In recent years genome-wide association studies (GWAS) have discovered thousands of genetic variants associated both with diseases and complex traits.
Geneticists had hoped these studies might transform precision medicine by enabling individualized disease risk prediction and treatments tailored to the variations in a person’s genes (a field known as pharmacogenomics). Most common diseases, however, are not caused by single genetic mutations, which are far more accessible to precision medicine strategies. Instead, GWAS have revealed that the majority of common diseases have a polygenic architecture, wherein multiple genetic variants, each by themselves of small effect, cumulatively impact disease risk.
Advances in statistical and clinical genetics have started to demonstrate the power of polygenic risk profiling in the form of polygenic risk scores to identify individuals at higher (and lower) risk of disease. This article reviews recent developments in polygenic risk profiling and the predictive utility of polygenic risk scores to uncover a person’s susceptibility to disease.
The Promise of Genetics and Genomics for MedicineDuring the past decade, significant progress in genomic technologies has allowed scientists and clinicians to explore the human genome in incredible detail. Consequently, the growth in our understanding of genetics and genomics may have the potential to transform all aspects of precision medicine, including prevention of disease manifestation, accurate diagnosis and prognosis of disease, pharmacogenomics, and motivating lifestyle changes to improve health.
When coupled with the ever-decreasing cost of DNA sequencing (see Figure 1), the era of genomic medicine can now be considered truly under way. The first human genome cost $2.7 billion and took almost 15 years to sequence. Now sequencing costs about $1,000 and can be done in a few days. In a few years, some believe the whole genome will be sequenced for as little as $100. As research continues to advance our understanding of genetics, genomic medicine will almost certainly lead to improvements in mortality and longevity, which will be positive for the life insurance industry and for society as a whole.
What is a Polygenic Risk Score?A polygenic risk score (PRS) is a metric that condenses information from tens, hundreds, thousands or even millions (“poly”) of a person’s genetic variants (“genic”) into a score that measures the individual’s genetic predisposition to specific diseases or complex traits.
Genome-wide association studies (GWAS) are a relatively new way for geneticists to search among the millions of variants in the human genome and identify those associated with certain diseases or complex traits. These genetic variants are known as single nucleotide polymorphisms or SNPs (pronounced “snips”). DNA has four different base nucleotides (DNA building blocks) and a SNP occurs when a nucleotide in a particular position in the genome is different.
Geneticists have observed that certain SNPs occur more commonly in people with a particular disease. Over the 13 years since the first GWAS was conducted (2005),2 millions of SNPs have been found and isolated. These SNPs have been shown to increase or decrease a person’s risk of developing complex diseases such as diabetes,3 breast cancer,4 and cardiovascular disease.5
Clinicians commonly test for high-penetrance single gene mutations such as BRCA1 or BRCA2, two genes that confer increased risk for breast cancer. Such simple one-to-one relationships between a gene and susceptibility to disease, however, are very rare. Rather, the genetic architecture of most disease is overwhelmingly polygenic, with multiple SNPs, each of small effect, cumulatively affecting disease risk. No single SNP can be used in prediction, as the increase or decrease in risk is extremely small. However, merging information across thousands or even millions of SNPs can be useful in predicting disease risk.
It is hoped that as GWAS grow in sample size and more SNPs are investigated, researchers will uncover additional SNPs associated with common diseases as well as SNPs that can influence a person’s response to medications.
And so we come to PRSs. These scores, which are calculated based on an individual’s SNPs, are generally expressed as risk percentiles correlated against the risk of particular disease in a given population. They are already proving most useful in terms of risk predictions in the extremes of the genetic risk distribution (see Figure 2).
An individual with a PRS in the 100th percentile for diabetes, for example, would be considered to have the highest genetic risk. However, for an individual with a PRS closer to the population mean, the score would offer little additional risk information (i.e., the person’s predicted risk, based on his or her genetics, will be similar to the population’s average risk).
In the past few years, academic researchers have developed PRSs for many conditions and have tested them in their studies. The pertinent question, however, remains: will PRSs transform clinical risk assessment in terms of disease prediction, stratification, and prognosis? And could they impact insurance medicine, underwriting, product development, and claims experience?
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