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

16.7K
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...
16.7K
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

19.9K
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,...
19.9K

You might also read

Related Articles

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

Sort by
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

Challenges and recommendations in establishing national human diversity genomic projects.

Nature methods·2026
Same author

Leveraging tumor dynamics to discover mutations influencing progression and treatment response for precision oncology.

Genome medicine·2026
Same author

Genome-wide analysis implicates inner ear development in Ménière disease.

American journal of human genetics·2026
Same author

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

Nature genetics·2026

Related Experiment Video

Updated: Apr 7, 2026

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

10.8K

A multivariate Bernoulli model to predict DNaseI hypersensitivity status from haplotype data.

Huwenbo Shi1, Bogdan Pasaniuc2, Kenneth L Lange3

  • 1Bioinformatics Interdepartmental Program, University of California, Los Angeles.

Bioinformatics (Oxford, England)
|July 4, 2015
PubMed
Summary

A new multivariate Bernoulli (MVB) model improves haplotype analysis for population inference and disease gene discovery. This model better captures genetic variant interactions, enhancing predictions of epigenetic marks like chromatin accessibility.

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

21.6K

Related Experiment Videos

Last Updated: Apr 7, 2026

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

10.8K
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

21.6K

Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Traditional hidden Markov models for haplotype analysis assume dependencies only between consecutive variants, limiting their accuracy.
  • The multivariate Bernoulli (MVB) distribution offers a more comprehensive approach by modeling interactions among all sets of variants.
  • This allows for the detection and utilization of long-range and higher-order interactions in genetic data.

Purpose of the Study:

  • To apply the MVB distribution for modeling haplotype data.
  • To develop an efficient algorithm for fitting sparse MVB models to haplotype data.
  • To evaluate the MVB model's performance in predicting DNaseI hypersensitivity (DH) status from population-scale haplotype data.

Main Methods:

  • Application of the multivariate Bernoulli (MVB) distribution to model haplotype data.
  • Development and implementation of a penalized estimation algorithm for sparse MVB models.
  • Fitting the MVB model to real-world haplotype and DNaseI hypersensitivity data from 59 individuals.

Main Results:

  • The MVB model successfully predicted DNaseI hypersensitivity status from genetic data, achieving a prediction R-squared of 0.12 in cross-validation.
  • Compared to linear regression (best linear unbiased prediction) and logistic regression, the MVB model demonstrated approximately 10% higher prediction accuracy (R-squared) on empirical data.
  • The MVB model effectively captures complex interactions within haplotype data.

Conclusions:

  • The MVB distribution provides a powerful and flexible framework for haplotype modeling, outperforming traditional methods.
  • This approach enhances the prediction of epigenetic marks, such as chromatin accessibility, from genetic data.
  • The developed method and software offer significant advantages for population inference and disease gene discovery.