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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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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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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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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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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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Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
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Semantic Modeling for SNPs Associated with Ethnic Disparities in HapMap Samples.

Hyoyoung Kim1, Won Gi Yoo2, Junhyung Park2

  • 1Department of Agricultural Biotechnology, Seoul National University, Seoul 151-742, Korea.

Genomics & Informatics
|April 22, 2014
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Summary

This study identifies ethnicity-specific single-nucleotide polymorphisms (SNPs) and genes using semantic network modeling. Findings reveal genetic variability

Keywords:
HapMap Projectethnic groupsmodelingsemanticsingle nucleotide polymorphism

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Area of Science:

  • Genomics and Bioinformatics
  • Human Genetics
  • Systems Biology

Background:

  • Single-nucleotide polymorphisms (SNPs) are crucial for understanding human diseases and ethnic disparities.
  • Phenotypes are influenced by complex biological networks, necessitating in-depth analysis of SNP impacts.
  • Existing research highlights the need for ethnicity-specific genetic insights.

Purpose of the Study:

  • To identify ethnicity-specific single-nucleotide polymorphisms (SNPs) and associated genes.
  • To construct a semantic network model for analyzing the biological implications of ethnicity-specific SNPs.
  • To explore the relationship between genetic variability and ethnicity-specific health disparities.

Main Methods:

  • Identified ethnicity-specific SNPs by removing overlapping SNPs from HapMap samples.
  • Mapped ethnicity-specific SNPs to UCSC RefGene lists to identify corresponding genes.
  • Constructed a semantic network model using curated entities (Gene, Pathway, Disease, Chemical, Drug, ClinicalTrials, SNP) and their relationships.

Main Results:

  • Identified ethnicity-specific genes: 22 in European Americans (CEU), 25 in Japanese (JPT), and 332 in Africans (YRI).
  • Semantic modeling revealed associations with specific diseases (e.g., Hypertension, Pelvic infection), drugs (e.g., Methylphenidate), and pathways (e.g., Heostasis, Prostate cancer).
  • The majority of findings align with previous studies, supporting the role of genetic variability in ethnicity-specific disparities.

Conclusions:

  • Semantic network modeling is effective for analyzing the functional implications of ethnicity-specific SNPs.
  • Identified novel ethnicity-specific genes and their potential links to diseases and biological pathways.
  • Genetic variability significantly contributes to observed ethnicity-specific disparities in health outcomes.