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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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...
Incomplete Dominance01:43

Incomplete Dominance

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.
Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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 SNPs

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: May 27, 2026

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information
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Finding disease variants in Mendelian disorders by using sequence data: methods and applications.

Iuliana Ionita-Laza1, Vlad Makarov, Seungtai Yoon

  • 1Department of Biostatistics, Columbia University, New York, NY 10032, USA. ii2135@columbia.edu

American Journal of Human Genetics
|December 6, 2011
PubMed
Summary

This study introduces a novel statistical method for identifying genetic disease causes. The approach improves gene ranking for Mendelian and complex traits by providing statistical uncertainty and adjusting for background variation.

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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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09:34

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

Area of Science:

  • Genetics and Genomics
  • Statistical Bioinformatics
  • Human Disease Genetics

Background:

  • Exome sequencing is crucial for identifying genetic causes of Mendelian and complex traits.
  • Current variant filtering methods are suboptimal, lacking statistical uncertainty measures.
  • Existing approaches rely on variant novelty, co-segregation, and external functional data.

Purpose of the Study:

  • To present a formal statistical approach for identifying disease-associated genes.
  • To offer a method that provides statistical uncertainty and adjusts for background variation.
  • To integrate functional or linkage information and accommodate various study designs.

Main Methods:

  • Developed a statistical framework for rapid computation of approximate gene p-values.
  • Incorporated adjustments for gene-specific background variation.
  • Validated the approach using simulations and applied it to real-world Mendelian disease datasets.

Main Results:

  • The proposed statistical method significantly improves gene ranking compared to filter-based approaches, especially with locus heterogeneity.
  • It successfully ranked the causative genes first in three Mendelian disease studies.
  • Achieved highly significant approximate p-values for Miller Syndrome (10^-6), Freeman-Sheldon Syndrome (1.0 x 10^-4), and Kabuki Syndrome (3.5 x 10^-5).

Conclusions:

  • The novel statistical approach enhances the identification of genes underlying Mendelian and complex traits.
  • It offers a statistically rigorous and computationally efficient alternative to current filtering methods.
  • This method provides a powerful tool for genetic disease research, improving diagnostic accuracy and discovery.