Genetic testing and implications for personalized medicine: changes in public and healthcare professional perspectives

Distinguishing different psychiatric disorders using DDx-PRS

Despite great progress on methods for case-control polygenic prediction (e.g. schizophrenia vs. control), there remains an unmet need for a method that genetically distinguishes clinically related disorders (e.g. schizophrenia (SCZ) vs. bipolar disorder (BIP) vs. depression (MDD) vs. control); such a method could have important clinical value, especially at disorder onset when differential diagnosis can be challenging. Here, we introduce a method, Differential Diagnosis-Polygenic Risk Score (DDx-PRS), that jointly estimates posterior probabilities of each possible diagnostic category (e.g. SCZ=50%, BIP=25%, MDD=15%, control=10%) by modeling variance/covariance structure across disorders, leveraging case-control polygenic risk scores (PRS) for each disorder (computed using existing methods) and prior clinical probabilities for each diagnostic category. DDx-PRS uses only summary-level training data and does not use tuning data, facilitating implementation in clinical settings. In simulations, DDx-PRS was well-calibrated (whereas a simpler approach that analyzes each disorder marginally was poorly calibrated), and effective in distinguishing each diagnostic category vs. the rest. We then applied DDx-PRS to Psychiatric Genomics Consortium SCZ/BIP/MDD/control data, including summary-level training data from 3 case-control GWAS ( N =41,917-173,140 cases; total N =1,048,683) and held-out test data from different cohorts with equal numbers of each diagnostic category (total N =11,460). DDx-PRS was well-calibrated and well-powered relative to these training sample sizes, attaining AUCs of 0.66 for SCZ vs. rest, 0.64 for BIP vs. rest, 0.59 for MDD vs. rest, and 0.68 for control vs. rest. DDx-PRS produced comparable results to methods that leverage tuning data, confirming that DDx-PRS is an effective method. True diagnosis probabilities in top deciles of predicted diagnosis probabilities were considerably larger than prior baseline probabilities, particularly in projections to larger training sample sizes, implying considerable potential for clinical utility under certain circumstances. In conclusion, DDx-PRS is an effective method for distinguishing clinically related disorders.

Screening for Familial Hypercholesterolemia in Children: What Can We Learn From Adult Screening Programs?

Familial hypercholesterolemia (FH), an autosomal dominant atherosclerotic disease, is a common monogenic subtype of cardiovascular disease. Patients with FH suffer an increased risk of early onset heart disease. Early identification of abnormally elevated cholesterol signpost clinicians to interventions that will significantly decrease risk of related morbidity and mortality. Cascade genetic testing can subsequently identify at-risk relatives. Accordingly, a number of screening approaches have been implemented for FH in countries including the UK and the Netherlands. However, incomplete identification of cases remains a challenge. Moreover, the potential for early intervention is now raising questions about the value of implementing universal cholesterol screening approaches that focus on children. In this report, we briefly discuss the potential benefit of such screening. Additionally, we submit that ever increasing genome technological capability will force a discussion of including genetic tests in these screening programs. We discuss the opportunities and challenges presented by such an approach. We close with recommendations that the success of such screening endeavors will rely on a better integrated practice model in public health genomics that bridges stakeholders including practitioners in primary care, clinical genetics and public health.