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An efficient family-based association test using multiple markers.

Xin Xu1, Cyril Rakovski, Xiping Xu

  • 1Program for Population Genetics, Harvard School of Public Health, 665 Huntington Avenue, Boston, MA 02115, USA. xin_xu@harvard.edu

Genetic Epidemiology
|July 27, 2006
PubMed
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A new multi-marker family-based association test (T(LC)) effectively identifies trait loci by combining single-marker statistics. This novel method demonstrates superior power in genetic association studies compared to existing tests.

Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Genetic association studies utilize multiple markers to pinpoint trait loci.
  • Existing methods for multi-marker analysis have limitations in power and applicability.

Purpose of the Study:

  • To introduce a novel multi-marker family-based association test (T(LC)).
  • To evaluate the type-I error rate and power of T(LC) against competing methods.

Main Methods:

  • Development of T(LC), a test linearly combining single-marker test statistics with data-driven weights.
  • Numerical studies comparing T(LC) with global haplotype (T(H)), multi-marker (T(MM)), and Bonferroni-corrected single-marker (T(B)) tests.
  • Application of T(LC) to a real-world dataset for nicotine dependence.

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Main Results:

  • T(LC) maintains well-controlled type-I error rates.
  • T(LC) exhibited the highest power in identifying common trait loci across examined scenarios.
  • T(B) was the second most powerful, while T(MM) and T(H) showed the poorest performance, with T(MM) outperforming T(H) when parental data was missing.

Conclusions:

  • The proposed T(LC) test is a powerful and reliable tool for genetic association studies.
  • T(LC) offers an improvement over existing multi-marker and single-marker tests for trait locus localization.
  • The test's performance was validated on a nicotine dependence dataset.