Livestock Genotyping Array Services

What CD Genomics does

We translate your research goal and cohort design into a microarray genotyping workflow that preserves data consistency across batches:

• align sample IDs and cohort metadata
• run microarray genotyping and genotype calling
• generate QC fields and flag unusable outputs (when applicable)
• package deliverables with documentation for downstream handoff

What you receive

Across species pages, deliverables are organized around a practical handoff:

• genotype matrix file(s) aligned to your sample IDs
• QC report fields for usability review
• methods/parameter notes describing file structure and QC field meaning
• optional lightweight cohort checks (if selected; project-dependent)

Detailed Workflow Steps

1. Sample Intake and Metadata Alignment: Upon arrival, samples are registered into the Laboratory Information Management System (LIMS). This crucial first step ensures that unique sample IDs are perfectly mapped to your provided cohort metadata, minimizing tracking errors for large-scale agricultural operations.

2. DNA QC Gatekeeping: Extracted genomic DNA undergoes rigorous purity and concentration checks. We typically assess A260/280 and A260/230 ratios using spectrophotometry to detect inhibitors, alongside fluorometric quantification. Degraded or contaminated samples are flagged early to prevent failed genotyping arrays.

3. SNP Microarray Genotyping: Cleared samples are normalized and prepared for the microarray protocol. This involves DNA amplification, fragmentation, precipitation, and resuspension, followed by hybridization onto species-specific SNP arrays. The arrays are subsequently washed and scanned using high-resolution fluorescence imaging systems.

4. Genotype Calling: The raw intensity files from the scanner are processed using specialized calling software. This translates the fluorescence signals into discrete biallelic genotypes (AA, AB, BB). The algorithm utilizes standardized cluster position files to ensure high call accuracy across the target loci.

5. QC Review (Sample & Marker Levels): A dual-tier quality control process is executed. Sample-level QC checks for overall sample call rates (often flagging samples below a 90-95% threshold, project-dependent). Marker-level QC checks individual SNP missingness rates, minor allele frequencies (MAF), and cluster plot integrity to flag poorly performing probes.

6. Deliverables Handoff: The final step packages the cleaned data. We organize the genotype matrix to perfectly reflect your metadata. Documentation, including a comprehensive QC summary and methods/parameters notes, is compiled to ensure the dataset is fully analysis-ready for downstream bioinformatics.