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Related Concept Videos

Heritability01:06

Heritability

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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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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

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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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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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Genomic Imprinting and Inheritance02:30

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Diploid organisms inherit genetic material through chromosomes from both parents. Copies of the same gene are known as alleles. In most cases, both alleles are simultaneously expressed and allow various cellular processes to function optimally. If one of the alleles is missing or mutated, the expression of the other allele can compensate; however, this is not true for all genes.
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X-linked Traits01:19

X-linked Traits

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In most mammalian species, females have two X sex chromosomes and males have an X and Y. As a result, mutations on the X chromosome in females may be masked by the presence of a normal allele on the second X. In contrast, a mutation on the X chromosome in males more often causes observable biological defects, as there is no normal X to compensate. Trait variations arising from mutations on the X chromosome are called “X-linked”.
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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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A Novel Bayesian Semi-parametric Model for Learning Heritable Imaging Traits.

Yize Zhao1, Xiwen Zhao1, Mansu Kim2

  • 1Department of Biostatistics, Yale University School of Public Health, NJ, USA.

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|March 18, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Bayesian model for brain imaging genetics, improving the identification of heritable traits. The new method constructs more accurate imaging quantitative traits (QTs) by considering voxel-level heritability, outperforming standard approaches.

Keywords:
Bayesian semi-parametric modelingHeritability estimationImaging genetics

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Area of Science:

  • Neuroimaging Genetics
  • Quantitative Trait Analysis
  • Statistical Genetics

Background:

  • Heritability analysis in brain imaging genetics aims to identify genetic influences on brain structure and function.
  • Current methods often use predefined atlases, leading to noise and diluted signals in regional quantitative traits (QTs).
  • This limits the power to dissect genetic underpinnings of brain imaging phenotypes.

Purpose of the Study:

  • To develop a novel semi-parametric Bayesian heritability estimation model.
  • To construct highly heritable imaging quantitative traits (QTs) by integrating genetic signals.
  • To improve the biological plausibility and clinical interpretability of identified brain regions.

Main Methods:

  • A new semi-parametric Bayesian model for heritability estimation.
  • Development of a novel brain parcellation driven by voxel-level heritability.
  • Incorporation of hierarchical sparsity, smoothness, and structural connectivity to ensure spatial contiguity.

Main Results:

  • The proposed method successfully identified highly heritable and biologically meaningful imaging QTs.
  • Demonstrated superior performance compared to the standard GCTA method in identifying robust QTs.
  • The new parcellation approach effectively reduced noise and signal dilution.

Conclusions:

  • The developed Bayesian model offers a powerful approach for constructing refined imaging QTs in brain imaging genetics.
  • This method enhances the discovery of genetic influences on brain imaging phenotypes.
  • The findings have implications for advancing our understanding of the genetic architecture of the brain.