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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...
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
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...
DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Related Experiment Video

Updated: Jun 4, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Quantitative trait prediction based on genetic marker-array data, a simulation study.

Wai-Ki Yip1, Christoph Lange

  • 1Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA. wkyip@hsph.harvard.edu

Bioinformatics (Oxford, England)
|February 3, 2011
PubMed
Summary
This summary is machine-generated.

Including significant single-nucleotide polymorphisms (SNPs) in regression models improves prediction accuracy for quantitative trait loci (QTL). This approach surpasses traditional methods by optimizing SNP selection based on prediction performance.

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An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
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Last Updated: Jun 4, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
10:17

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

Area of Science:

  • Genetics
  • Statistical Genomics
  • Bioinformatics

Background:

  • Quantitative trait loci (QTL) are crucial for understanding complex traits.
  • Genome-wide association studies (GWAS) identify significant genetic markers.
  • Predictive modeling in genetics often relies on genome-wide significant SNPs.

Purpose of the Study:

  • To evaluate the prediction quality of regression models using non-genome-wide significant SNPs.
  • To compare this approach with the standard method of including only significant SNPs.
  • To determine if including SNPs with small P-values enhances prediction accuracy.

Main Methods:

  • Simulation studies were employed to assess prediction models.
  • Regression models incorporated single-nucleotide polymorphism (SNP) genetic markers.
  • Mean square prediction error (MSPE) was used as the primary model metric.
  • The coefficient of determination (R(2)) guided the selection of SNPs for inclusion.

Main Results:

  • Models including SNPs with small P-values, not just genome-wide significant ones, showed improved prediction quality.
  • The R(2) value served as an effective guideline for optimizing SNP inclusion.
  • This enhanced approach outperformed the standard method of using only significant SNPs.

Conclusions:

  • Regression models can achieve better prediction quality for QTL by strategically including SNPs with small P-values.
  • Accurate estimation of trait heritability and the number of QTLs is essential for this method.
  • This strategy offers a more nuanced approach to genetic prediction beyond strict genome-wide significance thresholds.