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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

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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Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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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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Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

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Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
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Genome Annotation and Assembly03:36

Genome Annotation and Assembly

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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: Feb 20, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

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Data analysis in the post-genome-wide association study era.

Qiao-Ling Wang1, Wen-Le Tan1, Yan-Jie Zhao1

  • 1Department of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.

Chronic Diseases and Translational Medicine
|October 25, 2017
PubMed
Summary
This summary is machine-generated.

Genome-wide association studies (GWAS) have identified many genetic variants for complex diseases. Further analysis is needed to understand the biological mechanisms and enable clinical applications.

Keywords:
Copy number variationData miningGenome-wide association studyIntegrative data analysisPolymorphism

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Area of Science:

  • Genetics
  • Genomics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) have identified thousands of genetic loci associated with complex human diseases and traits.
  • The biological mechanisms underlying these identified loci remain largely unknown, limiting their clinical utility.

Purpose of the Study:

  • To outline strategies for in-depth data mining in the post-GWAS era.
  • To enhance the utility and significance of GWAS findings for understanding disease genetics and facilitating clinical applications.

Main Methods:

  • Fine-mapping of susceptibility regions to identify specific genes and elucidate biological mechanisms.
  • Joint analysis of susceptibility genes across different diseases.
  • Integration of GWAS data with transcriptome and epigenetic data (e.g., expression and methylation quantitative trait loci).
  • Genome-wide association analysis of DNA copy number variations related to diseases.

Main Results:

  • The proposed strategies aim to strengthen GWAS data through comprehensive post-GWAS analysis.
  • These methods will enable a deeper understanding of the genetic architecture of complex diseases.
  • The approach facilitates the translation of genetic findings into potential clinical applications.

Conclusions:

  • In-depth data mining and integration of multi-omics data are crucial in the post-GWAS era.
  • These advanced analytical strategies are essential for uncovering biological mechanisms and translating genetic discoveries into clinical practice.
  • Strengthening GWAS data through these methods will significantly improve our understanding of complex trait genetics.