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Tests for genetic association using family data.

Mei-Chiung Shih1, Alice S Whittemore

  • 1Department of Health Research and Policy, Stanford University, Stanford, California 94305, USA.

Genetic Epidemiology
|January 15, 2002
PubMed
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This study introduces new statistical methods for disease association studies using nuclear family data. These family-based methods offer greater power, especially with missing parental genetic information.

Area of Science:

  • Genetics
  • Biostatistics
  • Epidemiology

Background:

  • Disease association studies are crucial for understanding genetic contributions to health.
  • Existing methods like the Transmission Disequilibrium Test (TDT) have limitations in handling complex family structures and data types.

Purpose of the Study:

  • To develop and evaluate novel likelihood-based score statistics for disease association testing in nuclear families.
  • To extend family-based association testing to accommodate diverse phenotypes and missing data.

Main Methods:

  • Development of Nonfounder and Founder statistics, extensions of the TDT.
  • Application of statistics to analyze SRD5A2 gene polymorphism in prostate cancer families.
  • Simulations to compare power against Cox's partial likelihood score statistic for survival data.

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Main Results:

  • The Nonfounder statistic accommodates various phenotypes (quantitative, survival) and missing parental genotypes.
  • The Founder statistic weights parental genotypes based on their disease status and offspring affectedness.
  • Family-based statistics demonstrated superior power compared to Cox's score statistic when parental genotypes were frequently missing.

Conclusions:

  • The developed family-based score statistics provide a powerful and flexible approach for genetic association studies.
  • These methods are particularly advantageous in scenarios with incomplete parental genotype data.
  • The FGAP software is available for implementing these advanced statistical tests.