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Related Concept Videos

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...
Genetic Variation01:25

Genetic Variation

Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
Genes exist in different versions called alleles, which...
Incomplete Dominance01:43

Incomplete Dominance

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.
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,...
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%...
Genomic Imprinting and Inheritance02:30

Genomic Imprinting and Inheritance

Diploid organisms inherit genetic material through chromosomes from both parents. Copies of the same gene are known as alleles. In most cases, both alleles are simultaneously expressed and allow various cellular processes to function optimally. If one of the alleles is missing or mutated, the expression of the other allele can compensate; however, this is not true for all genes.
The expression of some genes depends on which parent passed the gene to the offspring, through a phenomenon known as...

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Related Experiment Video

Updated: Jul 6, 2026

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

RankVar: machine learning-based variant ranking and reinterpretation for rare genetic diseases.

Yuan Zhang1, Mian Umair Ahsan1, Peng Wang1

  • 1Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.

Genome Medicine
|July 4, 2026
PubMed
Summary

RankVar, a new machine learning tool, effectively prioritizes disease-causing variants from genomic data for rare genetic disorders. It improves diagnostic accuracy and aids in identifying novel disease genes.

Keywords:
Machine learningRare genetic diseaseVariant prioritization

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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

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Last Updated: Jul 6, 2026

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

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

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
09:34

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

Area of Science:

  • Genomics
  • Bioinformatics
  • Machine Learning

Background:

  • Integrating biological knowledge and phenotype data with sequencing is crucial for identifying disease genes.
  • Prioritizing causative variants from large datasets remains a significant challenge in genetic studies.

Purpose of the Study:

  • To develop and evaluate RankVar, a machine learning algorithm for prioritizing causative variants in rare diseases.
  • To leverage clinical notes and genome/exome sequencing data for improved variant prioritization.

Main Methods:

  • RankVar employs a random forest classifier trained on approximately 1 million variants.
  • The algorithm was tested on independent datasets including Mendelian diseases, birth defects, and autism spectrum disorders.
  • Sequencing data and phenotype information were utilized for training and validation.

Main Results:

  • RankVar achieved high top-10 variant accuracy across multiple datasets (e.g., 90.0% for CHOP).
  • The algorithm successfully identified X-linked and Y-linked disease-causal variants.
  • RankVar facilitated the reinterpretation of unsolved cases, identifying 61 candidate causal variants for hearing loss.

Conclusions:

  • RankVar demonstrates superior performance compared to existing methods for variant prioritization.
  • The algorithm accommodates diverse genetic models and X/Y chromosome variants.
  • RankVar offers a valuable framework for genetic diagnosis, reinterpretation, and novel disease gene discovery.