PREIMPLANTATION GENETIC TESTING: Preimplantation genetic testing for polygenic disease risk

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  • 1 Genomic Prediction Inc., North Brunswick, New Jersey, USA
  • 2 Department of Obstetrics, Gynecology and Reproductive Sciences, Rutgers University, New Brunswick, New Jersey, USA
  • 3 Department of Physics and Astronomy, Michigan State University, Hannah Administration Building, East Lansing, Michigan, USA

Correspondence should be addressed to N Treff; Email: nathan@genomicprediction.com

This paper forms part of an anniversary issue on Preimplantation Genetic Testing. The guest editor for this section was Professor Alan Handyside, School of Biosciences, University of Kent, Canterbury, UK

Since its introduction to clinical practice, preimplantation genetic testing (PGT) has become a standard of care for couples at risk of having children with monogenic disease and for chromosomal aneuploidy to improve outcomes for patients with infertility. The primary objective of PGT is to reduce the risk of miscarriage and genetic disease and to improve the success of infertility treatment with the delivery of a healthy child. Until recently, the application of PGT to more common but complex polygenic disease was not possible, as the genetic contribution to polygenic disease has been difficult to determine, and the concept of embryo selection across multiple genetic loci has been difficult to comprehend. Several achievements, including the ability to obtain accurate, genome-wide genotypes of the human embryo and the development of population-level biobanks, have now made PGT for polygenic disease risk applicable in clinical practice. With the rapid advances in embryonic polygenic risk scoring, diverse considerations beyond technical capability have been introduced.

Abstract

Since its introduction to clinical practice, preimplantation genetic testing (PGT) has become a standard of care for couples at risk of having children with monogenic disease and for chromosomal aneuploidy to improve outcomes for patients with infertility. The primary objective of PGT is to reduce the risk of miscarriage and genetic disease and to improve the success of infertility treatment with the delivery of a healthy child. Until recently, the application of PGT to more common but complex polygenic disease was not possible, as the genetic contribution to polygenic disease has been difficult to determine, and the concept of embryo selection across multiple genetic loci has been difficult to comprehend. Several achievements, including the ability to obtain accurate, genome-wide genotypes of the human embryo and the development of population-level biobanks, have now made PGT for polygenic disease risk applicable in clinical practice. With the rapid advances in embryonic polygenic risk scoring, diverse considerations beyond technical capability have been introduced.

Introduction

Many advances have been made since the first reported clinical use of PGT for a monogenic disease (PGT-M) (Handyside et al. 1990). For example, PGT has expanded to include routine evaluation of chromosomal aneuploidy (PGT-A) and structural rearrangements (PGT-SR). Utilization has now reached approximately one-third of all in vitro fertilization (IVF) cycles in the United States (Society for Assisted Reproductive Technology 2018; https://www.sart.org). PGT methods have also evolved, with the development of new, cost-effective and high-throughput human genome sequence analysis tools. As described in this review, machine learning applied to data from population-level biobanks now provides the opportunity to expand PGT to more common, genetic diseases which are polygenic in nature.

Population level polygenic disease risk

According to the World Health Organization (WHO), approximately 26% of the world population will die prematurely from a non-communicable disease (NCD), and total disease incidence is higher still. The WHO estimates that 80% of NCDs can be attributed to cardiovascular disease, respiratory disease, cancer, and diabetes (i.e. polygenic disorders) (World Health Organization 2014). Relative to the percentage of the population affected by a monogenic disease, the world-wide indication for polygenic disease risk may be more than ten times greater.

Significant national and commercial efforts have been made to establish large biobanks to help researchers improve population health (Table 1). Among these repositories is the United Kingdom BioBank (UKBB), which includes genome-wide DNA data from 500,000 individuals (Sudlow et al. 2015). The UKBB ‘aims to improve the prevention, diagnosis and treatment of a wide range of serious and life-threatening illnesses – including cancer, heart diseases, stroke, diabetes, arthritis, osteoporosis, eye disorders, depression and forms of dementia’. Although this repository is enriched for healthy individuals, the prevalence of disease (Table 2) has made it possible to develop useful polygenic risk scores for many common diseases, including those among the top ten causes of death.

Table 1

Examples of population level biobanks suitable for development of polygenic disease risk predictors.

Biobank nameNumber of individuals*
United Kingdom Biobank500,000
Biobank Graz1,200,000
‘All of Us’ Biobank1,000,000
The International Agency for Research on Cancer (IARC) Biobank (IBB)562,000
China Kadoorie Biobank510,000
FINNGEN Biobank500,000
Canadian Partnership for Tomorrow Project Biobank300,000
Qatar Biobank60,000
Biobank Japan200,000
Million Veteran Program1,000,000
Genome Asia 100K100,000

*Number of individuals set as recruitment goal.

Table 2

WHO estimates and UKBB values for common polygenic disease prevalence.

DiseaseWorldUKBB
Cardiovascular31%3%
Cancer17%9%
Diabetes9%5%

Polygenic risk scoring (PRS)

Genome-wide association studies (GWAS) were initially applied with the idea that small numbers of genetic loci would be adequate to explain variation in complex disease phenotypes in a population (Klein et al. 2005, Dewan et al. 2006). Advances in bioinformatics have led the GWAS methodology, reporting individually associated loci, to be superseded by genomic prediction methodology, with a focus on prediction competence through polygenic risk scoring (PRS). A result has been the rapid growth in research and development of PRS (Fig. 1). The PRS approach can be considered an evolution of the GWAS. While a GWAS measures a single locus’ correlation with a given disease, and typically defines success as a high degree of association of a given locus, a PRS approach measures capability to distinguish different phenotypes, such as disease and non-disease, using combinations of loci, and defines success as capability to accurately predict the phenotype of interest.

Figure 1
Figure 1

Thirty years of publication since the first PGT. PGD-preimplantation genetic diagnosis, PRS-polygenic risk scoring. (A) The first PGT cases reported (Handyside et al. 1990); (B) the first PGT birth reported (Handyside et al. 1992); (C) the first human whole genome sequence (WGS) published (Lander et al. 2001); (D) the first genome-wide association study (GWAS) reported (Klein et al. 2005); and (E) the first PGT for polygenic disease risk (PGT-P) reported (Treff et al. 2019a).

Citation: Reproduction 160, 5; 10.1530/REP-20-0071

The combination of PRS and standard clinical risk indicators (Chatterjee et al. 2016, Torkamani et al. 2018) has now made it possible to identify individuals with elevated polygenic disease risks equivalent to the elevated risk of individuals with monogenic diseases, including breast cancer, type 2 diabetes, and atrial fibrillation (Khera et al. 2018). Several studies have also demonstrated the ability of PRS to explain polygenic disease phenotypes directly from an individual’s DNA (without clinical indicators). For example, Lello and colleagues recently reported on PRS performance for diseases including hypothyroidism, hypertension, type 1 and 2 diabetes, breast cancer, prostate cancer, testicular cancer, gallstones, glaucoma, gout, atrial fibrillation, high cholesterol, asthma, basal cell carcinoma, malignant melanoma, and heart attack (Lello et al. 2019).

Infertile population polygenic disease risk

The WHO estimates that 10% of women are affected by infertility, with in vitro fertilization (IVF) considered the most effective treatment. Reviewed by Cedars et al. (2017), and now supported by the NIH, it is well established that infertile individuals are less healthy than fertile individuals, with increased risk for cardiovascular disease, cancer, and diabetes. Increased prevalence translates to better performance (higher utility) of clinical tests. Therefore, better performance of PRS in embryos derived from infertile couples can be expected, compared to the population without this indication. Determining whether IVF-derived embryos have an increased prevalence of high polygenic risk scores relative to the general population is in fact the objective of ongoing research. Initial data suggest that polygenic disease risk reduction performance with genetic testing in the UKBB and T1DBase (Burren et al. 2011) is significant (Treff et al. 2019a,b).

PGT for polygenic disease risk (PGT-P)

Four years following the first reported birth following PGT for a monogenic disease (Handyside et al. 1992), Nobel Laureate Robert Edwards and his colleague Joseph Schulman proposed that ‘many of the major human traits are highly polygenic, and that a large number of genes may possibly be analysed in embryos in the near future’ (Schulman & Edwards 1996). However, only recently has this become a reality, as the first reported clinical application of PGT for polygenic disease risk (PGT-P) was published in 2019 (Treff et al. 2019a), which includes analysis of multiple diseases (Fig. 2). This capability required several major developments: first, the availability of population-level genome-wide data with corresponding clinical phenotypes; second, the ability to perform accurate genomic prediction of a complex human trait (Lello et al. 2018); third, the capability to obtain embryonic genome-wide genotypes with accuracy equivalent to adults (Treff et al. 2019b); and fourth, the capability to achieve significant reduction in polygenic disease risk following genetic selection of a sibling (Treff et al. 2019a).

Figure 2
Figure 2

Example PGT-P report for a female embryo.

Citation: Reproduction 160, 5; 10.1530/REP-20-0071

The latter of these developments, demonstrating clinical utility in genetic selection across >2000 siblings, is an important outcome to highlight. First it requires that one appreciate that genotypes obtained in embryos are equivalent in accuracy to genotypes obtained in adults. With that in mind, one can evaluate adult sibling cohorts with known disease status to determine the utility PGT-P in sibling embryo selection. When compared to random selection, the use of genetic selection demonstrated 45–72% reduction in the prevalence of type 1 diabetes. This demonstrates that choice is significantly better than chance when considering a sibling embryo’s risk of polygenic disease.

Many additional considerations regarding the application of PGT-P are currently under investigation. For example, the quiet embryo hypothesis (Leese 2002) suggests that selecting more metabolically active embryos could increase the risk of adult-onset disease (including diabetes, cancer, and cardiovascular disease). Embryo selection based on morphology (Gardner et al. 2015) may contribute to this phenomena. As utilization of IVF and PGT-P increases, the correlation between embryo morphology and polygenic disease risk will be evaluated.

The concept that selection against one disease may increase the prevalence of another (pleiotropy) (Zheutlin et al. 2019) may be addressed using a ‘genomic index’ selection methodology, which provides combined risk scoring across multiple disease risks, much as PRS provides combined scoring across multiple genomic regions. Indeed, recent data suggest that the genetic loci used to predict different diseases are largely disjoint (Yong et al. 2020), such that selecting against one disease will not increase the risk of another. In fact, the impact of pleiotropy may be in the positive direction, where for example, selection against high risk of hypercholesterolemia might also result in reduced risk of myocardial infarction. This is one focus of ongoing research.

PGT-P ethics

Health care disparity may result from the limited representation of ethnicities made available by existing biobanks. Polygenic predictors constructed using a preponderance of a single ethnic group (typically, European ancestry) perform well when predictions are made within the same ethnic group, but less well when predictions are made outside the ancestral training set. Prediction accuracy decline follows the overall genetic distance between the training ancestry and target ancestry. This leads to a situation where, for example, predictions may be more beneficial for Europeans and less so for Africans and Native Americans. As Table 1 indicates, efforts throughout the world can be expected to eliminate this disparity when applying PGT-P in the near future.

The cost of IVF and PGT-P may also limit its use to individuals with the economic means to access care, causing increased disparity in health between wealthy and poor in the next generations. Several IVF programs have begun developing ‘low-cost’ IVF options, while others have developed methods which allow PGT without IVF (Munné et al. 2020). These efforts, in combination with national and commercial insurance policy changes, may help eliminate the potential for economic disparity in application of PGT-P.

A third ethical consideration involves appropriately defining what phenotypes should be tested. Mainstream media has focused on the possibility of using PGT-P to test for desirable traits, often referred to as selection for ‘designer babies’. A recent report evaluating selection for increased height and cognitive ability suggests that this application may not (yet) be powerful (Karavani et al. 2019). Still, some argue that selecting against embryos with high risk of disease is itself a designer baby outcome. However, an American Society for Reproductive Medicine Ethics Committee argues that testing embryos for adult onset conditions of lesser severity or lower penetrance is ethically justifiable for reasons of reproductive liberty (ASRM-Ethics-Committee 2018). It has been argued, in both PGT-M and PGT-P, that there is no need to select against diseases of lesser severity, on the grounds that they are treatable. However, even treatable diseases, like type 2 diabetes, impact lifespan.

The disease panel for which PGT-P screening is possible is largely determined by the availability of data for machine learning (Table 2). As the data grow, and as government, social, and health care policies are adapted, definitions of what is appropriate to test may evolve. These efforts have already begun with the European Commission’s recent Joint Research Centre report on ‘genome-wide association studies, polygenic scores and social science genetics: overview and policy implications’. The report suggests that the availability of PGT-P ‘illustrates how fast GWAS may influence the sector and demands the policy makers’ and society’s response on how to determine the margins and deal with these possibilities’. (European Commission 2019).

Gene editing

In 1996, Edwards and Schulman also raised the possibility of reducing the symptoms of inherited disease through ‘germinal DNA therapy’ (Schulman & Edwards 1996). The introduction of gene-editing (i.e. CRISPR/Cas9) in the preimplantation embryo has stirred considerable debate, and possible scientific misconduct (Cyranoski 2019), but also exciting new research on the opportunity to cure disease before pregnancy (Lea & Niakan 2019). Interestingly, this opportunity may lead to increased acceptance of PGT, where the outcome may no longer involve discarding embryos, but instead correction for the genetic abnormality with subsequent utilization for embryo transfer. New research involving the creation of gene-edited-embryo-derived embryonic stem cells (Ma et al. 2017) may be instrumental in the development of rigorous safety and efficacy data, but also to aid in development of the necessary genome-wide PGT methodologies. Applications beyond curing monogenic diseases may include curing Down Syndrome in embryos derived from Robertsonian translocation carriers (t21:21), restoring euploidy via polar body and parental DNA genetic analyses, and curing polygenic diseases through the identification and editing of multiple causative genetic loci. The prospect of targeted edits for the treatment of polygenic diseases remains unproven as the identification of the true causal set of loci is difficult to disentangle from loci which may only be correlated with the causal variants (i.e. those loci used for PGT-P). Much research remains in the effort to resolve the true genetic architecture for polygenic disorders, but the prospect of such a public health benefit remains tantalizing. In all cases, genome-wide PGT will remain a necessity, as will maturation of public policy (Ormond et al. 2017), if gene-editing is to make it to its intended clinical use.

Conclusion

Infertility is a growing public health concern, as is the global rate of premature death from polygenic disease. Initial application of PGT-P may be well suited to the infertile population given their increased risk of cardiovascular disease, cancer, and diabetes and the current clinical practice of selecting more metabolically active (not quiet) embryos for transfer. By combining the power of machine learning, large biobanks, and state-of-the-art molecular genetics, the introduction of PGT-P to clinical use may provide a means to reduce the prevalence of disease in humans. Evolving public policy around reproductive liberty and increased access to care, expansion of more diverse DNA repositories, and additional research on the relative disease risk reduction to offspring from parents with distinct indications, will be instrumental in realizing the full untapped potential of preimplantation genetic testing for polygenic disease risk.

Declaration of interest

Authors are employees, founders, and/or shareholders of Genomic Prediction.

Funding

This research did not receive any specific grant from any funding agency in the public, commercial or not-for-profit sector.

Author contribution statement

N R T, D M, L L, S H, and L C A M T wrote and edited the manuscript.

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  • ASRM Ethics Committee 2018 Use of preimplantation genetic testing for monogenic defects (PGT-M) for adult-onset conditions: an Ethics Committee opinion. Fertility & Sterility 109 989992. (https://doi.org/10.1016/j.fertnstert.2018.04.003)

    • Search Google Scholar
    • Export Citation
  • Burren OS, Adlem EC, Achuthan P, Christensen M, Coulson RM & Todd JA 2011 T1DBase: update 2011, organization and presentation of large-scale data sets for type 1 diabetes research. Nucleic Acids Research 39 D997D1001. (https://doi.org/10.1093/nar/gkq912)

    • Search Google Scholar
    • Export Citation
  • Cedars MI, Taymans SE, Depaolo LV, Warner L, Moss SB & Eisenberg ML 2017 The sixth vital sign: what reproduction tells us about overall health. Proceedings from a NICHD/CDC workshop. Human Reproduction Open 2017 hox008. (https://doi.org/10.1093/hropen/hox008)

    • Search Google Scholar
    • Export Citation
  • Chatterjee N, Shi J & Garcia-Closas M 2016 Developing and evaluating polygenic risk prediction models for stratified disease prevention. Nature Reviews: Genetics 17 392406. (https://doi.org/10.1038/nrg.2016.27)

    • Search Google Scholar
    • Export Citation
  • Cyranoski D 2019 The CRISPR-baby scandal: what’s next for human gene-editing. Nature 566 440442. (https://doi.org/10.1038/d41586-019-00673-1)

    • Search Google Scholar
    • Export Citation
  • Dewan A, Liu M, Hartman S, Zhang SS-M, Liu DTL, Zhao C, Tam POS, Chan WM, Lam DSC & Snyder M 2006 HTRA1 promoter polymorphism in wet age-related macular degeneration. Science 314 989992. (https://doi.org/10.1126/science.1133807)

    • Search Google Scholar
    • Export Citation
  • European Commission 2019. Genome-wide association studies, polygenic scores and social science genetics: overview and policy implications. JRC F7 – Knowledge Health and Consumer Safety, EUR 29815 EN.

    • Search Google Scholar
    • Export Citation
  • Gardner DK, Meseguer M, Rubio C & Treff NR 2015 Diagnosis of human preimplantation embryo viability. Human Reproduction Update 21 727747. (https://doi.org/10.1093/humupd/dmu064)

    • Search Google Scholar
    • Export Citation
  • Handyside AH, Kontogianni EH, Hardy K & Winston RM 1990 Pregnancies from biopsied human preimplantation embryos sexed by Y-specific DNA amplification. Nature 344 768770. (https://doi.org/10.1038/344768a0)

    • Search Google Scholar
    • Export Citation
  • Handyside AH, Lesko JG, Tarin JJ, Winston RM & Hughes MR 1992 Birth of a normal girl after in vitro fertilization and preimplantation diagnostic testing for cystic fibrosis. New England Journal of Medicine 327 905909. (https://doi.org/10.1056/NEJM199209243271301)

    • Search Google Scholar
    • Export Citation
  • Karavani E, Zuk O, Zeevi D, Barzilai N, Stefanis N C, Hatzimanolis A, Smyrnis N, Avramopoulos D, Kruglyak L & Atzmon G et al. 2019. Screening Human Embryos for Polygenic Traits Has Limited Utility. Cell 179 1424– 1435.e8. (https://doi.org/10.1016/j.cell.2019.10.033)

    • Search Google Scholar
    • Export Citation
  • Khera AV, Chaffin M, Aragam KG, Haas ME, Roselli C, Choi SH, Natarajan P, Lander ES, Lubitz SA & Ellinor PT 2018 Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nature Genetics 50 12191224. (https://doi.org/10.1038/s41588-018-0183-z)

    • Search Google Scholar
    • Export Citation
  • Klein RJ, Zeiss C, Chew EY, Tsai JY, Sackler RS, Haynes C, Henning AK, Sangiovanni JP, Mane SM & Mayne ST 2005 Complement factor H polymorphism in age-related macular degeneration. Science 308 385389. (https://doi.org/10.1126/science.1109557)

    • Search Google Scholar
    • Export Citation
  • Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, Devon K, Dewar K, Doyle M & Fitzhugh W 2001 Initial sequencing and analysis of the human genome. Nature 409 860921. (https://doi.org/10.1038/35057062)

    • Search Google Scholar
    • Export Citation
  • Lea RA & Niakan K 2019 Human germline genome editing. Nature Cell Biology 21 14791489. (https://doi.org/10.1038/s41556-019-0424-0)

  • Leese HJ 2002 Quiet please, do not disturb: a hypothesis of embryo metabolism and viability. BioEssays 24 845849. (https://doi.org/10.1002/bies.10137)

    • Search Google Scholar
    • Export Citation
  • Lello L, Avery SG, Tellier L, Vazquez AI, De Los Campos G & Hsu SDH 2018 Accurate genomic prediction of human height. Genetics 210 477497. (https://doi.org/10.1534/genetics.118.301267)

    • Search Google Scholar
    • Export Citation
  • Lello L, Raben TG, Yong SY, Tellier LCAM & Hsu SDH 2019 Genomic prediction of 16 complex disease risks including heart attack, diabetes, breast and prostate cancer. Scientific Reports 9 1528615286. (https://doi.org/10.1038/s41598-019-51258-x)

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