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When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
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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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Polygenic Epidemiology.

Frank Dudbridge1

  • 1Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, United Kingdom.

Genetic Epidemiology
|April 11, 2016
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Summary
This summary is machine-generated.

Polygenic epidemiology utilizes methods like polygenic scoring to analyze complex traits, even without identifying all individual genetic variants. This approach helps understand genetic correlations and predict disease risks.

Keywords:
Mendelian randomizationgenetic correlationgenetic risk predictionmissing heritability

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Area of Science:

  • Genetics
  • Epidemiology
  • Bioinformatics

Background:

  • Complex traits are influenced by numerous genetic variants, most of which remain unidentified.
  • Traditional genetic epidemiology faces challenges in dissecting the genetic architecture of complex diseases.

Purpose of the Study:

  • To review applications of polygenic epidemiology in understanding complex traits.
  • To highlight the utility of polygenic approaches despite incomplete variant identification.

Main Methods:

  • Polygenic scoring
  • Linear mixed models
  • Linkage disequilibrium (LD) scoring
  • Mendelian randomization

Main Results:

  • Established polygenic effects on traits and diseases.
  • Estimated genetic correlations between different traits.
  • Determined the approximate number of variants contributing to a trait.
  • Enabled subphenotyping of cases and prediction of individual disease risks.
  • Facilitated inference of causal effects using Mendelian randomization.

Conclusions:

  • Polygenic epidemiology offers valuable tools for genetic research.
  • These methods provide insights into complex traits even when specific causal variants are unknown.
  • The field is expected to continue advancing genetic understanding of complex traits.