongitudinal microbiome studiesmicrobiome association studies

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Area of Science:

  • Microbiome research
  • Statistical genetics
  • Human health and disease

Background:

  • Family-based and longitudinal studies are crucial for understanding the human microbiome's role in host traits.
  • Correlated data in these studies pose analytical challenges for standard statistical methods.
  • Existing methods like cSKAT are limited to Gaussian traits and single distance measures.

Purpose of the Study:

  • To develop a flexible statistical method for analyzing microbiome data with diverse host trait types (Gaussian, Binomial, Poisson).
  • To introduce an adaptive approach that eliminates the need to select an optimal distance measure.
  • To provide a robust and powerful tool for microbiome association studies in complex designs.

Main Methods:

  • Introduced GLMM-MiRKAT, a distance-based kernel association test using generalized linear mixed models (GLMM).
  • Developed an adaptive version, aGLMM-MiRKAT, to automatically handle multiple distance measures.
  • Validated the method through extensive simulations and application to real familial and longitudinal microbiome data.

Main Results:

  • Simulations demonstrated that aGLMM-MiRKAT controls type I error rates and exhibits robust power across various scenarios.
  • Application to real data revealed significant associations between microbial community composition and BMI status.
  • The study also identified disparities in microbial composition related to antibiotic use frequency.

Conclusions:

  • aGLMM-MiRKAT is a versatile and powerful analytical tool for microbiome association studies with diverse trait types.
  • The adaptive nature of aGLMM-MiRKAT simplifies analysis by removing the need for optimal distance measure selection.
  • This method offers valid statistical inference and broad applicability for complex human microbiome research.