Linkage Disequilibrium Analysis Service

Overview

Linkage Disequilibrium Analysis is a powerful method to assess non-random associations between alleles at different loci, revealing insights into genetic architecture, evolutionary history, and disease susceptibility. By analyzing patterns of LD, this approach identifies genomic regions under selection, maps trait-associated variants, and reconstructs population demographic events (e.g., bottlenecks, admixture). It helps pinpoint causal variants in complex traits, study recombination hotspots, and infer evolutionary forces shaping genetic diversity.

Our LD Analysis Service enhances your research with:

High-Resolution LD Mapping: Utilize advanced algorithms to detect fine-scale LD patterns across genomes.
Population-Specific Insights: Tailor analyses to diverse populations, accounting for genetic drift and migration.
Functional Annotation Integration: Link LD blocks to genes, regulatory elements, and phenotypic outcomes.
Haplotype Block Inference: Define recombination boundaries and identify conserved haplotypes.

What Is Linkage Disequilibrium Analysis

This dedicated bioinformatics service explores the non-random association of alleles at different loci within a population. Linkage Disequilibrium Analysis Service employs sophisticated statistical models and genomic data. It helps identify genetic markers in linkage, uncover population structure, and map disease-associated loci. Researchers leverage this service to gain insights into genetic diversity, evolutionary processes, and the genetic basis of complex traits, aiding in genetic research and breeding programs.

How to Measure

1. Sample Collection and Preparation

Sample Type Selection

Human: Blood, saliva, buccal swabs, formalin-fixed tissues. These samples are commonly used in human genetic studies to understand the genetic basis of diseases, population genetics, and evolutionary history.

Plants/Animals: Leaves, seeds, hair follicles, muscle/liver biopsies. For plant and animal genetics, these samples help in studying traits, breeding programs, and conservation genetics.

Microorganisms: Environmental metagenomic samples (soil, water), cultured isolates. Microbial samples are crucial for understanding microbial communities, their functions, and their roles in various ecosystems.

Genotyping/Sequencing Technologies

Table 1: Comparative genotyping techniques

Technology	Application Scenario	Key Advantages
SNP Arrays (e.g., Axiom)	Rapid population screens, cost-effective projects	High-throughput processing, low per-sample cost, established validation
RAD-seq	Non-model organisms, low-budget studies	Reduced genome representation, unbiased locus sampling, no reference genome required
WGS	Deep ancestry inference, rare variant detection	Comprehensive genomic coverage, no ascertainment bias, ideal for evolutionary studies
Pool-seq	Large population studies, pooled samples	Cost-effective allele frequency estimation, reduced individual genotyping needs

Data Quality Control (QC)

Sample-level QC: Remove duplicate samples; Exclude samples with low DNA concentration (<10 ng/μL); Identify and discard contaminated samples using control checks

Data-level QC:

Filter SNPs with: Missingness >20%; Hardy-Weinberg equilibrium p-value < 1×10⁻⁶ Minor allele frequency (MAF) <1%

Implement batch effect correction when applicable

2. Linkage Disequilibrium (LD) Analysis Workflow

LD Block Identification

Tools: Haploview, PLINK. These tools can identify LD blocks in the genome, which are regions where genetic markers are in strong linkage disequilibrium.

Application: Understanding LD blocks is important for association studies, as it helps in selecting tag SNPs that can represent the genetic variation in a region.

Output: LD block maps showing the extent of linkage disequilibrium between genetic markers.

LD Decay Analysis

Objective: To study how linkage disequilibrium changes with physical distance between genetic markers.

Methods: Calculate LD statistics (e.g., r²) for pairs of SNPs at different physical distances and plot LD decay curves.

Interpretation: LD decay curves can provide insights into the recombination rate, population history, and the effectiveness of natural selection.

LD-based Association Testing

Purpose: To identify genetic variants associated with traits or diseases by taking into account the linkage disequilibrium between markers.

Tools: PLINK, R packages (e.g., GenABEL). These tools can perform association tests while considering LD structure.

Considerations: Account for population stratification and relatedness to avoid false-positive associations.

3. Visualization and Reporting

LD Plots

Implementation: Haploview, LocusZoom. These tools can generate LD plots showing the linkage disequilibrium between genetic markers in a region.

Best Practices: Color-code the LD values to make the plots more informative. Include gene annotations and known trait-associated variants for context.

LD Decay Curves

Features: Plot LD statistics (e.g., r²) against physical distance between SNPs.

Enhancements: Add confidence intervals to show the variability of LD estimates. Compare LD decay curves between different populations or conditions.

Manhattan Plots for Association Results

Tools: qqman (R package), LocusZoom. Manhattan plots can display the association results of genetic variants with traits or diseases.

Integration: Overlay LD information on Manhattan plots to show the relationship between association signals and LD structure.

Figure 1: LD Workflow

What Can We do

Data Collection & Integration

Comprehensive Data Gathering:Gather diverse genetic data from public databases (e.g., dbSNP, 1000 Genomes) and private sources via collaboration.
Data Harmonization: Harmonize data from various formats into a unified one for LD analysis, handling marker mapping and missing data.

LD Calculation & Visualization

Advanced LD Estimation: Use advanced stats to calculate LD measures (D', r², Lewontin's D) for genetic marker pairs efficiently.
Interactive Visualization Tools: Offer interactive visualizations like heatmaps and LD decay plots to present LD results.

Population Stratification & Substructure Analysis

Population Structure Detection: Detect population structure using PCA, MDS, and clustering.
Stratified LD Analysis: Conduct stratified LD analysis for subgroups to get accurate estimates and identify true associations, crucial for GWAS.

Functional Annotation & Interpretation

Gene - LD Mapping: Map LD data to gene annotations to find genes in LD with variants.
Pathway Enrichment Analysis: Perform pathway enrichment analysis on these genes to understand biological mechanisms behind genetic associations.

Customized Analysis & Reporting

Tailored Analysis Pipelines: Develop tailored LD analysis pipelines based on client needs.
Detailed Reporting: Provide detailed reports with data, methods, LD patterns, functional interpretations, and statistical measures for reliability.

Our Advantages

Swift Analysis: Leverage powerful computational resources and optimized code to process large - scale genomic datasets rapidly. Whether it's a genome - wide scan or a targeted region analysis, we deliver results in a timely manner, saving researchers valuable time.
In - Depth Interpretation: Go beyond simple LD calculations by integrating gene annotation and pathway enrichment tools. We offer detailed functional insights into the genetic variants in LD, helping researchers understand the biological significance of their findings.
Tailored Services: Develop personalized LD analysis pipelines based on the unique research questions and data characteristics of each client. Whether it's a study on a rare disease or a population - specific trait, we adapt our services to meet specific needs.
Professional Guidance: Have a team of experienced geneticists and bioinformaticians available to provide expert advice and support throughout the LD analysis process. They can assist with data interpretation, study design, and troubleshooting any issues that may arise.
Reliable Protection: Implement robust data security measures to safeguard clients' sensitive genetic data. We comply with industry standards and regulations to ensure data confidentiality, integrity, and availability during the analysis and storage process.

Applications

Disease research

Disease Susceptibility Mapping: Uncover genetic variants in linkage disequilibrium that are linked to complex diseases like diabetes, heart disease, and cancer.

Pharmacogenomic Optimization: Determine how genetic variations in LD affect drug metabolism and efficacy.

Agricultural Genomics

Crop Improvement: Identify genetic markers in LD with desirable traits such as high yield, drought tolerance, and pest resistance in crops like wheat, rice, and corn. This accelerates breeding programs by allowing for marker-assisted selection.

Livestock Breeding: Locate genetic regions associated with traits like meat quality, milk production, and disease resistance in livestock such as cattle, pigs, and chickens. It aids in the development of more productive and resilient animal breeds.

Evolutionary Biology

Population Genetics Studies: Investigate the genetic diversity and evolutionary history of populations. By analyzing LD patterns, researchers can infer past population bottlenecks, migrations, and genetic drift events.

Speciation Research: Examine LD across species boundaries to understand the genetic basis of speciation. It helps in identifying genes that may have played a role in reproductive isolation and the formation of new species.

Conservation Biology

Endangered Species Protection: Assess the genetic health of endangered species by analyzing LD. This can reveal inbreeding levels, genetic bottlenecks, and the loss of genetic diversity, which are crucial for developing effective conservation strategies.

Habitat Fragmentation Impact: Study how habitat fragmentation affects LD in wildlife populations. It provides insights into the genetic consequences of habitat loss and isolation, guiding habitat restoration and management efforts.

Demo

Figure 2: Linkage disequilibrium decay for all scaffolds longer than 500 kb (Niu, 2019)

Case Study

Computing linkage disequilibrium aware genome embeddings using autoencoders

Journal：Bioinformatics

Published：2024

To achieve the proposed compression, many haploblocks need to be compressed and many autoencoders need to be trained. Given the variety of these blocks, optimization of each network's hyperparameters would consume time and resources beyond practicality, even feasibility. Therefore, this study seeks a standardized way to build autoencoders, which manages the trade-off between compression rate and reconstruction accuracy.

This study proposes a method to compress single nucleotide polymorphism (SNP) data, while leveraging the linkage disequilibrium (LD) structure and preserving potential epistasis. This method involves clustering correlated SNPs into haplotype blocks and training per-block autoencoders to learn a compressed representation of the block's genetic content. It provides an adjustable autoencoder design to accommodate diverse blocks and bypass extensive hyperparameter tuning. This study applied this method to genotyping data from Project MinE, and achieved 99% average test reconstruction accuracy—i.e. minimal information loss—while compressing the input to nearly 10% of the original size. This study demonstrates that haplotype-block based autoencoders outperform linear Principal Component Analysis (PCA) by approximately 3% chromosome-wide accuracy of reconstructed variants.

This study illustrates how the relationship between the block size and SNP accuracy is affected under different compression scales. Evidently, a more rigorous compression results in lower robustness of the SNP accuracy to increasing block sizes. Haploblocks can otherwise be assessed by quantifying their internal genetic variation using the average pairwise LD. The higher the LD within a block, the more similar the haplotypes are to each other, indicating lower variation. Overall, a higher degree of compression triggers the sensitivity of SNP accuracy to the variation in blocks. The positive correlation between SNP accuracy and LD gradually becomes weaker as bn increases.

Figure 3: The highest validation accuracies obtained from the grid search for each block, with bottleneck values ranging from 1 to 10. The accuracies are plotted against the number of SNPs in each block (left) and against the within-block average pairwise LD (right).

FAQs

Do you offer support for interpreting results?

Yes! Our team provides post-analysis support to help you interpret evolutionary patterns, test hypotheses, and contextualize findings within your field of study.

Is my genetic data safe with your service?

Data security is our top priority. We have implemented a comprehensive set of security measures to protect your sensitive genetic data. Firstly, all data transfers between your system and ours are encrypted using secure protocols such as SSL/TLS. This ensures that your data cannot be intercepted or tampered with during transmission. Secondly, we store your data on secure servers with restricted access. Only authorized personnel have permission to access the data, and they are bound by strict confidentiality agreements. We also regularly back up your data to prevent any loss in case of hardware failures or other unforeseen events.

References

Niu S, Song Q, Koiwa H, et al. Genetic diversity, linkage disequilibrium, and population structure analysis of the tea plant (Camellia sinensis) from an origin center, Guizhou plateau, using genome-wide SNPs developed by genotyping-by-sequencing. BMC Plant Biol. 2019 Jul 23;19(1):328. https://doi.org/10.1186/s12870-019-1917-5
Taş G, Westerdijk T, Postma E; et al. Computing linkage disequilibrium aware genome embeddings using autoencoders. Bioinformatics. 2024 Jun 3;40(6):btae326. https://doi.org/10.1093/bioinformatics/btae326

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