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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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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.
GWAS does not require the identification of the target gene involved in...
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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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Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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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.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

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In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
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Genomics02:02

Genomics

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Incomplete Dominance01:43

Incomplete Dominance

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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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Related Experiment Video

Updated: Jun 26, 2025

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

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Decoding polygenic diseases: advances in noncoding variant prioritization and validation.

Iris M Chin1, Zachary A Gardell1, M Ryan Corces1

  • 1Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA; Gladstone Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA; Department of Neurology, University of California San Francisco, San Francisco, CA, USA.

Trends in Cell Biology
|May 8, 2024
PubMed
Summary
This summary is machine-generated.

Genome-wide association studies (GWASs) identify genetic links to polygenic diseases. New methods now pinpoint causal variants in noncoding DNA using functional evidence, advancing disease mechanism research.

Keywords:
GWASfine-mappingfunctional genomicsnoncoding variantvariant effect predictionvariant prioritization

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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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Area of Science:

  • Genetics
  • Genomics
  • Molecular Biology

Background:

  • Genome-wide association studies (GWASs) are crucial for understanding the genetic basis of common polygenic diseases.
  • A key limitation of GWASs is their difficulty in assigning causality to specific genetic variants, particularly within the noncoding genome.
  • The noncoding genome's role in disease is increasingly recognized, yet challenging to decipher.

Purpose of the Study:

  • To review recent technological and methodological advancements for identifying causative variants in the noncoding genome.
  • To outline a workflow integrating analytical and empirical approaches for variant prioritization.
  • To highlight how this workflow facilitates moving from genetic associations to understanding molecular and cellular mechanisms of polygenic diseases.

Main Methods:

  • Leveraging orthogonal functional evidence to validate prioritized noncoding variants.
  • Employing analytical and empirical strategies for variant prioritization at scale.
  • Integrating diverse data types to bridge the gap between association and causality.

Main Results:

  • Significant progress has been made in identifying causative noncoding variants through integrated approaches.
  • Technological advances enable functional evidence generation at an unprecedented scale.
  • A clear pathway is emerging to link genetic variants to specific molecular and cellular disease mechanisms.

Conclusions:

  • The integration of advanced analytical and empirical methods is essential for deciphering the role of noncoding variants in polygenic diseases.
  • This approach moves beyond simple association to provide a foundation for mechanistic studies.
  • Future research can build upon this framework to develop genetically informed strategies for polygenic disease research.