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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,...
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

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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)...
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Published on: December 10, 2012

BAYESIAN MODEL SEARCH AND MULTILEVEL INFERENCE FOR SNP ASSOCIATION STUDIES.

Melanie A Wilson1, Edwin S Iversen, Merlise A Clyde

  • 1Department of Statistical Science, Duke University, Durham, North Carolina 27708-0251, USA.

The Annals of Applied Statistics
|December 24, 2010
PubMed
Summary

New Multilevel Inference of SNP Associations (MISA) methods improve genetic association studies. MISA offers higher statistical power for detecting associations and identifies novel variants, outperforming standard procedures.

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Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Genotyping technologies enable large-scale hypothesis-driven association studies.
  • Increasing study complexity necessitates advanced analytical methods to address challenges like multiple comparisons, SNP correlations, genetic parametrization, and missing data.

Purpose of the Study:

  • To introduce an efficient Bayesian model search strategy for genetic association studies.
  • To present the Multilevel Inference of SNP Associations (MISA) method for enhanced analysis of genetic markers and their parametrization.

Main Methods:

  • Developed an efficient Bayesian model search strategy to explore genetic markers and their parametrization.
  • Implemented MISA to compute multilevel posterior probabilities and Bayes factors at global, gene, and SNP levels.
  • Utilized prior distribution on SNP inclusion for intrinsic multiplicity correction.

Main Results:

  • MISA demonstrated higher statistical power in simulated datasets compared to standard procedures.
  • Applied to the North Carolina Ovarian Cancer Study (NCOCS), MISA identified variants missed by standard methods.
  • These identified variants were subsequently validated in independent studies.

Conclusions:

  • MISA provides a robust framework for analyzing complex genetic association studies.
  • The method offers improved power and identifies novel, externally validated genetic associations.
  • MISA is available as an R package on CRAN for broader accessibility.