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Related Concept Videos

Multiple Regression01:25

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
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...
Multiple Comparison Tests01:13

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...

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Related Experiment Video

Updated: May 29, 2026

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

Multilocus association testing with penalized regression.

Saonli Basu1, Wei Pan, Xiaotong Shen

  • 1Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA.

Genetic Epidemiology
|September 17, 2011
PubMed
Summary
This summary is machine-generated.

Penalized regression methods, like Lasso, offer variable selection for genetic association studies. However, for hypothesis testing in low to moderately high-dimensional data, Lasso-based tests may not outperform existing global tests.

Related Experiment Videos

Last Updated: May 29, 2026

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:

  • Genetics
  • Biostatistics
  • Computational Biology

Background:

  • Multilocus association analysis often involves numerous genetic markers, some of which may not be relevant to the trait of interest.
  • Penalized regression techniques, such as Lasso, are increasingly utilized for their automatic variable selection capabilities in high-dimensional data.
  • While effective for prediction, the performance of penalized methods for statistical inference, including hypothesis testing, remains less understood.

Purpose of the Study:

  • To investigate technical aspects and alternative implementations of hypothesis testing within Lasso penalized logistic regression.
  • To compare the performance of Lasso-based hypothesis tests against existing global tests, including those designed for high-dimensional data.
  • To evaluate the utility of penalized regression for statistical inference in genetic association studies, specifically in the context of kidney transplant recipients and acute rejection.

Main Methods:

  • Utilized Lasso penalized logistic regression for hypothesis testing in multilocus association analysis.
  • Compared various implementations of Lasso-based hypothesis tests.
  • Evaluated performance against established global tests, including variance component tests for high-dimensional data.
  • Applied methods to a cohort of kidney transplant recipients to assess genetic variants associated with acute rejection.

Main Results:

  • Statistical tests derived from Lasso penalized regression were not consistently more powerful than existing global tests for low to moderately high-dimensional data.
  • The study identified potential limitations in using a single "best" model selected by penalized regression for hypothesis testing.
  • Combining multiple tests, each based on a candidate model from penalized regression, showed promise for improved inference.

Conclusions:

  • For genetic association studies with low to moderately high-dimensional data, Lasso penalized regression-based hypothesis tests may not offer a significant power advantage over traditional global tests.
  • Relying on a single model selected by penalized regression for inference might be suboptimal.
  • A more promising avenue for statistical inference using penalized regression involves aggregating results from multiple candidate models.