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Related Concept Videos

Polygenic Traits01:18

Polygenic Traits

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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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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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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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Estimating Population Mean with Known Standard Deviation01:16

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To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
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Estimating Population Standard Deviation01:26

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When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Reference-Based Standardization Approach Stabilizing Small Batch Risk Prediction via Polygenic Score.

Yoichi Sutoh1,2, Tsuyoshi Hachiya1,2, Yayoi Otsuka-Yamasaki1,2

  • 1Division of Biomedical Information Analysis, Iwate Tohoku Medical Megabank Organization, Disaster Reconstruction Center, Iwate Medical University, Yahaba, Japan.

Genetic Epidemiology
|January 31, 2025
PubMed
Summary
This summary is machine-generated.

A new reference-based method standardizes polygenic scores (PGS) for personalized health. This approach validates PGS, improving risk communication for preventive behavioral changes.

Keywords:
behavioral changesgenetic counselinggenetic risk assessmentpolygenic scorepreventionstandardization methodology

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Area of Science:

  • Genetics
  • Bioinformatics
  • Preventive Medicine

Background:

  • Polygenic scores (PGS) show potential for motivating preventive health behaviors.
  • A clinically validated standardization methodology for PGS is currently lacking.
  • Standardization is crucial for reliable risk communication and clinical implementation of PGS.

Purpose of the Study:

  • To demonstrate the efficacy of a "reference-based" approach for standardizing polygenic scores (PGS).
  • To investigate the impact of reference population size, genotyping platform biases, and kinship on PGS computation.
  • To provide insights for establishing clinical guidelines for PGS implementation.

Main Methods:

  • Utilized a reference-based approach using general population PGS distribution for normalization and percentile determination.
  • Investigated the influence of reference population size on bootstrap standard error estimates for PGS percentiles.
  • Assessed deviations in PGS due to different genotyping platforms and evaluated the effect of matching ancestry and using shared genetic variants.

Main Results:

  • Reference population size impacts the bootstrap estimate of standard error for PGS percentiles, with effects diminishing at extreme percentiles.
  • Genotyping platform differences caused significant PGS deviations (p < 0.05), which were mitigated by matching ancestry and using shared variants.
  • The reference-based standardization approach recovered approximately 9.6% of the positive predictive value of PGS compared to naive genotype calculations.

Conclusions:

  • The reference-based approach shows efficacy in standardizing polygenic scores.
  • Understanding influences like reference size and genotyping platforms is essential for accurate PGS computation.
  • This study offers fundamental insights for developing clinical guidelines for reliable PGS-based risk communication.