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Optimum study design for detecting imprinting and maternal effects based on partial likelihood.

Fangyuan Zhang1, Abbas Khalili2, Shili Lin1

  • 1Department of Statistics, The Ohio State University, 1958 Neil Avenue, Columbus, Ohio 43210, U.S.A.

Biometrics
|August 20, 2015
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Summary

For common diseases, recruiting additional siblings in family studies boosts statistical power. For rare diseases, however, recruiting more families is more effective with limited genomic resources.

Keywords:
AscertainmentAssociation studyImprinting effectMaternal effectPartial likelihoodStudy design

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Area of Science:

  • Genetics
  • Biostatistics
  • Epidemiology

Background:

  • Genomic study resources are finite, necessitating optimal study designs for family-based research.
  • Choosing between genotyping additional siblings or recruiting more families impacts statistical power.

Purpose of the Study:

  • To systematically evaluate study designs for family-based genomic studies.
  • To determine the most resource-efficient design for detecting imprinting and maternal effects.

Main Methods:

  • Utilized a partial likelihood formulation for a systematic approach.
  • Derived asymptotic properties and computed information content for different designs.

Main Results:

  • Recruiting additional siblings is advantageous for common diseases.
  • For rare diseases, recruiting additional families provides more information with limited resources.

Conclusions:

  • Offers a practical strategy for selecting optimal family-based study designs.
  • Informs resource allocation for genomic studies based on disease prevalence.