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...
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
Principles of Pharmacogenetics: Types of Genetic Variants01:27

Principles of Pharmacogenetics: Types of Genetic Variants

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...
Pharmacogenetic Phenotypes: Alterations in Pharmacokinetics, Drug Targets and Biologic Milieu01:29

Pharmacogenetic Phenotypes: Alterations in Pharmacokinetics, Drug Targets and Biologic Milieu

Genetic variations significantly influence drug response through pharmacokinetics, receptor interactions, and biologic milieu modifications. Pharmacokinetic alterations impact drug metabolism and clearance, affecting efficacy and toxicity. Variants in drug-metabolizing enzymes, such as CYP2C9 and CYP2C19, alter drug activation and elimination. For example, CYP2C9 loss-of-function variants require lower warfarin doses to prevent excessive bleeding, while CYP2C19 variants reduce clopidogrel...

You might also read

Related Articles

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

Sort by
Same author

Cervical Rotation-Traction Manipulation of Different Treatment Frequency in Cervical Radiculopathy: Study Protocol for a Randomized Controlled Trial.

Journal of pain research·2026
Same author

Protective mutations associated with APOE in Alzheimer's disease.

Molecular psychiatry·2026
Same author

Introgressed mitochondrial fragments from archaic hominins alter nuclear genome function in modern humans.

Science advances·2026
Same author

Causal Impact of Genetically Predicted Leptin Levels on Atrial Fibrillation Risk: Evidence from Bidirectional Mendelian Randomization Analysis.

Endocrine, metabolic & immune disorders drug targets·2026
Same author

Comment on: The association of bone density and hip fracture risk among Asian women.

Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA·2026
Same author

The miR-140-5p/BMP2 axis mediates T-2 toxin-related injury in both fetal and adult chondrocytes.

Toxicon : official journal of the International Society on Toxinology·2025
Same journal

Single-Cell and Multiomics Characterization of p21 in Cancer Progression and Therapeutic Sensitivity.

Human mutation·2026
Same journal

COL1A1 and SERPINE1 as Potential Therapeutic Targets in Diabetic Retinopathy: A Study Incorporating RNA Transcriptomics, Single-Cell RNA Sequencing, and Proteomics.

Human mutation·2026
Same journal

Autosomal Dominant Missense <i>DAG1</i> Variant Linked to Mild-Moderate LGMD R16.

Human mutation·2026
Same journal

RETRACTION: "Differential Effects of AKT1(p.E17K) Expression on Human Mammary Luminal Epithelial and Myoepithelial Cells".

Human mutation·2026
Same journal

Diagnostic Yield of Genome Sequencing in an Iranian Exome-Negative Autosomal-Recessive Intellectual Disability Cohort.

Human mutation·2026
Same journal

Exploring the Functional Impact of Individual <i>DDX41</i> Variants With a Fast and Robust Cell-Based Method.

Human mutation·2026
See all related articles

Related Experiment Video

Updated: Jun 20, 2026

Tissue Collection and RNA Extraction from the Human Osteoarthritic Knee Joint
06:06

Tissue Collection and RNA Extraction from the Human Osteoarthritic Knee Joint

Published on: July 22, 2021

Translating Osteoarthritis Genetic Risk Into Biomarkers: Opportunities, Pitfalls, and Implementation Considerations.

Tao Meng1,2, Lina Ma2,3, Xiaoqing Zhang1,2

  • 1Department of Orthopedics, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China, tjtcm.cn.

Human Mutation
|June 19, 2026
PubMed
Summary
This summary is machine-generated.

Converting osteoarthritis (OA) genetic discoveries into clinical biomarkers is challenging. This review outlines strategies to translate genetic insights into actionable tools for risk stratification and personalized OA patient care.

Keywords:
biomarker translationclinical implementationeQTL/pQTLfine-mappinggenetic riskosteoarthritispolygenic risk scoresingle-cell multiomicsspatial transcriptomicsvariant interpretation

Related Experiment Videos

Last Updated: Jun 20, 2026

Tissue Collection and RNA Extraction from the Human Osteoarthritic Knee Joint
06:06

Tissue Collection and RNA Extraction from the Human Osteoarthritic Knee Joint

Published on: July 22, 2021

Area of Science:

  • Genetics and genomics
  • Biomarker discovery
  • Osteoarthritis research

Background:

  • Osteoarthritis (OA) is a complex joint disease with variable patient outcomes, lacking effective early risk stratification tools.
  • Human genetic studies have identified numerous OA-associated loci, but translating these polygenic, noncoding signals into clinical biomarkers remains a significant hurdle.
  • Existing OA genomic progress necessitates systematic conversion of genetic discoveries into actionable biomarkers for risk prediction, subtyping, and therapeutic development.

Purpose of the Study:

  • To review strategies for converting osteoarthritis genetic risk into practical biomarker approaches.
  • To highlight clinically useful outputs from genetic discoveries, including risk stratification, molecular panels, and imaging-omics models.
  • To identify common pitfalls in OA biomarker development and outline requirements for responsible deployment.

Main Methods:

  • Combining statistical variant interpretation with joint-resolved biology to prioritize effector genes and regulatory modes.
  • Utilizing fine-mapping and molecular quantitative trait loci (QTL) evidence.
  • Integrating single-cell and spatial atlases of cartilage, synovium, and subchondral bone to establish anatomical and cellular contexts.

Main Results:

  • Three classes of clinically useful outputs are identified: polygenic risk-informed stratification, compact molecular panels from accessible biospecimens, and imaging-omics models.
  • Common reasons for biomarker pipeline failure in OA include uncertain variant-to-gene assignment, population portability issues, tissue accessibility bias, and technical platform variations.
  • Responsible deployment requires standardized assays, clinically meaningful evaluation, external replication in diverse cohorts, and clear governance.

Conclusions:

  • Translating OA genetic discoveries into clinically actionable biomarkers requires integrating statistical genetics with detailed biological context.
  • Developing robust OA biomarkers necessitates addressing challenges in variant interpretation, tissue accessibility, and cross-platform standardization.
  • Future OA biomarker development should focus on responsible deployment through rigorous validation, diverse cohort replication, and ethical governance.