QTL Location Analysis Service

Overview

We offer comprehensive QTL location analysis services to identify genomic regions associated with complex traits across diverse populations. By integrating high-throughput genotyping and advanced statistical mapping methods, the service reveals genotype-phenotype relationships, locates significant loci, and supports functional annotation. This empowers research in trait dissection, molecular breeding, and precision agriculture.

What is QTL Location Analysis

QTL (Quantitative Trait Loci) location analysis is a method used to identify specific genomic regions that contribute to the variation of complex, quantitative traits within a population. These traits, such as yield, height, or disease resistance, are typically influenced by multiple genes and environmental interactions. By analyzing the statistical association between genetic markers and phenotypic data, QTL analysis enables the mapping of loci that have measurable effects on such traits.

In biological research, QTL location analysis serves as a foundational tool for understanding the genetic architecture underlying observable characteristics. It facilitates marker-assisted selection in breeding programs, supports functional genomics studies, and guides the discovery of candidate genes responsible for trait expression. In both plant and animal systems, the identification of QTLs enables researchers to dissect trait heritability, optimize genetic improvement strategies, and better understand adaptive mechanisms shaped by evolutionary pressures.

How to Measure QTL Location

QTL location analysis quantifies the association between genetic markers and phenotypic traits to pinpoint genomic regions influencing quantitative traits. Key methodologies include:

Genotyping and Marker Selection

High-resolution marker data is essential for accurate QTL mapping. This includes:

SNP Genotyping: Detects single nucleotide polymorphisms across the genome.
Microsatellite Markers: Useful in species with limited genomic resources.
Whole-Genome Resequencing: Enables dense coverage for fine-mapping QTLs.

Phenotypic Trait Evaluation

Precise, replicated phenotypic measurements across environments enhance mapping power and reliability. Traits must be quantifiable and standardized.

Mapping Techniques

Linkage Mapping: Utilizes structured populations (e.g., F2, RILs) to detect co-segregation between markers and traits.
Association Mapping (GWAS): Applies to natural populations, leveraging historical recombination and population structure.
Interval Mapping & Composite Interval Mapping (CIM): Identifies QTLs within confidence intervals while accounting for background noise.
Mixed Linear Models (MLM): Controls for population structure and kinship, improving mapping accuracy.

Statistical Outputs

LOD Scores: Indicate strength of marker-trait associations.
P-values and Effect Sizes: Assess significance and impact of identified QTLs.
Confidence Intervals: Define the genomic boundaries of a QTL.
Heritability Estimates: Quantify the proportion of trait variance explained by QTLs.

Post-Mapping Validation

Identified QTLs are validated via independent datasets, fine-mapping, or candidate gene analysis, often supported by gene annotation databases.

What Can We do

Genomic Data Acquisition: Select optimal strategies for genotyping based on research objectives, including whole-genome resequencing, SNP arrays, and genotyping-by-sequencing (GBS).

Phenotype Collection and Standardization: Help design experiments and collect high-quality phenotypic data across environments to ensure reliable trait measurement.

QTL Mapping and Analysis: Perform robust QTL detection using advanced statistical approaches such as interval mapping, composite interval mapping (CIM), and genome-wide association studies (GWAS).

Statistical Evaluation: Calculate key indicators including LOD scores, p-values, explained variance, and confidence intervals for each identified QTL.

Visualisation of Results: Generate intuitive graphical outputs, such as Manhattan plots, LOD score curves, and QTL heatmaps, for clear interpretation.

Report Generation: Deliver comprehensive, publication-ready reports summarising the methodology, results, annotated QTL regions, and actionable recommendations for downstream applications.

Our QTL Location Analysis Service Workflow

QTL Location Analysis Workflow

Data Analysis

Data collection on QTLs(Aloryi KD et al.,2022)

Demo Results

QTL flanking region of SBI-10L (Kiranmayee et al.2020)

Our Advantages

Integrated QTL Platforms: Supports both linkage and association mapping with flexible pipelines adaptable to diverse population structures and trait complexities.

Precision Mapping: Employs dense genotyping and high-resolution statistical models to narrow QTL intervals and improve candidate gene identification.

Cross-Species Expertise: Applicable across crop plants, livestock, model organisms, and wild populations-enabling broad utility in agricultural and ecological studies.

Reproducibility & Rigor: Follows strict quality control and statistical validation procedures to ensure the reliability and reproducibility of mapping results.

Interdisciplinary Support: Combines molecular biology, quantitative genetics, and bioinformatics to deliver holistic, end-to-end analysis solutions.

Applications of Genetic Diversity

Agricultural Applications

(1) Crop Breeding and Trait Improvement

QTL mapping enables the identification of genomic regions associated with yield, drought tolerance, disease resistance, and other agronomic traits. This supports marker-assisted selection (MAS) and genomic selection (GS), accelerating breeding cycles and improving genetic gains.

(2) Germplasm Characterisation

By associating phenotypic traits with genetic markers, QTL analysis aids in characterising the functional variation in breeding populations and wild relatives. This helps preserve genetic resources and strategically integrates desirable traits into elite lines.

(3) Pathogen and Stress Resistance Research

Identifying QTLs related to resistance against pests, pathogens, and abiotic stresses such as salinity or temperature extremes informs targeted breeding for more resilient crop varieties.

Animal Applications

(1) Livestock Genetic Improvement

QTL analysis helps uncover genomic regions influencing growth rate, milk yield, meat quality, fertility, and feed efficiency. These findings support marker-assisted selection in cattle, swine, poultry, and aquaculture species.

(2) Conservation of Genetic Resources

For endangered species or rare breeds, QTL mapping identifies adaptive traits and supports conservation breeding by linking genetic variation to fitness-related traits, such as disease resistance or reproductive success.

(3) Health and Disease Resistance

Mapping QTLs associated with immunity and disease susceptibility contributes to understanding host-pathogen interactions and informs the development of healthier, more disease-resilient animals.

Environmental and Ecological Applications

(1) Adaptation to Environmental Change

QTL location analysis in wild species can reveal genomic regions involved in adaptation to temperature, altitude, or other environmental variables, offering insights into evolutionary processes and species resilience.

(2) Restoration and Reintroduction Programs

Identifying adaptive traits through QTL mapping aids in selecting source populations for ecological restoration and designing reintroduction strategies that promote long-term survival.

(3) Invasive Species Management

QTL studies on invasive species can identify traits linked to rapid spread or high adaptability, informing risk assessments and management practices for protecting native biodiversity.

Case Study

QTL-Seq identified a genomic region on chromosome 1 for soil-salinity tolerance in F2 progeny of Thai salt-tolerant rice donor line "Jao Khao"

Journal：Frontiers in Plant Science

Published: 2024

Background
Results

Owing to advances in high-throughput genome sequencing, QTL-Seq mapping of salt tolerance traits is a major platform for identifying soil-salinity tolerance QTLs to accelerate marker-assisted selection for salt-tolerant rice varieties. We performed QTL-BSA-Seq in the seedling stage of rice from a genetic cross of the extreme salt-sensitive variety, IR29, and "Jao Khao" (JK), a Thai salt-tolerant variety.

The findings confirm that the CMS and TN traits are tightly linked to the long arm of chromosome 1 rather than to chromosome 3. The validated qCMS-TN1 QTL can be used for gene/QTL pyramiding in marker-assisted selection to expedite breeding for salt resistance in rice at the seedling stage.

Marker-assisted development pipeline used in this study(Prasit Khunsanit et al.,2024)

FAQs

Why is QTL location analysis important in genetic research?

QTL location analysis uncovers the genetic basis of complex quantitative traits by identifying genomic regions that influence phenotypic variation. This is essential for advancing our understanding of trait heritability, dissecting gene–trait relationships, and facilitating precise molecular breeding strategies in both plants and animals.

How does QTL analysis benefit breeding programs?

By pinpointing loci associated with desirable traits, QTL analysis allows breeders to incorporate marker-assisted selection (MAS) and genomic selection (GS). This improves selection efficiency, accelerates genetic gains, and reduces breeding cycles, ultimately resulting in high-yielding, stress-resistant, and well-adapted varieties or breeds.

Can QTL mapping be performed without a reference genome?

Yes. While having a reference genome enhances resolution, QTL analysis can still be conducted in species lacking complete genome assemblies by using techniques such as reduced-representation sequencing (e.g., GBS, RAD-seq) combined with linkage or association mapping in structured or natural populations.

What types of populations are suitable for QTL analysis?

QTL mapping can be applied to a variety of population types. Linkage mapping typically uses structured populations like F2, recombinant inbred lines (RILs), or backcrosses, while association mapping (GWAS) is used in natural or diverse populations with historical recombination, enabling broader allele discovery.

How reliable are QTL mapping results?

The reliability of QTL analysis depends on multiple factors: the quality of phenotypic data, marker density, population size, and statistical rigor. Our service includes strict quality control, model optimization, and validation strategies, such as replication in independent datasets or fine-mapping, to ensure credible and reproducible results.

References

Aloryi, K. D., Okpala, N. E., Amo, A., Bello, S. F., Akaba, S., & Tian, X. (2022). A meta-quantitative trait loci analysis identified consensus genomic regions and candidate genes associated with grain yield in rice. Frontiers in Plant Science, 13, 1035851. https://doi.org/10.3389/fpls.2022.1035851
Kiranmayee, K. N. S. U., Hash, C. T., Sivasubramani, S., Ramu, P., Amindala, B. P., Rathore, A., Kishor, P. B. K., Gupta, R., & Deshpande, S. P. (2020). Fine-Mapping of Sorghum Stay-Green QTL on Chromosome 10 Revealed Genes Associated with Delayed Senescence. Genes, 11(9), 1026. https://doi.org/10.3390/genes11091026
Khunsanit, P., Jitsamai, N., Thongsima, N., Chadchawan, S., Pongpanich, M., Henry, I. M., Comai, L., Suriya-Arunroj, D., Budjun, I., & Buaboocha, T. (2024). QTL-Seq identified a genomic region on chromosome 1 for soil-salinity tolerance in F2 progeny of Thai salt-tolerant rice donor line "Jao Khao". Frontiers in Plant Science, 15, 1424689. https://doi.org/10.3389/fpls.2024.1424689

* Designed for biological research and industrial applications, not intended for individual clinical or medical purposes.