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Related Concept Videos

Single Nucleotide Polymorphisms-SNPs01:05

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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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Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
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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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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.
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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...
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The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Simultaneous SNP selection and adjustment for population structure in high dimensional prediction models.

Sahir R Bhatnagar1,2, Yi Yang3, Tianyuan Lu4,5

  • 1Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Québec, Canada.

Plos Genetics
|May 5, 2020
PubMed
Summary
This summary is machine-generated.

We developed ggmix, a penalized linear mixed effects model (LMM) for high-dimensional genetic data. This method simultaneously selects single nucleotide polymorphisms (SNPs) and adjusts for population structure, improving prediction accuracy for complex traits.

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Complex traits result from environmental factors and genetic variants.
  • Detecting genetic associations is challenging due to low statistical power and population structure.
  • Standard linear mixed effects models (LMM) are not suitable for high-dimensional genetic data.

Purpose of the Study:

  • To develop a penalized LMM (ggmix) for simultaneous SNP selection and population structure adjustment in high-dimensional settings.
  • To provide a computationally efficient and scalable algorithm for genetic prediction models.
  • To improve the accuracy of polygenic risk scores and instrumental variable selection.

Main Methods:

  • Developed a general penalized LMM with a single random effect (ggmix).
  • Implemented a blockwise coordinate descent algorithm with automatic tuning parameter selection.
  • Validated the method through simulations and three real-world genetic datasets.

Main Results:

  • ggmix produced more parsimonious models with higher prediction accuracy compared to two-stage and principal component adjustment methods.
  • The method demonstrated robustness with highly correlated markers and when causal SNPs were in the kinship matrix.
  • Achieved better performance in SNP selection and population structure adjustment.

Conclusions:

  • ggmix offers a powerful and efficient approach for analyzing high-dimensional genetic data.
  • The method enhances the construction of polygenic risk scores and instrumental variable selection in Mendelian randomization.
  • An R package for ggmix is available on CRAN for broader application.