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Genome-wide Association Studies-GWAS01:11

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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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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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Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
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Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
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Heritability01:06

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Heritability is a statistical concept that measures the degree to which genetic differences among individuals contribute to trait variations within a population. It is a fundamental idea in genetics, often prone to misinterpretation. Heritability is expressed as a percentage, reflecting the proportion of variation in a specific trait across a population that can be linked to genetic differences. However, it's important to understand that heritability does not determine how "genetic"...
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Updated: Jun 10, 2025

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Methodologies underpinning polygenic risk scores estimation: a comprehensive overview.

Carene Anne Alene Ndong Sima1, Kathryn Step1, Yolandi Swart1

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Human Genetics
|October 19, 2024
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Summary

Polygenic risk scores (PRS) show promise for disease prediction but require diverse population data. New methods are needed to ensure PRS accuracy and equity across all ancestries.

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Area of Science:

  • Genomics
  • Personalized Medicine
  • Population Genetics

Background:

  • Polygenic risk scores (PRS) are developed using genome-wide association studies (GWAS), predominantly from European ancestry populations.
  • Existing PRS models show limited validation and potential bias in non-European populations, impacting clinical utility.
  • There's a critical need for risk prediction frameworks that are inclusive of diverse genetic backgrounds and consider complex interactions.

Purpose of the Study:

  • To review and analyze the strengths and limitations of various polygenic risk score (PRS) construction methods.
  • To highlight recent advancements in PRS calculation methodologies.
  • To identify future research directions for developing robust PRS across diverse populations.

Main Methods:

  • Analysis of traditional weighted PRS construction methods.
  • Evaluation of novel Bayesian and Frequentist penalized regression approaches for PRS.
  • Review of current literature on PRS development and application across different ancestries.

Main Results:

  • Traditional PRS methods have limitations when applied to diverse populations.
  • Newer penalized regression methods offer potential improvements in PRS construction.
  • The development of PRS is complex, influenced by genetic variants across various ancestral backgrounds.

Conclusions:

  • PRS have significant potential for enhancing disease risk prediction and personalized medicine.
  • Further research is essential to create PRS models that are accurate and equitable across diverse populations.
  • Ethical considerations, including bias and fairness, must guide the implementation of PRS.