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Related Concept Videos

Pedigree Analysis01:35

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Overview
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Genome-wide Association Studies-GWAS01:11

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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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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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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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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Genetic Screens02:46

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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
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Related Experiment Video

Updated: Jun 6, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Accelerating Heritability, Genetic Correlation, and Genome-Wide Association Imaging Genetic Analyses in Complex

Brian Donohue1, Si Gao1, Thomas E Nichols2

  • 1Department of Psychiatry and Behavioral Sciences, University of Texas, Health Science Center Houston, Houston, Texas, USA.

Human Brain Mapping
|November 27, 2024
PubMed
Summary

New computational methods accelerate imaging genetics analyses for large biobanks. These fast, non-iterative approaches reduce complexity by 10^4- to 10^6-fold, enabling practical genome-wide association studies (GWAS) and heritability analyses.

Keywords:
Big Data geneticsGWASgenetic correlationheritabilitypedigree

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Area of Science:

  • Neuroscience
  • Genetics
  • Computational Biology

Background:

  • Large-scale biobanks offer rich imaging genetics datasets for studying brain health and disease.
  • Analyzing high-resolution neuroimaging and genetic data is computationally intensive, especially with related subjects.

Purpose of the Study:

  • To develop computationally efficient methods for imaging genetics analyses in large, complex datasets.
  • To accelerate variance component (VC) methods for heritability, genetic correlation, and genome-wide association studies (GWAS).

Main Methods:

  • Developed fast, non-iterative simplifications for classical variance component (VC) methods.
  • Linearized computational complexity from N^2-3 to N^~1 while maintaining high fidelity (r ~ 0.95) with VC results.
  • Leveraged parallel computing on central and graphics processing units (CPU and GPU).

Main Results:

  • Achieved a 10^4- to 10^6-fold reduction in computational complexity.
  • Demonstrated the practicality of voxel-wise heritability, genetic correlation, and GWAS for large biobanks like UK Biobank.
  • New methods are integrated into the open-source SOLAR-Eclipse software.

Conclusions:

  • The developed methods significantly enhance the feasibility of conducting advanced imaging genetics analyses on massive datasets.
  • These advancements facilitate deeper understanding of genetic and environmental influences on the human brain.
  • Open-source software availability promotes wider adoption and further research in the field.