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

17.2K
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...
17.2K
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

98
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...
98
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

20.4K
A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
20.4K
Pleiotropy01:33

Pleiotropy

44.4K
Pleiotropy is the phenomenon in which a single gene impacts multiple, seemingly unrelated phenotypic traits. For example, defects in the SOX10 gene cause Waardenburg Syndrome Type 4, or WS4, which can cause defects in pigmentation, hearing impairments, and an absence of intestinal contractions necessary for elimination. This diversity of phenotypes results from the expression pattern of SOX10 in early embryonic and fetal development. SOX10 is found in neural crest cells that form melanocytes,...
44.4K
Incomplete Dominance01:43

Incomplete Dominance

32.9K
Gregor Mendel's work (1822 - 1884) was primarily focused on pea plants. Through his initial experiments, he determined that every gene in a diploid cell has two variants called alleles inherited from each parent. He suggested that amongst these two alleles, one allele is dominant in character and the other recessive. The combination of alleles determines the phenotype of a gene in an organism.
32.9K
Exon Recombination02:32

Exon Recombination

4.3K
The evolution of new genes is critical for speciation. Exon recombination, also known as exon shuffling or domain shuffling, is an important means of new gene formation. It is observed across vertebrates, invertebrates, and in some plants such as potatoes and sunflowers. During exon recombination, exons from the same or different genes recombine and produce new exon-intron combinations, which might evolve into new genes. 
Exon shuffling follows “splice frame rules.” Each exon...
4.3K

You might also read

Related Articles

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

Sort by
Same author

Improved Identification of Large-effect Rare Genetic Variants using Haplotype Aggregated Allele-specific Expression Data.

medRxiv : the preprint server for health sciences·2025
Same author

A Plot Twist: When RNA Yields Unexpected Findings in Paired DNA-RNA Germline Genetic Testing.

Genes·2025
Same author

Apolipoprotein B outperforms low density lipoprotein particle number as a marker of cardiovascular risk in the UK Biobank.

European journal of preventive cardiology·2025
Same author

Author Correction: Meta-prediction of coronary artery disease risk.

Nature medicine·2025
Same author

Multimodal AI correlates of glucose spikes in people with normal glucose regulation, pre-diabetes and type 2 diabetes.

Nature medicine·2025
Same author

Allele Specific Expression Quality Control Fills Critical Gap in Transcriptome Assisted Rare Variant Interpretation.

bioRxiv : the preprint server for biology·2025

Related Experiment Video

Updated: Apr 18, 2026

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
06:41

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Published on: August 20, 2019

14.5K

Symptom-driven idiopathic disease gene identification.

Bhuvan Molparia1,2, Phillip H Pham3, Ali Torkamani1,2,3,4,5

  • 1Scripps Translational Science Institute, La Jolla, California, USA.

Genetics in Medicine : Official Journal of the American College of Medical Genetics
|January 16, 2015
PubMed
Summary

We developed a novel gene ranking method using genetic networks and patient symptoms to identify disease-causing genes for rare genetic disorders. This approach aids in prioritizing candidate genes and finding more affected individuals.

More Related Videos

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.9K
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

Related Experiment Videos

Last Updated: Apr 18, 2026

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
06:41

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Published on: August 20, 2019

14.5K
Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.9K
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

Area of Science:

  • Genomics
  • Medical Genetics
  • Bioinformatics

Background:

  • Mendelian disorders stem from rare genetic variants, but many diseases lack identified causative genes.
  • The full spectrum of rare genetic diseases is likely underestimated, necessitating advanced discovery methods.
  • Whole-genome sequencing generates numerous candidate genes, complicating diagnosis due to disease rarity and varied symptoms.

Purpose of the Study:

  • To introduce a method for ranking candidate genes from family sequencing studies.
  • To develop a phenotype-informed network (PIN) ranking approach for prioritizing disease-causing genes.
  • To demonstrate the utility of symptom-driven network analysis for gene identification.

Main Methods:

  • Developed a genetic network-based method called phenotype informed network (PIN) ranking.
  • Applied PIN ranking to prioritize candidate genes identified through family-based sequencing.
  • Utilized a case study extending PIN ranking, where disease symptoms guided gene discovery.

Main Results:

  • Simulations confirmed the method's capability to identify the correct disease-causing gene in most instances.
  • The PIN-rank tool is publicly available for researchers.
  • The method successfully prioritizes candidate genes based on phenotypic data.

Conclusions:

  • A novel method has been created to prioritize candidate disease-causing genes.
  • This approach leverages genetic networks and phenotypic information.
  • The method is valuable for both candidate gene prioritization and identifying additional patients with rare genetic disorders.