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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...

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Related Experiment Video

Updated: Jun 21, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

A comprehensive approach to haplotype-specific analysis by penalized likelihood.

Jung-Ying Tzeng1, Howard D Bondell

  • 1Department of Statistics, North Carolina State University, Campus Box 7566, Raleigh NC 27695, USA. jytzeng@stat.ncsu.edu

European Journal of Human Genetics : EJHG
|July 9, 2009
PubMed
Summary

This study introduces a novel penalized regression method for haplotype analysis, offering a more powerful way to understand gene effects in disease. The approach reveals haplotype structures without needing a baseline, improving genetic association studies.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Related Experiment Videos

Last Updated: Jun 21, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

  • Genetics
  • Statistical Genetics
  • Computational Biology

Background:

  • Haplotypes are crucial for understanding candidate gene roles in disease etiology.
  • Standard haplotype analysis often lacks power and fails to fully reveal complex haplotype information.
  • Current methods typically rely on a baseline haplotype, limiting comprehensive effect structure depiction.

Purpose of the Study:

  • To propose a penalized regression approach for systematic evaluation of haplotype effect patterns and structures.
  • To develop a model-based haplotype analysis for detecting and characterizing haplotypic association signals.
  • To provide a tool for comprehending candidate regions identified from genomic scans.

Main Methods:

  • A penalized regression model with an L1 penalty on pairwise haplotype effect differences.
  • Simultaneous estimation and comparison of all haplotype effects, avoiding baseline selection.
  • Theoretical design of penalty weights to balance likelihood and penalty terms.

Main Results:

  • The proposed method effectively detects and characterizes haplotypic association signals.
  • It outputs a haplotype group structure based on effect size.
  • Simulation studies demonstrate superior performance in identifying haplotype effect structures compared to traditional methods.

Conclusions:

  • The penalized regression approach offers a powerful and informative method for haplotype association analysis.
  • This model-based strategy enhances the understanding of genetic contributions to disease.
  • The method provides a robust tool for analyzing complex genetic data and candidate regions.