Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
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...
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are observed.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Proximal regularization of deep residual neural networks applied to high-dimensional genomic data.

Briefings in bioinformatics·2026
Same author

tvsfglasso: Time-varying scale-free graphical lasso for network estimation from time-series data.

PLoS computational biology·2025
Same author

HMFGraph: Novel Bayesian approach for recovering biological networks.

PLoS computational biology·2025
Same author

Developing risk prediction models for type 2 diabetes and assessing the role of circulating metabolic biomarkers in five independent Finnish cohorts with over 22,000 individuals.

Journal of clinical epidemiology·2025
Same author

Robust multi-outcome regression with correlated covariate blocks using fused LAD-lasso.

Journal of applied statistics·2025
Same author

Posterior estimation of longitudinal variance components from nonlongitudinal data using Bayesian Gaussian process model.

Genetics·2025

Related Experiment Video

Updated: May 18, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

A decision rule for quantitative trait locus detection under the extended Bayesian LASSO model.

Crispin M Mutshinda1, Mikko J Sillanpää

  • 1Department of Mathematics and Statistics, University of Helsinki, FIN-00014 Helsinki, Finland.

Genetics
|September 18, 2012
PubMed
Summary

This study introduces a new Bayesian decision rule for quantitative trait locus (QTL) mapping. This method improves QTL detection by using Bayes factors, avoiding computationally intensive permutations.

More Related Videos

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

Related Experiment Videos

Last Updated: May 18, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

Area of Science:

  • Genetics and Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Bayesian shrinkage analysis is a leading method for large-scale quantitative trait locus (QTL) mapping.
  • Current QTL detection methods often rely on phenotype permutation, which is computationally intensive and contradicts Bayesian principles.
  • A need exists for a more efficient and philosophically consistent Bayesian approach to QTL detection.

Purpose of the Study:

  • To propose a fully Bayesian decision rule for QTL detection.
  • To implement this rule within the extended Bayesian LASSO framework for QTL mapping.
  • To offer a method free from hypothetical data generation and computationally intensive permutations.

Main Methods:

  • Developed a novel decision rule based on Bayes factors for evaluating evidence of QTL presence.
  • Integrated the decision rule into the extended Bayesian LASSO model for QTL mapping.
  • Evaluated the method's performance using simulations and real-world genetic data.

Main Results:

  • The proposed Bayesian decision rule demonstrated remarkable performance in simulations.
  • The new method effectively detects QTLs without relying on phenotype permutation.
  • The approach aligns with Bayesian philosophy by avoiding hypothetical data generation.

Conclusions:

  • The fully Bayesian decision rule offers a superior alternative for large-scale QTL mapping.
  • This method enhances computational efficiency and adheres to Bayesian principles.
  • The approach is validated through simulations and practical application to real data.