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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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Human Genetics01:28

Human Genetics

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Human genetics provides a profound framework for understanding the interplay between genetic predispositions and human psychology. At the heart of this discipline lies the study of how genes influence physical traits, behaviors, and susceptibility to diseases. Each person carries a unique genetic code that subtly or significantly shapes their psychological and behavioral landscape.
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Epistasis Analysis01:09

Epistasis Analysis

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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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Genetic Screens02:46

Genetic Screens

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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...
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Related Experiment Video

Updated: Nov 9, 2025

A Phenotyping Regimen for Genetically Modified Mice Used to Study Genes Implicated in Human Diseases of Aging
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AMD Genetics: Methods and Analyses for Association, Progression, and Prediction.

Qi Yan1, Ying Ding2, Daniel E Weeks2,3

  • 1Department of Obstetrics and Gynecology, Columbia University Irving Medical Center, New York, NY, USA.

Advances in Experimental Medicine and Biology
|April 13, 2021
PubMed
Summary
This summary is machine-generated.

Genome-wide association studies (GWAS) have identified over 30 genetic loci for age-related macular degeneration (AMD). Emerging methods predict AMD progression using genetic data and novel analytical approaches.

Keywords:
AMD geneticsGWASMachine learningPredictionProgressionStatistical methods

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Area of Science:

  • Ophthalmology
  • Genetics
  • Neuroscience

Background:

  • Age-related macular degeneration (AMD) is a leading cause of vision loss in the elderly.
  • AMD is a complex neurodegenerative disease with a significant genetic component.
  • Genome-wide association studies (GWAS) have been instrumental in identifying AMD-associated genetic loci.

Purpose of the Study:

  • To review recent GWAS findings for AMD risk and progression.
  • To present novel methods for predicting AMD development and progression using genetic data.
  • To discuss advanced statistical methods for analyzing high-dimensional genetic data in AMD research.

Main Methods:

  • Review of published genome-wide association studies (GWAS) for AMD.
  • Exploration of emerging predictive models utilizing large-scale genetic datasets.
  • Discussion of novel statistical and analytical techniques for complex genetic data analysis.

Main Results:

  • Over 30 genetic loci associated with AMD risk and progression have been identified and confirmed.
  • Emerging methods show promise for predicting AMD development and progression.
  • Novel analytical methods are being developed to address challenges in high-dimensional genetic data analysis.

Conclusions:

  • GWAS has significantly advanced our understanding of AMD genetics.
  • Predictive modeling using genetic data holds potential for personalized AMD management.
  • Continued development of sophisticated analytical methods is crucial for future AMD genetic research.