PGS has great business value

Scientific Background

        For most common complex diseases, genetic risk cannot be attributed to only a single or several genes. Instead, numerous common variants have a contribution to the overall risk, each with a tiny effect. In fact, the number of such contributing variants usually ranges from 5,000 to 100,000, accounting for as many as 1% of all genomic common variants (Zhang et al. 2018). In other words, most complex diseases exhibit a highly polygenic architecture. These include psychiatric disorders, coronary artery disease, atrial fibrillation, inflammatory bowel disease, type 2 diabetes, Alzheimer, rheumatoid arthritis, obesity etc.

        Consequently, screening high-risk individuals by focusing on rare monogenic mutations of a handful of previously reported high-risk genes only benefits a small proportion of the population who are carriers. Unless we adopt a polygenic scoring approach, most people would miss out on the benefits of genetic risk assessment.

Polygenic scoring is a technique of statistical estimation that evaluates the overall genetic risk by assigning a tiny weight to each variant’s genotype and aggregating them to a genome-wide sum score. Initially, its realization can be very simple (clumping plus thresholding), but now the mainstream modelling includes some complicated penalized regression or Bayesian approaches. The training of PGS relies on a base dataset that is the summary statistics of one large GWAS on our interested phenotype, and another target dataset with individual-level genotype and phenotype is usually needed for hyperparameter optimization and performance evaluation.

Main Benefits

The major utility of polygenic scoring is to predict an individual’s risk for all kinds of complex diseases. They inform us about an individual’s relative risk compared to the remainder of the population, and can bring many additional benefits.

Ÿ   Polygenic scoring is applicable at any scale of genotype data, whether array genotyping, exome sequencing or whole genome sequencing, with varying accuracy.

Ÿ   Polygenic scoring gives physicians new tools for diagnosis and treatment.

Ÿ   It helps to stratify patient population according to risk and identify individuals who would most benefit from additional monitoring or early preventative measures to offset the risk of disease.

Ÿ   It would greatly improve the scale that a genetic testing report can cover and tremendously enrich the contents delivered to consumers. Curated pretrained PGSs in this catalog have covered 219 traits. If we use famous publicly-available large cohorts (UK Biobank, Japan Biobank, FinnGen) for training, much more could be covered. For example, the UK Biobank comprises over 600 diseases and over 2,800 other traits. As a comparison, CircleDNA currently appears to cover around 120 common diseases and 160 rare mendelian disorders.

Ÿ   By combining with environmental factors or clinical factors, PRS provide more accurate risk assessment and stratification, demonstrating a way to make personalized lifestyle recommendations based on an individual’s genetic profile. It also strengthens users’ motivation and willingness to provide personal environmental or clinical information, which they see as beneficial to their own health prediction.

Potential Value

Both individual consumers of genetic testing service and clinicians can benefit from polygenic scoring. It provides 5~10 times more information for the users than traditional monogenic approach, although in general its accuracy is still insufficient for clinical-level use. However, most clients, as layman in genetics, are more concerned about whether they have high risks for all kinds of common severe diseases, other than the exact odds ratio, relative risks or effect size. Particularly, polygenic scoring greatly broadens the scope of cancer risk prediction by covering over 100 types of cancers. Even though high cancer PGSs are far from being regarded as diagnostic evidence, they still give the clients a suggestion about on what types of cancers they should go ahead for clinical-level cancer screening tests. Noteworthily, for several diseases like coronary artery disease, PGS succeeds in identifying individuals with risk equivalent to monogenic mutations (Khera et al., 2018). Besides, research in the past decade show that polygenic scoring might be the best way to identify people with high risks of psychiatric disorders such as depression, autism, ADHD and schizophrenia, which are extremely polygenic, highly inheritable and lacking in biological useful and valid biomarkers, making another selling point to parents of children and teenagers.

We also see insurance companies like Swiss Re. and Hannover Re. show interest in incorporating polygenic scoring into health insurance services. Undoubtedly, it is promising that polygenic scoring could add great value to profitability of DTC genetic testing service.

On top of that, we see much greater potential value beyond financial gain.

Ÿ   Polygenic scoring is at the cutting edge of genomic medicine research. It is a very hot topic with many papers receiving hundreds to thousands of citations. Research institutions in some leading areas, like Harvard-MIT Broad Institute, Oxford University and Stanford University, have already been applying for relevant patents (e.g. US 2021/0065846 A1) and translating these advances into businesses. For example, Allelica Inc., a company with valuation around 10 million USD, is aiming to provide cardiovascular disease polygenic scoring directly to consumers and is backed by famous research groups. Being the first in Asia to incorporate polygenic scoring explicitly to the service would add much to Prenetics’s brand value by demonstrating its innovation capacity to the media. We believe that

Ÿ   Polygenic scoring is a complex statistical methodology that requires PhD-level expertise and sophistication in modelling. Building up a cooperative team for specialized PGS bioinformatic analysis service will also bring advantage in getting Prenetics a participating role in emerging large biobank projects all around Asia, like the Hong Kong Genome Project that plans to perform whole-genome sequencing on 50,000 subjects. The availability of local population data from such large cohort would also address the current challenge of cross-ancestry application.