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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.
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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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Pleiotropy is the phenomenon in which a single gene impacts multiple, seemingly unrelated phenotypic traits. For example, defects in the SOX10 gene cause Waardenburg Syndrome Type 4, or WS4, which can cause defects in pigmentation, hearing impairments, and an absence of intestinal contractions necessary for elimination. This diversity of phenotypes results from the expression pattern of SOX10 in early embryonic and fetal development. SOX10 is found in neural crest cells that form melanocytes,...
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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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Related Experiment Video

Updated: Apr 6, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Phenome-Wide Association Studies: Embracing Complexity for Discovery.

Sarah A Pendergrass1, Anurag Verma, Anna Okula

  • 1Biomedical and Translational Informatics Program, Geisinger Health System, Danville, Pa., USA.

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Summary
This summary is machine-generated.

Integrating complex biological data, including phenome and exposome, enhances understanding of genetic architecture

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Area of Science:

  • Genetics and Systems Biology
  • Genomic Medicine

Background:

  • Biological systems' complexity offers insights into genetic architecture's impact on traits and drug response.
  • Genome-wide association studies (GWAS) provide valuable data, but a comprehensive view of genetic architecture's phenotypic impact is still developing.

Purpose of the Study:

  • To explore how incorporating greater biological complexity into analyses can improve understanding of genetic factors.
  • To highlight the utility of phenome-wide association studies (PheWAS) in uncovering genetic influences on diseases and drug responses.

Main Methods:

  • Leveraging diverse -omic resources, phenome, and exposome data.
  • Integrating analyses of pleiotropy and genetic interactions.
  • Expanding on traditional Genome-wide association studies (GWAS) with phenome-wide association studies (PheWAS).

Main Results:

  • Increased complexity in analyses reveals deeper insights into genetic architecture.
  • Phenome-wide association studies (PheWAS) offer a powerful approach to dissecting genetic influences.
  • Integrating diverse data types enhances the interpretation of genetic associations.

Conclusions:

  • Incorporating greater complexity, including phenome and exposome data, is crucial for a comprehensive understanding of genetic architecture.
  • Phenome-wide association studies (PheWAS) are vital for advancing genetic research in diseases and pharmacology.
  • Future research should focus on integrating multi-omics data and complex biological interactions for robust genetic discoveries.