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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...
Epistasis Analysis01:09

Epistasis Analysis

Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. 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...
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...
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...

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Related Experiment Video

Updated: May 7, 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

Mapping quantitative trait loci by controlling polygenic background effects.

Shizhong Xu1

  • 1Department of Botany and Plant Sciences, University of California, Riverside, California 92521.

Genetics
|October 1, 2013
PubMed
Summary
This summary is machine-generated.

A new mixed-model method improves quantitative trait loci (QTL) mapping by analyzing genetic variance components. This approach enhances QTL mapping resolution and can be applied to genome-wide association studies (GWAS).

Keywords:
bin genotypeepistasismixed modelquantitative trait locusrice

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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

  • Quantitative genetics
  • Statistical genomics
  • Plant breeding

Background:

  • Mapping quantitative trait loci (QTL) is crucial for understanding complex traits.
  • Existing methods may not fully capture the intricate genetic architecture, including epistasis.
  • Accurate genetic variance component estimation is essential for precise QTL detection.

Purpose of the Study:

  • To develop and validate a novel mixed-model method for QTL mapping.
  • To incorporate multiple polygenic covariance structures, including epistatic interactions.
  • To apply the method to rice yield component traits and assess its performance.

Main Methods:

  • Calculated six kinship matrices using genome-wide markers.
  • Partitioned total genetic variance into six components (additive, dominance, and epistatic variances).
  • Integrated these components into a mixed-model QTL mapping framework.

Main Results:

  • Simulation studies demonstrated improved QTL mapping resolution with epistatic covariance structures.
  • Analysis of rice traits revealed varying contributions of different genetic variances across traits.
  • Detected numerous QTL effects for yield, tiller number, grain number, and grain weight.

Conclusions:

  • The new mixed-model method effectively incorporates complex genetic architectures for QTL mapping.
  • It provides insights into the relative importance of different genetic variances in complex traits.
  • The method is applicable to polygenic-effect-adjusted genome-wide association studies (GWAS) in various species.