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Related Concept Videos

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,...
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...
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.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...
Polygenic Traits01:18

Polygenic Traits

When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
Polygenic Traits01:18

Polygenic Traits

When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...

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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

Phenotype forecasting with SNPs data through gene-based Bayesian networks.

Alberto Malovini1, Angelo Nuzzo, Fulvia Ferrazzi

  • 1IRCCS Multimedica, Via Fantoli 16/15, I-20138 Milano, Italy. alberto.malovini@unipv.it

BMC Bioinformatics
|February 12, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for genetic analysis using Bayesian networks and single nucleotide polymorphisms (SNPs). The new gene-based approach improves predictive accuracy compared to traditional SNP or haplotype analyses.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Modeling

Background:

  • Bayesian networks are effective for learning genetic models from association studies.
  • High dimensionality in genetic data poses challenges for Bayesian network learning.
  • Dimensionality reduction techniques are crucial for efficient genetic data analysis.

Purpose of the Study:

  • To develop a new strategy for reducing dimensionality in genetic data for Bayesian network analysis.
  • To create a gene-based predictive model using single nucleotide polymorphisms (SNPs).
  • To improve the efficiency and accuracy of genetic association studies.

Main Methods:

  • Mapping SNPs related to the same gene into a single meta-variable.
  • Utilizing classification trees to assign states to meta-variables.
  • Applying a hold-out experiment with five repetitions for performance evaluation.

Main Results:

  • The proposed gene-based meta-variable approach achieved a mean accuracy of 64.28%.
  • This outperformed the traditional SNP-based network (58.99%) and haplotype-based network (54.57%).
  • The new method demonstrated consistently superior prediction performance across all test sets.

Conclusions:

  • The developed approach effectively derives a gene-based predictive model from SNP data.
  • The model is more parsimonious than single SNP models while identifying predictive SNP configurations.
  • This method offers a viable alternative for analyzing association study data.