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

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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%...
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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,...
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...
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

You might also read

Related Articles

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

Sort by
Same author

AI-guided analysis of human pancreatic islet sociology reveals distinct cell compositional changes in type 1 diabetes.

bioRxiv : the preprint server for biology·2026
Same author

Adaptive Fisher's method using weakly geometric grid for combining <i>p</i>-values with application to COVID-19 surveillance.

Journal of the Royal Statistical Society. Series C, Applied statistics·2026
Same author

Points to consider for the reporting of variants of uncertain significance in germline genetic and genomic testing: A statement of the American College of Medical Genetics and Genomics (ACMG).

Genetics in medicine : official journal of the American College of Medical Genetics·2026
Same author

Impact of sex chromosomes and gonad type in stress susceptibility in corticostriatal brain regions.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Unraveling Tissue-Specific Molecular Signatures and Convergent Pathway Enrichments in Suicidal Behavior.

bioRxiv : the preprint server for biology·2026
Same author

Quantitative and qualitative patient-reported analysis of misdiagnosis and/or late diagnosis of metastatic lobular cancer.

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

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026
Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026
Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Jun 29, 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

Smarter clustering methods for SNP genotype calling.

Yan Lin1, George C Tseng, Soo Yeon Cheong

  • 1Department of Biostatistics, Department of Medicine, Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA. liny@upmc.edu

Bioinformatics (Oxford, England)
|October 2, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces novel genotype calling methods that leverage family data and external information for improved accuracy in both disomic and trisomic individuals. The developed methods enhance SNP genotyping by incorporating pedigree information, outperforming existing approaches.

More Related Videos

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Related Experiment Videos

Last Updated: Jun 29, 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

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Standard single nucleotide polymorphism (SNP) genotyping methods often fail to optimize genotype calling by ignoring family pedigree data.
  • Model-based clustering can improve genotype calls by utilizing prior information on measurement distributions.
  • Genotyping trisomic individuals presents unique challenges not addressed by existing literature.

Purpose of the Study:

  • To investigate the impact of incorporating external information into clustering algorithms for disomic and trisomic genotype calling.
  • To propose and evaluate new family-based genotype calling methods.
  • To address the specific challenges of genotyping trisomic individuals.

Main Methods:

  • Developed two novel methods for genotype calling using family data: a modified K-means algorithm incorporating pedigree information and a likelihood-based method combining mixture models with pedigree data.
  • Compared the performance of these new methods against existing approaches using simulation studies.
  • Validated the methods on a real dataset from the Illumina platform.

Main Results:

  • The proposed family-based methods demonstrate improved genotype calling accuracy compared to existing methods.
  • Incorporating pedigree information significantly enhances the performance of clustering algorithms for SNP genotyping.
  • The likelihood-based method shows particular promise for complex genotyping scenarios, including trisomic data.

Conclusions:

  • External information, especially family pedigree data, is crucial for accurate SNP genotype calling.
  • The developed methods offer a significant advancement in genotyping technology, particularly for complex genetic data and trisomic individuals.
  • The R package 'SNPCaller' provides accessible tools for implementing these enhanced genotype calling strategies.