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Related Concept Videos

Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
Gene Evolution - Fast or Slow?02:05

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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
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Genome-wide Association Studies-GWAS01:11

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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.
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Related Experiment Video

Updated: Jun 7, 2026

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Published on: August 21, 2016

Genomic selection and complex trait prediction using a fast EM algorithm applied to genome-wide markers.

Ross K Shepherd1, Theo H E Meuwissen, John A Woolliams

  • 1School of Information and Communication Technology, CQUniversity, Rockhampton 4702, Australia. r.shepherd@cqu.edu.au

BMC Bioinformatics
|October 26, 2010
PubMed
Summary

A new expectation-maximization (EM) algorithm, emBayesB, offers fast and accurate genomic selection for complex traits. This method efficiently maps quantitative trait loci (QTL) using dense single nucleotide polymorphism (SNP) data, matching Bayesian accuracy in a fraction of the time.

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Last Updated: Jun 7, 2026

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Published on: August 21, 2016

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:

  • Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Dense genome-wide markers (e.g., single nucleotide polymorphisms - SNPs) are valuable for human disease studies and livestock breeding.
  • Genome-wide association studies (GWAS) link SNPs to complex traits, assuming linkage disequilibrium (LD) with quantitative trait loci (QTL).
  • Full Bayesian models can incorporate prior information but are computationally intensive.

Purpose of the Study:

  • To develop a computationally efficient algorithm for genomic selection and QTL mapping.
  • To implement an expectation-maximization (EM) algorithm that incorporates prior information on SNP effects and LD with QTL.
  • To demonstrate the performance of the proposed algorithm using simulated genomic selection data.

Main Methods:

  • Proposed an expectation-maximization (EM) algorithm named emBayesB.
  • emBayesB models the proportion of SNPs in LD with QTL and incorporates prior distributions of SNP effects.
  • Calculated posterior probabilities of LD with QTL and estimated mixture prior hyperparameters for each SNP.

Main Results:

  • The emBayesB algorithm achieves prediction accuracy comparable to full Bayesian analysis but is significantly faster.
  • The algorithm accurately identified QTL explaining over 1% of the total genetic variation.
  • A computational approach for very large SNP panels was developed.

Conclusions:

  • emBayesB provides a fast and accurate method for genomic selection and complex trait prediction using dense SNP data.
  • The algorithm effectively maps QTL, offering similar accuracy to Bayesian methods with substantially reduced computational time.
  • This approach enhances the feasibility of utilizing genome-wide SNP data in breeding programs and disease studies.