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Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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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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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.
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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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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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Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
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Pleiotropy is the phenomenon in which a single gene impacts multiple, seemingly unrelated phenotypic traits. For example, defects in the SOX10 gene cause Waardenburg Syndrome Type 4, or WS4, which can cause defects in pigmentation, hearing impairments, and an absence of intestinal contractions necessary for elimination. This diversity of phenotypes results from the expression pattern of SOX10 in early embryonic and fetal development. SOX10 is found in neural crest cells that form melanocytes,...
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Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information
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MDVarP: modifier ~ disease-causing variant pairs predictor.

Hong Sun1, Yunqin Chen2, Liangxiao Ma3

  • 1Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine, Center for Biomedical Informatics, School of Medicine, Shanghai Children's Hospital, Shanghai Jiao Tong University, Shanghai, 200062, China. shpolor@163.com.

Biodata Mining
|October 8, 2024
PubMed
Summary
This summary is machine-generated.

We developed MDVarP, a novel computational model, to identify genetic interactions between modifier and disease-causing variants. This tool aids in understanding disease variability and can advance personalized medicine.

Keywords:
Interacting unitPhenotypical expressionPredictionVariants

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Genetic modifiers significantly influence disease phenotypes by altering the impact of disease-causing variants.
  • Individual disease manifestations vary due to these complex genetic interactions.
  • Identifying specific modifier-disease-causing variant interactions remains a significant challenge in genetic research.

Purpose of the Study:

  • To develop a computational framework for identifying modifier-disease-causing variant combinations.
  • To accurately predict genetic interactions that contribute to phenotypic variability.
  • To provide a tool for prioritizing variant pairs associated with phenotypic modulation.

Main Methods:

  • Development of MDVarP, an ensemble model utilizing 1000 random forest predictors.
  • Training and validation of the model using datasets with established genetic interaction evidence.
  • MDVarP outputs classification labels ('Associated-pair' or 'Nonrelevant-pair') and prediction scores.

Main Results:

  • MDVarP demonstrated high accuracy and precision in identifying modifier-disease-causing variant pairs.
  • The model successfully identified 25 novel modifier-disease-causing variant combinations with supporting evidence.
  • Validation using an independent dataset confirmed the model's predictive capabilities.

Conclusions:

  • MDVarP effectively prioritizes variant pairs involved in phenotypic modulation.
  • The framework enhances the understanding of functional contributions from disease-causing and modifier variants.
  • This approach has potential applications in personalized medicine and disease prevention strategies.