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Related Concept Videos

Polygenic Traits01:18

Polygenic Traits

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...
Polygenic Traits01:18

Polygenic Traits

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...
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
Uncertainty: Overview00:59

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor 't,' or...

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Related Experiment Video

Updated: May 26, 2026

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

Characterizing the Uncertainty, Misclassification and Inconsistency of Polygenic Prediction.

Yingzhe Zhang, Rui Zhang, Tian Ge

    Medrxiv : the Preprint Server for Health Sciences
    |May 25, 2026
    PubMed
    Summary
    This summary is machine-generated.

    Polygenic risk scores (PRS) show promise for precision medicine but face uncertainty. This study introduces a framework to calibrate PRS estimates and uncertainties, improving risk prediction and clinical decision-making.

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    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

    Published on: October 11, 2018

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    Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
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    Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

    Published on: June 6, 2025

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
    07:35

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

    Published on: October 11, 2018

    Area of Science:

    • Genetics and Bioinformatics
    • Statistical Genetics
    • Precision Medicine

    Background:

    • Polygenic risk scores (PRSs) are valuable for precision medicine but suffer from uncertainty in individual risk estimates.
    • Limited agreement across different PRSs for the same disease hinders clinical translation.

    Purpose of the Study:

    • To develop a unified inferential framework for calibrating PRS point estimates and uncertainties.
    • To characterize the joint impact of PRS accuracy, uncertainty, and correlation on misclassification and inconsistency.
    • To evaluate strategies for integrating PRS and using uncertainty-aware thresholding to improve risk stratification.

    Main Methods:

    • Developed a unified inferential framework for PRS calibration (quantitative traits and binary phenotypes).
    • Analyzed the interplay of PRS accuracy, uncertainty, and pairwise correlation on classification outcomes.
    • Evaluated PRS integration and probabilistic thresholding strategies using independent datasets.

    Main Results:

    • Individual and population-level misclassification and inconsistency rates are predictable.
    • PRS integration and uncertainty-aware strategies reduce misclassification and enhance risk stratification concordance.
    • Classification instability in PRSs is a predictable statistical consequence of uncertainty.

    Conclusions:

    • Established a principled foundation for interpreting and communicating PRS-based risk by incorporating uncertainty.
    • Demonstrated that PRS classification instability is statistically predictable.
    • Provided a framework to improve clinical decision-making in precision medicine through uncertainty-aware PRS utilization.