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Updated: May 8, 2026
05:53
Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
Published on: June 21, 2018
A fast multilocus test with adaptive SNP selection for large-scale genetic-association studies.
Han Zhang1, Jianxin Shi1, Faming Liang2
1Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
European Journal of Human Genetics : EJHG
|September 12, 2013
Summary
AdaJoint is a novel multilocus test for genetic association studies. It efficiently identifies key genetic variants, improving detection of disease susceptibility loci like in pancreatic cancer.
Area of Science:
- Genetics
- Statistical Genetics
- Genomic Association Studies
Background:
- Multiple genetic variants can jointly influence disease outcomes.
- Multilocus tests may offer greater power than single-marker tests for detecting susceptibility loci.
Purpose of the Study:
- To propose and evaluate AdaJoint, a new multilocus test for genetic association.
- To assess AdaJoint's performance in identifying joint effects of genetic markers.
Main Methods:
- AdaJoint employs a variable selection procedure to identify subsets of genetic markers with strong association signals.
- A computationally efficient algorithm is used for P-value evaluation, adjusting for multiple comparisons.
- The method was compared to existing multilocus tests via simulation studies.
Main Results:
- AdaJoint demonstrated robust performance across simulations, outperforming other commonly used multilocus tests.
- Analysis of pancreatic cancer genome-wide association studies revealed a strong association between CLPTM1L and cancer risk, driven by two single-nucleotide polymorphisms (SNPs).
- AdaJoint successfully mapped cis-regulating methylation quantitative trait loci in breast tissues, identifying multiple SNPs jointly regulating CpG sites.
Conclusions:
- AdaJoint is a powerful and efficient tool for multilocus association analysis.
- The method enhances the detection of complex genetic influences on disease risk and regulatory mechanisms.
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