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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...
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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.
GWAS does not require the identification of the target gene involved in...
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
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...

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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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A principal components-based clustering method to identify variants associated with complex traits.

Mary Helen Black1, Richard M Watanabe

  • 1Department of Preventive Medicine, Keck School of Medicine of USC, Los Angeles, CA 90089-9011, USA.

Human Heredity
|March 11, 2011
PubMed
Summary
This summary is machine-generated.

Orthoblique principal components-based clustering (OPCC) improves genetic association studies by identifying causal single nucleotide polymorphisms (SNPs) more effectively. This method offers enhanced power and precision in pinpointing disease-associated variants compared to traditional principal components analysis (PCA).

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Area of Science:

  • Genetics
  • Statistical genomics
  • Bioinformatics

Background:

  • Existing multivariate methods like principal components analysis (PCA) for genetic association studies have limitations in power and identifying specific causal markers.
  • Accurately identifying subsets of markers associated with disease traits is crucial for understanding genetic contributions to complex diseases.

Purpose of the Study:

  • To introduce orthoblique principal components-based clustering (OPCC) as a novel method for identifying marker subsets associated with quantitative traits.
  • To evaluate the performance of OPCC against traditional methods using simulations and a real-world example of type 2 diabetes.

Main Methods:

  • Orthoblique principal components-based clustering (OPCC) was developed as an alternative to PCA for marker association analysis.
  • The method's utility was demonstrated through simulation studies and application to type 2 diabetes genetic data.

Main Results:

  • OPCC demonstrated comparable or superior statistical power across various linkage disequilibrium structures and genotype data availability scenarios.
  • Simulations confirmed OPCC's ability to accurately reduce large marker sets to a subset containing the causal variant or its proxy.

Conclusions:

  • OPCC is an effective data reduction technique for detecting associations between gene variants and disease-related traits.
  • OPCC surpasses PCA by isolating the effects of causal single nucleotide polymorphisms (SNPs) within large candidate regions, improving association testing.