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

16.4K
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...
16.4K
Principles of Pharmacogenetics: Types of Genetic Variants01:27

Principles of Pharmacogenetics: Types of Genetic Variants

72
The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...
72
Biostatistics: Overview01:20

Biostatistics: Overview

1.0K
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...
1.0K
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

19.0K
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.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
19.0K
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

1.2K
Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...
1.2K
Epistasis Analysis01:09

Epistasis Analysis

6.1K
Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
6.1K

You might also read

Related Articles

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

Sort by
Same author

nanoASM: Long-Read Allele-Specific DNA Methylation Profiling Enables Functional Annotation of Regulatory Noncoding Variants in Human Prostate Tissues.

bioRxiv : the preprint server for biology·2026
Same author

Performance of Polygenic Risk Scores for Atherosclerotic Cardiovascular Disease in the All of Us Program.

Circulation. Genomic and precision medicine·2026
Same author

A decision-oriented framework for genomic testing across the prostate cancer continuum.

Cancer genetics·2026
Same author

Polygenic scores for risk of pancreatic ductal adenocarcinoma: evaluation of novel and published models.

NPJ precision oncology·2026
Same author

Combining multiplexed assays of variant effect for enhanced BRCA2 variant classification.

Nature communications·2026
Same author

Personalized Environment and Genes Study (PEGS) Dataset-a resource for genomic, exposomic, and geospatial data.

Scientific data·2026

Related Experiment Video

Updated: Mar 14, 2026

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration
04:41

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Published on: January 9, 2020

19.5K

Incorporating Functional Annotations for Fine-Mapping Causal Variants in a Bayesian Framework Using Summary

Wenan Chen1, Shannon K McDonnell1, Stephen N Thibodeau2

  • 1Department of Health Sciences Research, Division of Biostatistics, Mayo Clinic, Rochester, Minnesota 55905.

Genetics
|September 23, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian framework for integrating functional annotations to enhance genome-wide association studies (GWAS). The new method improves causal variant identification accuracy, outperforming existing approaches.

Keywords:
Bayesian fine mappingannotationscausal variantssummary statistics

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

11.5K
In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

21.4K

Related Experiment Videos

Last Updated: Mar 14, 2026

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration
04:41

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Published on: January 9, 2020

19.5K
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

11.5K
In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

21.4K

Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical genomics

Background:

  • Functional annotations improve genome-wide association studies (GWAS) power and fine-mapping accuracy.
  • Optimal strategies for incorporating numerous functional annotations in GWAS remain unclear.

Purpose of the Study:

  • To develop a systematic Bayesian framework for incorporating functional annotations in GWAS.
  • To enhance the accuracy of identifying causal variants using summary statistics.

Main Methods:

  • Developed a Bayesian framework utilizing maximum a posteriori (MAP) estimation and cross-validation for parameter tuning.
  • Extended the CAVIARBF fine-mapping method to work with summary statistics.
  • Derived exact Bayes factor calculations for quantitative traits using summary statistics.

Main Results:

  • The proposed Bayesian framework achieved superior accuracy in identifying causal variants compared to PAINTOR and other annotation strategies.
  • Individual screening of annotations can lead to overly optimistic results when dealing with many moderate-effect annotations.
  • Application to lipid traits and cis-expression quantitative trait loci (eQTL) data demonstrated significant improvements, particularly in eQTL analysis.

Conclusions:

  • The proposed Bayesian framework offers a robust and accurate method for integrating functional annotations in GWAS.
  • The findings highlight the importance of systematic annotation incorporation over simple screening methods.
  • The approach effectively increases the identification of high-probability causal variants, especially in eQTL studies.