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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...
Background and Environment Affect Phenotype02:27

Background and Environment Affect Phenotype

Although the genetic makeup of an organism plays a major role in determining the phenotype, there are also several environmental factors, such as temperature, oxygen availability, presence of mutagens, that can alter an organism’s phenotype.
An example of how genetic background affects phenotype can be seen in horses. The Extension gene in horses is responsible for their coat color. A wild-type gene (EE) produces black pigment in the coat, while a mutant gene (ee) produces red pigment. A...
Punnett Squares01:00

Punnett Squares

Overview
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...
Incomplete Dominance01:43

Incomplete Dominance

Gregor Mendel's work (1822 - 1884) was primarily focused on pea plants. Through his initial experiments, he determined that every gene in a diploid cell has two variants called alleles inherited from each parent. He suggested that amongst these two alleles, one allele is dominant in character and the other recessive. The combination of alleles determines the phenotype of a gene in an organism.

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Related Experiment Video

Updated: Jun 3, 2026

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

Incorporating scientific knowledge into phenotype development: penalized latent class regression.

Jeannie-Marie S Leoutsakos1, Karen Bandeen-Roche, Elizabeth Garrett-Mayer

  • 1Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, U.S.A. jeannie-marie@jhu.edu

Statistics in Medicine
|March 12, 2011
PubMed
Summary

This study introduces penalized latent class regression to improve psychiatric disorder classification using genetic data. The method enhances precision and reduces bias, aiding in the development of refined psychiatric phenotypes.

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Area of Science:

  • Biostatistics
  • Psychiatric Genetics
  • Computational Biology

Background:

  • Psychiatric genetics research is limited by the absence of a clear diagnostic taxonomy.
  • Latent class analysis (LCA) is a statistical method for identifying unobserved subgroups within a population.
  • Existing LCA methods may not fully leverage available scientific information or handle complex covariate relationships.

Purpose of the Study:

  • To develop a penalized latent class regression method that incorporates additional scientific information into the measurement model estimation.
  • To improve the precision and reduce bias in estimating latent classes, particularly in the presence of differential measurement.
  • To enhance the utility of latent classes for predicting outcomes like dementia by including genetic and clinical covariates.

Main Methods:

  • Description of a penalized latent class regression model building upon existing work.
  • Simulation studies to evaluate the performance of ridge and LASSO penalty functions.
  • Application of the methodology to data from the Cache County Study on Memory and Aging, including APO-E genotype and clinical covariates.

Main Results:

  • Penalized latent class regression improved the precision of estimates and reduced bias in simulation studies.
  • Class-specific penalization enhanced latent class separation when true separation existed.
  • The method was computationally more efficient than Bayesian analysis.
  • In the Cache County Study, incorporating covariates and applying penalization improved dementia prediction utility and precision.

Conclusions:

  • Penalized latent class regression offers a valuable tool for psychiatric genetics by improving phenotype refinement.
  • The methodology is particularly useful when dealing with numerous collinear covariates or violations of standard latent class model assumptions.
  • Further research into novel penalty functions could lead to even more refined psychiatric phenotypes.