What is Quantitative Trait Locus (QTL)?

What is Quantitative Trait Locus (QTL)?

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Quantitative Trait Locus (QTL)

Quantitative Trait Locus (QTL) designate specific genomic regions governing quantitative traits. The identification of QTL necessitates the utilization of genetic or molecular markers. By establishing a correlation between these markers and the target quantitative trait, one can pinpoint the location of one or more QTL on the same chromosome. Essentially, this process involves linking the markers with the QTL, creating a chain of genetic information.

Illustration of molQTLs.Illustration of molQTLs. (Aguet et al., 2023)

Quantitative Character

Quantitative traits encompass all measurable characteristics, exhibiting a continuum of variation rather than discrete categories. Individuals manifest degrees of these traits rather than distinct qualitative differences. Key features include the complexity of describing inter-individual differences, the continuous nature of variation within populations, the polygenic control often involved, and the susceptibility to environmental influences.

The inheritance patterns of quantitative traits can be broadly categorized into three scenarios:

  • When two parents with differing expressions of a quantitative trait are crossed, the offspring (F1 generation) typically exhibit traits falling between those of the parents, with the mean approaching the median of the parental traits.
  • In the subsequent generation (F2), the range of variation among individuals significantly widens, often following a normal distribution, while the mean remains closer to that of the F1 generation.
  • Genes governing quantitative traits are subject to environmental influences, resulting in continuous variation even among individuals with the same genotype, including purebred parents or F1 progeny.

CD Genomics high-throughput sequencing and bioinformatics analysis services to address diverse facets within the realm of population genetics.

How to Do Quantitative Trait Loci (QTL) Mapping?

Mapping methods for localizing Quantitative Trait Loci (QTL) involve techniques akin to those used for single gene localization, aiming to position QTL on the genetic map and determine the distance between QTL and genetic markers, often expressed as recombination rates. These methods encompass various approaches based on marker number, statistical analysis methods, and labeled intervals.

  • Marker Number-based Methods: Single marker, double marker, and multiple marker approaches.
  • Statistical Analysis Methods: Variance and mean analysis, regression and correlation analysis, moment estimation, and maximum likelihood methods.
  • Labeled Intervals: Zero interval mapping, single interval mapping, and multi-interval mapping.

Additionally, comprehensive techniques such as QTL Composite Interval Mapping (CIM), Multi-Interval Mapping (MIM), and Multi-Trait Mapping (MTM) integrate multiple methods. Interval mapping (IM) and composite interval mapping (CIM) are particularly prominent.

Interval Mapping (IM)

Proposed by Lander and Botstein in 1989, IM employs a linear model incorporating individual quantitative trait observations and bipartite marker genotypic variables. It utilizes the maximum likelihood method to assess the likelihood ratio of a QTL's presence within an interval between neighboring markers, thereby estimating its effect. IM can deduce QTL locations, systematically search for QTL across chromosomes, and reduce the sample size required for QTL detection. However, IM has limitations including its inability to estimate genotype-environment interactions (Q×E), detect complex genetic effects, and address interference between neighboring QTLs.

Composite Interval Mapping (CIM)

CIM amalgamates interval mapping with multiple regression. CIM assumes that quantitative traits are governed by multiple genes and incorporates additional genetic markers to control background genetic effects. It leverages the strengths of IM while enhancing mapping accuracy and efficiency by controlling for background genetic effects. However, like IM, CIM faces challenges in estimating interactions between genotypes and environments, analyzing complex genetic effects, and selecting conditioning factors when marker density is high.

How to Design A QTL Localization Experiment?

Acquisition of Relevant Materials

Identify a trait of interest meeting specific criteria: research significance, value creation, distinct phenotypic differences, and sustained research relevance.


  • Analyze trait data from field populations using statistical methods like simple mean and ANOVA.
  • Plot data to observe distribution characteristics, such as a bar-line graph indicating normal distribution for traits like "number of leaves," signaling a quantitative trait.
  • Purpose of phenotypic analysis: Identify parent lines for population construction, typically representing extreme trait values. For instance, selecting autogamous lines with maximum and minimum leaf counts.
  • Basis for parental selection includes typicality, polymorphism, purity, and hybrid progeny fertility, aiming for lines with superior agronomic traits and genetic characteristics.

Population Construction

  • QTL localization analysis requires population construction, categorized into temporary segregating populations and permanent segregating populations.
  • Temporary segregating populations (e.g., F2, F2:3, BC1) are easier to establish but lack stability over time due to genetic composition changes.
  • Permanent segregating populations (e.g., Recombinant Inbred Lines - RIL, Double Haploids - DH) require extensive effort for construction but offer stable genetic compositions. Each strain within these populations is genetically pure, facilitating repeated use through continual offspring reproduction.

rinciples of mapping quantitative trait loci. (Mauricio et al., 2001)Principles of mapping quantitative trait loci. (Mauricio et al., 2001)

Marker Screening

Current methods for initial QTL locus screening encompass SSR, InDel, SNP, among others. SNP molecular marker technology involves sequencing-based molecular labeling, identifying nucleotide sequence differences between alleles at the same locus. These differences arise from single-base deletions or insertions, mutations, or substitutions in DNA sequences. In laboratory settings, SSR and InDel markers are commonly employed priming markers. The prerequisite for screening these markers is the availability of DNA samples.

Construction of Linkage Maps

The foundation for constructing linkage maps lies in the exchange and recombination of chromosomes during meiosis. Genes on non-homologous chromosomes segregate independently, while genes on homologous chromosomes undergo exchange and recombination, with recombination frequency increasing with genetic distance. Genes on the same chromosome tend to remain linked during inheritance, forming gene chains. Recombination between them occurs through exchange between non-sister chromatids of homologous chromosome pairs.

Linkage Map Construction Method

Polymorphic markers and field phenotypic data are used to create linkage maps using software like IciMapping. QTL loci are identified based on LOD values on the linkage map.

Fine Localization of QTL

  • Theoretical Basis: High-density molecular markers, sufficient exchange singletons, precise phenotypic localization.
  • Approach: Expand populations, isolate exchange singletons, and develop new molecular markers within defined intervals to refine localization. Precise localization aims to narrow the QTL interval to a few centimeters or less, necessitating population expansion and increased exchange singletons.

Prediction of Candidate Genes

Examine annotated genes within QTL intervals. Non-targeted quantitative proteomics analysis can identify differentially expressed proteins between parental lines, revealing candidate genes controlling traits of interest.

Candidate Gene Function Analysis

Methods include bioinformatics analysis, luciferase activity assays, in situ hybridization, CRISPR-Cas9, and overexpression studies, assessing gene function at the gene, protein, and phenotype levels.

Bioinformatics Analysis

  • GO Enrichment Analysis: Investigates biological process, molecular function, and cellular component aspects.
  • KEGG Clustering Analysis: Annotates metabolic and signaling pathways associated with candidate genes.
  • Phylogenetic Tree Analysis: Identifies published genes with high homology to candidate genes.


  1. Mauricio, Rodney. "Mapping quantitative trait loci in plants: uses and caveats for evolutionary biology." Nature Reviews Genetics 2.5 (2001): 370-381.
  2. Aguet, François, et al. "Molecular quantitative trait loci." Nature Reviews Methods Primers 3.1 (2023): 4.
* For Research Use Only. Not for use in diagnostic procedures.
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