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Related Concept Videos

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...
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...
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
Heritability01:06

Heritability

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" a trait is,...
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism

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Related Experiment Video

Updated: May 10, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Genome-wide association studies with high-dimensional phenotypes.

Pekka Marttinen1, Jussi Gillberg, Aki Havulinna

  • 1Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University, Aalto, Finland

Statistical Applications in Genetics and Molecular Biology
|June 14, 2013
PubMed
Summary

Canonical correlation analysis offers higher power for high-dimensional phenotype association studies. This method is computationally feasible for genome-wide association studies (GWAS) with sufficient sample sizes, outperforming alternatives.

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • High-dimensional phenotypes enhance association study findings but complicate multiple testing.
  • Existing methods for joint phenotype testing offer potential power gains but lack comparative assessment.

Purpose of the Study:

  • To compare existing methods for high-dimensional phenotype association studies.
  • To provide guidelines for method selection in genome-wide association studies (GWAS).

Main Methods:

  • Comparison of statistical methods using simulated and real metabolomics data.
  • Application to genome-wide data by dividing the genome into blocks of correlated genetic markers.
  • Utilizing canonical correlation analysis (CCA) and its sparse variant, alongside regression models with latent confounding factors.

Main Results:

  • Canonical correlation analysis demonstrated higher power than alternative methods in simulations and real data.
  • CCA is computationally tractable for GWAS with adequate sample size relative to data dimensionality.
  • Sparse CCA and latent factor models show promise for smaller sample sizes relative to high-dimensional data.

Conclusions:

  • Canonical correlation analysis is a powerful and computationally feasible approach for high-dimensional phenotype association studies.
  • Method choice depends on sample size relative to the number of phenotype and genotype variables.
  • Sparse CCA and latent factor models offer viable alternatives in low-sample, high-dimensional scenarios.