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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,...
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%...
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...
Genetic Screens02:46

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...

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Updated: Jun 14, 2026

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

Simultaneous QTL detection and genomic breeding value estimation using high density SNP chips.

Roel F Veerkamp1, Klara L Verbyla, Han A Mulder

  • 1Animal Breeding and Genomics Centre, ASG Wageningen UR, PO Box 65, 8200 AB Lelystad, The Netherlands . Roel.Veerkamp@wur.nl.

BMC Proceedings
|April 13, 2010
PubMed
Summary

This study detected quantitative trait loci (QTL) and predicted breeding values using genomic selection models. Results show models are robust, but trait complexity impacts accuracy, highlighting the need to understand QTL number before selection.

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Area of Science:

  • Animal Genetics
  • Quantitative Genetics
  • Genomic Selection

Background:

  • Analysis of a simulated dataset from the 13th QTL-MAS workshop.
  • Objective: detect quantitative trait loci (QTL) and predict breeding values for animals lacking phenotypic data.
  • Application of multiple parameterizations using Gibbs sampling, considering all single nucleotide polymorphisms (SNPs) simultaneously.

Purpose of the Study:

  • To detect QTL within a simulated dataset.
  • To predict breeding values for animals without phenotypic information using genomic selection.
  • To evaluate different parameterizations for genomic prediction models.

Main Methods:

  • Utilized Gibbs sampling for parameter estimation.
  • Employed multiple parameterizations considering all SNPs simultaneously.
  • Compared prediction accuracy across different models, including one using selected SNPs associated with detected QTL.

Main Results:

  • Fourteen QTL were successfully detected across different time points.
  • High correlations in estimated breeding values were observed between most models.
  • A model using 14 SNPs associated with QTL showed near-unity correlations with full parameterizations.

Conclusions:

  • Nine out of 18 target QTL were detected; six for inflection point were missed.
  • Genomic selection models demonstrated considerable robustness regarding the accuracy of estimated breeding values.
  • Investigating the number of QTL underlying quantitative traits is crucial before selecting a genomic selection model.