O-056 Evaluating the utility of screening human IVF embryos with polygenic risk scores for complex diseases

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
S Carmi ◽  
D Backenroth ◽  
A Green ◽  
O Weissbrod ◽  
O Zuk ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Reduction ◽  
Disease Risk ◽  
Threshold Model ◽  
Complex Diseases ◽  
Risk Scores ◽  
Polygenic Risk ◽  
Social Concerns ◽  
Future Child ◽  
Human Ivf

Abstract Study question It is now feasible to screen human IVF embryos with “polygenic risk scores” for predicting complex disease risk. What is the expected risk reduction? Summary answer Under some conditions, prioritizing embryos based on polygenic risk scores can lead to substantial disease risk reductions. However, only excluding high-risk embryos is less effective. What is known already Recent genetic studies have identified numerous mutations associated with complex diseases, leading to the development of accurate polygenic risk scores (PRSs) for disease risk prediction. Given that genomes of human IVF embryos can now be sequenced with relative ease, it has become technically feasible to use PRSs for prioritization of embryos for transfer. Clearly, such use is associated with ethical and social concerns, from inequality to eugenics. Nevertheless, polygenic embryo screening is already offered to consumers, with little research so far on expected outcomes. Our previous evaluation of screening IVF embryos for polygenic traits showed little current utility. Study design, size, duration This is a theoretical/computational study based on statistical genetics theory and simulations. Participants/materials, setting, methods We used the liability threshold model to estimate the disease risk given the PRS. We considered screening for a single disease (with known prevalence and PRS accuracy), and assumed that n viable embryos are available. We calculated the risk of the child given these parameters and the prioritization strategy, either when parents are random or when their disease status is known. We also used simulations based on genomic data from a schizophrenia case-control study. Main results and the role of chance We modeled the disease risk of a hypothetical future child when the PRSs of embryos are used for prioritization, relative to random selection. When selecting an embryo at random among those who do not have an extremely high risk (typically, top 2% of the PRS distribution), the relative risk reduction (RRR) is limited, and is under 10% for currently realistic scenarios. In contrast, selecting the lowest risk embryo for implantation results in substantial RRRs of ∼20-50% already with n = 5 embryos and realistic disease parameters. For example, the RRR for schizophrenia is > 40% with current PRSs, a result we validated with simulated genomes of parents and children based on genotypes from a schizophrenia study. When one of the parents is known to be affected, selecting the lowest risk embryo out of n = 5 may restore the risk of the future child to nearly normal levels. Limitations, reasons for caution Our analytical modeling is based on several simplifying assumptions regarding the dependence of the risk on the PRS and the accuracy of the PRS. Further, the estimated risk reductions depend on the availability of n = 5 embryos that could lead to a live birth. Wider implications of the findings Under some conditions, prioritizing embryos for transfer based on polygenic risk scores could lead to substantial disease risk reductions. However, predicted outcomes vary considerably with prioritization strategies and disease and PRS parameters. The emerging ethical and social concerns and the challenges in communicating these results need to be urgently discussed. Trial registration number Not applicable

scholarly journals Cascade screening following a polygenic risk score test: what is the disease risk of a sibling conditional on a high score of a proband?

2021 ◽  
Author(s):  
Shai Carmi
Keyword(s):  
Disease Risk ◽  
Threshold Model ◽  
Short Note ◽  
Score Test ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Cascade Screening ◽  
Polygenic Risk ◽  
Using Data ◽  
The Uk

Polygenic risk scores (PRSs) for predicting disease risk have become increasingly accurate, leading to increasing popularity of PRS tests. Consider an individual whose PRS test has placed him/her at the top q-quantile of genetic risk. Recently, Reid et al. (Circ Genom Precis Med. 2021;14:e003262) have investigated whether such a finding should motivate cascade screening in the proband's siblings. Specifically, using data from the UK biobank, Reid et al. computed the empirical probability of a sibling of the proband to also have a PRS at the top q-quantile. In this short note, I use the liability threshold model to compute this probability analytically, showing excellent agreement with the empirical results of Reid et al., including that this probability is disease-independent. Further, I compute the probability of a sibling of the proband to be affected, as a function of the quantile threshold q, the proportion of variance explained by the score, and the disease prevalence.

scholarly journals A smoothed version of the Lassosum penalty for fitting integrated risk models

2021 ◽  
Author(s):  
Georg Hahn ◽  
Dmitry Prokopenko ◽  
Sharon M. Lutz ◽  
Kristina Mullin ◽  
Rudolph E. Tanzi ◽  
...  
Keyword(s):  
High Risk ◽  
Disease Susceptibility ◽  
Risk Model ◽  
Disease Risk ◽  
State Of The Art ◽  
Risk Scores ◽  
Risk Models ◽  
Polygenic Risk ◽  
Integrated Risk ◽  
Genotype Information

AbstractPolygenic risk scores are a popular means to predict the disease risk or disease susceptibility of an individual based on its genotype information. When adding other important epidemiological covariates such as age or sex, we speak of an integrated risk model. Methodological advances for fitting more accurate integrated risk models are of immediate importance to improve the precision of risk prediction, thereby potentially identifying patients at high risk early on when they are still able to benefit from preventive steps/interventions targeted at increasing their odds of survival, or at reducing their chance of getting a disease in the first place. This article proposes a smoothed version of the “Lassosum” penalty used to fit polygenic risk scores and integrated risk models. The smoothing allows one to obtain explicit gradients everywhere for efficient minimization of the Lassosum objective function while guaranteeing bounds on the accuracy of the fit. An experimental section demonstrates the increased accuracy of the proposed smoothed Lassosum penalty compared to the original Lassosum algorithm, allowing it to draw equal with state-of-the-art methodology such as LDpred when evaluated via the AUC (area under the ROC curve) metric.

scholarly journals Identifying individuals with high risk of Alzheimer’s disease using polygenic risk scores

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ganna Leonenko ◽  
Emily Baker ◽  
Joshua Stevenson-Hoare ◽  
Annerieke Sierksma ◽  
Mark Fiers ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Genetic Risk ◽  
Prediction Accuracy ◽  
Genetic Risk Score ◽  
Low Risk ◽  
Risk Scores ◽  
Sample Mean ◽  
Polygenic Risk ◽  
Reliable Identification

AbstractPolygenic Risk Scores (PRS) for AD offer unique possibilities for reliable identification of individuals at high and low risk of AD. However, there is little agreement in the field as to what approach should be used for genetic risk score calculations, how to model the effect of APOE, what the optimal p-value threshold (pT) for SNP selection is and how to compare scores between studies and methods. We show that the best prediction accuracy is achieved with a model with two predictors (APOE and PRS excluding APOE region) with pT<0.1 for SNP selection. Prediction accuracy in a sample across different PRS approaches is similar, but individuals’ scores and their associated ranking differ. We show that standardising PRS against the population mean, as opposed to the sample mean, makes the individuals’ scores comparable between studies. Our work highlights the best strategies for polygenic profiling when assessing individuals for AD risk.

03 TOWARDS DISEASE INTERCEPTION: CONTRIBUTION OF POLYGENIC RISK SCORES TOWARDS AGE-DEPENDENT RISK OF IBD IN A HIGH-RISK POPULATION

2019 ◽  
Vol 156 (3) ◽  
pp. S73-S74
Author(s):  
Elizabeth A. Spencer ◽  
Kyle Gettler ◽  
Drew Helmus ◽  
Shannon Telesco ◽  
Amy Hart ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Scores ◽  
High Risk Population ◽  
Risk Population ◽  
Polygenic Risk ◽  
Age Dependent

scholarly journals S175. CLINICAL OUTCOMES IN PEOPLE AT HIGH RISK FOR PSYCHOSIS RELATED TO INTERACTIONS BETWEEN POLYGENIC RISK SCORES AND CHILDHOOD ADVERSITY

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S104-S104
Author(s):  
Anja Richter ◽  
Evangelos Vassos ◽  
Matthew J Kempton ◽  
Mark van der Gaag ◽  
Lieuwe de Haan ◽  
...  
Keyword(s):  
Risk Factors ◽  
Sexual Abuse ◽  
High Risk ◽  
Prospective Study ◽  
Physical Abuse ◽  
Clinical Outcomes ◽  
Childhood Trauma ◽  
Childhood Adversity ◽  
Risk Scores ◽  
Polygenic Risk

Abstract Background Genetic vulnerability to psychosis is polygenic, involving multiple genes with small individual effects (Psychiatric Genomics Consortium (PGC), 2014). The risk of psychosis is also related to environmental factors, such as childhood trauma (Lardinois et al, 2011). Although the onset of psychosis is thought to result from the interaction of genetic and environmental risk factors (Walker & Diforio, 1997), the extent to which the influence of childhood trauma depends on genetic susceptibility remains unclear. We sought to address this issue in a large prospective study of people at clinical high risk (CHR) for psychosis. These individuals present with psychotic and affective symptoms, and are at increased risk of developing both schizophreniform and affective psychoses. Methods We studied subjects of European ancestry, drawn from EU-GEI, a large multi-centre prospective study of people at CHR for psychosis. At baseline, DNA was obtained from subjects who met the CAARMS criteria for the CHR state (n=266) and healthy controls (HC; n=42). Childhood trauma was assessed using the childhood trauma questionnaire (CTQ), which comprises 5 subdomains: emotional abuse, physical abuse, sexual abuse, physical neglect, and emotional neglect. Polygenic risk scores (PRSs) for schizophrenia (SCZ), bipolar disorder (BD) and major depressive disorder (MDD) were constructed separately, using results from meta-analyses by the corresponding Disorder Working Groups of the PGC. The CHR subjects were clinically monitored for up to 5 years and clinical outcomes were assessed in terms of transition to psychosis (as defined by the CAARMS), remission from the CHR state (subject no longer meets CAARMS inclusion criteria) and level of functioning (GAF Disability Scale). Logistic regression models were used to investigate the association between each PRSs and childhood trauma as predictors of transition and remission, adjusted by population stratification using the first 10 principal components, age, sex and site. All findings are reported at p&lt;0.017, Bonferroni-corrected for the 3 PRSs. Results Within the CHR sample, the onset of psychosis during follow up was related to interactions between the BD PRS and the total childhood trauma score (OR=0.959, 95% CI 0.930–0.988, p=0.006), and between the BD PRS and physical abuse (OR=0.787, 95% CI 0.689–0.900, p&lt;0.001). Remission from the CHR state was related to an interaction between the SCZ PRS and childhood sexual abuse (OR: 1.110, 95% CI 1.004–1.226, p=0.041). Discussion These data indicate that clinical outcomes in CHR subjects are related to interactions between the polygenic risk for psychotic disorders and childhood adversity. The measurement of interactions between genomic and environmental risk factors may help to predict individual outcomes in people at high risk in a clinical setting.

scholarly journals Translating polygenic risk scores for clinical use by estimating the confidence bounds of risk prediction

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jiangming Sun ◽  
Yunpeng Wang ◽  
Lasse Folkersen ◽  
Yan Borné ◽  
Inge Amlien ◽  
...  
Keyword(s):  
Risk Prediction ◽  
Complex Disease ◽  
Disease Risk ◽  
Disease Status ◽  
Risk Scores ◽  
Correct Prediction ◽  
Confidence Bounds ◽  
Conformal Prediction ◽  
Polygenic Risk ◽  
Individual Level

AbstractA promise of genomics in precision medicine is to provide individualized genetic risk predictions. Polygenic risk scores (PRS), computed by aggregating effects from many genomic variants, have been developed as a useful tool in complex disease research. However, the application of PRS as a tool for predicting an individual’s disease susceptibility in a clinical setting is challenging because PRS typically provide a relative measure of risk evaluated at the level of a group of people but not at individual level. Here, we introduce a machine-learning technique, Mondrian Cross-Conformal Prediction (MCCP), to estimate the confidence bounds of PRS-to-disease-risk prediction. MCCP can report disease status conditional probability value for each individual and give a prediction at a desired error level. Moreover, with a user-defined prediction error rate, MCCP can estimate the proportion of sample (coverage) with a correct prediction.

scholarly journals Incorporating family history of disease improves polygenic risk scores in diverse populations

2021 ◽  
Author(s):  
Margaux L.A. Hujoel ◽  
Po-Ru Loh ◽  
Benjamin M. Neale ◽  
Alkes L. Price
Keyword(s):  
Family History ◽  
Threshold Model ◽  
South Asians ◽  
Target Population ◽  
Risk Scores ◽  
Diverse Populations ◽  
Polygenic Risk ◽  
History Of Disease ◽  
History Of ◽  
Large Improvement

AbstractPolygenic risk scores derived from genotype data (PRS) and family history of disease (FH) both provide valuable information for predicting disease risk, enhancing prospects for clinical utility. PRS perform poorly when applied to diverse populations, but FH does not suffer this limitation. Here, we explore methods for combining both types of information (PRS-FH). We analyzed 10 complex diseases from the UK Biobank for which family history (parental and sibling history) was available for most target samples. PRS were trained using all British individuals (N=409K), and target samples consisted of unrelated non-British Europeans (N=42K), South Asians (N=7K), or Africans (N=7K). We evaluated PRS, FH, and PRS-FH using liability-scale R2, focusing on three well-powered diseases (type 2 diabetes, hypertension, depression) with R2 > 0.05 for PRS and/or FH in each target population. Averaging across these three diseases, PRS attained average prediction R2 of 5.8%, 4.0%, and 0.53% in non-British Europeans, South Asians, and Africans, confirming poor cross-population transferability. In contrast, PRS-FH attained average prediction R2 of 13%, 12%, and 10%, respectively, representing a large improvement in Europeans and an extremely large improvement in Africans; for each disease and each target population, the improvement was highly statistically significant. PRS-FH methods based on a logistic model and a liability threshold model performed similarly when covariates were not included in predictions (consistent with simulations), but the logistic model outperformed the liability threshold model when covariates were included. In conclusion, including family history greatly improves the accuracy of polygenic risk scores, particularly in diverse populations.

scholarly journals Utility of polygenic embryo screening for disease depends on the selection strategy

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Todd Lencz ◽  
Daniel Backenroth ◽  
Einat Granot-Hershkovitz ◽  
Adam Green ◽  
Kyle Gettler ◽  
...  
Keyword(s):  
Complex Disease ◽  
Disease Risk ◽  
Threshold Model ◽  
Absolute Risk ◽  
Risk Scores ◽  
Selection Strategy ◽  
Case Control Studies ◽  
Disease Case ◽  
The Impact

Polygenic risk scores (PRSs) have been offered since 2019 to screen in vitro fertilization embryos for genetic liability to adult diseases, despite a lack of comprehensive modeling of expected outcomes. Here we predict, based on the liability threshold model, the expected reduction in complex disease risk following polygenic embryo screening for a single disease. A strong determinant of the potential utility of such screening is the selection strategy, a factor that has not been previously studied. When only embryos with a very high PRS are excluded, the achieved risk reduction is minimal. In contrast, selecting the embryo with the lowest PRS can lead to substantial relative risk reductions, given a sufficient number of viable embryos. We systematically examine the impact of several factors on the utility of screening, including: variance explained by the PRS, number of embryos, disease prevalence, parental PRSs, and parental disease status. We consider both relative and absolute risk reductions, as well as population-averaged and per-couple risk reductions, and also examine the risk of pleiotropic effects. Finally, we confirm our theoretical predictions by simulating ‘virtual’ couples and offspring based on real genomes from schizophrenia and Crohn’s disease case-control studies. We discuss the assumptions and limitations of our model, as well as the potential emerging ethical concerns.

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