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Multiple Allele Traits

Multiple Allele Traits

The Concept of Multiple Allelism

Multiple Allele Traits

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Genome-wide Association Studies-GWAS

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...

Epistasis Analysis

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...

Polygenic Traits

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 Traits

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...

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Related Experiment Video

Updated: Jun 25, 2026

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

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

Published on: July 27, 2021

Gene-trait similarity regression for multimarker-based association analysis.

Jung-Ying Tzeng¹, Daowen Zhang, Sheng-Mao Chang

¹Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695, USA. jytzeng@stat.ncsu.edu

|February 13, 2009

Summary

We developed a novel regression method to find links between traits and genetic markers. This approach uses similarity measures for both traits and genetic data, offering a flexible tool for genetic association studies.

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

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

Published on: June 21, 2018

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Last Updated: Jun 25, 2026

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

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

Published on: July 27, 2021

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

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

Published on: June 21, 2018

Area of Science:

Genetics
Statistical genetics
Bioinformatics

Background:

Identifying associations between genetic variations and phenotypic traits is crucial in understanding complex diseases.
Current methods often require complex genotype phasing or are limited to specific trait types.

Purpose of the Study:

To introduce a flexible, similarity-based regression method for detecting trait-genotype associations.
To provide a unified framework connecting haplotype sharing and variance-component approaches.

Main Methods:

Regressing trait similarity between individuals on their haplotype similarities.
Utilizing phase-independent similarity measures to avoid genotype phasing.
Employing a score test with a derived limiting distribution for significance detection.
Incorporating covariates and accommodating general trait types (quantitative and qualitative).

Main Results:

The proposed method effectively detects associations between traits and multimarker genotypes.
Demonstrated a close connection between gene-trait similarity regression and random effects haplotype analysis.
Established that the method is applicable to various trait types and can incorporate covariates.

Conclusions:

The similarity-based regression offers a powerful and versatile approach for genetic association studies.
The unified framework simplifies the analytical properties of sharing statistics in genetic analysis.
This method enhances the ability to study the genetic architecture of complex traits.