QTL Analysis and Mapping

QTL Analysis

What is QTL Analysis?

A quantitative trait locus (QTL) is a region of DNA associated with a specific phenotype or trait that varies within a population. Typically, QTLs are associated with traits with continuous variance, such as height or skin color, rather than traits with discrete variance, such as hair or eye color.

QTL mapping is a statistical analysis to identify which molecular markers lead to a quantitative change of a particular trait. Since a single locus may include many variants, imputation or whole-genome sequencing is a key prerequisite for QTL mapping to enable precise identification of the contributing molecular marker. QTLs have been expanded to include variants that act at different levels throughout the genotype-to-phenotype continuum.

Types of QTL Analysis

QTL analysis is an effective means of annotating variants that are associated with disease. By understanding the functional effects of variants, it allows for the distinction between variants that are involved with disease, from those that are correlated with disease. By leveraging different QTL analyses, the network of molecular interactions of variants and the genes they affect begin to come into view, and provide evidence for which underlying genes and pathways are truly driving disease. This enables the investment of time, resources, and funding in targets that are most likely to be involved with disease.


Expression quantitative trait loci (eQTL) are genetic variants that affect the expression of one or more genes. An eQTL can act in cis (locally) or in trans (at a distance, eg, on a different chromosome) to its gene target. eQTL mapping requires genome-wide genotyping by arrays or WGS and gene expression analysis by RNA-Seq for each sample.

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Methylation quantitative trait loci (meQTL) are genetic variants that affect patterns of DNA methylation. meQTLs can influence methylation across extended genomic regions. meQTL mapping requires genome-wide genotyping and DNA methylation analysis with either arrays or sequencing.

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Chromatin accessibility quantitative trait loci (caQTL) are genetic variants that affect nucleosome packing, positioning, and chromatin accessibility. caQTL mapping requires genome-wide genotyping by arrays or WGS and chromatin accessibility analysis by a method such as ATAC-Seq or Hi-C.

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Binding quantitative trait loci (bQTL) are genetic variants that affect transcription factor binding. bQTL mapping requires genome-wide genotyping by arrays or WGS and ChIP-Seq.

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Protein quantitative trait loci (pQTL) are genetic variants that affect the quantity of that particular protein. pQTL requires genome-wide association studies (GWAS) using arrays, whole-genome (WGS), or whole-exome sequencing (WES).

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QTL Analysis Applications

We support the following workflows for in-depth analysis of basic and clinical research data:

Complex Disease
Illumina complex disease research

From variant discovery to risk profiling, modern genomics tools are helping researchers investigate the molecular etiology of complex diseases.

Genetic & Rare Diseases
QTL analysis and rare diseases

NGS technology is helping to drive breakthroughs in genetic disease testing by facilitating early detection and diagnosis.

QTL agrigenomic applications

Genomics is helping farmers and breeders develop more productive crops and livestock, improve sustainability, and meet the growing challenges of feeding the world.

Cancer Research
QTL Analysis and Cancer Research

Today’s genomic technologies can help you uncover new insights into cancer biology and etiology for tomorrow’s research breakthroughs.

QTL Analysis FAQ

QTL detection or mapping is the ability to statistically associate genotypic data with a phenotype of interest.
Genome-Wide Association Studies (GWAS) analyze the full genome of interest to identify loci related to a phenotype. In contrast, QTL analysis defines which molecular markers are linked to a phenotype.
QTL analysis requires samples of the same species that exhibit different phenotypes of the trait or traits of interest, or different genotypes for the trait(s). The protocol or method depends on the specific goals of the study.
Meta QTL analysis combines multiple genetic maps to gain wider coverage and improve the ability to link traits of interest to molecular markers.
Webinar: From Variants to Function with Multiomics

In this session, hear from three panelists who will share how we can begin to understand what variants mean physiologically through a multiomics approach. You'll learn how powerful combinations of high-throughput experimental assays, single-cell approaches, and computational analyses are accelerating the ability to link variants to function, and by extension, link genotype to phenotype.

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Webinar: From Variants to Function with Multiomics

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