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

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...
Statistical Analysis System (SAS)01:14

Statistical Analysis System (SAS)

SAS, short for Statistical Analysis System, is a powerful data analysis, management, and visualization tool. Developed by the SAS Institute in the early 1970s, SAS has evolved into a comprehensive software suite used across various industries for statistical analysis, business intelligence, and predictive modeling.
Applications: SAS finds applications in numerous fields, including healthcare for clinical trial analysis, finance for risk assessment, marketing for customer data analysis, and...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
Biostatistics: Overview01:20

Biostatistics: Overview

Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:

You might also read

Related Articles

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

Sort by
Same author

Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function.

Nature communications·2018
Same author

Cross-ancestry genome-wide association analysis of corneal thickness strengthens link between complex and Mendelian eye diseases.

Nature communications·2018
Same author

Common Coding Variants in <i>SCN10A</i> Are Associated With the Nav1.8 Late Current and Cardiac Conduction.

Circulation. Genomic and precision medicine·2018
Same author

Common and Rare Coding Genetic Variation Underlying the Electrocardiographic PR Interval.

Circulation. Genomic and precision medicine·2018
Same author

Correction: Single-trait and multi-trait genome-wide association analyses identify novel loci for blood pressure in African-ancestry populations.

PLoS genetics·2018
Same author

Pharmacometabolomic signature links simvastatin therapy and insulin resistance.

Metabolomics : Official journal of the Metabolomic Society·2018
Same journal

Abstracts from Specialized Centers of Research Excellence (SCORE) on Sex Differences 2025 annual meeting.

BMC proceedings·2026
Same journal

Conference abstracts the 1st UDOM scientific conference on health: healthy lives and well-being for all: opportunities and challenges.

BMC proceedings·2026
Same journal

Entrepreneurship beyond the lab: commercializing your creative outputs.

BMC proceedings·2026
Same journal

The need to strengthen laboratory leadership, systems, and networks to enhance outbreak detection and resilience in Africa: proceedings of a regional workshop.

BMC proceedings·2026
Same journal

Abstracts from the Globesync Community Research and Sustainability 2025 (GlobeCoReS 2025).

BMC proceedings·2026
Same journal

Bauru International Craniofacial Symposium: Comprehensive Care, Policy and Advocacy Proceedings.

BMC proceedings·2026
See all related articles

Related Experiment Video

Updated: May 24, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

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

Published on: December 10, 2012

Application of Bayesian regression with singular value decomposition method in association studies for sequence data.

Soonil Kwon1, Xiaofei Yan, Jinrui Cui

  • 1Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA. Xiuqing.Guo@cshs.org.

BMC Proceedings
|March 1, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new Bayesian regression with singular value decomposition (BRSVD) method for genetic association studies. BRSVD effectively analyzes multiple single-nucleotide polymorphisms (SNPs) simultaneously, overcoming limitations of traditional methods when the number of SNPs exceeds sample size.

More Related Videos

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Related Experiment Videos

Last Updated: May 24, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

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

Published on: December 10, 2012

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Genetic association studies often face a high-dimensional data problem where the number of single-nucleotide polymorphisms (SNPs) (k) far exceeds the sample size (n).
  • Conventional single-SNP association tests struggle with multiple testing issues and cannot simultaneously evaluate the impact of multiple SNPs.
  • Existing methods are inadequate for analyzing complex genetic data when k >> n.

Purpose of the Study:

  • To develop a novel statistical method for evaluating the simultaneous contribution of multiple SNPs to disease traits in genetic association studies.
  • To address the challenge of high-dimensional genetic data where the number of SNPs is much larger than the sample size (k >> n).
  • To provide a practical alternative to single-SNP association tests and penalized regression methods for analyzing sequence data.

Main Methods:

  • Developed the Bayesian regression with singular value decomposition (BRSVD) method.
  • Reduced the dimensionality of the design matrix from k to n using singular value decomposition.
  • Employed Markov chain Monte Carlo simulation with a Gibbs sampler and conjugate prior densities for model evaluation.
  • Incorporated permutation to generate empirical p-values.

Main Results:

  • The BRSVD method effectively reduces the dimensionality of genetic data.
  • Model evaluation through simulation demonstrated the method's robustness.
  • Application to Genetic Analysis Workshop 17 sequence data confirmed its practicality.
  • BRSVD showed comparable or superior performance to single-SNP association tests and penalized regression methods.

Conclusions:

  • The Bayesian regression with singular value decomposition (BRSVD) is a practical and effective method for genetic association studies with high-dimensional SNP data.
  • BRSVD offers a robust approach to simultaneously analyze multiple SNPs, overcoming limitations of conventional methods.
  • This method provides a valuable tool for identifying genetic factors contributing to disease pathophysiology in large-scale genetic datasets.