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

15.4K
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...
15.4K
Fineness of Cement01:15

Fineness of Cement

497
The fineness of cement directly influences the rate of hydration, as the hydration begins at the surface of the cement particles. In addition to hydration, the fineness of cement is vital for various properties of concrete including workability, gypsum requirement, and long-term behavior. The fineness of cement is represented in terms of the specific surface of cement which is typically measured in square meters per kilogram, with several methods available for this determination.
Direct...
497
Fineness Modulus01:19

Fineness Modulus

1.5K
The fineness modulus (FM) of aggregate is a numerical index that measures the coarseness or fineness of the particles. It is calculated by adding the cumulative percentages of aggregate retained on each of a specified series of sieves and dividing the sum by 100.
Consider performing sieve analysis on sand through a set of ASTM sieves. The weight of aggregate retained in each sieve and pan placed at the bottom is recorded, as given in Column B of Table 1.
To determine the fineness modulus of...
1.5K
Association Areas of the Cortex01:21

Association Areas of the Cortex

9.2K
Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
9.2K
Associative Learning01:27

Associative Learning

1.3K
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
1.3K
Somatosensory, Motor, and Association Cortex01:23

Somatosensory, Motor, and Association Cortex

2.5K
The somatosensory cortex in the parietal lobes is crucial for interpreting sensory data such as touch, temperature, and proprioception. The somatosensory cortex, situated in the parietal lobes, plays a vital role in interpreting sensory information like touch, temperature, and proprioception—awareness of body position. This specialized brain region features an organized structure wherein neurons at the top primarily process sensations originating from the lower body. In contrast, those at...
2.5K

You might also read

Related Articles

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

Sort by
Same author

A Bayesian framework for longitudinal EHR and genetic discovery.

Nature·2026
Same author

Genetic and transcriptomic determinants of disseminated coccidioidomycosis identify a founder variant in <i>NLRX1</i> and ancestry-specific rare variants in immune response genes.

medRxiv : the preprint server for health sciences·2026
Same author

Quantitative trait loci mapping of gene expression and chromatin accessibility in primary fibroblasts reveals shared allelic effects between Latin American and European ancestries.

BMC genomics·2026
Same author

Embeddings of clinical codes enable knowledge-grounded AI in medicine.

NPJ digital medicine·2026
Same author

Integrative analyses elucidate transcriptional regulatory functions of risk alleles for metabolic liver disease.

Nature genetics·2026
Same author

<i>Trans</i>-eQTLs reveal the architecture of human gene regulatory networks.

medRxiv : the preprint server for health sciences·2026

Related Experiment Video

Updated: Jan 27, 2026

iCLIP - Transcriptome-wide Mapping of Protein-RNA Interactions with Individual Nucleotide Resolution
10:45

iCLIP - Transcriptome-wide Mapping of Protein-RNA Interactions with Individual Nucleotide Resolution

Published on: April 30, 2011

59.3K

Probabilistic fine-mapping of transcriptome-wide association studies.

Nicholas Mancuso1, Malika K Freund2, Ruth Johnson3

  • 1Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA. nmancuso@mednet.ucla.edu.

Nature Genetics
|March 31, 2019
PubMed
Summary

Transcriptome-wide association studies (TWAS) can identify genes linked to diseases, but linkage disequilibrium can create false signals. This study introduces a probabilistic method to accurately pinpoint causal genes for complex traits, even with limited expression data.

More Related Videos

Author Spotlight: Unveiling Neural Mechanisms Through Automated Evaluation of Motor Learning and Myelin Plasticity Studies Using the Erasmus Ladder
08:51

Author Spotlight: Unveiling Neural Mechanisms Through Automated Evaluation of Motor Learning and Myelin Plasticity Studies Using the Erasmus Ladder

Published on: December 15, 2023

2.0K
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

4.8K

Related Experiment Videos

Last Updated: Jan 27, 2026

iCLIP - Transcriptome-wide Mapping of Protein-RNA Interactions with Individual Nucleotide Resolution
10:45

iCLIP - Transcriptome-wide Mapping of Protein-RNA Interactions with Individual Nucleotide Resolution

Published on: April 30, 2011

59.3K
Author Spotlight: Unveiling Neural Mechanisms Through Automated Evaluation of Motor Learning and Myelin Plasticity Studies Using the Erasmus Ladder
08:51

Author Spotlight: Unveiling Neural Mechanisms Through Automated Evaluation of Motor Learning and Myelin Plasticity Studies Using the Erasmus Ladder

Published on: December 15, 2023

2.0K
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

4.8K

Area of Science:

  • Genetics
  • Bioinformatics
  • Systems Biology

Background:

  • Transcriptome-wide association studies (TWAS) identify gene-trait associations using predicted gene expression.
  • Linkage disequilibrium (LD) can lead to spurious associations at non-causal genes in TWAS.
  • Prioritizing causal genes for complex traits and diseases remains a challenge.

Purpose of the Study:

  • To develop a probabilistic framework to address confounding effects of LD in TWAS.
  • To accurately identify causal genes associated with complex traits and diseases.
  • To improve gene prioritization for functional studies.

Main Methods:

  • Developed a probabilistic model to account for correlations among TWAS signals due to LD.
  • Incorporated expression quantitative trait loci (eQTL) weights in expression prediction.
  • Leveraged cross-tissue expression prediction when causal tissue data is unavailable.
  • Applied the framework to analyze lipid traits.

Main Results:

  • Demonstrated that LD can induce significant gene-trait associations at non-causal genes.
  • The probabilistic framework assigns a probability to each gene in a risk region for explaining the observed association signal.
  • The approach maintains accuracy even without expression data from the causal tissue.
  • Generated credible sets of genes containing the causal gene with high confidence (e.g., 90%).

Conclusions:

  • The developed probabilistic framework effectively prioritizes causal genes in TWAS.
  • The method provides a reliable way to identify genes for functional validation.
  • Integrative analysis of lipid traits using this approach yielded genes with strong evidence for causality.