Genetic risks scores are most accurate in the populations with the most data

Genetic risks scores are most accurate in the populations with the most data
One way to lower the chance that a future child might develop a disease later in life is to use a genetic test to identify embryos with a lower risk for that disease. For chronic diseases like heart disease or schizophrenia, a single gene doesn’t usually drive risk - rather hundreds to over one million DNA differences (variants) are aggregated into a genetic risk score that measures risk. A limitation of genetic risk scores is that since they are based on large genetic studies done using people of European descent, they are most predictive in that population. However, that does not mean they only work in that population. We have found that even when using strict accuracy requirements, genetic risk scores can identify people of non-European descent with a higher chance of developing disease. As more diverse databases and genetic risk score models become available, Orchid scientists will use them to make their tests even better and to provide people of non-European descent with results for additional diseases in the near future.

Written by Orchid Team

Key Points:

  • Genetic risk scores have more predictive power in people of European descent because they are based on large genetic studies which have been done primarily in that population. 
  • Genetic risk scores can identify people of non-European descent who have a higher chance of developing a disease using genetic risk scores even with the strict accuracy requirement that the odds of developing that disease are at least twice as high for individuals above the 97th genetic risk score percentile. 
  • We measure ancestry precisely to make sure that people get only the appropriate results.
  • With more scientists around the world doing large genetic studies with more diverse participants, people of non-European descent will be able to receive results for additional diseases in the near future.

Genetic risks scores are most accurate in the populations with the most data

Orchid’s mission is to help everyone have a healthy baby. We do this through genetic screening of embryos created during in vitro fertilization (IVF). A key part of this screening is identifying the embryo(s) with the lowest chance for developing any of a wide variety of diseases.  

For example, Orchid offers a genetic screen that can determine the chance that an embryo will develop any of twelve chronic diseases that affect large numbers of people later in life. These diseases include Alzheimer’s disease, atrial fibrillation, bipolar disorder, breast cancer, celiac disease, coronary artery disease, inflammatory bowel disease, prostate cancer, severe obesity, schizophrenia, type 1 diabetes, and type 2 diabetes. This type of screening uses genetic risk scores made up of hundreds to over one million different DNA variants. 

Until recently, most large genetic studies have primarily been done with people of European descent. Because genetic risk scores are usually developed using these databases, they work best in people of European descent and are less predictive, but still useful, in other populations [1,2]. 

The table below shows how well these scores work for different populations, averaged across 245 traits [1]:

Table 1: Performance difference by ancestry. The table shows how accurate polygenic scores are on average for different populations, averaged across 245 traits. Data taken from [1]. Relative predictive performance is given on the scale of Pearson correlation coefficients between continuous traits and genetic risk scores rather than on the variance explained scale (=squared Pearson correlation coefficient).

Reduced performance in people of non-European descent is not unique to genetic risk scores. In every area of medicine, the population who gets treated more often and/or for whom the most data is available receives the best results. In almost every case that means people of European descent. A few examples where medical interventions do not work as well in other populations because they were tested in people of European descent include:

  • Prostate screening. Black men have a 2-3 times higher mortality rate compared to White men [3].  
  • A1C blood levels. Black people have higher levels of A1C, a measure of blood sugar levels, compared to White people [4]. This can lead to misdiagnosis of prediabetes and type 2 diabetes.
  • IVF protocols. Black women are half as likely as White women to have a live birth after IVF treatment [5]. 

However, even with reduced effectiveness, medical interventions still provide benefit to people of non-European descent and genetic risk scores are no exception. Orchid can identify people of non-European descent with a higher chance of developing disease using genetic risk scores.

Everyone can, to varying degrees, benefit from genetic risk scores

After carefully evaluating each disease separately for each ancestry, we are able report on the following diseases for each ancestry group as of July 2022:

Table 2: Genetic risk score reports returned by disease and ancestry. The table shows which diseases Orchid reports in different ancestries. Definitions: European–White European ancestry and Ashkenazi Jewish; South Asian–India, Pakistan, and some neighboring countries; East Asian–China, Korea, Japan, and some neighboring countries; African–Black Sub-Saharan African ancestry. The exact set of diseases may differ for each customer, depending on their precise ancestry. 1People of Middle Eastern descent receive at a minimum the same reports as people of South Asian descent. While people of Latin American descent are heterogeneous genetically with differing amounts of European, Native American, and African ancestry, many receive the same reports as people of South Asian descent.

As you can see, Orchid is able to provide reports to everyone to varying degrees. People of European descent receive all twelve reports, people of South Asian descent receive ten, people of East Asian descent receive nine, and people of African descent receive two. 

Many people do not fit neatly into one of the four categories outlined in Table 2. That is why we use a technique called principal component analysis (PCA) to measure each person’s ancestry precisely so that we can generate the appropriate genetic risk scores for them. After adjusting for their unique ancestry, we make sure to only deliver a report for a given disease if the odds of developing that disease are at least twice as high for individuals above the 97th genetic risk score percentile.

We also take additional steps to ensure that people of non-European descent receive the most accurate and appropriate results from genetic risk scores possible.

  • We adjust each genetic risk model so that our genetic risk scores are ancestrally unbiased. We make adjustments when necessary to correct for the fact that some genetic risk models can predict that everyone from a certain population has a higher chance of developing a disease [2].
  • We use genetic risk scoring methods that are specifically designed to improve accuracy across different ancestries for each disease and ancestry group [6,7]. Because of this, we can be confident that our results are at least as accurate as the state-of-the-art in each disease and ancestry group. 

Genetic risk scores will continue to improve for non-Europeans 

Genetic risk scores will continue to get better and better for people of non-European ancestry as new, large genetic studies are done that include these populations. Scientists around the world recognize how important this is and multiple efforts are underway.

One of the most exciting studies in the U.S. is the All of Us research program. This National Institutes of Health (NIH) sponsored program hopes to enroll a million genetically diverse people. Genetic risk scores derived from this data will undoubtedly perform even better than current ones in non-European populations.

As data like this becomes available, we will continue to refine our models to deliver improved genetic risk scores for everyone. As an example, when a new prostate cancer genetic risk score model was released, Orchid scientists quickly validated it, updated the model, and added it to the product in June 2022. People of African descent could now receive results for their genetic risk for prostate cancer.

Improvements like these will keep coming. In the not too distant future, genetic risk scores will become ever more available and accurate for everyone. 

References 

  1. Privé F, Aschard H,  Carmi S,  Folkersen L, Hoggart C, O’Reilly PF, Vilhjálmsson BJ. High-resolution portability of 245 polygenic scores when derived and applied in the same cohort. Am J Hum Genet 2022; 109: 12-23. Free access: https://doi.org/10.1101/2021.02.05.21251061 , https://www.cell.com/ajhg/fulltext/S0002-9297(21)00420-1 
  2. Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet 2019; 51: 584–591 https://doi.org/10.1038/s41588-019-0379-x  
  3. U.S. Department of Health and Human Services, National Institutes of Health, National Cancer Institute. (September 11, 2015). The Biology of Cancer Health Disparities. Retrieved from https://www.cancer.gov/research/progress/discovery/biology-cancer-health-disparities 
  4. Herman WH, Ma Y, Uwaifo G, Haffner S, Khan SE, Horton ES, et. al. for Diabetes Prevention Program Research Group.  Differences in A1C by Race and Ethnicity Among Patients With Impaired Glucose Tolerance in the Diabetes Prevention Program. Diabetes Care 2007; 30: 2453–2457. https://doi.org/10.2337/dc06-2003  
  5. McQueen DB, Schufreider A, Lee SM, Feinberg, EC, Uhler ML, Racial disparities in in vitro fertilization outcomes Fertil. Steril. 2015; 104: 398-402. https://doi.org/10.1016/j.fertnstert.2015.05.012 
  6. Weissbrod O, Kanai M, Shi H, Gazal S, Peyrot WJ, Khera AV, et. al. Leveraging fine-mapping and multipopulation training data to improve cross-population polygenic risk scores. Nat Genet 2022; 54: 450–458 https://doi.org/10.1038/s41588-022-01036-9 
  7. Ruan Y, Lin Y-F, Feng Y-C A, Chen C-Y, Lam M, Guo Z, et. al. Improving Polygenic Prediction in Ancestrally Diverse Populations Nat Genet 2022; 54: 573–580 Free access: https://www.medrxiv.org/content/10.1101/2020.12.27.20248738v2 , https://doi.org/10.1038/s41588-022-01054-7 
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