Societal implications of expanded universal carrier screening: a scoping review.

Carrier screening aims to identify couples at risk of conceiving children with a recessive condition. Until recently, carrier screening was primarily offered ancestry-based. Technological advances now facilitate expanded universal carrier screening (EUCS). This scoping review aimed to map EUCS's potential societal implications based on both theoretical studies and empirical evidence. To this aim, we performed a CoCites search to find relevant articles, including articles describing carrier screening for at-risk populations, based on five selected query articles. Forty articles were included. Three main potential societal implications were identified: (1) unwanted medicalization, (2) stigmatization and discrimination of carriers and people affected with the conditions screened and (3) challenges in achieving equitable access. Within these themes, potential positive implications are reduction of ethnic stigmatization in ancestry-based offers and increased equity. Potential negative implications are reinforcement of disability-based stigmatization, less possibility for developing expertise in healthcare and societal pressure to partake in screening. Empirical evidence on all these implications is however scarce. In conclusion, both positive and negative potential societal implications of implementing EUCS, primarily theoretical, were identified, even in at-risk groups where evidence is mostly lacking. Empirical research in EUCS pilots is needed to identify which societal implications are likely to occur and therefore should be overcome when implementing EUCS.

Do we measure or compute polygenic risk scores? Why language matters.

Here, we argue that polygenic risk scores (PRSs) are different epistemic objects as compared to other biomarkers such as blood pressure or sodium level. While the latter two may be subject to variation, measured inaccurately or interpreted in various ways, blood flow has pressure and sodium is available in a concentration that can be quantified and visualised. In stark contrast, PRSs are calculated, compiled or constructed through the statistical assemblage of genetic variants. How researchers frame and name PRSs has consequences for how we interpret and value their results. We distinguish between the tangible and inferential understanding of PRS and the corresponding languages of measurement and computation, respectively. The conflation of these frames obscures important questions we need to ask: what PRS seeks to represent, whether current ways of 'doing PRS' are optimal and responsible, and upon what we base the credibility of PRS-based knowledge claims.

Improving reporting standards for polygenic scores in risk prediction studies.

Polygenic risk scores (PRSs), which often aggregate results from genome-wide association studies, can bridge the gap between initial discovery efforts and clinical applications for the estimation of disease risk using genetics. However, there is notable heterogeneity in the application and reporting of these risk scores, which hinders the translation of PRSs into clinical care. Here, in a collaboration between the Clinical Genome Resource (ClinGen) Complex Disease Working Group and the Polygenic Score (PGS) Catalog, we present the Polygenic Risk Score Reporting Standards (PRS-RS), in which we update the Genetic Risk Prediction Studies (GRIPS) Statement to reflect the present state of the field. Drawing on the input of experts in epidemiology, statistics, disease-specific applications, implementation and policy, this comprehensive reporting framework defines the minimal information that is needed to interpret and evaluate PRSs, especially with respect to downstream clinical applications. Items span detailed descriptions of study populations, statistical methods for the development and validation of PRSs and considerations for the potential limitations of these scores. In addition, we emphasize the need for data availability and transparency, and we encourage researchers to deposit and share PRSs through the PGS Catalog to facilitate reproducibility and comparative benchmarking. By providing these criteria in a structured format that builds on existing standards and ontologies, the use of this framework in publishing PRSs will facilitate translation into clinical care and progress towards defining best practice.

Critical examination of current response shift methods and proposal for advancing new methods.

PURPOSE: This work is part of an international, interdisciplinary initiative to synthesize research on response shift in results of patient-reported outcome measures. The objective is to critically examine current response shift methods. We additionally propose advancing new methods that address the limitations of extant methods. METHODS: Based on literature reviews, this critical examination comprises design-based, qualitative, individualized, and preference-based methods, latent variable models, and other statistical methods. We critically appraised their definition, operationalization, the type of response shift they can detect, whether they can adjust for and explain response shift, their assumptions, and alternative explanations. Overall limitations requiring new methods were identified. RESULTS: We examined 11 methods that aim to operationalize response shift, by assessing change in the meaning of one's self-evaluation. Six of these methods distinguish between change in observed measurements (observed change) and change in the construct that was intended to be measured (target change). The methods use either (sub)group-based or individual-level analysis, or a combination. All methods have underlying assumptions to be met and alternative explanations for the inferred response shift effects. We highlighted the need to address the interpretation of the results as response shift and proposed advancing new methods handling individual variation in change over time and multiple time points. CONCLUSION: No single response shift method is optimal; each method has strengths and limitations. Additionally, extra steps need to be taken to correctly interpret the results. Advancing new methods and conducting computer simulation studies that compare methods are recommended to move response shift research forward.

Personalizing Breast Cancer Screening Based on Polygenic Risk and Family History.

BACKGROUND: We assessed the clinical utility of a first-degree breast cancer family history and polygenic risk score (PRS) to inform screening decisions among women aged 30-50 years. METHODS: Two established breast cancer models evaluated digital mammography screening strategies in the 1985 US birth cohort by risk groups defined by family history and PRS based on 313 single nucleotide polymorphisms. Strategies varied in initiation age (30, 35, 40, 45, and 50 years) and interval (annual, hybrid, biennial, triennial). The benefits (breast cancer deaths averted, life-years gained) and harms (false-positive mammograms, overdiagnoses) were compared with those seen with 3 established screening guidelines. RESULTS: Women with a breast cancer family history who initiated biennial screening at age 40 years (vs 50 years) had a 36% (model range = 29%-40%) increase in life-years gained and 20% (model range = 16%-24%) more breast cancer deaths averted, but 21% (model range = 17%-23%) more overdiagnoses and 63% (model range = 62%-64%) more false positives. Screening tailored to PRS vs biennial screening from 50 to 74 years had smaller positive effects on life-years gained (20%) and breast cancer deaths averted (11%) but also smaller increases in overdiagnoses (10%) and false positives (26%). Combined use of family history and PRS vs biennial screening from 50 to 74 years had the greatest increase in life-years gained (29%) and breast cancer deaths averted (18%). CONCLUSIONS: Our results suggest that breast cancer family history and PRS could guide screening decisions before age 50 years among women at increased risk for breast cancer but expected increases in overdiagnoses and false positives should be expected.

Cost-Effectiveness of Risk-Stratified Colorectal Cancer Screening Based on Polygenic Risk: Current Status and Future Potential.

BACKGROUND: Although uniform colonoscopy screening reduces colorectal cancer (CRC) mortality, risk-based screening may be more efficient. We investigated whether CRC screening based on polygenic risk is a cost-effective alternative to current uniform screening, and if not, under what conditions it would be. METHODS: The MISCAN-Colon model was used to simulate a hypothetical cohort of US 40-year-olds. Uniform screening was modeled as colonoscopy screening at ages 50, 60, and 70 years. For risk-stratified screening, individuals underwent polygenic testing with current and potential future discriminatory performance (area under the receiver-operating curve [AUC] of 0.60 and 0.65-0.80, respectively). Polygenic testing results were used to create risk groups, for which colonoscopy screening was optimized by varying the start age (40-60 years), end age (70-85 years), and interval (1-20 years). RESULTS: With current discriminatory performance, optimal screening ranged from once-only colonoscopy at age 60 years for the lowest-risk group to six colonoscopies at ages 40-80 years for the highest-risk group. While maintaining the same health benefits, risk-stratified screening increased costs by $59 per person. Risk-stratified screening could become cost-effective if the AUC value would increase beyond 0.65, the price per polygenic test would drop to less than $141, or risk-stratified screening would lead to a 5% increase in screening participation. CONCLUSIONS: Currently, CRC screening based on polygenic risk is unlikely to be cost-effective compared with uniform screening. This is expected to change with a greater than 0.05 increase in AUC value, a greater than 30% reduction in polygenic testing costs, or a greater than 5% increase in adherence with screening.

Novel citation-based search method for scientific literature: a validation study.

BACKGROUND: We recently developed CoCites, a citation-based search method that is designed to be more efficient than traditional keyword-based methods. The method begins with identification of one or more highly relevant publications (query articles) and consists of two searches: the co-citation search, which ranks publications on their co-citation frequency with the query articles, and the citation search, which ranks publications on frequency of all citations that cite or are cited by the query articles. METHODS: We aimed to reproduce the literature searches of published systematic reviews and meta-analyses and assess whether CoCites retrieves all eligible articles while screening fewer titles. RESULTS: A total of 250 reviews were included. CoCites retrieved a median of 75% of the articles that were included in the original reviews. The percentage of retrieved articles was higher (88%) when the query articles were cited more frequently and when they had more overlap in their citations. Applying CoCites to only the highest-cited article yielded similar results. The co-citation and citation searches combined were more efficient when the review authors had screened more than 500 titles, but not when they had screened less. CONCLUSIONS: CoCites is an efficient and accurate method for finding relevant related articles. The method uses the expert knowledge of authors to rank related articles, does not depend on keyword selection and requires no special expertise to build search queries. The method is transparent and reproducible.

Reflection on modern methods: Revisiting the area under the ROC Curve.

The area under the receiver operating characteristic (ROC) curve (AUC) is commonly used for assessing the discriminative ability of prediction models even though the measure is criticized for being clinically irrelevant and lacking an intuitive interpretation. Every tutorial explains how the coordinates of the ROC curve are obtained from the risk distributions of diseased and non-diseased individuals, but it has not become common sense that therewith the ROC plot is just another way of presenting these risk distributions. We show how the ROC curve is an alternative way to present risk distributions of diseased and non-diseased individuals and how the shape of the ROC curve informs about the overlap of the risk distributions. For example, ROC curves are rounded when the prediction model included variables with similar effect on disease risk and have an angle when, for example, one binary risk factor has a stronger effect; and ROC curves are stepped rather than smooth when the sample size or incidence is low, when the prediction model is based on a relatively small set of categorical predictors. This alternative perspective on the ROC plot invalidates most purported limitations of the AUC and attributes others to the underlying risk distributions. AUC is a measure of the discriminative ability of prediction models. The assessment of prediction models should be supplemented with other metrics to assess their clinical utility.

Validity of polygenic risk scores: are we measuring what we think we are?

Polygenic risk scores (PRSs) have become the standard for quantifying genetic liability in the prediction of disease risks. PRSs are generally constructed as weighted sum scores of risk alleles using effect sizes from genome-wide association studies as their weights. The construction of PRSs is being improved with more appropriate selection of independent single-nucleotide polymorphisms (SNPs) and optimized estimation of their weights but is rarely reflected upon from a theoretical perspective, focusing on the validity of the risk score. Borrowing from psychometrics, this paper discusses the validity of PRSs and introduces the three main types of validity that are considered in the evaluation of tests and measurements: construct, content, and criterion validity. This introduction is followed by a discussion of three topics that challenge the validity of PRS, namely, their claimed independence of clinical risk factors, the consequences of relaxing SNP inclusion thresholds and the selection of SNP weights. This discussion of the validity of PRS reminds us that we need to keep questioning if weighted sums of risk alleles are measuring what we think they are in the various scenarios in which PRSs are used and that we need to keep exploring alternative modeling strategies that might better reflect the underlying biological pathways.

Proprietary Algorithms for Polygenic Risk: Protecting Scientific Innovation or Hiding the Lack of It?

Direct-to-consumer genetic testing companies aim to predict the risks of complex diseases using proprietary algorithms. Companies keep algorithms as trade secrets for competitive advantage, but a market that thrives on the premise that customers can make their own decisions about genetic testing should respect customer autonomy and informed decision making and maximize opportunities for transparency. The algorithm itself is only one piece of the information that is deemed essential for understanding how prediction algorithms are developed and evaluated. Companies should be encouraged to disclose everything else, including the expected risk distribution of the algorithm when applied in the population, using a benchmark DNA dataset. A standardized presentation of information and risk distributions allows customers to compare test offers and scientists to verify whether the undisclosed algorithms could be valid. A new model of oversight in which stakeholders collaboratively keep a check on the commercial market is needed.

Evaluation of polygenic risk models using multiple performance measures: a critical assessment of discordant results.

PURPOSE: The area under the receiver operating characteristic curve (AUC) is commonly used for evaluating the improvement of polygenic risk models and increasingly assessed together with the net reclassification improvement (NRI) and integrated discrimination improvement (IDI). We evaluated how researchers described and interpreted AUC, NRI, and IDI when simultaneously assessed. METHODS: We reviewed how researchers described definitions of AUC, NRI, and IDI and how they computed each metric. Next, we reviewed how the increment in AUC, NRI, and IDI were interpreted, and how the overall conclusion about the improvement of the risk model was reached. RESULTS: AUC, NRI, and IDI were correctly defined in 63, 70, and 0% of the articles. All statistically significant values and almost half of the nonsignificant were interpreted as indicative of improvement, irrespective of the values of the metrics. Also, small, nonsignificant changes in the AUC were interpreted as indication of improvement when NRI and IDI were statistically significant. CONCLUSION: Researchers have insufficient knowledge about how to interpret the various metrics for the assessment of the predictive performance of polygenic risk models and rely on the statistical significance for their interpretation. A better understanding is needed to achieve more meaningful interpretation of polygenic prediction studies.

Finding Wealth in Waste: Irreplicability Re-Examined.

Irreplicability is framed as crisis, blamed on sloppy science motivated by perverse stimuli in research. Structural changes to the organization of science, targeting sloppy science (e.g., open data, pre-registration), are proposed to prevent irreplicability. While there is an unquestionable link between sloppy science and failures to replicate/reproduce scientific studies, they are currently conflated. This position can be understood as a result of the erosion of the role of theory in science. The history, sociology, and philosophy of science reveal alternative explanations for irreplicability to show it is part of proper, informative and valuable science. Irreplicability need not equate research waste. Sloppy science is the problem, also when results do replicate. Hence, the solution should focus on opposing sloppy research.

Correction: A collaborative translational research framework for evaluating and implementing the appropriate use of human genome sequencing to improve health.

[This corrects the article DOI: 10.1371/journal.pmed.1002631.].

A collaborative translational research framework for evaluating and implementing the appropriate use of human genome sequencing to improve health.

In a Policy Forum, Muin Khoury and colleagues discuss research on the clinical application of genome sequencing data.

Cost Effectiveness of Age-Specific Screening Intervals for People With Family Histories of Colorectal Cancer.

BACKGROUND & AIMS: Relative risk of colorectal cancer (CRC) decreases with age among individuals with a family history of CRC. However, no screening recommendations specify less frequent screening with increasing age. We aimed to determine whether such a refinement would be cost effective. METHODS: We determined the relative risk for CRC for individuals based on age and number of affected first-degree relatives (FDRs) using data from publications. For each number of affected FDRs, we used the Microsimulation Screening Analysis model to estimate costs and effects of colonoscopy screening strategies with different age ranges and intervals. Screening was then optimized sequentially, starting with the youngest age group, and allowing the interval of screening to change at certain ages. Strategies with an incremental cost effectiveness ratio below $100,000 per quality-adjusted life year were considered cost effective. RESULTS: For people with 1 affected FDR (92% of those with a family history), screening every 3 years beginning at an age of 40 years is most cost effective. If no adenomas are found, the screening interval can gradually be extended to 5 and 7 years, at ages 45 and 55 years, respectively. From a cost-effectiveness perspective, individuals with more affected FDRs should start screening earlier and at shorter intervals. However, frequency can be reduced if no abnormalities are found. CONCLUSIONS: Using a microsimulation model, we found that for individuals with a family history of CRC, it is cost effective to gradually increase the screening interval if several subsequent screening colonoscopies have negative results and no new cases of CRC are found in family members.