Related Experiment Video
Updated: May 29, 2026
06:52
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
Published on: September 17, 2019
Heritability across the distribution: an application of quantile regression
Jessica A R Logan1, Stephen A Petrill, Sara A Hart
1Department of Human Development and Family Science, College of Education and Human Ecology, The Ohio State University, Columbus, OH, USA. Logan.251@osu.edu
Behavior Genetics
|August 31, 2011
Summary
Quantile regression offers a novel approach to twin data analysis, revealing genetic and environmental influences on skills across their distribution. This method provides more detailed insights than traditional approaches for understanding reading ability etiology.
Area of Science:
- Behavioral Genetics
- Quantitative Genetics
- Developmental Psychology
Background:
- Twin studies are crucial for dissecting genetic and environmental influences on traits.
- Traditional methods may not capture the full spectrum of etiological influences across a trait's distribution.
Purpose of the Study:
- Introduce and evaluate quantile regression as a novel method for twin data analysis.
- Compare quantile regression with existing methods for assessing genetic and environmental etiology.
- Examine the genetic and environmental influences on reading-related skills at multiple distribution points.
Main Methods:
- Applied quantile regression to analyze genetic and environmental influences in twin data.
- Utilized data from 304 pairs of first-grade same-sex twins from the Western Reserve Reading Project.
- Compared findings with the Cherny et al. method for four reading-related outcomes.
Main Results:
- Both quantile regression and the Cherny et al. method indicated variation in genetic and shared environmental influences on non-word reading.
- Quantile regression provided more granular detail on the location and magnitude of these influences.
- The methods showed broadly similar, yet nuanced, findings regarding reading skill etiology.
Conclusions:
- Quantile regression is a valuable tool for a more detailed understanding of genetic and environmental contributions to skills.
- The method enhances traditional twin analysis by examining etiological factors across the entire distribution of a trait.
- Further applications of quantile regression can deepen insights into the development of various abilities.
Related Concept Videos
01:06Heritability
Heritability is a statistical concept that measures the degree to which genetic differences among individuals contribute to trait variations within a population. It is a fundamental idea in genetics, often prone to misinterpretation. Heritability is expressed as a percentage, reflecting the proportion of variation in a specific trait across a population that can be linked to genetic differences. However, it's important to understand that heritability does not determine how "genetic" a trait is,...
01:26Distributions to Estimate Population Parameter
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
01:32Probability Distributions
The probability of a random variable x is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson probability...
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson probability...
01:15Quartile
Quartiles are numbers that separate the data into quarters. Quartiles may or may not be part of the data. To find the quartiles, first, find the median or second quartile. The first quartile, Q1, is the middle value of the lower half of the data, and the third quartile, Q3, is the middle value, or median, of the upper half of the data. To get the idea, consider the same data set:
1; 1; 2; 2; 4; 6; 6.8; 7.2; 8; 8.3; 9; 10; 10; 11.5
The median or second quartile is seven. The lower half of the...
1; 1; 2; 2; 4; 6; 6.8; 7.2; 8; 8.3; 9; 10; 10; 11.5
The median or second quartile is seven. The lower half of the...
01:08Outliers and Influential Points
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the vertical...
01:00Detection of Gross Error: The Q Test
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...