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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...

Genomics

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

Epistasis

Epistasis

In addition to multiple alleles at the same locus influencing traits, numerous genes or alleles at different locations may interact and influence phenotypes in a phenomenon called epistasis. For example, rabbit fur can be black or brown depending on whether the animal is homozygous dominant or heterozygous at a TYRP1 locus. However, if the rabbit is also homozygous recessive at a locus on the tyrosinase gene (TYR), it will have an unshaded coat that appears white, regardless of its TYRP1...

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

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Integration of Multi-omics Data for Expression Quantitative Trait Loci (eQTL) Analysis and eQTL Epistasis.

Mingon Kang¹, Jean Gao²

¹Department of Computer Science, University of Nevada, Las Vegas, Las Vegas, NV, USA.

Methods in Molecular Biology (Clifton, N.J.)

|December 19, 2019

Summary

This study explores expression quantitative trait loci (eQTL) mapping by integrating multi-omics data. It introduces novel methods for detecting nonlinear causal relationships, enhancing our understanding of gene regulation.

Keywords:

Copy number variation DNA methylation Gene expression Integrative analysis Multi-omics Single-nucleotide polymorphism eQTL epistasis eQTL mapping study

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Area of Science:

Genetics and Genomics
Systems Biology
Bioinformatics

Background:

Expression quantitative trait loci (eQTL) mapping identifies genetic loci regulating gene expression.
Understanding gene regulatory interactions is crucial for deciphering biological mechanisms.
Complex biological systems involve intricate interactions across multiple processes.

Purpose of the Study:

To introduce multi-omics data commonly used in eQTL studies.
To present integrative methodologies for incorporating multi-omics data into eQTL analysis.
To describe a statistical approach for detecting nonlinear causal relationships between eQTLs (eQTL epistasis).

Main Methods:

Review of multi-omics data types (SNPs, CNVs, DNA methylation, gene expression).
Description of integrative statistical methodologies for multi-omics eQTL studies.
Introduction of a statistical approach for identifying eQTL epistasis.

Main Results:

Demonstrates the utility of integrating diverse omics data for eQTL mapping.
Highlights the importance of advanced statistical methods for uncovering complex genetic interactions.
Provides a framework for detecting nonlinear causal relationships in gene regulation.

Conclusions:

Multi-omics data integration significantly enhances eQTL mapping capabilities.
Novel statistical approaches, like eQTL epistasis detection, are vital for understanding complex gene regulation.
This work advances the study of genetic mechanisms in biological systems.