Concordance of a High Polygenic Score Among Relatives: Implications for Genetic Counseling and Cascade Screening

Taking the risk. A systematic review of ethical reasons and moral arguments in the clinical use of polygenic risk scores

Debates about the prospective clinical use of polygenic risk scores (PRS) have grown considerably in the last years. The potential benefits of PRS to improve patient care at individual and population levels have been extensively underlined. Nonetheless, the use of PRS in clinical contexts presents a number of unresolved ethical challenges and consequent normative gaps that hinder their optimal implementation. Here, we conducted a systematic review of reasons of the normative literature discussing ethical issues and moral arguments related to the use of PRS for the prevention and treatment of common complex diseases. In total, we have included and analyzed 34 records, spanning from 2013 to 2023. The findings have been organized in three major themes: in the first theme, we consider the potential harms of PRS to individuals and their kin. In the theme "Threats to health equity," we consider ethical concerns of social relevance, with a focus on justice issues. Finally, the theme "Towards best practices" collects a series of research priorities and provisional recommendations to be considered for an optimal clinical translation of PRS. We conclude that the use of PRS in clinical care reinvigorates old debates in matters of health justice; however, open questions, regarding best practices in clinical counseling, suggest that the ethical considerations applicable in monogenic settings will not be sufficient to face PRS emerging challenges.

Combining Mendelian randomization with the sibling comparison design

Mendelian randomization (MR) is a popular epidemiologic study design that uses genetic variants as instrumental variables (IVs) to estimate causal effects, while accounting for unmeasured confounding. The validity of the MR design hinges on certain IV assumptions, which may sometimes be violated due to dynastic effects, population stratification, or assortative mating. Since these mechanisms act through parental factors it was recently suggested that the bias resulting from violations of the IV assumptions can be reduced by combing the MR design with the sibling comparison design, which implicitly controls for all factors that are constant within families. In this article, we provide a formal discussion of this combined MR-sibling design. We derive conditions under which the MR-sibling design is unbiased, and we relate these to the corresponding conditions for the standard MR and sibling comparison designs. We proceed by considering scenarios where all three designs are biased to some extent, and discuss under which conditions the MR-sibling design can be expected to have less bias than the other two designs. We finally illustrate the theoretical results and conclusions with an application to real data, in a study of low-density lipoprotein and diastolic blood pressure using data from the Swedish Twin Registry.

Concordance of a High Lipoprotein(a) Concentration Among Relatives

Importance

Lipoprotein(a) (Lp[a]) concentrations are a highly heritable and potential causal risk factor for atherosclerotic cardiovascular disease (ASCVD). Recent consensus statements by the European Atherosclerosis Society and American Heart Association recommend screening of relatives of individuals with high Lp(a) concentrations, but the expected yield of this approach has not been quantified in large populations.

Objective

To measure the prevalence of high Lp(a) concentrations among first- and second-degree relatives of individuals with high Lp(a) concentrations compared with unrelated participants.

Design, Setting, and Participants

In this cross-sectional analysis, pairs of first-degree (n = 19 899) and second-degree (n = 9715) relatives with measured Lp(a) levels from the UK Biobank study and random pairs of unrelated individuals (n = 184 764) were compared. Data for this study were collected from March 2006 to August 2010 and analyzed from December 2021 to August 2023.

Exposure

Serum Lp(a) levels, with a high Lp(a) level defined as at least 125 nmol/L.

Main Outcome and Measure

Concordance of clinically relevant high Lp(a) levels in first- and second-degree relatives of index participants with high Lp(a) levels.

Results

A total of 52 418 participants were included in the analysis (mean [SD] age, 57.3 [8.0] years; 29 825 [56.9%] women). Levels of Lp(a) were correlated among pairs of first-degree (Spearman ρ = 0.45; P < .001) and second-degree (Spearman ρ = 0.22; P < .001) relatives. A total of 1607 of 3420 (47.0% [95% CI, 45.3%-48.7%]) first-degree and 514 of 1614 (31.8% [95% CI, 29.6%-34.2%]) second-degree relatives of index participants with high Lp(a) levels also had elevated concentrations compared with 4974 of 30 258 (16.4% [95% CI, 16.0%-16.9%]) pairs of unrelated individuals. The concordance in high Lp(a) levels was generally consistent among subgroups (eg, those with prior ASCVD, postmenopausal women, and statin users). The odds ratios for relatives to have high Lp(a) levels if their index relative had a high Lp(a) level compared with those whose index relatives did not have high Lp(a) levels were 7.4 (95% CI, 6.8-8.1) for first-degree relatives and 3.0 (95% CI, 2.7-3.4) for second-degree relatives.

Conclusions and Relevance

The findings of this cross-sectional study suggest that the yield of cascade screening of first-degree relatives of individuals with high Lp(a) levels is over 40%. These findings support recent recommendations to use this approach to identify additional individuals at ASCVD risk based on Lp(a) concentrations.

Polygenic risk score penetrance & recurrence risk in familial Alzheimer disease

OBJECTIVE

To compute penetrance and recurrence risk using a genome-wide PRS (including and excluding the APOE region) in families with Alzheimer's disease.

METHODS

Genotypes from the National Institute on Aging Late-Onset Alzheimer's Disease Family-Based Study and a study of familial Alzheimer's disease in Caribbean Hispanics were used to compute PRS with and without variants in the 2 MB region flanking APOE. PRS was calculated in using clumping/thresholding and Bayesian methods and was assessed for association with Alzheimer's disease and age at onset. Penetrance and recurrence risk for carriers in highest and lowest PRS quintiles were compared separately within APOE-ε4 carriers and non-carriers.

RESULTS

PRS excluding the APOE region was strongly associated with clinical and neuropathological diagnosis of AD. PRS association with AD was similar in participants who did not carry an APOE-ε4 allele (OR = 1.74 [1.53-1.91]) compared with APOE-ε4 carriers (1.53 [1.4-1.68]). Compared to the lowest quintile, the highest PRS quintile had a 10% higher penetrance at age 70 (p = 0.0006) and a 20% higher penetrance at age 80 (p < 10e-05). Stratifying by APOE-ε4 allele, PRS in the highest quintile was significantly more penetrant than the lowest quintile, both, within APOE-ε4 carriers (14.5% higher at age 80, p = 0.002) and non-carriers (26% higher at 80, p < 10e-05). Recurrence risk for siblings conferred by a co-sibling in the highest PRS quintile increased from 4% between the ages of 65-74 years to 39% at age 85 and older.

INTERPRETATION

PRS can be used to estimate penetrance and recurrence risk in familial Alzheimer's disease among carriers and non-carries of APOE-ε4.

Polygenic Scores in the Direct-to-Consumer Setting: Challenges and Opportunities for a New Era in Consumer Genetic Testing

Direct-to-consumer (DTC) genetic tests have generated considerable scholarly attention and public intrigue. Although the current consumer genetic testing regime relies on the reporting of individual variants of interest to consumers, there has recently been interest in the possibility of integrating polygenic scores (PGS), which aggregate genetic liability for disease across the entire genome. While PGS have thus far been extensively explored as clinical and public health tools, the use of PGS in consumer genetic testing has not yet received systematic attention, even though they are already in use for some consumer genetic tests. In this narrative review, we highlight the ethical, legal, and social implications of the use of PGS in DTC genetic tests and synthesize existing solutions to these concerns. We organize these concerns into three domains: (1) industry variation; (2) privacy and commercialization; and (3) patient safety and risk. While previously expressed concerns in these domains will remain relevant, the emergence of PGS-based DTC genetic tests raises challenges that will require novel approaches.

Ethical layering in AI-driven polygenic risk scores-New complexities, new challenges

Researchers aim to develop polygenic risk scores as a tool to prevent and more effectively treat serious diseases, disorders and conditions such as breast cancer, type 2 diabetes mellitus and coronary heart disease. Recently, machine learning techniques, in particular deep neural networks, have been increasingly developed to create polygenic risk scores using electronic health records as well as genomic and other health data. While the use of artificial intelligence for polygenic risk scores may enable greater accuracy, performance and prediction, it also presents a range of increasingly complex ethical challenges. The ethical and social issues of many polygenic risk score applications in medicine have been widely discussed. However, in the literature and in practice, the ethical implications of their confluence with the use of artificial intelligence have not yet been sufficiently considered. Based on a comprehensive review of the existing literature, we argue that this stands in need of urgent consideration for research and subsequent translation into the clinical setting. Considering the many ethical layers involved, we will first give a brief overview of the development of artificial intelligence-driven polygenic risk scores, associated ethical and social implications, challenges in artificial intelligence ethics, and finally, explore potential complexities of polygenic risk scores driven by artificial intelligence. We point out emerging complexity regarding fairness, challenges in building trust, explaining and understanding artificial intelligence and polygenic risk scores as well as regulatory uncertainties and further challenges. We strongly advocate taking a proactive approach to embedding ethics in research and implementation processes for polygenic risk scores driven by artificial intelligence.

Clinical genetic counseling and translation considerations for polygenic scores in personalized risk assessments: A Practice Resource from the National Society of Genetic Counselors

Polygenic scores (PGS) are primed for use in personalized risk assessments for common, complex conditions and population health screening. Although there is growing evidence supporting the clinical validity of these scores in certain diseases, presently, there is no consensus on best practices for constructing PGS or demonstrated clinical utility in practice. Despite these evidence gaps, individuals can access their PGS information through commercial entities, research programs, and clinical programs. This prompts the immediate need for educational resources for clinicians encountering PGS information in clinical practice. This practice resource is intended to increase genetic counselors' and other healthcare providers' understanding and comfort with PGS used in personalized risk assessments. Drawing on best practices in clinical genomics, we discuss the unique considerations for polygenic-based (1) testing, (2) clinical genetic counseling, and (3) translation to population health services. This practice resource outlines the emerging uses of PGS, as well as the critical limitations of this technology that need to be addressed before wide-scale implementation.

Systematic comparison of family history and polygenic risk across 24 common diseases

Family history is the standard indirect measure of inherited susceptibility in clinical care, whereas polygenic risk scores (PRSs) have more recently demonstrated potential for more directly capturing genetic risk in many diseases. Few studies have systematically compared how these overlap and complement each other across common diseases. Within FinnGen (N = 306,418), we leverage family relationships, up to 50 years of nationwide registries, and genome-wide genotyping to examine the interplay of family history and genome-wide PRSs. We explore the dynamic for three types of family history across 24 common diseases: first- and second-degree family history and parental causes of death. Covering a large proportion of the burden of non-communicable diseases in adults, we show that family history and PRS are independent and not interchangeable measures, but instead provide complementary information on inherited disease susceptibility. The PRSs explained on average 10% of the effect of first-degree family history, and first-degree family history 3% of PRSs, and PRS effects were independent of both early- and late-onset family history. The PRS stratified the risk similarly in individuals with and without family history. In most diseases, including coronary artery disease, glaucoma, and type 2 diabetes, a positive family history with a high PRS was associated with a considerably elevated risk, whereas a low PRS compensated completely for the risk implied by positive family history. This study provides a catalogue of risk estimates for both family history of disease and PRSs and highlights opportunities for a more comprehensive way of assessing inherited disease risk across common diseases.

Polygenic Risk Scores for Cardiovascular Disease: A Scientific Statement From the American Heart Association

Cardiovascular disease is the leading contributor to years lost due to disability or premature death among adults. Current efforts focus on risk prediction and risk factor mitigation' which have been recognized for the past half-century. However, despite advances, risk prediction remains imprecise with persistently high rates of incident cardiovascular disease. Genetic characterization has been proposed as an approach to enable earlier and potentially tailored prevention. Rare mendelian pathogenic variants predisposing to cardiometabolic conditions have long been known to contribute to disease risk in some families. However, twin and familial aggregation studies imply that diverse cardiovascular conditions are heritable in the general population. Significant technological and methodological advances since the Human Genome Project are facilitating population-based comprehensive genetic profiling at decreasing costs. Genome-wide association studies from such endeavors continue to elucidate causal mechanisms for cardiovascular diseases. Systematic cataloging for cardiovascular risk alleles also enabled the development of polygenic risk scores. Genetic profiling is becoming widespread in large-scale research, including in health care-associated biobanks, randomized controlled trials, and direct-to-consumer profiling in tens of millions of people. Thus, individuals and their physicians are increasingly presented with polygenic risk scores for cardiovascular conditions in clinical encounters. In this scientific statement, we review the contemporary science, clinical considerations, and future challenges for polygenic risk scores for cardiovascular diseases. We selected 5 cardiometabolic diseases (coronary artery disease, hypercholesterolemia, type 2 diabetes, atrial fibrillation, and venous thromboembolic disease) and response to drug therapy and offer provisional guidance to health care professionals, researchers, policymakers, and patients.

Human genetics and its impact on cardiovascular disease

Cardiovascular disease (CVD) is a major cause of death worldwide. Given that CVD is a highly heritable trait, researchers have attempted to fully understand the genetic basis of CVD for a long time. The human genome comprises 3,100 Mbp per haploid genome and 6,200 Mbp in total (diploid genome). However, there is a tendency for rare genetic variations to exhibit a large effect size, whereas common genetic variations have a small effect on diseases, because of natural selection. In this sense, dividing genetic variations into two groups based on allele frequency (and effect sizes on diseases) is a good idea. We know there are several important genes (especially lipid-related genes) in which rare genetic variations are apparently associated with CVD risk, while a polygenic risk score comprising common genetic variations appears to work quite well among general populations. That information can be used not only for risk stratification but also for discoveries for novel pharmacologic targets. In this review article, we provide the important and simple idea that human genetics is important for CVD because it is a highly heritable trait, and we believe that it will lead to precision medicine in this field.

Assessment of practical applicability and clinical relevance of a commonly used LDL-C polygenic score in patients with severe hypercholesterolemia

BACKGROUND AND AIMS

Low-density lipoprotein cholesterol (LDL-C) levels vary in patients with familial hypercholesterolemia (FH) and can be explained by a single deleterious genetic variant or by the aggregate effect of multiple, common small-effect variants that can be captured in a polygenic score (PS). We set out to investigate the contribution of a previously published PS to the inter-individual LDL-C variation and coronary artery disease (CAD) risk in patients with a clinical FH phenotype.

METHODS

First, in a cohort of 628 patients referred for genetic FH testing, we evaluated the distribution of a PS for LDL-C comprising 12 genetic variants. Next, we determined its association with coronary artery disease (CAD) risk using UK Biobank data.

RESULTS

The mean PS was higher in 533 FH-variant-negative patients (FH/M-) compared with 95 FH-variant carriers (1.02 vs 0.94, p < 0.001). 39% of all patients had a PS equal to the top 20% from a population-based reference cohort and these patients were less likely to carry an FH variant (OR 0.22, 95% CI 0.10-0.48) compared with patients in the lowest 20%. In UK Biobank data, the PS explained 7.4% of variance in LDL-C levels and was associated with incident CAD. Addition of PS to a prediction model using age and sex and LDL-C did not increase the c-statistic for predicting CAD risk.

CONCLUSIONS

This 12-variant PS was higher in FH/M- patients and associated with incident CAD in UK Biobank data. However, the PS did not improve predictive accuracy when added to the readily available characteristics age, sex and LDL-C, suggesting limited discriminative value for CAD.

New prevention scenarios: polygenic risk

Although coronary heart disease is a highly preventable disease, it is still the leading cause of morbidity and mortality in developed countries. This is also due to the fact that the risk models used in clinical practice have proved ineffective in identifying people at risk: up to 30% of cases of myocardial infarction do not have traditional risk factors used in risk estimation models. Although the genetic component of myocardial infarction has been known for many years, with an inheritance rate of between 40% and 60%, it is not yet used as a risk factor in primary prevention models such as the Heart Card or the European SCORE. Recent advances in genomics and the use of clinical big data have allowed the development of genetic risk scores called Polygenic Risk Score (PRS), capable of identifying populations with average LDL-C levels, but with the same risk of heart attack of carriers of hypercholesterolaemia. The clinical usefulness of the PRS lies precisely in identifying high-risk individuals who are invisible to traditional models. The clinical applications of PRS for coronary artery disease are discussed in this report.