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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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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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Single Nucleotide Polymorphisms-SNPs01:05

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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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Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
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Comparing Copy Number Variations and SNPs02:26

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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
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Related Experiment Video

Updated: Jul 24, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Tuning Parameters for Polygenic Risk Score Methods Using GWAS Summary Statistics from Training Data.

Wei Jiang1, Ling Chen2, Matthew J Girgenti3

  • 1Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.

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|July 3, 2023
PubMed
Summary
This summary is machine-generated.

PRStuning offers a novel method to optimize polygenic risk score (PRS) models using only genome-wide association study (GWAS) summary statistics. This approach enhances genetic risk prediction accuracy without needing individual-level data.

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Area of Science:

  • Genetics
  • Biostatistics
  • Computational Biology

Background:

  • Predicting genetic risks for common diseases is crucial for prevention and treatment.
  • Polygenic risk score (PRS) methods combine single nucleotide polymorphism (SNP) effects from genome-wide association studies (GWAS).
  • Current PRS methods often require external individual-level GWAS data for hyperparameter tuning, posing privacy and security challenges and potentially reducing predictive accuracy.

Approach:

  • Propose PRStuning, a novel method for automatic hyperparameter tuning of PRS methods.
  • Utilize only GWAS summary statistics from training data, eliminating the need for external datasets.
  • Employ an empirical Bayes approach to mitigate overfitting by shrinking predicted performance based on estimated disease genetic architecture.

Key Points:

  • PRStuning accurately predicts PRS method performance across various parameters.
  • The method effectively selects optimal hyperparameters for PRS models.
  • Demonstrated success in extensive simulations and real-world data applications.

Conclusions:

  • PRStuning provides a privacy-preserving and efficient solution for PRS hyperparameter optimization.
  • The method enhances the accuracy and reliability of genetic risk prediction.
  • Facilitates the development of more effective PRS models for common diseases.