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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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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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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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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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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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Related Experiment Video

Updated: Jun 17, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Simplifying causal gene identification in GWAS loci.

Marijn Schipper1, Jacob Ulirsch2,3,4, Danielle Posthuma1,5

  • 1Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

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Summary

We developed CALDERA, a new tool for prioritizing genes linked to diseases identified through genome-wide association studies (GWAS). CALDERA outperforms existing methods, aiding in the discovery of novel drug targets.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Genome-wide association studies (GWAS) identify genetic variants associated with diseases, but pinpointing causal genes within these loci is difficult.
  • Current gene prioritization tools often rely on complex models and may contain biases.
  • Accurate gene prioritization is crucial for understanding disease mechanisms and developing targeted therapies.

Purpose of the Study:

  • To develop and validate a novel, data-driven gene prioritization tool named CALDERA.
  • To address limitations of existing tools, including complex models and potential biases.
  • To improve the identification of causal genes in GWAS loci for potential drug target discovery.

Main Methods:

  • Constructed a curated truth set of causal genes from 406 GWAS loci.
  • Developed CALDERA using a logistic regression model with L1 regularization, incorporating confounder correction.
  • Benchmarked CALDERA against FLAMES, L2G, and cS2G using three independent datasets of resolved GWAS loci.

Main Results:

  • CALDERA demonstrated superior performance, outperforming existing methods in two out of three independent benchmarking datasets.
  • CALDERA successfully prioritized genes with expected biological properties, such as high mutation intolerance (pLI > 90%).
  • The tool showed a statistically significant association with mutation intolerance (OR = 1.751, P = 8.45×10-3).

Conclusions:

  • CALDERA offers a powerful and reliable solution for prioritizing potentially causal genes within GWAS loci.
  • The tool's performance and ability to identify biologically relevant genes suggest its utility in genetic research.
  • CALDERA may facilitate the identification of novel genetics-driven drug targets, advancing precision medicine.