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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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Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research. Archival research relies on looking at past records or data sets to look for interesting patterns or relationships. For example, a researcher might access the academic records of all individuals who enrolled in college within the past ten years and...
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Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
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In cross-sectional research, a researcher compares multiple segments of the population at the same time. If they were interested in people's dietary habits, the researcher might directly compare different groups of people by age. Instead of following a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old...
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Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration
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Bench Research Informed by GWAS Results.

Nikolay V Kondratyev1, Margarita V Alfimova1, Arkadiy K Golov1,2

  • 1Mental Health Research Center, 115522 Moscow, Russia.

Cells
|November 27, 2021
PubMed
Summary
This summary is machine-generated.

Complex traits, often highly polygenic, are challenging to study. This review explains genome-wide association studies (GWAS) for biologists to interpret genetic findings for complex traits.

Keywords:
GWAScomplex traitspolygenic scores

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Area of Science:

  • Genetics
  • Genomics
  • Quantitative Genetics

Background:

  • Complex traits, crucial in biology and medicine, are typically hereditary and highly polygenic.
  • Their intricate genetic architecture complicates traditional reverse genetics approaches.
  • Genome-wide association studies (GWAS) have emerged as a key tool for investigating these traits.

Purpose of the Study:

  • To provide 'wet biologists' with methods for interpreting genome-wide association study (GWAS) results.
  • To clarify potentially counterintuitive aspects of GWAS findings.
  • To evaluate the utility of GWAS data in experimental studies of complex traits.

Main Methods:

  • Review of existing literature and methodologies in genome-wide association studies.
  • Explanation of statistical approaches for analyzing genetic associations with complex traits.
  • Discussion of experimental validation strategies for GWAS findings.

Main Results:

  • Genome-wide association studies (GWAS) identify numerous genetic factors, often with small individual effects, associated with complex traits.
  • Interpretation of GWAS results requires understanding their statistical nature and limitations.
  • The application of GWAS findings in experimental settings is feasible but requires careful consideration.

Conclusions:

  • Genome-wide association studies (GWAS) offer valuable insights into the genetic basis of complex traits.
  • Bridging the gap between GWAS data and experimental biology is essential for advancing our understanding.
  • Future research should focus on integrating GWAS findings into functional studies to elucidate the mechanisms underlying complex traits.