Multivariate linkage analysis using the electrophysiological phenotypes in the COGA alcoholism data
2005

Linkage Analysis of Alcoholism Data

Sample size: 819 publication Evidence: moderate

Author Information

Author(s): Heping Zhang, Xiaoyun Zhong, Yuanqing Ye

Primary Institution: Yale University School of Medicine

Hypothesis

Multivariate linkage analysis using several correlated traits may provide greater statistical power to detect susceptibility genes.

Conclusion

The study identified candidate markers associated with alcoholism, particularly on chromosome 4.

Supporting Evidence

  • The multivariate analysis found linkage evidence on chromosome 4 around markers GABRB1 and FABP2.
  • Previous studies have identified GABRB1 as associated with alcoholism.
  • The analysis included 2,457 measurements from 819 individuals.

Takeaway

The researchers looked at how different traits related to alcoholism can help find genes that might cause it, and they found some important markers.

Methodology

The study used a variance-component model to analyze repeated measurements of electrophysiological phenotypes.

Limitations

The multivariate analysis may introduce noise if one phenotype has a strong linkage signal while others do not.

Participant Demographics

The dataset includes 143 nuclear and multigenerational families with a total of 819 individuals.

Statistical Information

P-Value

0.00006

Statistical Significance

p<0.0001

Digital Object Identifier (DOI)

10.1186/1471-2156-6-S1-S118

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