Human 85K SNP Genotyping Array Service

Overview

Large human cohorts require more than a collection of individual genotype files. They need a consistent marker framework, traceable sample handling, cohort-aware quality control, and data structures that can move directly into statistical genetics. CD Genomics provides a human SNP genotyping array service built around an approximately 85K-marker configuration for research teams that need standardized genome-wide genotypes across tens, hundreds, or thousands of samples.

The service connects study-design review, DNA quality assessment, array processing, genotype calling, data harmonization, and optional downstream analysis. Rather than treating genotyping as an isolated laboratory step, we organize the workflow around the research question: identifying population structure, detecting unexpected relatedness, studying runs of homozygosity, preparing a GWAS dataset, or generating array data suitable for genotype imputation.

Approximately 85K human SNP markers: genome-wide cohort profiling using a predefined and project-confirmed marker configuration.
Scalable cohort processing: coordinated sample layouts and batch-aware review for population and multicenter research projects.
Integrated quality control: sample-level, marker-level, plate-level, batch-level, and cohort-level checks as applicable.
Analysis-ready delivery: optional population structure, relatedness, ROH, GWAS, and imputation-preparation outputs.

Researchers who need a different species, another marker density, or a targeted method can review our broader SNP Genotyping Service.

Human research cohort DNA processed with an approximately 85K-marker SNP array into QC-ready genotype data Figure 1: Human 85K SNP array service overview from cohort DNA to standardized genotypes and population-genomics outputs.

Why Use an Approximately 85K-Marker Human SNP Array?

An approximately 85K-marker array occupies a practical middle ground between small targeted panels and higher-density or sequencing-based approaches. Because the loci are predefined, the same marker framework can be applied across the cohort, supporting consistent genotype representation and efficient data management. This is particularly useful when the study priority is comparison across many individuals rather than open-ended discovery of every genomic variant.

The array is best suited to research projects that need:

Genome-wide common-variant information across a large, consistently processed cohort
Population stratification and ancestry-aware cohort characterization
Detection of duplicates, close relatives, and cryptic relatedness
ROH and homozygosity pattern analysis at a resolution appropriate to the finalized marker set
GWAS-oriented genotype data, subject to study power, phenotype quality, marker coverage, and analysis design
Genotype data prepared for phasing and imputation against a suitable reference panel

The array is not designed for comprehensive novel-variant discovery, direct detection of all rare variants, or complete characterization of structural variation. Projects centered on these questions may be better served by sequencing.

Marker Coverage and Study Design

The service uses an approximately 85K-marker human SNP array configuration with genome-wide coverage that can include chromosomes 1–22 and X. The final marker count and composition are confirmed during project setup because available content can vary with the selected array configuration, reference build, and any technically feasible marker supplementation.

Marker density alone does not determine whether an array is appropriate. Study population, linkage disequilibrium patterns, ancestry composition, phenotype definition, relatedness, target allele frequency, and the intended downstream analysis all influence study utility. Our project review therefore begins with the research objective rather than a one-size-fits-all marker claim.

Study-Design Input	Why It Matters
Cohort size and sampling scheme	Affects statistical power, plate layout, batch planning, and the handling of related participants.
Population and ancestry composition	Influences marker informativeness, population-structure assessment, and reference-panel selection for imputation.
Research endpoint	Determines whether the workflow should prioritize PCA, relatedness, ROH, association testing, or imputation preparation.
Reference assembly	Controls chromosome coordinates, allele representation, annotation, and downstream compatibility.
Phenotype and covariate data	Supports association-ready sample mapping and helps identify metadata inconsistencies before analysis.
Requested marker additions	Requires probe and platform feasibility review; supplementation is not guaranteed for every locus.

Project-specific marker supplementation or an alternative density can be evaluated where the platform and probe design allow. This review does not imply unrestricted custom array design.

Illustrative genome-wide distribution of approximately 85K SNP markers across human chromosomes 1 through 22 and X Figure 3: Illustrative marker-coverage concept for an approximately 85K human SNP array; final content depends on project configuration.

Human 85K SNP Genotyping Array Workflow

1. Project Consultation and Design Review

We define the cohort, study endpoint, sample layout, genome build, marker configuration, metadata structure, optional analyses, and final deliverables.

2. Sample Receipt and Identity Review

Sample identifiers and accompanying metadata are checked for completeness, uniqueness, and compatibility with the proposed plate map.

3. DNA Quality Control and Normalization

DNA quantity, purity, integrity, and concentration consistency are evaluated using methods appropriate to the submitted material and project scope. Samples are normalized for array processing when applicable.

4. Array Processing and Signal Acquisition

Samples undergo the platform-specific amplification, hybridization, washing, staining, scanning, and signal collection workflow associated with the finalized array configuration.

5. Genotype Calling and Technical QC

Genotype clusters are evaluated, sample- and marker-level metrics are calculated, and potential plate or batch effects are reviewed before data release.

6. Cohort Analysis and Delivery

Approved samples and markers are harmonized into analysis-ready files. Optional modules may include population structure, relatedness, ROH, GWAS preparation, or imputation preparation.

Workflow for project design, DNA QC, SNP array processing, genotype calling, cohort QC, and data delivery Figure 2: End-to-end workflow for cohort-scale human SNP array genotyping.

Sample Requirements

Purified human genomic DNA is the standard input. Exact acceptance criteria are confirmed after review of the array configuration, cohort size, extraction method, and available sample volume. This prevents fixed specifications from being applied to projects with different matrices or operational constraints.

Input	Preferred Project Information	Review Points
Purified genomic DNA	Unique sample ID, concentration, volume, extraction method, source material, and storage buffer	Quantity, purity, integrity, concentration consistency, and compatibility with array processing
Biospecimens requiring extraction	Sample type, collection method, storage conditions, shipping history, and available volume	Extraction feasibility must be confirmed before acceptance; purified DNA may be recommended
Sample manifest	One row per sample with unique IDs and non-identifying study metadata	Duplicate IDs, missing fields, plate-map compatibility, and data-transfer requirements
Phenotype and covariate table	Variables required for the planned research analysis	Coding consistency, missingness, allowed values, and sample-ID correspondence
Requested marker list	rsID and/or genomic coordinate, reference build, alleles, and priority	Platform compatibility, probe feasibility, redundancy, and final configuration impact

Please do not ship samples until the project team has confirmed the sample specification and logistics.

Bioinformatics and Genotype Quality Control

Array processing produces genotype calls, but a population-genomics dataset also requires cohort-level review. The QC plan is agreed before analysis so that filters are appropriate for the population, study design, array content, and intended endpoint. Fixed thresholds are not imposed without considering these factors.

Sample-Level Review

Genotyping completeness and missingness distribution
Heterozygosity patterns and potential outlier samples
Duplicate or near-duplicate detection
Relatedness and identity-by-descent estimates
Genetic versus reported sex concordance when suitable chromosome content and validated metadata are available
Population structure and ancestry outlier review using principal components when included in scope
Plate, batch, and processing-date patterns

Marker-Level Review

Marker completeness and missingness distribution
Allele-frequency summaries
Cluster-quality and genotype-calling review where supported by platform outputs
Hardy–Weinberg equilibrium checks when appropriate to the study design and analysis subset
Allele, strand, chromosome, coordinate, and reference-build harmonization
Duplicate markers, ambiguous alleles, and cross-batch consistency

Downstream-Ready Preparation

Linkage disequilibrium pruning for PCA or relatedness workflows
Population structure and principal-component analysis
Relatedness and IBD summaries
Runs of homozygosity analysis
GWAS-ready sample and variant tables
Pre-phasing and imputation preparation, including reference-build and allele checks

Researchers who need dedicated downstream interpretation can combine the service with Population Structure Analysis or Genome-wide Association Analysis.

What You Receive

Deliverables are finalized during project design and may include:

Raw or platform-native intensity and genotype-calling files, where contractually and technically available
Sample manifest and processing summary
Analysis-ready genotype data in PLINK 2 formats (.pgen, .pvar, .psam) or PLINK 1 binary formats (.bed, .bim, .fam)
VCF files when compatible with the selected workflow, reference build, and allele representation
Sample-level and marker-level QC tables
Exclusion and flagging reports with documented criteria
PCA coordinates and plots, relatedness/IBD tables, or ROH summaries when ordered
GWAS-ready files or imputation-preparation outputs when included in scope
Methods summary and data dictionary

The final delivery package is designed around traceability: researchers can see which samples and markers were retained, flagged, or excluded and which decisions were applied before downstream analysis.

Research Applications

Population Structure and Ancestry Research

Use genome-wide genotype patterns to characterize cohort heterogeneity, identify genetic clusters, visualize ancestry gradients, and define covariates for downstream analyses.

Relatedness and Sample Identity

Detect duplicated samples, unexpected close relatives, and cryptic relatedness that may affect association tests or cohort composition.

Runs of Homozygosity

Measure long homozygous segments and compare ROH burden or distribution across research groups, subject to marker density and QC design.

GWAS-Oriented Cohort Genotyping

Prepare standardized genotypes, QC metrics, population covariates, and sample mappings for association analysis of complex traits or disease-associated variation in research cohorts.

Genotype Imputation Preparation

Harmonize build, chromosome, coordinate, strand, allele, and marker identifiers before phasing and imputation. Imputation performance depends on marker overlap, data quality, ancestry match, and reference-panel composition.

Cohort Stratification and Sample Selection

Use PCA, relatedness, heterozygosity, and missingness information to select balanced or independent subsets for follow-up experiments.

Illustrative Demo Results

The demo below uses simulated data and is intended to explain potential deliverables rather than report service performance. It combines a three-cluster genotype intensity plot, a sample-level completeness distribution, a PCA scatter plot, and a relatedness heatmap. Any review boundary shown is illustrative and is not a CD Genomics guarantee or universal acceptance threshold.

Illustrative simulated SNP array outputs showing genotype clusters, sample completeness, PCA, and relatedness patterns Figure 4: Illustrative simulated genotype-calling, QC, population-structure, and relatedness outputs; values are not service specifications.

Case Study: Population-Optimized SNP Arrays in the Taiwan Precision Medicine Initiative

The Taiwan Precision Medicine Initiative provides a cohort for large-scale studies

Journal: Nature

Published: 2025

Yang H-C, Kwok P-Y, Li L-H, et al. The Taiwan Precision Medicine Initiative provides a cohort for large-scale studies. Nature. 2025;648(8092):117–127.

Large population cohorts can be limited when the available SNP content and reference resources do not adequately represent the study population. The Taiwan Precision Medicine Initiative established a large Han Chinese research cohort and used population-optimized SNP arrays to support standardized genotyping and downstream genetic analyses.

The study used two customized SNP arrays, TPMv1 and TPMv2. Genotyping was organized in batches, and the investigators applied sample- and marker-level QC, genotype harmonization, relatedness analysis, population-structure analysis, GWAS workflows, and phasing/imputation validation. For the imputation assessment, 6,000 genotyped variants on chromosomes 5, 13, and 18 were masked in 1,000 individuals and compared with their imputed genotypes.

After the reported QC measures, the study had genotype data from 165,596 individuals processed with TPMv1 and 321,360 with TPMv2, totaling 486,956 participants with matching research records. The masked-variant validation reported an average correlation of 0.906 between imputed and observed genotypes, and 96.3% of masked SNPs had an INFO score above 0.7. The investigators also used shared, QC-passed SNPs for PCA, ancestry characterization, relatedness assessment, and association analyses.

Original summary of a published Taiwan cohort study using population-optimized SNP arrays, cohort QC, and imputation validation Figure 5: Original summary of the published Taiwan Precision Medicine Initiative SNP-array workflow and reported cohort-level analyses. Source: Yang et al. (2025).

The study shows how standardized SNP-array genotyping can be scaled across a very large cohort when marker selection, batch normalization, sample QC, marker QC, population structure, relatedness, and imputation are treated as connected parts of one research workflow. For service planning, the transferable lesson is the importance of cohort-aware design and QC—not the adoption of a universal marker count or fixed performance threshold.

Choosing Between SNP Array, GBS, Low-Pass WGS, and WGS

The most appropriate method depends on cohort size, ancestry, allele-frequency range, discovery requirements, reference resources, analysis objectives, and budget. Method selection should begin with the research question rather than a preferred technology.

Method	Best Fit	Main Strength	Important Limitation
Approximately 85K human SNP array	Large human cohorts focused on predefined genome-wide markers	Consistent loci, efficient cohort processing, and direct compatibility with common genotype analyses	Does not comprehensively discover novel, rare, or structural variants
Genotyping By Sequencing	Projects needing reduced-representation sequencing or flexible marker discovery, especially outside standardized human-array settings	Combines variant discovery and genotyping without a fixed array	Locus representation and missingness can vary with library and sequencing factors
Low-pass whole-genome sequencing	Population-scale projects seeking broader genome representation with imputation	Samples the genome beyond a predefined array and can support imputed genotypes	Requires sequencing, imputation, and additional computational QC; utility depends on coverage and reference panel
Whole Genome Re-sequencing	Projects prioritizing broad variant discovery and richer genomic characterization	Most comprehensive option among these methods for sequence-based discovery	Higher data volume, analytical complexity, and project cost than a fixed SNP array

Why CD Genomics for Human Cohort SNP Genotyping

Confidence in a population-scale array project comes from decisions that remain traceable from marker configuration and sample layout through cohort-level delivery. CD Genomics connects laboratory processing, quality control, and downstream requirements so the genotype dataset is designed for the research question rather than treated as an isolated assay output.

Cohort-aware study planning: marker configuration, sample layout, metadata, QC criteria, and statistical endpoints are connected before processing begins. This reduces the risk of producing technically valid genotypes that do not fit the planned analysis.

Integrated laboratory and bioinformatics workflow: sample review, array processing, genotype calling, harmonization, and optional population-genomics analyses are coordinated within one project plan, with documented handoffs and deliverables.

Transparent QC and traceable decisions: QC is reported at sample, marker, plate, batch, and cohort levels as applicable. Flagging and exclusion criteria are documented so the released dataset can be audited and reused.

Method flexibility and analysis-ready outputs: an approximately 85K array is recommended only when it matches the research question. File formats, genome build, allele representation, identifiers, and metadata mapping are agreed in advance for the intended analysis.

Any sample, marker, output, timing, or performance specification not confirmed for the individual project remains subject to feasibility review. This transparency helps identify method limitations before they become unexplained gaps in a large cohort.

FAQs

Does the service always contain exactly 85,000 SNPs?

No. "85K" describes an approximate configuration. The final count can vary with the selected array version, genome build, QC status, and any technically feasible marker supplementation. The confirmed marker manifest is part of project setup.

Can I add SNPs that are important to my study?

Project-specific supplementation can be evaluated when probe design and the selected platform support the requested loci. Please provide rsIDs or genomic coordinates, reference build, alleles, and marker priorities. Feasibility is reviewed before the configuration is finalized.

Is an approximately 85K array suitable for GWAS?

It can support GWAS-oriented research, but suitability depends on cohort size, effect size, allele frequency, phenotype quality, ancestry composition, marker coverage, imputation strategy, and statistical design. A power and design review is recommended before genotyping.

Can the data be used for genotype imputation?

Yes, the workflow can prepare data for imputation by checking reference build, chromosome coordinates, strand, alleles, duplicates, missingness, and marker overlap. Imputation accuracy is not determined by the array alone; ancestry match and reference-panel composition are also important.

Which QC thresholds will be used?

Thresholds are set according to the study population, analysis endpoint, array configuration, and cohort distributions. The project plan can define initial review criteria, while final decisions are documented in the QC report.

What DNA should I submit?

Purified genomic DNA is the standard input. Required quantity, concentration, purity, integrity, volume, and buffer are confirmed after the array and cohort scope are reviewed. Samples should not be shipped before specifications are approved.

How are batch effects handled in large cohorts?

Batch planning begins with plate layout and sample randomization or balancing where appropriate. During analysis, sample and marker metrics are reviewed by plate and batch, and genotype consistency, missingness, intensity patterns, and population covariates can be evaluated for systematic differences.

Can the array discover novel variants?

No. An SNP array assays predefined loci. If novel, rare, or structural variant discovery is central to the research question, low-pass or standard-depth whole-genome sequencing may be more appropriate.

What files will I receive?

Typical deliveries include genotype files, sample and marker QC tables, a manifest, a data dictionary, and a methods summary. PLINK, VCF, PCA, relatedness, ROH, GWAS-ready, or imputation-preparation outputs are included when specified in the project scope.

How long does a project take?

The schedule depends on sample count, input quality, array availability, marker configuration, repeat requirements, downstream analysis, and delivery format. A project-specific timeline is provided after technical review.

Use the SNP Genotyping Service when the project requires a different species, marker density, or genotyping technology. Consider Genotyping By Sequencing or Whole Genome Re-sequencing when sequence-based marker discovery is required. For downstream cohort analysis, explore Population Structure Analysis and Genome-wide Association Analysis.

Next step: prepare the cohort size, sample source, ancestry composition, reference build, phenotype structure, requested markers, intended analyses, and preferred delivery formats for a project-design review.

References

Marees AT, de Kluiver H, Stringer S, et al. A tutorial on conducting genome-wide association studies: Quality control and statistical analysis. International Journal of Methods in Psychiatric Research. 2018;27(2):e1608. View article by DOI.
Anderson CA, Pettersson FH, Clarke GM, Cardon LR, Morris AP, Zondervan KT. Data quality control in genetic case-control association studies. Nature Protocols. 2010;5(9):1564–1573. View article by DOI.
Laurie CC, Doheny KF, Mirel DB, et al. Quality control and quality assurance in genotypic data for genome-wide association studies. Genetic Epidemiology. 2010;34(6):591–602. View article by DOI.
Das S, Forer L, Schönherr S, et al. Next-generation genotype imputation service and methods. Nature Genetics. 2016;48(10):1284–1287. View article by DOI.
Sharma S, Nagar SD, Pemu P, et al. Genetic ancestry and population structure in the All of Us Research Program cohort. Nature Communications. 2025;16(1):4123. View article by DOI.
Yang H-C, Kwok P-Y, Li L-H, et al. The Taiwan Precision Medicine Initiative provides a cohort for large-scale studies. Nature. 2025;648(8092):117–127. View article by DOI.

For research purposes only, not intended for clinical diagnosis, treatment, or individual health assessments.

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