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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
Published on: June 21, 2018
Modeling genetic imprinting effects of DNA sequences with multilocus polymorphism data.
Sheron Wen1, Chenguang Wang, Arthur Berg
1Department of Statistics, University of Florida, Gainesville, Florida 32611, USA. xwen4@ufl.edu
Algorithms for Molecular Biology : AMB
|August 13, 2009
Summary
This study introduces a new algorithmic model for analyzing single nucleotide polymorphisms (SNPs) and genetic imprinting. The model identified a specific DNA haplotype influencing pain sensitivity, with effects varying by parental origin.
Area of Science:
- Genetics
- Genomics
- Bioinformatics
Background:
- Single nucleotide polymorphisms (SNPs) are common DNA variations crucial for understanding complex traits.
- Genetic imprinting, where gene expression depends on parental origin, adds complexity to inheritance patterns.
Purpose of the Study:
- To extend an algorithmic model for haplotype analysis of SNPs to estimate DNA sequence-level genetic imprinting effects.
- To identify DNA sequence variants with differential expression based on parental origin.
Main Methods:
- Developed and applied an algorithmic model for haplotype analysis of SNPs.
- Incorporated statistical inference to detect parental origin-specific expression.
- Analyzed genetic data from a pain genetics project.
Main Results:
- Identified a significant effect of DNA haplotype GAC (from three specific SNPs at the kappa-opioid receptor) on pain sensitivity.
- Demonstrated that the expression of this haplotype is significantly dependent on the parent from which it is inherited (p = 0.008).
Conclusions:
- The developed model provides a method for identifying parent-of-origin effects at the DNA sequence level.
- This haplotype discovery and statistical inference model can be a valuable tool for analyzing complex quantitative traits.
- Advances in SNP identification facilitate the application of such models in genetic research.
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