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Related Concept Videos

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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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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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Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.
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Genetic Variation01:25

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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.
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Single Nucleotide Polymorphisms-SNPs01:05

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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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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
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Considerations in assessing germline variant pathogenicity using cosegregation analysis.

Sophie Belman1,2, Michael T Parsons3, Amanda B Spurdle3

  • 1University of Utah, Salt Lake City, UT, USA.

Genetics in Medicine : Official Journal of the American College of Medical Genetics
|August 11, 2020
PubMed
Summary
This summary is machine-generated.

Cosegregation analysis, crucial for genetic testing interpretation, is more accurately performed using Bayes factors than the meiosis counting method favored by ACMG/AMP guidelines. An improved penetrance model enhances Bayes factor accuracy, especially for high-penetrant variants.

Keywords:
Bayes factorcosegregation analysisfull-likelihood Bayes factormeiosis countingpenetrance

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Area of Science:

  • Genetics
  • Bioinformatics
  • Medical Genomics

Background:

  • The American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) provide guidelines for interpreting genetic test results.
  • Cosegregation analysis is a key component of these guidelines, with two primary methods: meiosis counting and Bayes factor-based quantitative analysis.
  • The ACMG/AMP guidelines currently utilize only meiosis counting, and the accuracy of both methods requires further investigation.

Purpose of the Study:

  • To evaluate the accuracy of meiosis counting versus Bayes factor-based cosegregation analysis.
  • To assess these methods for classifying cancer-associated genes.
  • To identify limitations and propose improvements for cosegregation analysis.

Main Methods:

  • Analysis of hypothetical, simulated, and real-life genetic data.
  • Evaluation of meiosis counting and Bayes factor approaches.
  • Development and application of an improved penetrance model for Bayes factor analysis.

Main Results:

  • Meiosis counting can lead to incorrect variant classifications in non-Mendelian inheritance patterns.
  • Existing Bayes factor methods may use inappropriate penetrance models.
  • An improved penetrance model enhances the accuracy of Bayes factor analysis, considering factors like pleiotropy and population genetics.
  • A webserver, COOL (Co-segregation Online), is available for accurate Bayes factor cosegregation analysis.

Conclusions:

  • An appropriate penetrance model significantly improves the accuracy of Bayes factor cosegregation analysis for high-penetrant variants.
  • Bayes factor analysis, particularly with an improved penetrance model, is a superior alternative to meiosis counting when feasible.
  • Accurate cosegregation analysis is vital for reliable genetic variant interpretation in clinical settings.