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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Mixed model with correction for case-control ascertainment increases association power.

Tristan J Hayeck1, Noah A Zaitlen2, Po-Ru Loh3

  • 1Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.

American Journal of Human Genetics
|April 21, 2015
PubMed
Summary

We developed a new statistical method, the liability-threshold mixed linear model (LTMLM), for genetic association studies. This approach improves power for low-prevalence diseases in case-control studies, outperforming existing methods.

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Area of Science:

  • Genetics
  • Biostatistics
  • Epidemiology

Background:

  • Mixed-model methods are standard for genetic association studies.
  • Existing methods lose power in case-control studies, especially for low-prevalence diseases.
  • No prior solution addressed this power loss.

Purpose of the Study:

  • Introduce a novel association statistic, the liability-threshold mixed linear model (LTMLM).
  • Demonstrate LTMLM's improved power and controlled false-positive rates for low-prevalence diseases.
  • Address the power deficit of current methods in case-control genetic studies.

Main Methods:

  • Developed a χ(2) score statistic using posterior mean liabilities (PMLs) within the liability-threshold model.
  • Estimated individual PMLs considering case-control status and the genetic relationship matrix (GRM).
  • Utilized a multivariate Gibbs sampler for PML estimation and Haseman-Elston regression for heritability.

Main Results:

  • LTMLM exhibited a well-controlled false-positive rate in simulations.
  • LTMLM demonstrated superior power compared to existing mixed-model methods for low-prevalence diseases.
  • A real-world dataset (Wellcome Trust Case Control Consortium 2) showed a 4.3% improvement in χ(2) statistics with LTMLM.

Conclusions:

  • LTMLM offers a significant advancement for genetic association studies in case-control designs.
  • The method is particularly beneficial for diseases with low prevalence, enhancing statistical power.
  • Future applications with larger sample sizes are expected to yield even greater power increases.