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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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Heritability01:06

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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"...
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Behavioral Genetics and Its Designs01:23

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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.
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Inheritance01:25

Inheritance

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Gregor Mendel's pioneering work on the principles of inheritance fundamentally transformed our understanding of how traits are transmitted from generation to generation. His experiments with pea plants laid the groundwork for the discovery of genes, discrete units within organisms that control heredity.
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Variability: Analysis01:11

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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.
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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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A modeling of complex trait phenotypic variance determinants.

Shobbir Hussain1

  • 1Department of Life Sciences, University of Bath, Claverton Down, Bath BA2 7AY, United Kingdom.

PNAS Nexus
|November 12, 2024
PubMed
Summary

Complex traits like height are largely explained by additive genetics. However, complex diseases may involve genotype-by-environment interactions, suggesting twin studies may overestimate heritability.

Area of Science:

  • Genetics
  • Quantitative Trait Genetics
  • Behavioral Genetics

Background:

  • Additive genetic architecture explains complex traits like human height.
  • Pedigree-based studies (e.g., twin studies) often overestimate heritability for complex diseases.
  • Current models may not fully capture the genetic mechanisms underlying complex diseases.

Purpose of the Study:

  • To propose a theoretical model for complex trait heritability.
  • To investigate the role of non-additive genetic mechanisms, specifically genotype-by-environment interactions.
  • To address discrepancies between heritability estimates from different study designs.

Main Methods:

  • Theoretical modeling of genetic architecture.
  • Analysis of complex traits and disease susceptibility.

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  • Comparison of heritability estimates from different methodologies.

    • Main Results:

    • Additive genetic architecture can fully capture heritability for some complex traits.
    • For complex diseases, a significant portion of heritability may be missed by current additive models.
    • Somatic variant interactions and heritable genotype-by-environment interactions are proposed as key components.

    Conclusions:

    • Heritability estimates from twin studies may be systematically inflated.
    • Genotype-by-environment interactions represent an underappreciated source of phenotypic variance.
    • A more comprehensive model is needed to understand the genetic basis of complex diseases.