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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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Introduction to z Scores01:06

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A z score (or standardized value) is measured in units of the standard deviation. It tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
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Introduction to z Scores01:05

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A z score (or standardized value) is measured in units of the standard deviation. It indicates how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
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z Scores and Area Under the Curve01:17

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z scores are the standardized values obtained after converting a normal distribution into a standard normal distribution. A z score is measured in units of the standard deviation. The z score tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a z score of...
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Interpreting R Charts01:22

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R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
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Interpreting Run Charts01:25

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Run charts, essentially line graphs plotted over time, serve as fundamental yet effective tools for process analysis. They chronicle data sequentially, facilitating the identification of trends, shifts, or cyclical movements. This graphical representation is instrumental in determining whether a process is stable or exhibits signs of potential instability indicative of special cause variation. In the healthcare domain, run charts depict infection rates over time, enabling hospitals to monitor...
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Related Experiment Video

Updated: Jan 28, 2026

A Simple Composite Phenotype Scoring System for Evaluating Mouse Models of Cerebellar Ataxia
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Interpreting polygenic scores, polygenic adaptation, and human phenotypic differences.

Noah A Rosenberg1, Michael D Edge2, Jonathan K Pritchard1,3,4

  • 1Department of Biology, Stanford University, Stanford, CA, USA.

Evolution, Medicine, and Public Health
|March 7, 2019
PubMed
Summary
This summary is machine-generated.

Polygenic scores can show population differences in traits, but these may not reflect genetic differences. Environmental factors significantly influence trait distributions, especially in health disparities.

Keywords:
adaptationhealth disparitieshuman variationpolygenic scorespopulation genetics

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Area of Science:

  • Population genetics
  • Evolutionary biology
  • Human genomics

Background:

  • Recent polygenic score analyses prompt discussions on the genetic basis and evolutionary significance of population-level phenotype distributions.
  • Understanding genetic and environmental contributions to traits is crucial for interpreting population differences.

Purpose of the Study:

  • To highlight limitations in research concerning polygenic scores, polygenic adaptation, and population differences.
  • To examine how genetic and environmental factors interact to shape trait distributions among populations.
  • To evaluate the null hypothesis that genetic propensity differences are small for selectively neutral phenotypes.

Main Methods:

  • Analysis of polygenic scores and their relationship to phenotypic distributions.
  • Modeling the interplay of genetic and environmental contributions to complex traits.
  • Illustrating the null hypothesis using health disparities between African Americans and European Americans.

Main Results:

  • Phenotypic differences among populations do not necessarily equate to corresponding differences in genetic propensity due to combined genetic and environmental influences.
  • Under selective neutrality, predicted genetic propensity differences contributing to population-level phenotypic variation are minimal.
  • The study provides a framework for interpreting population differences in trait distributions, considering both genetic and environmental effects.

Conclusions:

  • Limitations in polygenic score research necessitate careful interpretation of findings regarding human population differences.
  • Environmental factors play a significant role in observed phenotypic variations across populations, potentially masking or exaggerating genetic contributions.
  • Further research must integrate genetic and environmental perspectives to accurately understand the evolutionary significance of population-level trait differences.