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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...
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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

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Related Experiment Video

Updated: Jun 2, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Exploiting genome structure in association analysis.

Seyoung Kim1, Eric P Xing

  • 1School of Computer Science, Carnegie Mellon University , Pittsburgh, Pennsylvania.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|May 10, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces stochastic block lasso, a novel method for genome-wide association studies. It improves the detection of trait-associated single-nucleotide polymorphisms (SNPs) by utilizing linkage disequilibrium structure.

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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) identify trait-associated single-nucleotide polymorphisms (SNPs).
  • Existing methods often overlook chromosomal structural information like recombination events.
  • Linkage disequilibrium (LD) structure between SNPs is crucial but not fully exploited.

Purpose of the Study:

  • To develop a novel association mapping method that incorporates prior knowledge of LD structure.
  • To enhance the power of detecting true associations and reduce false positives in GWAS.
  • To leverage recombination rates and distances between adjacent SNPs for improved marker identification.

Main Methods:

  • Proposed a new approach: stochastic block lasso for association mapping.
  • Employed a linear regression framework with genotypes as input and phenotype as output.
  • Utilized a sparsity-enforcing Laplacian prior and a first-order Markov process to model LD structure.

Main Results:

  • Demonstrated significant advantages in incorporating LD structure for marker identification.
  • Showcased improved power in detecting true associations compared to existing methods.
  • Validated findings on HapMap-simulated and mouse datasets.

Conclusions:

  • Integrating prior knowledge of LD structure substantially benefits whole-genome association studies.
  • Stochastic block lasso offers a powerful tool for identifying associated markers by considering genomic structure.
  • The method effectively combines information from multiple nearby SNPs for robust association mapping.