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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,...
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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)...
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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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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Published on: June 21, 2018

Imputation of missing single nucleotide polymorphism genotypes using a multivariate mixed model framework.

M P L Calus1, R F Veerkamp, H A Mulder

  • 1Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, PO Box 65, 8200 AB Lelystad, The Netherlands. mario.calus@wur.nl

Journal of Animal Science
|March 2, 2011
PubMed
Summary
This summary is machine-generated.

Genotype imputation accuracy improves with surrounding marker information, especially when linkage disequilibrium is high. A multivariate mixed model is superior for low SNP density, while Beagle excels at high density.

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Area of Science:

  • Animal genetics
  • Genomic prediction
  • Statistical genetics

Background:

  • Accurate genotype imputation is crucial for genomic selection and breeding programs.
  • Existing imputation methods face challenges with missing data and varying marker densities.
  • Multivariate mixed models offer a framework to integrate relatedness and marker information.

Purpose of the Study:

  • To evaluate genotype imputation accuracy using a multivariate mixed model with surrounding marker information.
  • To compare this approach with population-based imputation algorithms (FastPHASE, Beagle) under various scenarios.
  • To assess the impact of marker density and linkage disequilibrium (LD) on imputation accuracy.

Main Methods:

  • Utilized a multivariate mixed model predicting genotypes using nearby loci and additive genetic relationships.
  • Employed Monte Carlo simulations to assess imputation accuracy across five missing genotype scenarios.
  • Compared imputation accuracies with FastPHASE and Beagle, and predicted accuracies using selection index theory.

Main Results:

  • Surrounding marker information improved imputation accuracy only when the individuals being imputed were genotyped for those markers.
  • Accuracy gains were limited at low LD but substantial at high LD between SNPs.
  • The multivariate mixed model outperformed Beagle at low SNP density with reduced SNP panels, while Beagle was superior at high SNP density.

Conclusions:

  • Multivariate BLUP models incorporating surrounding marker information enhance imputation accuracy when SNPs are in high LD.
  • The effectiveness of imputation depends on the genotypic information available for surrounding markers.
  • Selection index theory accurately predicts imputation accuracy within the multivariate mixed model framework.