A phenome-wide association study, commonly known as PheWAS, represents a powerful research approach that broadly scans for links between an individual’s genetic makeup and a wide range of observable traits or health conditions. This method systematically investigates the connections between genetic variations and various aspects of human health.
Unraveling the Human Phenome and Genome
PheWAS analyzes two fundamental biological components: the genome and the phenome. The human genome refers to an individual’s complete set of DNA. This vast genetic instruction manual includes both protein-coding DNA sequences, which make up about 1-2% of the genome, and various types of non-coding DNA.
The phenome, on the other hand, represents the totality of an individual’s observable characteristics, traits, diseases, and health measurements over time. This encompasses a broad spectrum, including morphology, physiological properties, biochemical markers, and behaviors. Phenomic data is often derived from extensive sources like electronic health records (EHRs), which contain detailed diagnostic codes, laboratory results, and clinical observations. Other sources include surveys, biorepositories linked to medical records, and various clinical data.
How PheWAS Identifies Connections
PheWAS employs a systematic approach to identify connections between genetic variants and phenotypes. Unlike traditional studies that test one gene for one trait, PheWAS begins with a specific genetic variation, such as a single-nucleotide polymorphism (SNP), and then systematically examines its association with thousands of different phenotypes simultaneously. This “genotype-to-phenotype” direction of inference is a key distinction from genome-wide association studies (GWAS), which typically start with a phenotype and search for associated genetic variants.
The methodology often leverages large clinical datasets, particularly from electronic health records (EHRs), where phenotypes are defined by groupings of International Classification of Disease (ICD) codes, known as “phecodes.” Statistical tests calculate p-values, indicating the relevance of a genotype to a particular phenotype. Due to the large number of comparisons, statistical corrections are often applied to control for false positives. This broad scanning capability allows PheWAS to uncover unexpected associations that might be missed by more targeted research.
Real-World Insights from PheWAS Research
PheWAS has been instrumental in both confirming previously known gene-disease relationships and uncovering novel associations. It has successfully replicated many SNP-trait associations initially identified by GWAS. Beyond known links, PheWAS has revealed surprising connections between genetic variants and seemingly unrelated conditions, demonstrating the concept of pleiotropy, where a single genetic variant influences multiple distinct traits.
One example involves a genetic variant associated with multiple sclerosis (MS) that PheWAS also found to be linked with erythematous conditions, including rosacea, suggesting a shared genetic origin. In another instance, a gene previously associated with type 2 diabetes might also show associations with related conditions like fasting glucose levels, hyperinsulinemia, or obesity. Furthermore, PheWAS has identified novel associations, such as a nonsense variant in the ARMS2 gene linked to age-related macular degeneration (AMD). The strongest associations for a specific SNP (rs1061170) in the complement factor H gene, known to be associated with AMD, were indeed found for various macular degeneration diagnoses in PheWAS. These discoveries highlight the utility of PheWAS in expanding our understanding of the complex interplay between genes and health.
Impact on Personalized Health
The insights derived from PheWAS research significantly contribute to a deeper understanding of disease mechanisms. By identifying widespread genetic influences on various traits and conditions, PheWAS helps to unravel the intricate biological pathways involved in health and disease. This expanded knowledge aids in identifying potential drug targets, as understanding the pleiotropic effects of genes can inform the development of new therapeutics or the repurposing of existing drugs for different conditions.
PheWAS findings also advance risk prediction models, allowing for a more tailored assessment of an individual’s susceptibility to a range of health issues based on their genetic profile. This approach fosters the development of personalized medicine, where prevention strategies and treatments can be customized to an individual’s unique genetic makeup. Ultimately, by revealing complex genetic-phenotypic relationships, PheWAS moves the field closer to delivering more effective and individualized healthcare interventions.