Zubair Khalid

Virologist/Molecular Biologist | Veterinarian | Bioinformatician

Conventional & Molecular Virology • Vaccine Development • Computational Biology

Dr. Zubair Khalid is a veterinarian and virologist specializing in conventional and molecular virology, vaccine development, and computational biology. Dedicated to advancing animal health through innovative research and multi-omics approaches.

Dr. Zubair Khalid - Veterinarian, Virologist, and Vaccine Development Researcher specializing in Computational Biology, Multi-omics, Animal Health, and Infectious Disease Research

Category: Guides

Lgc Genomics: A Practical Guide to Genotyping and Genomic Services

Lgc Genomics refers to a suite of validated genotyping and genomic analysis services offered by LGC (Laboratory of the Government Chemist), a leading provider of high throughput single nucleotide polymorphism (SNP) detection, custom assay development, and next generation sequencing (NGS) support. This guide is for laboratory scientists, bioinformaticians, and project managers who need a source bounded, practical framework to evaluate, implement, and troubleshoot LGC Genomics workflows in agriculture, human genetics, and infectious disease research. The core of LGC’s offering is the KASP (Competitive Allele Specific PCR) genotyping platform, a homogeneous, fluorescence based endpoint detection system that requires no post PCR processing. For a broad overview of genotyping technologies and their applications, see the NCBI Bookshelf general biotechnology chapter.

Understanding how KASP assays function and where they fit into your project is essential. LGC Genomics also provides whole genome genotyping arrays, custom NGS panels, and DNA extraction services. Their platform is built around a two allele scoring system that calls SNP genotypes based on relative fluorescence ratios. For training in SNP data analysis and common bioinformatics approaches, the EMBL EBI Training resources offer excellent modular courses.

At a Glance

Aspect Key Information
Primary service High throughput SNP genotyping via KASP chemistry
Typical applications Marker assisted selection, quantitative trait locus mapping, population genetics, clinical sample screening
Sample requirements 5 50 ng purified DNA per reaction, compatible with blood, tissue, saliva, and plant leaf extracts
Turnaround time 24 48 hours for KASP runs, longer for custom NGS panels
Data output Genotype calls in CSV or VCF format with quality scores
Strengths Scalable from 96 to 1536 well plates, low cost per data point, no probe labeling required
Limitations Requires prior SNP sequence information, not suitable for rare variant discovery beyond targeted sets
Key decision factor Choose KASP when you have known SNPs and need thousands of samples run across multiple markers

Core Concepts and Decision Criteria

LGC Genomics derives its genotyping power from KASP, an allele specific PCR system that uses two forward primers (each with a unique tail) and one common reverse primer. Fluorescently labeled cassettes bind to the tails during amplification, generating a signal that is read at endpoint. The decision to adopt LGC’s platform depends on several variables.

First, evaluate your marker density needs. If you are genotyping fewer than 10 SNPs across hundreds or thousands of samples, KASP is often more cost effective than array based or sequencing approaches. For large scale genome wide scans (thousands of markers), consider LGC’s bespoke genotyping arrays or partnering with NGS services. Second, consider sample quality. DNA that is degraded or contaminated can produce ambiguous calls. LGC’s protocols require a minimum A260/280 ratio of 1.8. For guidance on sample preparation and quality assurance, the Galaxy Training Network provides practical tutorials on DNA extraction validation.

Third, weigh the need for custom assay design. LGC offers a free online primer design tool for KASP, but primers must target flanking regions that are conserved across your population. If you are working with non model organisms, you may need to sequence flanking regions first. For a case study in applying LGC genotyping to track genomic diversity in influenza household transmission, see the publication in J Infect Influenza household transmission and genomic diversity. That study used targeted SNP panels to map transmission chains, demonstrating the utility of LGC’s approach in infectious disease epidemiology.

Practical Workflow or Implementation Sequence

The following steps outline a typical project using LGC Genomics services. This workflow is based on standard protocols and has been validated in diverse settings, including the development of reference materials for methylated cell free DNA Development characterization and inter laboratory validation of methylated human cell free DNA candidate reference materials.

Step 1: Define Your Markers and Design Assays

Identify the SNPs of interest from prior sequencing, public databases (e.g., dbSNP), or literature. Submit the flanking sequences (at least 50 bases on each side of the SNP) to LGC’s online design portal. They return candidate KASP assays with predicted success scores. Validate in silico using Bioconductor packages like SNPRelate to check for strand compatibility and cluster separation.

Step 2: Prepare DNA Samples

Extract DNA from your material using a method that yields high molecular weight, pure DNA. Quantify using a fluorometric assay (e.g., PicoGreen) and normalize concentrations to 5 20 ng per microliter. Dispense samples into 96 or 384 well plates. Include at least two no template controls and two known positive controls per plate.

Step 3: Order and Set Up Reaction Mix

Order lyophilized KASP master mix and assay primers from LGC. Resuspend primers in TE buffer. Prepare reaction master mix per manufacturer instructions. For a 10 microliter reaction, combine 5 microliters of 2x KASP master mix, 0.14 microliters of primer mix, and 4.86 microliters of water. Add 5 microliters of master mix to each well, followed by 5 microliters of normalized DNA.

Step 4: Run the Thermal Protocol

Use a real time PCR instrument capable of endpoint fluorescence detection (e.g., BioRad CFX, Applied Biosystems QuantStudio). Typical protocol: 94°C for 15 minutes (activation), then 10 cycles of 94°C for 20 seconds, 61°C for 60 seconds (with touchdown of 0.6°C per cycle), then 26 cycles of 94°C for 20 seconds, 55°C for 60 seconds. Read fluorescence at endpoint after a final 30°C hold. For step by step training, the Galaxy Training Network offers a module on qPCR data processing that applies to KASP endpoint reads.

Step 5: Analyze Genotype Calls

Export fluorescence data (FAM and HEX signals) from the instrument. LGC provides automatic clustering software, but you should manually inspect clusters in a scatter plot. Samples with low total fluorescence (below 10% of the positive control) should be flagged as no call. For advanced quality control and batch correction, use R packages from Bioconductor such as gwas3 or SNPolisher. Typical output is a genotype table with columns for sample ID, marker name, allele call (e.g., AA, AB, BB), and quality score.

Step 6: Submit Data to Repository (Optional)

If your funding requires public data sharing, deposit raw endpoint fluorescence values and genotype calls into the NCBI Sequence Read Archive. You can also upload study level metadata. The SRA accepts genotype data in VCF format, which can be converted from LGC’s output using standard bioinformatics tools.

Common Mistakes

Even with a robust platform like LGC Genomics, errors can occur. Avoid these frequent pitfalls.

Mistake 1: Poor DNA quality or quantity. Degraded DNA or insufficient template (below 1 ng per reaction) leads to failed amplification and ambiguous calls. Always quantify with a fluorometer and run a pre genotyping QC gel. For lessons from wastewater surveillance where sample quality is variable, read the study on designing effective SARS CoV 2 wastewater systems Designing effective SARS CoV 2 wastewater surveillance system. That paper emphasizes the need for rigorous sample pre processing before genotyping.

Mistake 2: Ignoring primer specificity. KASP primers must be allele specific. If your target region contains repetitive elements or structural variants, you may get off target amplification. Always BLAST your flanking sequences against the relevant genome before ordering assays.

Mistake 3: Overlooking batch effects. Running samples on different instruments or with different master mix lots can shift cluster positions. Include a set of control samples across all plates to normalize calls. Without controls, you may misclassify heterozygotes.

Mistake 4: Misinterpreting rare alleles. For markers with minor allele frequency below 1%, the small number of homozygous minor samples may form a sparsely populated cluster. In these cases, use a Bayesian calling algorithm available in LGC’s software or a Bioconductor tool to avoid false homozygous calls.

Limits of Interpretation

LGC Genomics provides genotype data, not phenotype predictions or biological interpretations. A few key limits to keep in mind.

Genotype calls are probabilistic. The endpoint readout relies on a predefined cluster model. If a sample falls between clusters (i.e., low confidence call), you cannot simply assign a genotype by eye. Further validation by Sanger sequencing is recommended for low confidence samples, especially if the marker is used for clinical or regulatory decisions. For example, in chronic lymphocytic leukemia studies, high risk molecular features can eclipse genomic complexity when predicting outcomes High risk molecular features may eclipse genomic complexity in predicting chronic lymphocytic leukemia outcomes. This illustrates that genotype data alone does not capture functional consequences.

Population stratification can bias allele frequency estimates. If your sample set includes multiple subpopulations, allele calls may appear to deviate from Hardy Weinberg equilibrium due to structure, not genotyping error. Use principal component analysis on genome wide data (if available) to adjust.

Assays are not validated for all species. LGC’s design tools assume a diploid genome with known reference sequence. For polyploid crops (e.g., wheat, strawberry) or highly duplicated genomes, KASP may produce multiallelic patterns that are difficult to interpret. Consult LGC’s support for polyploid specific pipelines.

The platform cannot detect novel mutations. KASP is a targeted assay. If your SNP of interest mutates to a third allele (e.g., A to C instead of A to G), the assay will fail or produce an unexpected signal. For discovery, you must use sequencing, not genotyping. The recent single cell transcriptomics study in silkworm wings Single cell and spatial transcriptomics define 20E driven developmental reprogramming in silkworm wing disc demonstrates that combined transcriptomic and genomic approaches reveal mechanisms that a pure genotyping screen would miss.

Frequently Asked Questions

Q1: What is the difference between KASP and TaqMan genotyping?

Both are allele specific endpoint PCR methods. TaqMan uses hydrolysis probes with fluorophores, while KASP uses proprietary fluorescently labeled cassettes that bind to tailed primers. KASP typically costs less per data point because it uses a universal cassette rather than custom probes for each SNP. However, TaqMan can sometimes offer higher precision for low abundance alleles. For a general introduction to PCR based genotyping chemistries, see the NCBI Bookshelf molecular biology section.

Q2: Can I use LGC Genomics for non human or non model organisms?

Yes, but with caveats. You need at least 50 bases of flanking sequence on each side of the SNP from your organism’s genome. If a reference genome is not available, you must first obtain flanking sequences via targeted amplicon sequencing or DNA walking. LGC has successfully designed KASP assays for cattle, soybean, and many other species.

Q3: How do I ensure data reproducibility across multiple plates?

Use the same master mix lot for all plates in a study. Include a set of identical control samples on every plate (e.g., three DNA samples of known genotype and one no template control). Apply plate wise clustering using software that normalizes fluorescence to the median of controls. The Galaxy Training Network has a workflow for plate normalization that can be adapted.

Q4: What are the minimum sample requirements for a KASP genotyping project?

LGC recommends at least 200 nanograms of high quality DNA per sample to allow for triplicate reactions. In practice, 5 ng per reaction is sufficient if DNA is pure, but you should always include replicates for low concentration samples. For projects with fewer than 96 samples, consider using lower density plates or outsourcing to LGC’s custom service.

References and Further Reading

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