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Related Concept Videos

Variation01:19

Variation

An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
One-Way ANOVA01:18

One-Way ANOVA

One-way ANOVA analyzes more than three samples categorized by one factor. For example, it can compare the average mileage of sports bikes. Here, the data is categorized by one factor - the company. However, one-way ANOVA cannot be used to simultaneously compare the sample mean of three or more samples categorized by two factors. An example of two factors would be sports bikes from different companies driven in different terrains, such as a desert or snowy landscape. Here, two-way ANOVA is used...
Variability: Analysis01:11

Variability: Analysis

Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
Two-Way ANOVA01:17

Two-Way ANOVA

The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the means for...
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism

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Related Experiment Video

Updated: Jun 16, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Variation explained in mixed-model association mapping.

G Sun1, C Zhu, M H Kramer

  • 1Department of Agronomy, Kansas State University, Manhattan, KS, USA.

Heredity
|February 11, 2010
PubMed
Summary
This summary is machine-generated.

A new R(2)-like statistic, R(LR)(2), is proposed for linear mixed models in quantitative trait locus (QTL) mapping. This statistic effectively measures model-data agreement and quantifies QTL effects in complex trait dissection.

Related Experiment Videos

Last Updated: Jun 16, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Area of Science:

  • Genetics and Bioinformatics
  • Statistical Genomics

Background:

  • Genomic mapping of complex traits requires integrating genetics and statistics.
  • R(2) statistic is commonly used in quantitative trait locus (QTL) mapping to assess phenotypic variation explained by molecular markers.
  • Linear mixed models (LMMs) are used for complex trait dissection but lack a well-established R(2) statistic.

Purpose of the Study:

  • To evaluate R(2)-like statistics for LMMs in association mapping.
  • To identify a statistic that reflects model-data agreement and QTL effect.
  • To provide a general measure for QTL effects in mixed-model association mapping.

Main Methods:

  • Assessed performance of several R(2)-like statistics for LMMs.
  • Proposed and validated the likelihood-ratio-based R(2) (R(LR)(2)).
  • Compared R(LR)(2) with traditional R(2) in fixed models.

Main Results:

  • The likelihood-ratio-based R(2) (R(LR)(2)) meets critical requirements for an R(2)-like statistic.
  • R(LR)(2) generalizes to the standard R(2) in fixed models without random effects.
  • R(LR)(2) aids in understanding the overlap between population structure and kinship in association mapping.

Conclusions:

  • R(LR)(2) provides a general and intuitive measure for QTL effects in mixed-model association mapping.
  • Comparing R(LR)(2) values offers a bridge between statistical analysis and the genetics of complex traits.
  • This statistic is valuable for dissecting complex traits across species.