Genotyping for research and studies: from study design to data delivery

Reviewed by Dr. Daniel Wallerstorfer, molecular biologist and founder of Novogenia · Updated: 13 October 2026 · Reading time: 9 min

In short

For studies and cohorts, a genotyping lab takes over the lab work: DNA extraction, targeted SNP genotyping, quality control, sample management and export of structured raw data in your pipeline’s format. Analysis and interpretation stay with your research team. Saliva samples work well: in a comparison study, DNA from saliva was comparable to DNA from blood for genotyping. The research partner is responsible for ethics approval and participant consent; the framework is set by the GDPR (Arts. 9 and 89), Section 66 GTG in Austria and the Human Research Act in Switzerland.

Half a million genotypes: why cohort research relies on genotyping

UK Biobank shows what genotyping can do for research. Genome-wide genotype data are available for all of its roughly 500,000 participants – aged 40 to 69 at recruitment – linked to lifestyle, lab and imaging data. This resource opens up many opportunities to discover new genetic associations and the genetic bases of complex traits (Bycroft et al., Nature, 2018).

And the sample? A study from the University of Cambridge compared saliva and blood from the same 79 participants. 78 of 79 saliva samples yielded sufficient DNA – for individual genotyping assays as well as for genome-wide chip arrays. Genotype call rates and concordance were above 97% for DNA from both saliva and blood. The authors conclude that saliva sampling can make recruitment for studies easier and reduce the effort of collecting samples across several study centers (Abraham et al., BMC Medical Genomics, 2012).

The logical conclusion: the saliva kit can travel to the participants instead of everyone traveling to a blood draw. And the lab work does not have to happen in-house – whether you run a feasibility study or a large cohort.

What a genotyping lab takes over for your project

Not every research institution runs its own high-throughput lab – and for many projects, building one does not pay off. Equipment, maintenance and qualified staff tie up funds that often sit idle once the project ends. With an external lab, you get capacity exactly when your study needs it.

You bring the research question, the study design and the samples. The lab takes over the steps for which you would otherwise need equipment, staff and routine:

Building block	What the lab takes over	What you define up front
Sample material	preparation, extraction and quality control of DNA from saliva or other sample materials; alternatively, you supply already extracted DNA	which material, which sample kit, who collects the samples
SNP genotyping	targeted genotyping of defined markers, reproducible and at high throughput	marker list and study goal
Sample management	unique registration, storage and tracking of every study sample during the project	sample identifiers, shipping routes, time points
Data export	structured raw data in the format of your statistical analysis or pipeline	formats, annotations, identifiers
Scaling	from pilot cohort to large study population, including across several study phases	phases, sample numbers, timeframe

The model follows your study design: a one-off contract analysis of a completed cohort or a project-accompanying lab service across several phases. You set the timeframe within the project – it depends on method, sample numbers and study phases.

From first call to data delivery: how a project runs

Project call: You describe the study design, markers and planned sample numbers; together you clarify method, throughput and schedule.
Quote and planning: a transparent quote and an analysis workflow that fits your cohort and timeframe.
Ethics and consent: Your research team obtains ethics approval and participant consent – before the first sample is collected.
Sample collection: with saliva sample kits or as ready-prepared samples.
Sample receipt and quality control: Each sample is registered, prepared and checked before the analysis starts.
Genotyping: standardized and reproducible, even for large sample numbers.
Data export and follow-up runs: structured raw data in your format; for multi-phase studies, further runs follow at consistent quality.

Tip for your inquiry: The more specific your first briefing, the faster the plan comes together. State the research question and study design, the marker list or at least the number of markers, the planned sample numbers per phase, the sample material, the desired timeframe and your pipeline’s data format. Also say whether the samples have already been collected or collection is still being planned.

Targeted rather than blanket: planning the panel

Targeted SNP genotyping plays to its strengths when the variants you are interested in are already known: you determine defined markers across the whole cohort – efficiently and in a well-standardized way. So plan the panel from the hypothesis:

Marker list with unique identifiers: such as rs numbers, so that lab and analysis speak the same language.
Only what answers the question: Every additional marker creates data that must be protected, stored and explained. Data minimization applies in research too (Art. 89(1) GDPR).
Pilot first: Start with a pilot cohort, check sample quality and data format, then scale.
From project to product: If your research is to become a genetic test of your own – with marker selection, panel design, assay development, validation and a pilot – that is a development project. More on the page Development partner for new genetic tests.

Raw data that fit your pipeline

Raw data are like a plug: they are only useful if they fit your socket. That is why you agree at the start which formats, annotations and identifiers your pipeline expects – so the data delivered flow into your statistical analysis without rework.

Analysis and interpretation stay with you and your research team. This division of labor has an advantage: scientific interpretation stays where the research question was born. It is important to distinguish a completely different case: we advise against having raw data from consumer tests interpreted by third parties – why is explained in Judging genetic tests. How companies use contract genotyping and raw data for their own products is covered in Contract lab: genotyping and raw data on demand.

Ethics approval and consent: the research partner’s job

The research partner is responsible for the study protocol, ethics approval and participant consent – the university, clinic or company that conducts the study. The lab provides lab services and data. Each country sets the legal framework a little differently:

EU: Research with genetic data is governed by the GDPR. Processing needs a basis under Art. 9(2) – explicit consent or a research provision in Union or national law (point (j)) – and appropriate safeguards such as data minimization and, where possible, pseudonymization (Art. 89(1)). Consent can relate to certain areas of scientific research when recognized ethical standards are met (Recital 33).
Germany: The Genetic Diagnostics Act does not apply to genetic examinations and analyses for research purposes (Section 2(2) No. 1 GenDG). Without consent, Section 27 BDSG only permits processing if it is necessary and the controller’s interests significantly outweigh those of the data subject; the data are anonymized as soon as the research purpose allows.
Austria: Genetic analyses for scientific purposes may only be performed on de-identified samples; linking them back to the donor only happens in facilities with valid consent. Results may only be networked or published if the donor cannot be identified (Section 66 GTG).
Switzerland: Genetic tests in research on human diseases and on the structure and function of the human body are governed not by the GUMG but by the Human Research Act (HFG) (Art. 2(4) GUMG). A research project requires authorization from the responsible ethics committee (Art. 45 HFG), participants give written informed consent (Art. 16 HFG), and further use of biological material and genetic data in uncoded or coded form requires consent (Art. 32 HFG).

For the collaboration, this means: clarify in advance who has which data protection role – often the research team decides on purposes and means, and the lab works on its behalf. Put this in a contract. How to determine roles is explained in Data protection for white-label genetic tests.

Confidentiality: codes instead of names

Good research needs trust – from participants and from sponsors alike. Three tools help:

Coded samples: The lab sees sample identifiers, not names; the research team keeps the key separately. The GDPR calls this pseudonymization (Art. 4(5)). Pseudonymized data remain personal data because they can be re-attributed using additional information (Recital 26).
Confidentiality in the contract: If the lab works on your behalf, it ensures that everyone with access is bound by confidentiality (Art. 28(3)(b) GDPR). Protect study design, marker lists and results with a non-disclosure agreement as well.
Traceability: Every sample is uniquely registered and traceable throughout the project – from receipt through quality control to data export. A mixed-up sample can invalidate an entire analysis.

Checklist for your genotyping project

Research question, marker list with unique identifiers and sample numbers are defined.
Ethics approval and participant consent are in place – including the rules on further use of samples and data.
The data protection roles of research team and lab are set out in a contract.
Samples are coded; the key stays with the research team.
Sample material, sample kit and shipping route are defined; a pilot cohort tests the process.
Formats, annotations and identifiers for the data export are agreed.
Timeframe, study phases and follow-up runs are planned.
It is settled what happens to samples and data after the project ends.

How we do it at Novogenia

How we support research projects at Novogenia:

Our own lab: We analyze study samples in our own genetic lab in Eugendorf near Salzburg, Austria – since 2009, CLIA-certified and working according to ISO 15189.
Services: targeted SNP genotyping of defined markers; preparation, extraction and quality control of sample material; sample management and data export.
Data in your format: We deliver structured raw data in the format your statistical analysis or pipeline needs. We agree on formats, annotations and identifiers at the start; analysis and interpretation stay with your research team.
Flexible scaling: start with a pilot cohort, then scale up – as a one-off contract analysis or as a project-accompanying lab service across several study phases.
Planning: a transparent quote and an analysis workflow we plan around your cohort and your timeframe.
Sample kits: Our non-medical-device kits are suitable, among other things, for research applications without a medical intended purpose.

Frequently asked questions

Does our study need ethics approval?

That depends on the country and the project, and it is the research partner’s responsibility. In Switzerland, research on human diseases and on the structure and function of the human body involving biological material or health-related personal data requires authorization from the responsible ethics committee (Arts. 2 and 45 HFG). In Germany and Austria, it depends on the applicable law and your institution’s rules.

Does Germany’s Genetic Diagnostics Act apply to research projects?

No. The GenDG does not apply to genetic examinations and analyses or to the handling of genetic samples and data for research purposes (Section 2(2) No. 1 GenDG). The GDPR and the Federal Data Protection Act still apply – for example Section 27 BDSG on processing special categories of personal data for research purposes.

In what format do we receive the data?

As structured raw data in the format your statistical analysis or pipeline needs. You agree with the lab at the start which formats, annotations and identifiers those are. Analysis and interpretation stay with your research team.

Can we start with a small cohort?

Yes. You can start with a pilot cohort and then scale up sample processing. Options include a one-off contract analysis or a project-accompanying lab service across several study phases; the quote and workflow are based on your cohort and timeframe.

Are saliva samples suitable for genotyping in studies?

Yes. In a comparison study, 78 of 79 saliva samples yielded sufficient DNA, and genotype call rates and concordance were above 97% for DNA from both saliva and blood (Abraham et al., BMC Medical Genomics, 2012). How samples are collected is set out in the study protocol.

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Note: This guide is general information for businesses and professionals and does not replace medical or legal advice. The genetic traits determined in a genetic analysis are well documented (each gene included is backed by at least three independent studies); recommendations derived from them are logical conclusions, not scientifically proven. Regulations can change; errors and changes excepted.