Complex Disease Genomics
Microbial Genomics Oncology Genetic & Rare Disease Complex Disease Genomics Agrigenomics Cellular & Molecular Biology Research Population Genomics Pharmacogenomics Drug Discovery & Development Immunogenomics Neurogenomics Forensic Genomics Precision Health

Genome-wide association studies for disease research

Identify genetic variants linked to complex diseases

Female scientist using a single pipette; microscope and other lab equipment in the background on the lab bench.

What are genome-wide association studies?

Genome-wide association studies (GWAS) are research approaches that scan and compare the genomes of many individuals in an effort to identify genetic variants associated with specific traits or diseases. Using high-throughput genomic technologies, GWAS analyzes the DNA of individuals in large populations to uncover variants such as single nucleotide polymorphisms (SNPs) and copy number variations (CNVs) that may influence disease risk. These studies are essential for understanding the genetic architecture of complex diseases and guide research into the development of targeted therapies used in precision medicine.1,2

GWAS for common variant discovery

Complex diseases are often linked to common genetic variants, while the role of rare or low-frequency variants is still not well understood. Large-scale GWAS using microarrays are an efficient and cost-effective way to find loci and impute common SNP variants associated with disease. However, microarrays have limits when it comes to detecting low-frequency SNP variants. Whole-genome sequencing, with its base-by-base resolution, can identify both common and rare variants that may be linked to disease.

Benefits of genome-wide association studies

Novel variant discovery

Find novel variant–trait associations with a growing number of reported trait and disease associations.3,4

Genomic insights

Use genotype information for clinical research, disease prediction with polygenic risk scores, disease prevention, guidance for treatment decisions, and optimization of drug development, selection, and dosage.

Scalable, sharable data

Create shareable data sets that enable analysis across increasingly large and diverse sample populations.

Learn more about:

Polygenic risk scores
Microarray software for GWAS analysis

Opportunities for GWAS and genetic diseases

GWAS for many diseases and disorders have not yet been performed and the large majority of participants in GWAS to date are of European ancestry. As the European population accounts for just ~16% of the global population, there is a recognized need for more diverse GWAS data sets.5,6


In addition to ethnic diversity, there is a need to perform GWAS on diverse diseases within specific sub-groups. This will help provide clues about which genes and gene pathways could be involved in disease mechanisms and pathogenesis.

Learn more about multi-ethnic genotyping

Successfully identified variants for specific complex diseases

GWAS with the commonly used case-control setup approach, which compares two large groups of individuals—one case group affected by a disease and one healthy control group—have successfully identified variants for specific complex diseases, such as:

  • Type 2 diabetes7
  • Parkinson’s disease8
  • Crohn’s disease9
  • Various types of heart disease including coronary artery, atrial fibrillation, cardiomyopathy, and others10-13
  • Multiple types of cancer, including breast, colorectal, and more11

Prioritizing functional genetic variants through advanced sequencing approaches

Genome-wide association studies have identified thousands of variants with putative roles in different diseases. However, going from statistical associations to true insight into disease mechanisms remains a challenge. Advances in sequencing technologies have facilitated the development of strategies for assaying GWAS SNPs for potential functional relevance.

View webinar

GWAS applications

Explore how GWAS is offering powerful genomic insights to shape our understanding of drug development studies, complex diseases, cancer risk, and more.

Using GWAS multiomics to find therapeutic targets

Read how researchers used a multiomics approach to amplify GWAS in finding targets for drug development.

Understanding complex diseases using GWAS

Read this article to explore the last twenty years of GWAS-related work that has reshaped our understanding of complex diseases such as diabetes, arthritis, cancer, and dementia.

Identification of new targets for osteoarthritis

Learn how researchers leveraged the UK Biobank and arcOGEN resources to perform a genome-wide meta-analysis for osteoarthritis across ~17.5 million single nucleotide variants in 455,221 individuals to identify 65 genome-wide significant variants.

GWAS and cancer risk

Read how scientists performed pan-cancer and cross-population GWAS meta-analysis to identify novel cancer risk loci and highlight shared heritability between breast and prostate cancer.

Thumbnail

Understanding variant to function research

Variant to function (V2F) research focuses on mapping genetic variants to disease in order to discover new biomarkers, understand how these variants affect cellular processes, and make breakthroughs that will change how we treat complex diseases. Watch this video to learn more about V2F research.

Hear from GWAS experts

Using GWAS to map complex genetic traits

Researchers performed large GWAS studies to identify disease-associated DNA risk loci and develop polygenic risk scores.

Read interview

From GWAS to NGS: Genetics of children’s complex genetic disease

Professors at Children's Hospital of Philadelphia discuss how they used NGS to map variants to causal genes.

Listen to podcast

Using GWAS to power panomics-based drug discovery

Read how GWAS was used, along with imaging, multiomic technologies, and big data, to uncover novel diagnostic and therapeutic targets for common chronic diseases.

Read interview

GWAS workflows

This example of a GWAS workflow highlights the use of the Infinium Global Screening Array-24, a powerful and cost-effective BeadChip for population-scale genetic studies, variant screening, and precision medicine research. It covers an extensive range of diseases, enabling validation of disease associations, risk profiling, preemptive screening research, and pharmacogenomics studies.

1
Select content

Infinium Global Screening Array-24 Kit

A scalable, cost-effective solution for population genetics, pharmacogenomics studies, and precision medicine research.

DesignStudio Custom Assay Design Tool

A free, user-friendly assay design tool for creating custom microarray probes and sequencing panels optimized for specific genomic content of interest.

2
Process and scan arrays

iScan System

The iScan System delivers high-quality data for a broad range of genomic applications with the flexibility to meet various throughput needs.

Infinium Automation Packages

These automation option packages are equipped with robot control software for Infinium assays, enabling streamlined sample preparation workflows and reduced errors.

3
Track, analyze, and report

Clarity LIMS

A highly customizable laboratory information management system to track samples and manage workflows efficiently and securely.

GenomeStudio Software

This powerful solution supports the genotyping analysis of microarray data with performance-optimized tools and a user-friendly graphical interface to convert data into meaningful results quickly and easily.

Beeline Software

A flexible software solution to reduce array data size and facilitate analysis of large array experiments with automated sample statistics and allele calling.

Featured GWAS products

More GWAS resources

Using GWAS for trait mapping in crops

This webinar discusses eRD-GWAS, or "expression read depth genome-wide association study," a genome-wide approach for identifying genes whose expression patterns affect phenotypic traits.

Related methods

Whole-genome sequencing

Learn how to obtain a high-resolution view of the entire genome with whole-genome sequencing.

Microarrays

Analyze genetic variation on any scale, for a broad range of applications using microarrays.

SNP genotyping

See how to efficiently detect single nucleotide polymorphisms and variants using microarrays and sequencing.

CNV detection

Learn about solutions to identify copy number variations using microarrays or sequencing.

Male scientist holding an 8 lane pipette in one hand and a library tube in the other; tubes are filled with clear liquid; lab equipment in the foreground and background.

Additional resources

NGS for beginners

Find resources designed to educate on the basics of next-generation sequencing.

Genomics Research Hub

Explore the latest genomics discoveries and data analysis innovations by Illumina scientists.

Illumina resources and tools

Find everything you need to get started with genomics, from training to experiment planning, purchasing, and more.

Interested in learning more about GWAS?

Speak to a specialist to get answers on how to get started with GWAS.

References
  1. National Human Genome Research Institute. Genome-wide Association Studies (GWAS). genome.gov/genetics-glossary/Genome-Wide-Association-Studies-GWAS. Accessed October 24, 2025.
  2. National Human Genome Research Institute. Genome-wide Association Studies Fact Sheet. genome.gov/about-genomics/fact-sheets/Genome-Wide-Association-Studies-Fact-Sheet. Accessed October 24, 2025.
  3. Tam V, Patel N, Turcotte M, et al. Benefits and limitations of genome-wide association studies. Nat Reviews. 2019;20:467-484. doi: 10.1038/s41576-019-0127-1
  4. Cerezo M, Sollis E, Ji y, et al. The NHGRI-EBI GWAS Catalog: standards for reusability, sustainability and diversity, Nucleic Acids Research. Nucleic Acids Research. 2025;53(1):D998–D1005. https://doi.org/10.1093/nar/gkae1070
  5. Martin AR, Kanai M, Kamatani Y, et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genetics. 2019; 51: 584-591. doi: 10.1038/s41588-021-00797-z
  6. Ju D, Hui D, Hammond DA, et al. Importance of Including Non-European Populations in Large Human Genetic Studies to Enhance Precision Medicine. Annu Rev Biomed Data Sci. 2022;5:321-339. doi: 10.1146/annurev-biodatasci-122220-112550
  7. Shojima N, Yamauchi T. Progress in genetics of type 2 diabetes and diabetic complications. J Diabetes Investig. 2023 Apr;14(4):503-515. doi: 10.1111/jdi.13970
  8. Arya R, Haque AKMA, Shakya H, et al. Parkinson's Disease: Biomarkers for Diagnosis and Disease Progression. Int J Mol Sci. 2024 Nov 18;25(22):12379. doi: 10.3390/ijms252212379
  9. Sazonovs A, Stevens CR, Venkataraman GR, et al. Large-scale sequencing identifies multiple genes and rare variants associated with Crohn's disease susceptibility. Nat Genet. 2022 Sep;54(9):1275-1283. doi: 10.1038/s41588-022-01156-2
  10. Aherrahrou R, Reinberger T, Hashmi S, et al. GWAS breakthroughs: mapping the journey from one locus to 393 significant coronary artery disease associations. Cardiovasc Res. 2024 Nov 5;120(13):1508-1530. doi: 10.1093/cvr/cvae161
  11. Tcheandjieu C, Zhu X, Hilliard AT, et al. Large-scale genome-wide association study of coronary artery disease in genetically diverse populations. Nat Med. 2022 Aug;28(8):1679-1692. doi: 10.1038/s41591-022-01891-3
  12. Roselli C, Surakka I, Olesen MS, et al. Meta-analysis of genome-wide associations and polygenic risk prediction for atrial fibrillation in more than 180,000 cases. Nat Genet. 2025 Mar;57(3):539-547. doi: 10.1038/s41588-024-02072-3 
  13. Tadros R, Zheng SL, Grace C. et al. Large-scale genome-wide association analyses identify novel genetic loci and mechanisms in hypertrophic cardiomyopathy. Nat Genet 57, 530–538 (2025). doi: 10.1038/s41588-025-02087-4