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Related Concept Videos

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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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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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: Jul 6, 2025

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Using multi-scale genomics to associate poorly annotated genes with rare diseases.

Christina Canavati1,2, Dana Sherill-Rofe1, Lara Kamal2,3

  • 1Department of Developmental Biology and Cancer Research, Institute of Medical Research - Israel-Canada, The Hebrew University of Jerusalem, Jerusalem, 9112102, Israel.

Genome Medicine
|January 4, 2024
PubMed
Summary

EvORanker, a new algorithm, links mutated genes to clinical phenotypes using genomic data. It accurately identifies disease genes, especially for poorly annotated ones, aiding in diagnosing genetic disorders.

Keywords:
DLGAP2EvORankerGene-based prioritizationLPCAT3

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Next-generation sequencing (NGS) has advanced genetic disorder research but leaves many patients undiagnosed.
  • Undiagnosed cases stem from hard-to-detect variants and mutations in novel disease genes.
  • Identifying disease-causing genes requires sophisticated analytical tools.

Purpose of the Study:

  • To introduce EvORanker, an algorithm for linking mutated genes to clinical phenotypes.
  • To improve the diagnosis of genetic disorders by prioritizing candidate disease genes.
  • To provide a user-friendly web tool for gene prioritization.

Main Methods:

  • EvORanker integrates clinical data, multi-scale phylogenetic profiling, and omics data.
  • The algorithm was validated using solved exomes and simulated genomes.
  • Comparative analysis with existing methods was performed.

Main Results:

  • EvORanker identified the correct disease gene in 69% of top candidates and 95% within the top 5.
  • The algorithm demonstrated superior performance for poorly annotated genes compared to existing methods.
  • EvORanker successfully identified candidate genes in previously unsolved genetic syndromes.

Conclusions:

  • Clade-based phylogenetic profiling is an effective strategy for prioritizing disease genes.
  • EvORanker demonstrates high efficacy in associating poorly annotated genes with patient phenotypes.
  • The EvORanker web tool is publicly accessible for research use.