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Incomplete Dominance01:43

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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.
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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.
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Related Experiment Video

Updated: Jul 23, 2025

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information
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Deep structured learning for variant prioritization in Mendelian diseases.

Matt C Danzi1, Maike F Dohrn1,2, Sarah Fazal1

  • 1Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA.

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|July 13, 2023
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Summary

MAVERICK, a new AI tool, accurately predicts pathogenic variants for Mendelian diseases. It aids in diagnosing patients and discovering novel disease genes, significantly advancing genetic research.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Automated variant evaluation is crucial for diagnosing monogenic diseases.
  • Identifying causative variants in known and novel genes accelerates research.

Purpose of the Study:

  • Introduce MAVERICK (Mendelian Approach to Variant Effect pRedICtion built in Keras).
  • Develop an AI tool to classify variants and assess pathogenicity in Mendelian diseases.

Main Methods:

  • MAVERICK utilizes an ensemble of transformer-based neural networks.
  • It classifies single nucleotide variants (SNVs) and indels for dominant or recessive inheritance.

Main Results:

  • MAVERICK outperforms existing pathogenicity prediction tools.
  • It ranked the causative variant in the top five for over 95% of 644 solved Mendelian disease cases.
  • It identified novel disease genes, including one for motor neuron disease.

Conclusions:

  • MAVERICK significantly advances automated causal variant identification in Mendelian diseases.
  • The tool aids in clinical diagnostics and genetic research discovery.