Connecting the dots: How phenotypic data enhances genetic insights in research 

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Genetic data is at the heart of precision medicine. Understanding the influence of genetics on health has helped us to recognise risk factors for disease, predict how patients might respond to treatments, and develop targeted treatments. But how does this knowledge connect to phenotypic data – the expression of the interaction between genes and the environment?

Phenotypic data is a collection of the observable traits of an organism. It can refer to anything from a common characteristic, such as height or hair colour, to the presence or absence of a disease. Often collected and stored by patient registries and biobanks, phenotypic data provides a comprehensive view of an individual's health when combined with genetic data. In this blog, we will outline the significance of phenotypic data, focusing on how it complements genomic information to provide a holistic view of patient health.

Understanding genetic data

Genetic data is a blueprint of an organism's genetic makeup and encompasses all the information related to the structure and function of an organism's genome. It includes the sequence of molecules in an organism's genes, the function of each gene, the regulatory elements governing gene expression, and the web of interactions between genes and proteins.

Each of the trillions of cells in the human body contains a complete copy of our genome, composed of approximately 6 billion DNA letters. Since the first whole genome was sequenced by the Human Genome Project in the early 2000s, researchers have been amassing genomic data from diverse populations worldwide. Now, we can map individual DNA sequences to identify differences from single-nucleotide polymorphisms to larger structural variants, including some which have significant health implications.

Genetic data has helped us to better understand disease pathogenesis and heritability. It has allowed us to pinpoint genetic predispositions that can make developing a disease much more likely. And, it has helped us understand why people react differently to diseases and conditions, as well as to treatments and interventions.

The limitations of genetic data

While genetic data has been transforming healthcare for the better, relying solely on it to get an understanding of an individual's health does come with some limitations. While genetic data can tell us a lot about potential risk factors for various health conditions, it doesn't guarantee that those diseases will actually develop.

Aside from this, variations in genome depth coverage can lead to false positives and negatives when reading a genome – causing issues of analytical validity. Structural variants, which can be duplications or deletions of large segments of DNA, can also pose a problem for current sequencing technologies. At the same time, interpreting genomic data can also be a bit like piecing together a puzzle with some missing parts. Our understanding of genetics and how it relates to specific diseases is continually evolving, so it can be difficult to create a full picture of someone's health using their DNA data alone.

Complementary nature of phenotypic data

Phenotypic data goes beyond the genotype and includes all observable characteristics that make an individual unique, taking into account their lifestyle, environment, behaviour, and clinical observations. An analogy to describe how both genetic and phenotypic data come together is to picture a full-colour portrait of your health, where your genes provide the sketch, but your environment and experiences fill in the details.

Genotype-phenotype databases are critical to understanding the link between genetic variants and the pathogenesis of disease. These databases allow researchers to make the change from studying individual genes to deciphering variants in tens, hundreds, or even thousands of genes and assigning pathogenicity to genetic variants, enabling more precise diagnoses and more powerful targeted drug development. Overall, phenotypic data isn't just about symptoms; it combines genetics and environmental factors to provide a deeper understanding of an individual's health.

Phenotypic data offers the context and real-world insights needed to fully understand genetic data; it's the bridge that connects genes to observable traits and behaviours, revealing how lifestyle choices, environmental factors, and behaviour impact health outcomes. For instance, it can illustrate how a genetic predisposition for a certain condition may or may not manifest depending on an individual's lifestyle and environment

One example: Research shows that carriers of the G-allele of the PNPLA3 variant are at an increased risk of NASH. However, this risk is significantly amplified by the consumption of a high-fat diet, particularly one rich in saturated fats. The genetic variant alone does not guarantee NASH development; rather, it predisposes individuals to greater risk when combined with specific lifestyle factors.

A more comprehensive approach to health assessment, combining both genetic and phenotypic data, empowers healthcare providers to make more informed decisions. It not only enables early disease detection but also allows for personalised treatment plans that consider an individual's unique genetic traits and the real-life factors influencing their health.

Real-world applications

The synergy of genetic and phenotypic data has already shown impactful real-world applications within healthcare. For instance, within the UK's National Health Service (NHS), the integration of patient records, encompassing both genetic and phenotypic data, has allowed healthcare providers to identify unique patient phenotypes with specific treatment responses or healthcare requirements. For instance, tumour genome analysis is currently being used to tailor targeted cancer treatments, and non-invasive prenatal screening leverages both genetic and phenotypic data for more accurate risk assessment.

In addition to this, the combination of artificial intelligence (AI) and precision medicine has brought together genetic and phenotypic data to provide personalised diagnoses and prognoses. Notable examples involve the utilisation of deep learning techniques for the detection of conditions such as malaria and cervical cancer, as well as for the prediction of infectious disease outbreaks, environmental toxin exposure, and allergen levels.

In the realm of broader public health research, phenotypic data plays a pivotal role in evaluating how genetic information interacts with environmental, social, and behavioural factors to comprehend and prevent chronic diseases. Precision medicine initiatives utilise this blend of data to enhance healthcare outcomes for patients globally.


The synergy between genetic and phenotypic data represents a transformative advancement in healthcare and research, bridging the gap between predisposition and real-world health outcomes. Genetic data lays the foundation by identifying risk factors and genetic traits, while phenotypic data provides the crucial context, illustrating how lifestyle, environment, and behaviour influence health. This holistic approach, as demonstrated in real-world applications and precision medicine initiatives, enables personalised healthcare and deeper insights into disease risk. It not only enhances patient care but also holds the potential to reshape healthcare and research, offering a more comprehensive understanding of health on a global scale.

To learn more about genetics and precision medicine, download our "Guide: Genetics essentials for clinical research professionals" below! 


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