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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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Genomics02:02

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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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Evolutionary Relationships through Genome Comparisons02:54

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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...
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Genome Annotation and Assembly03:36

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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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Related Experiment Video

Updated: Jun 30, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Statistical Learning of Large-Scale Genetic Data: How to Run a Genome-Wide Association Study of Gene-Expression Data

Anton Sugolov1, Eric Emmenegger2, Andrew D Paterson3,4

  • 1Department of Mathematics,Faculty of Arts and Sciences, University of Toronto, Toronto, Canada.

Statistics in Biosciences
|March 18, 2024
PubMed
Summary

This workshop engaged students in genome-wide association studies (GWAS) using real genetic data. Participants learned statistical genetics by analyzing large datasets and interpreting results, fostering interest in the field.

Keywords:
1000 Genomes ProjectData VisualizationGene ExpressionGenome-wide Association StudyHands-on ExperienceLarge-scale Data AnalysisMultiple Hypothesis TestingOpen ResourceReproducible Research

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Area of Science:

  • Genetics
  • Statistics
  • Data Science

Background:

  • Engaging students in statistics requires hands-on experience with large datasets.
  • Statistical genetics is crucial for understanding genetic influences on traits and diseases.

Purpose of the Study:

  • To develop and evaluate a workshop for teaching genome-wide association studies (GWAS) to students with basic data science knowledge.
  • To motivate students by connecting genetic data analysis to real-world health insights.

Main Methods:

  • A week-long workshop was designed for high-school or junior undergraduate students.
  • Students performed a GWAS on open-source gene expression and human genetics data.
  • A detailed manual and archived scripts facilitated hands-on learning and reproducible research.

Main Results:

  • Students successfully generated approximately 1.4 million p-values from a real scientific study.
  • The workshop fostered engagement with core statistical concepts like regression and multiple hypothesis testing.
  • Personalized presentations on disease genetics enhanced student motivation.

Conclusions:

  • Hands-on GWAS workshops effectively teach statistical genetics to students with data science backgrounds.
  • Connecting data analysis to personal health contexts increases student motivation and learning.
  • Open-source data and tools promote reproducible research and accessible training in genetics.