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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

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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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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...
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Point mutations are genetic alterations involving the change of a single nucleotide base pair in DNA. Depending on how the alteration affects protein synthesis, they can lead to various consequences.Point mutations fall into the following types:Silent mutations occur when a nucleotide change does not alter the amino acid sequence due to the redundancy of the genetic code. For instance, changing ACC to ACA still encodes threonine, leaving the protein function unaffected. This occurs because...
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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,...
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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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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
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MAPPIN: a method for annotating, predicting pathogenicity and mode of inheritance for nonsynonymous variants.

Nehal Gosalia1,2, Aris N Economides1,2, Frederick E Dewey1

  • 1Regeneron Genetics Center, Tarrytown, NY 10591, USA.

Nucleic Acids Research
|October 5, 2017
PubMed
Summary

MAPPIN accurately predicts the pathogenicity and mode of inheritance for nonsynonymous single nucleotide variants (nsSNVs). This method distinguishes dominant, recessive, and benign nsSNVs, improving variant prioritization for genetic diseases.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Nonsynonymous single nucleotide variants (nsSNVs) are a significant cause of genetic diseases.
  • Current prediction tools struggle to differentiate between dominant and recessive disease-causing variants.
  • Understanding the functional impact and inheritance mode of nsSNVs is crucial for genetic diagnostics.

Purpose of the Study:

  • To develop a novel prediction method, MAPPIN, for annotating, predicting pathogenicity, and determining the mode of inheritance for nsSNVs.
  • To differentiate nsSNVs with dominant, recessive, and benign effects.
  • To enhance the prioritization of disease-causing genetic variants.

Main Methods:

  • Utilized a random forest algorithm for variant classification.
  • Applied MAPPIN to curated datasets of Mendelian disease mutations.
  • Validated performance against independent datasets like Deciphering Developmental Disorders Study and Human Gene Mutation Database.
  • Compared MAPPIN's performance with existing tools such as CADD and Eigen.

Main Results:

  • MAPPIN accurately predicted pathogenicity for all tested Mendelian disease mutations.
  • Achieved 70.3% accuracy in predicting the mode of inheritance for nsSNVs.
  • Demonstrated high accuracy in predicting pathogenicity (87.3%) and mode of inheritance (78.5%) on the Deciphering Developmental Disorders Study dataset.
  • Significantly discriminated between dominant and recessive mutations from the Human Gene Mutation Database.
  • Outperformed CADD and Eigen in predicting disease inheritance modes.

Conclusions:

  • MAPPIN is the first algorithm capable of predicting both pathogenicity and mode of inheritance for nsSNVs.
  • Provides an additional layer of information crucial for variant interpretation and clinical decision-making.
  • Offers a significant advancement in the field of genetic variant analysis and disease gene discovery.