Workflow solutions for low-pass WGS and imputation analysis
From challenging plant and animal sample types to high-throughput library preparation and downstream genotype imputation, each step can affect the quality and usability of low-pass WGS data. Integrated workflow solutions can help support consistent sample processing, efficient library preparation, scalable automation, and analysis-ready results.
Sample homogenization
Plant tissues, seeds, animal tissues, and other agricultural inputs can be difficult to process consistently. Variability at this first step can carry through extraction, library preparation, sequencing, and downstream analysis. Homogenization solutions for plant and animal samples can help create more uniform starting material, supporting reliable DNA recovery across large genotyping studies.
Homogenization solutions for plant and animal samples can help create more uniform starting material, supporting reliable DNA recovery across large genotyping studies.
Nucleic acid extraction
Successful genotyping studies depend on DNA that is suitable for efficient library preparation and sequencing. Agricultural samples can introduce challenges such as variable tissue composition, inhibitors, degraded inputs, or inconsistent yields.
Scalable extraction workflows help labs process diverse sample types while maintaining the consistency needed for high-throughput projects.
Sample and library quality control
Poor sample or library quality can reduce the usefulness of sequencing data before analysis begins. QC checkpoints help confirm that DNA inputs and prepared libraries are suitable before committing samples to sequencing.
Assessing concentration, quality, and library size distribution can help identify failed or variable samples earlier, reduce wasted sequencing capacity, and support more consistent data generation.
DNA library preparation
Library preparation is a key step in generating sequencing-ready DNA for low-pass WGS genotyping. Efficient library conversion, compatibility with commonly used sequencing platforms, and support for variable sample quality are important considerations for agricultural research workflows.
NEXTFLEX® HT Agrigenomics Low-Pass WGS kits are designed to support high-throughput library preparation from breeder-relevant DNA samples and generate libraries compatible with Illumina® and Element® sequencing platforms.
Modular and scalable automation
As low-pass WGS projects scale, manual steps can limit throughput and introduce variability. Automation can help standardize key workflow steps, improve efficiency, and support consistent processing across larger sample sets.
Modular automation solutions can be configured to support different throughput needs across sample preparation, nucleic acid extraction, QC, and library preparation workflows.
Imputation analysis
Low-pass WGS generates genome-wide sequencing data at reduced coverage, making imputation an important step for inferring missing genotypes and producing more complete variant information.
CURIO analysis solutions can help support low-pass WGS data interpretation, converting sequencing data into genotype information for downstream breeding research applications.
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FAQs
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When is low-pass WGS a good fit for genotyping studies?
Low-pass WGS can be a good fit when researchers need genome-wide genotyping across large sample sets but do not require high-depth sequencing for every sample. It is commonly used in plant and animal breeding research where cost, throughput, and broad genomic coverage are important considerations.
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How does low-pass WGS differ from SNP arrays?
SNP arrays analyze a predefined set of markers, so they depend on prior knowledge of the variants included on the array. Low-pass WGS sequences across the genome at reduced coverage, which can provide broader genomic information and more flexibility across populations, species, or breeding programs.
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Why is imputation used with low-pass WGS?
Because low-pass WGS sequences the genome at reduced coverage, not every genotype is directly observed. Imputation uses the available sequencing data and a reference panel or population-level information to infer missing genotypes and generate more complete variant information for downstream analysis.
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What factors can affect imputation results?
Imputation performance can be influenced by sample quality, sequencing data quality, coverage level, variant density, population structure, and the quality and relevance of the reference panel. A well-designed workflow helps generate data that is more suitable for reliable downstream interpretation.
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Can low-pass WGS be used for both plant and animal breeding research?
Yes. Low-pass WGS can be applied to plant and animal breeding research, but workflow requirements may vary depending on sample type, genome complexity, available reference resources, and project goals. Upstream sample preparation and downstream imputation strategy should be matched to the specific study design.
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