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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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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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Correlations02:20

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Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
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Each human somatic cell contains 6 billion base-pairs of DNA. Each base-pair is 0.34 nm long, which means that each diploid cell contains a staggering 2 meters of DNA. How is such a long DNA strand packed inside a nucleus measuring only 10 - 20 microns in diameter? 
The chromatin
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Most chemical reactions in cells require enzymes—biological catalysts that speed up the reaction without being consumed or permanently changed. They reduce the activation energy needed to convert the reactants into products. Enzymes are proteins, that usually work by binding to a substrate—a reactant molecule that they act upon.
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Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
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Calculating and Interpreting the Linear Correlation Coefficient01:11

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The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
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Related Experiment Video

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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plmmr: an R package to fit penalized linear mixed models for genome-wide association data with complex correlation

Tabitha K Peter1, Anna C Reisetter1, Yujing Lu1

  • 1Department of Biostatistics, University of Iowa, 145 N Riverside Dr, Iowa 52242, United States.

Briefings in Bioinformatics
|January 31, 2026
PubMed
Summary

We developed plmmr, an R package for penalized linear mixed models, to address confounding in high-dimensional data. It estimates correlations to enhance prediction and handles large datasets using memory mapping for genome-wide association studies.

Keywords:
Rlassolinear mixed modelspenalized regressionstatistical genetics

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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Area of Science:

  • Genetics
  • Statistical modeling
  • Bioinformatics

Background:

  • High-dimensional data analysis presents challenges due to observational confounding.
  • Accurate correlation estimation is crucial for robust regression modeling.

Purpose of the Study:

  • Introduce plmmr, an open-source R package for penalized linear mixed models.
  • Improve prediction accuracy in high-dimensional datasets by estimating inter-observation correlations.

Main Methods:

  • Implement penalized linear mixed models using R.
  • Utilize memory mapping for efficient analysis of genome-scale data exceeding RAM.
  • Develop a file-backing approach for handling large datasets.

Main Results:

  • plmmr effectively estimates correlations in high-dimensional data.
  • The package enhances prediction using the best linear unbiased predictor.
  • Demonstrated computational efficiency on real genome-wide association studies data.

Conclusions:

  • plmmr provides a scalable solution for analyzing large, high-dimensional genetic datasets.
  • The package facilitates improved predictive modeling in the presence of complex correlation structures.
  • Open-source availability promotes wider adoption in genetic research.