Strategic vision for improving human health at The Forefront of Genomics

Ultra-deep sequencing of Hadza hunter-gatherers recovers vanishing gut microbes

The gut microbiome modulates immune and metabolic health. Human microbiome data are biased toward industrialized populations, limiting our understanding of non-industrialized microbiomes. Here, we performed ultra-deep metagenomic sequencing on 351 fecal samples from the Hadza hunter-gatherers of Tanzania and comparative populations in Nepal and California. We recovered 91,662 genomes of bacteria, archaea, bacteriophages, and eukaryotes, 44% of which are absent from existing unified datasets. We identified 124 gut-resident species vanishing in industrialized populations and highlighted distinct aspects of the Hadza gut microbiome related to in situ replication rates, signatures of selection, and strain sharing. Industrialized gut microbes were found to be enriched in genes associated with oxidative stress, possibly a result of microbiome adaptation to inflammatory processes. This unparalleled view of the Hadza gut microbiome provides a valuable resource, expands our understanding of microbes capable of colonizing the human gut, and clarifies the extensive perturbation induced by the industrialized lifestyle.

XPA tumor variants lead to defects in NER that sensitize cells to cisplatin

Nucleotide excision repair (NER) neutralizes treatment with platinum (Pt)-based chemotherapy by removing Pt lesions from DNA. Previous study has identified that missense mutation or loss of either of the NER genes Excision Repair Cross Complementation Group 1 and 2 ( ERCC1 and ERCC2 ) leads to improved patient outcomes after treatment with Pt-based chemotherapies. Although most NER gene alterations found in patient tumors are missense mutations, the impact of such mutations in the remaining nearly 20 NER genes is unknown. Towards this goal, we previously developed a machine learning strategy to predict genetic variants in an essential NER scaffold protein, Xeroderma Pigmentosum Complementation Group A (XPA), that disrupt repair activity on a UV-damaged substrate. In this study, we report in-depth analyses of a subset of the predicted NER-deficient XPA variants, including in vitro analyses of purified recombinant protein and cell-based assays to test Pt agent sensitivity in cells and determine mechanisms of NER dysfunction. The most NER deficient variant Y148D had reduced protein stability, weaker DNA binding, disrupted recruitment to damage, and degradation resulting from tumor missense mutation. Our findings demonstrate that tumor mutations in XPA impact cell survival after cisplatin treatment and provide valuable mechanistic insights to further improve variant effect prediction efforts. More broadly, these findings suggest XPA tumor variants should be considered when predicting patient response to Pt-based chemotherapy.

Significance

A destabilized, readily degraded tumor variant identified in the NER scaffold protein XPA sensitizes cells to cisplatin, suggesting that XPA variants can be used to predict response to chemotherapy.

Clinical variants in Caenorhabditis elegans expressing human STXBP1 reveal a novel class of pathogenic variants and classify variants of uncertain significance

Purpose

Modeling disease variants in animals is useful for drug discovery, understanding disease pathology, and classifying variants of uncertain significance (VUS) as pathogenic or benign.

Methods

Using Clustered Regularly Interspaced Short Palindromic Repeats, we performed a Whole-gene Humanized Animal Model procedure to replace the coding sequence of the animal model's unc-18 ortholog with the coding sequence for the human STXBP1 gene. Next, we used Clustered Regularly Interspaced Short Palindromic Repeats to introduce precise point variants in the Whole-gene Humanized Animal Model-humanized STXBP1 locus from 3 clinical categories (benign, pathogenic, and VUS). Twenty-six phenotypic features extracted from video recordings were used to train machine learning classifiers on 25 pathogenic and 32 benign variants.

Results

Using multiple models, we were able to obtain a diagnostic sensitivity near 0.9. Twenty-three VUS were also interrogated and 8 of 23 (34.8%) were observed to be functionally abnormal. Interestingly, unsupervised clustering identified 2 distinct subsets of known pathogenic variants with distinct phenotypic features; both p.Tyr75Cys and p.Arg406Cys cluster away from other variants and show an increase in swim speed compared with hSTXBP1 worms. This leads to the hypothesis that the mechanism of disease for these 2 variants may differ from most STXBP1-mutated patients and may account for some of the clinical heterogeneity observed in the patient population.

Conclusion

We have demonstrated that automated analysis of a small animal system is an effective, scalable, and fast way to understand functional consequences of variants in STXBP1 and identify variant-specific intensities of aberrant activity suggesting a genotype-to-phenotype correlation is likely to occur in human clinical variations of STXBP1.

Evaluation of software impact designed for biomedical research: Are we measuring what's meaningful?

Software is vital for the advancement of biology and medicine. Through analysis of usage and impact metrics of software, developers can help determine user and community engagement. These metrics can be used to justify additional funding, encourage additional use, and identify unanticipated use cases. Such analyses can help define improvement areas and assist with managing project resources. However, there are challenges associated with assessing usage and impact, many of which vary widely depending on the type of software being evaluated. These challenges involve issues of distorted, exaggerated, understated, or misleading metrics, as well as ethical and security concerns. More attention to the nuances, challenges, and considerations involved in capturing impact across the diverse spectrum of biological software is needed. Furthermore, some tools may be especially beneficial to a small audience, yet may not have comparatively compelling metrics of high usage. Although some principles are generally applicable, there is not a single perfect metric or approach to effectively evaluate a software tool's impact, as this depends on aspects unique to each tool, how it is used, and how one wishes to evaluate engagement. We propose more broadly applicable guidelines (such as infrastructure that supports the usage of software and the collection of metrics about usage), as well as strategies for various types of software and resources. We also highlight outstanding issues in the field regarding how communities measure or evaluate software impact. To gain a deeper understanding of the issues hindering software evaluations, as well as to determine what appears to be helpful, we performed a survey of participants involved with scientific software projects for the Informatics Technology for Cancer Research (ITCR) program funded by the National Cancer Institute (NCI). We also investigated software among this scientific community and others to assess how often infrastructure that supports such evaluations is implemented and how this impacts rates of papers describing usage of the software. We find that although developers recognize the utility of analyzing data related to the impact or usage of their software, they struggle to find the time or funding to support such analyses. We also find that infrastructure such as social media presence, more in-depth documentation, the presence of software health metrics, and clear information on how to contact developers seem to be associated with increased usage rates. Our findings can help scientific software developers make the most out of the evaluations of their software so that they can more fully benefit from such assessments.

Estimating clinical risk in gene regions from population sequencing cohort data

While pathogenic variants can significantly increase disease risk, it is still challenging to estimate the clinical impact of rare missense variants more generally. Even in genes such as BRCA2 or PALB2, large cohort studies find no significant association between breast cancer and rare missense variants collectively. Here, we introduce REGatta, a method to estimate clinical risk from variants in smaller segments of individual genes. We first define these regions by using the density of pathogenic diagnostic reports and then calculate the relative risk in each region by using over 200,000 exome sequences in the UK Biobank. We apply this method in 13 genes with established roles across several monogenic disorders. In genes with no significant difference at the gene level, this approach significantly separates disease risk for individuals with rare missense variants at higher or lower risk (BRCA2 regional model OR = 1.46 [1.12, 1.79], p = 0.0036 vs. BRCA2 gene model OR = 0.96 [0.85, 1.07] p = 0.4171). We find high concordance between these regional risk estimates and high-throughput functional assays of variant impact. We compare our method with existing methods and the use of protein domains (Pfam) as regions and find REGatta better identifies individuals at elevated or reduced risk. These regions provide useful priors and are potentially useful for improving risk assessment for genes associated with monogenic diseases.

Including multiracial individuals is crucial for race, ethnicity and ancestry frameworks in genetics and genomics

Current ontologies of race, ethnicity and genetic ancestry rely on categorization, but have limitations — as exemplified by multiracial individuals. We argue that including these individuals will foster inclusion by better capturing complex identities, with equity benefits for the full human population.

Translating Genetic Discovery into a Mechanistic Understanding of Pediatric Movement Disorders: Lessons from Genetic Dystonias and Related Disorders

The era of next-generation sequencing has increased the pace of gene discovery in the field of pediatric movement disorders. Following the identification of novel disease-causing genes, several studies have aimed to link the molecular and clinical aspects of these disorders. This perspective presents the developing stories of several childhood-onset movement disorders, including paroxysmal kinesigenic dyskinesia, myoclonus-dystonia syndrome, and other monogenic dystonias. These stories illustrate how gene discovery helps focus the research efforts of scientists trying to understand the mechanisms of disease. The genetic diagnosis of these clinical syndromes also helps clarify the associated phenotypic spectra and aids the search for additional disease-causing genes. Collectively, the findings of previous studies have led to increased recognition of the role of the cerebellum in the physiology and pathophysiology of motor control-a common theme in many pediatric movement disorders. To fully exploit the genetic information garnered in the clinical and research arenas, it is crucial that corresponding multi-omics analyses and functional studies also be performed at scale. Hopefully, these integrated efforts will provide us with a more comprehensive understanding of the genetic and neurobiological bases of movement disorders in childhood.

Coronary Artery Calcium Score and Polygenic Risk Score for the Prediction of Coronary Heart Disease Events

Importance

Coronary artery calcium score and polygenic risk score have each separately been proposed as novel markers to identify risk of coronary heart disease (CHD), but no prior studies have directly compared these markers in the same cohorts.

Objective

To evaluate change in CHD risk prediction when a coronary artery calcium score, a polygenic risk score, or both are added to a traditional risk factor-based model.

Design, Setting, and Participants

Two observational population-based studies involving individuals aged 45 years through 79 years of European ancestry and free of clinical CHD at baseline: the Multi-Ethnic Study of Atherosclerosis (MESA) study involved 1991 participants at 6 US centers and the Rotterdam Study (RS) involved 1217 in Rotterdam, the Netherlands.

Exposure

Traditional risk factors were used to calculate CHD risk (eg, pooled cohort equations [PCEs]), computed tomography for the coronary artery calcium score, and genotyped samples for a validated polygenic risk score.

Main Outcomes and Measures

Model discrimination, calibration, and net reclassification improvement (at the recommended risk threshold of 7.5%) for prediction of incident CHD events were assessed.

Results

The median age was 61 years in MESA and 67 years in RS. Both log (coronary artery calcium+1) and polygenic risk score were significantly associated with 10-year risk of incident CHD (hazards ratio per SD, 2.60; 95% CI, 2.08-3.26 and 1.43; 95% CI, 1.20-1.71, respectively), in MESA. The C statistic for the coronary artery calcium score was 0.76 (95% CI, 0.71-0.79) and for the polygenic risk score, 0.69 (95% CI, 0.63-0.71). The change in the C statistic when each was added to the PCEs was 0.09 (95% CI, 0.06-0.13) for the coronary artery calcium score, 0.02 (95% CI, 0.00-0.04) for the polygenic risk score, and 0.10 (95% CI, 0.07-0.14) for both. Overall categorical net reclassification improvement was significant when the coronary artery calcium score (0.19; 95% CI, 0.06-0.28) but was not significant when the polygenic risk score (0.04; 95% CI, -0.05 to 0.10) was added to the PCEs. Calibration of the PCEs and models with coronary artery calcium and/or polygenic risk scores was adequate (all χ2<20). Subgroup analysis stratified by the median age demonstrated similar findings. Similar findings were observed for 10-year risk in RS and in longer-term follow-up in MESA (median, 16.0 years).

Conclusions and Relevance

In 2 cohorts of middle-aged to older adults from the US and the Netherlands, the coronary artery calcium score had better discrimination than the polygenic risk score for risk prediction of CHD. In addition, the coronary artery calcium score but not the polygenic risk score significantly improved risk discrimination and risk reclassification for CHD when added to traditional risk factors.

Population monitoring of trisomy 21: problems and approaches

Trisomy 21 (Down syndrome) is the most common autosomal aneuploidy among newborns. About 90% result from meiotic nondisjunction during oogenesis, which occurs around conception, when also the most profound epigenetic modifications take place. Thus, maternal meiosis is an error prone process with an extreme sensitivity to endogenous factors, as exemplified by maternal age. This contrasts with the missing acceptance of causal exogenous factors. The proof of an environmental agent is a great challenge, both with respect to ascertainment bias, determination of time and dosage of exposure, as well as registration of the relevant individual health data affecting the birth prevalence. Based on a few exemplary epidemiological studies the feasibility of trisomy 21 monitoring is illustrated. In the nearer future the methodical premises will be clearly improved, both due to the establishment of electronic health registers and to the introduction of non-invasive prenatal tests. Down syndrome is a sentinel phenotype, presumably also with regard to other congenital anomalies. Thus, monitoring of trisomy 21 offers new chances for risk avoidance and preventive measures, but also for basic research concerning identification of relevant genomic variants involved in chromosomal nondisjunction.

The functional impact of 1,570 individual amino acid substitutions in human OTC

Deleterious mutations in the X-linked gene encoding ornithine transcarbamylase (OTC) cause the most common urea cycle disorder, OTC deficiency. This rare but highly actionable disease can present with severe neonatal onset in males or with later onset in either sex. Individuals with neonatal onset appear normal at birth but rapidly develop hyperammonemia, which can progress to cerebral edema, coma, and death, outcomes ameliorated by rapid diagnosis and treatment. Here, we develop a high-throughput functional assay for human OTC and individually measure the impact of 1,570 variants, 84% of all SNV-accessible missense mutations. Comparison to existing clinical significance calls, demonstrated that our assay distinguishes known benign from pathogenic variants and variants with neonatal onset from late-onset disease presentation. This functional stratification allowed us to identify score ranges corresponding to clinically relevant levels of impairment of OTC activity. Examining the results of our assay in the context of protein structure further allowed us to identify a 13 amino acid domain, the SMG loop, whose function appears to be required in human cells but not in yeast. Finally, inclusion of our data as PS3 evidence under the current ACMG guidelines, in a pilot reclassification of 34 variants with complete loss of activity, would change the classification of 22 from variants of unknown significance to clinically actionable likely pathogenic variants. These results illustrate how large-scale functional assays are especially powerful when applied to rare genetic diseases.

Demographic Characteristics Associated With Perceptions of Personal Utility in Genetic and Genomic Testing: A Systematic Review

Importance

The expansion of genetic and genomic testing in health care has led to recognition that these tests provide personal as well as clinical utility to patients and families. However, available systematic reviews on this topic have not reported the demographic backgrounds of participants in studies of personal utility, leaving generalizability unclear.

Objective

To determine the demographic characteristics of participants in studies examining the personal utility of genetic and genomic testing in health care.

Evidence Review

For this systematic review, we utilized and updated the results of a highly cited 2017 systematic review on the personal utility of genetics and genomics, which identified relevant articles published between January 1, 2003, and August 4, 2016. We also used the original methods to update this bibliography with literature published subsequently up to January 1, 2022. Studies were screened for eligibility by 2 independent reviewers. Eligible studies reported empirical data on the perspectives of patients, family members, and/or the general public in the US on the personal utility of any type of health-related genetic or genomic test. We utilized a standardized codebook to extract study and participant characteristics. We summarized demographic characteristics descriptively across all studies and by subgroup based on study and participant characteristics.

Findings

We included 52 studies with 13 251 eligible participants. Sex or gender was the most frequently reported demographic characteristic (48 studies [92.3%]), followed by race and ethnicity (40 studies [76.9%]), education (38 studies [73.1%]), and income (26 studies [50.0%]). Across studies, participants disproportionately were women or female (mean [SD], 70.8% [20.5%]), were White (mean [SD], 76.1% [22.0%]), had a college degree or higher (mean [SD], 64.5% [19.9%]), and reported income above the US median (mean [SD], 67.4% [19.2%]). Examination of subgroups of results by study and participant characteristics evidenced only small shifts in demographic characteristics.

Conclusions and Relevance

This systematic review examined the demographic characteristics of individual participants in studies of the personal utility of health-related genetic and genomic testing in the US. The results suggest that participants in these studies were disproportionately White, college-educated women with above-average income. Understanding the perspectives of more diverse individuals regarding the personal utility of genetic and genomic testing may inform barriers to research recruitment and uptake of clinical testing in currently underrepresented populations.

High-throughput functional mapping of variants in an arrhythmia gene, KCNE1, reveals novel biology

Background

KCNE1 encodes a 129-residue cardiac potassium channel (IKs) subunit. KCNE1 variants are associated with long QT syndrome and atrial fibrillation. However, most variants have insufficient evidence of clinical consequences and thus limited clinical utility.

Results

Here, we demonstrate the power of variant effect mapping, which couples saturation mutagenesis with high-throughput sequencing, to ascertain the function of thousands of protein coding KCNE1 variants. We comprehensively assayed KCNE1 variant cell surface expression (2,554/2,709 possible single amino acid variants) and function (2,539 variants). We identified 470 loss-of-surface expression and 588 loss-of-function variants. Out of the 588 loss-of-function variants, only 155 had low cell surface expression. The latter half of the protein is dispensable for protein trafficking but essential for channel function. 22 of the 30 KCNE1 residues (73%) highly intolerant of variation were in predicted close contact with binding partners KCNQ1 or calmodulin. Our data were highly concordant with gold standard electrophysiological data (ρ = -0.65), population and patient cohorts (32/38 concordant variants), and computational metrics (ρ = -0.55). Our data provide moderate-strength evidence for the ACMG/AMP functional criteria for benign and pathogenic variants.

Conclusions

Comprehensive variant effect maps of KCNE1 can both provide insight into IKs channel biology and help reclassify variants of uncertain significance.

Implementation and evaluation of personal genetic testing as part of genomics analysis courses in German universities

PURPOSE

Due to the increasing application of genome analysis and interpretation in medical disciplines, professionals require adequate education. Here, we present the implementation of personal genotyping as an educational tool in two genomics courses targeting Digital Health students at the Hasso Plattner Institute (HPI) and medical students at the Technical University of Munich (TUM).

METHODS

We compared and evaluated the courses and the students' perceptions on the course setup using questionnaires.

RESULTS

During the course, students changed their attitudes towards genotyping (HPI: 79% [15 of 19], TUM: 47% [25 of 53]). Predominantly, students became more critical of personal genotyping (HPI: 73% [11 of 15], TUM: 72% [18 of 25]) and most students stated that genetic analyses should not be allowed without genetic counseling (HPI: 79% [15 of 19], TUM: 70% [37 of 53]). Students found the personal genotyping component useful (HPI: 89% [17 of 19], TUM: 92% [49 of 53]) and recommended its inclusion in future courses (HPI: 95% [18 of 19], TUM: 98% [52 of 53]).

CONCLUSION

Students perceived the personal genotyping component as valuable in the described genomics courses. The implementation described here can serve as an example for future courses in Europe.

Pharmacogenomic Clinical Decision Support: A Scoping Review

Clinical decision support (CDS) is often cited as an essential part of pharmacogenomics (PGx) implementations. A multitude of strategies are available; however, it is unclear which strategies are effective and which metrics are used to quantify clinical utility. The objective of this scoping review was to aggregate previous studies into a cohesive depiction of the current state of PGx CDS implementations and identify areas for future research on PGx CDS. Articles were included if they (i) described electronic CDS tools for PGx and (ii) reported metrics related to PGx CDS. Twenty of 3,449 articles were included and provided data on PGx CDS metrics from 15 institutions, with 93% of programs located at academic medical centers. The most common tools in CDS implementations were interruptive post-test alerts. Metrics for clinical response and alert response ranged from 12-73% and 21-98%, respectively. Few data were found on changes in metrics over time and measures that drove the evolution of CDS systems. Relatively few data were available regarding support of optimal approaches for PGx CDS. Post-test alerts were the most widely studied approach, and their effectiveness varied greatly. Further research on the usability, effectiveness, and optimization of CDS tools is needed.

Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis

Diagnostics require precision and predictive ability to be clinically useful. Integration of multi-omic with clinical data is crucial to our understanding of disease pathogenesis and diagnosis. However, interpretation of overwhelming amounts of information at the individual level requires sophisticated computational tools for extraction of clinically meaningful outputs. Moreover, evolution of technical and analytical methods often outpaces standardisation strategies. RNA is the most dynamic component of all -omics technologies carrying an abundance of regulatory information that is least harnessed for use in clinical diagnostics. Gene expression-based tests capture genetic and non-genetic heterogeneity and have been implemented in certain diseases. For example patients with early breast cancer are spared toxic unnecessary treatments with scores based on the expression of a set of genes (e.g., Oncotype DX). The ability of transcriptomics to portray the transcriptional status at a moment in time has also been used in diagnosis of dynamic diseases such as sepsis. Gene expression profiles identify endotypes in sepsis patients with prognostic value and a potential to discriminate between viral and bacterial infection. The application of transcriptomics for patient stratification in clinical environments and clinical trials thus holds promise. In this review, we discuss the current clinical application in the fields of cancer and infection. We use these paradigms to highlight the impediments in identifying useful diagnostic and prognostic biomarkers and propose approaches to overcome them and aid efforts towards clinical implementation.

Hadza Prevotella Require Diet-derived Microbiota Accessible Carbohydrates to Persist in Mice

Industrialization has transformed the gut microbiota, reducing the prevalence of Prevotella relative to Bacteroides. Here, we isolate Bacteroides and Prevotella strains from the microbiota of Hadza hunter-gatherers of Tanzania, a population with high levels of Prevotella. We demonstrate that plant-derived microbiota-accessible carbohydrates (MACs) are required for persistence of Prevotella copri but not Bacteroides thetaiotaomicron in vivo. Differences in carbohydrate metabolism gene content, expression, and in vitro growth reveal that Hadza Prevotella strains specialize in degrading plant carbohydrates, while Hadza Bacteroides isolates use both plant and host-derived carbohydrates, a difference mirrored in Bacteroides from non-Hadza populations. When competing directly, P. copri requires plant-derived MACs to maintain colonization in the presence of B. thetaiotaomicron, as a no MAC diet eliminates P. copri colonization. Prevotella's reliance on plant-derived MACs and Bacteroides' ability to use host mucus carbohydrates could explain the reduced prevalence of Prevotella in populations consuming a low-MAC, industrialized diet.

The Right to Ask, the Need to Answer-When Patients Meet Research: How to Cope with Time

Reaching a diagnosis and its communication are two of the most meaningful events in the physician-patient relationship. When facing a disease, most of the patients' expectations rely on the hope that their clinicians would be able to understand the cause of their illness and eventually end it. Rare diseases are a peculiar subset of conditions in which the search for a diagnosis might reveal a long and painful journey scattered by doubts and requiring, in most cases, a long waiting time. For many individuals affected by a rare disease, turning to research might represent their last chance to obtain an answer to their questions. Time is the worst enemy, threatening to disrupt the fragile balance among affected individuals, their referring physicians, and researchers. It is consuming at all levels, draining economic, emotional, and social resources, and triggering unpredictable reactions in each stakeholder group. Managing waiting time is one of the most burdensome tasks for all the parties playing a role in the search for a diagnosis: the patients and their referring physicians urge to obtain a diagnosis in order to know the condition they are dealing with and establish proper management, respectively. On the other hand, researchers need to be objective and scientifically act to give a rigorous answer to their demands. While moving towards the same goal, patients, clinicians, and researchers might have different expectations and perceive the same waiting time as differently hard or tolerable. The lack of information on mutual needs and the absence of effective communication among the parties are the most common mechanisms of the failure of the therapeutic alliance that risk compromising the common goal of a proper diagnosis. In the landscape of modern medicine that goes faster and claims high standards of cure, rare diseases represent an exception where physicians and researchers should learn to cope with time in order to care for patients.

The foundation and architecture of precision medicine in neurology and psychiatry

Neurological and psychiatric diseases have high degrees of genetic and pathophysiological heterogeneity, irrespective of clinical manifestations. Traditional medical paradigms have focused on late-stage syndromic aspects of these diseases, with little consideration of the underlying biology. Advances in disease modeling and methodological design have paved the way for the development of precision medicine (PM), an established concept in oncology with growing attention from other medical specialties. We propose a PM architecture for central nervous system diseases built on four converging pillars: multimodal biomarkers, systems medicine, digital health technologies, and data science. We discuss Alzheimer's disease (AD), an area of significant unmet medical need, as a case-in-point for the proposed framework. AD can be seen as one of the most advanced PM-oriented disease models and as a compelling catalyzer towards PM-oriented neuroscience drug development and advanced healthcare practice.

Prognostic evaluation of polygenic risk score underlying pan-cancer analysis: evidence from two large-scale cohorts

BACKGROUND

Polygenic risk score (PRS) has been demonstrated to be effective in identifying individuals at high risk of developing cancer, but its prognostic value remains unclear.

METHODS

We constructed site-specific PRSs by aggregating the risk effect of independent variants derived from previous genome-wide association studies (GWASs) across 17 cancer types. The Cox proportional hazards model was used to evaluate the association of each PRS with cancer survival, leveraging data from two prospective European cohorts, namely the UK Biobank involving 19,628 incident cases and The Cancer Genome Atlas involving 7079 prevalent cases. The combined PRS (CPRS), determined by merging site-specific PRSs, was further used to assess the prognostic effect of PRS on overall cancer in a sex-specific manner.

FINDINGS

We discovered that the cancer risk-related PRS was associated with neither overall survival (OS) nor cancer-specific survival (CSS) of each site-specific cancer with an underlying false discovery rate (FDR) P > 0.05, as evidenced by consistent findings from the two cohorts. Furthermore, the fixed-effect meta-analysis of the two cohorts provided no evidence to support for an association between CPRS and overall cancer survival in both males [OS: hazard ratio (HR)meta = 1.00, Pmeta = 0.760; CSS: HRmeta = 1.01, Pmeta = 0.447] and females (OS: HRmeta = 0.97, Pmeta = 0.067; CSS: HRmeta = 0.96, Pmeta = 0.054). Similar results were observed across multiple sensitivity analyses.

INTERPRETATION

Our findings indicate that the risk-specific PRS might not be a clinically useful tool in cancer prognosis prediction and further studies focusing on the development of polygenic prognostic score are warranted.

FUNDING

This project was funded by the National Natural Science Foundation of China (82173601 and 82073631), and Priority Academic Program Development of Jiangsu Higher Education Institutions (Public Health and Preventive Medicine).

Perspectives on the future of dysmorphology

The field of clinical genetics and genomics continues to evolve. In the past few decades, milestones like the initial sequencing of the human genome, dramatic changes in sequencing technologies, and the introduction of artificial intelligence, have upended the field and offered fascinating new insights. Though difficult to predict the precise paths the field will follow, rapid change may continue to be inevitable. Within genetics, the practice of dysmorphology, as defined by pioneering geneticist David W. Smith in the 1960s as "the study of, or general subject of abnormal development of tissue form" has also been affected by technological advances as well as more general trends in biomedicine. To address possibilities, potential, and perils regarding the future of dysmorphology, a group of clinical geneticists, representing different career stages, areas of focus, and geographic regions, have contributed to this piece by providing insights about how the practice of dysmorphology will develop over the next several decades.

Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing

Most transcriptome-wide association studies (TWASs) so far focus on European ancestry and lack diversity. To overcome this limitation, we aggregated genome-wide association study (GWAS) summary statistics, whole-genome sequences and expression quantitative trait locus (eQTL) data from diverse ancestries. We developed a new approach, TESLA (multi-ancestry integrative study using an optimal linear combination of association statistics), to integrate an eQTL dataset with a multi-ancestry GWAS. By exploiting shared phenotypic effects between ancestries and accommodating potential effect heterogeneities, TESLA improves power over other TWAS methods. When applied to tobacco use phenotypes, TESLA identified 273 new genes, up to 55% more compared with alternative TWAS methods. These hits and subsequent fine mapping using TESLA point to target genes with biological relevance. In silico drug-repurposing analyses highlight several drugs with known efficacy, including dextromethorphan and galantamine, and new drugs such as muscle relaxants that may be repurposed for treating nicotine addiction.

Public Health Genetics: Surveying Preparedness for the Next Generation of Public Health Professionals

Since the Human Genome Project's completion in 2003, the need for increased population genetic literacy has grown exponentially. To address this need, public health professionals must be educated appropriately to serve the public best. This study examines the current state of public health genetics education within existing master of public health (MPH) programs. A total of 171 MPH Council on Education for Public Health Accreditation (CEPH)-accredited programs across the nation were identified via a preliminary internet search. The American Public Health Association (APHA) Genomics Forum Policy Committee created 14 survey questions to assess the current status of incorporating genetics/genomics education within MPH programs. Using the Qualtrics survey system through the University of Pittsburgh, a link to the anonymous survey was sent to each director's email address obtained from their program's website. There were 41 survey responses, with 37 finished to completion, for a response rate of 21.6% (37/171). A total of 75.7% (28/37) of respondents reported having courses containing genetics/genomics information in their programs' coursework. Only 12.6% reported such coursework to be required for program completion. Commonly listed barriers to incorporating genetics/genomics include limited faculty knowledge and lack of space in existing courses and programs. Survey results revealed the incongruous and limited incorporation of genetics/genomics within the context of graduate-level public health education. While most recorded programs report offering public health genetics coursework, the extent and requirement of such instruction are not considered necessary for program completion, thereby potentially limiting the genetic literacy of the current pool of public health professionals.

Understanding cancer predisposition in Singapore: What's next

Knowledge of an underlying genetic predisposition to cancer allows the use of personalised prognostic, preventive and therapeutic strategies for the patient and carries clinical implications for family members. Despite great progress, we identified six challenging areas in the management of patients with hereditary cancer predisposition syndromes and suggest recommendations to aid in their resolution. These include the potential for finding unexpected germline variants through somatic tumour testing, optimal risk management of patients with hereditary conditions involving moderate-penetrance genes, role of polygenic risk score in an under-represented Asian population, management of variants of uncertain significance, clinical trials in patients with germline pathogenic variants and technology in genetic counselling. Addressing these barriers will aid the next step forward in precision medicine in Singapore. All stakeholders in healthcare should be empowered with genetic knowledge to fully leverage the potential of novel genomic insights and implement them to provide better care for our patients.

Democratizing clinical-genomic data: How federated platforms can promote benefits sharing in genomics

Since the first sequencing of the human genome, associated sequencing costs have dramatically lowered, leading to an explosion of genomic data. This valuable data should in theory be of huge benefit to the global community, although unfortunately the benefits of these advances have not been widely distributed. Much of today's clinical-genomic data is siloed and inaccessible in adherence with strict governance and privacy policies, with more than 97% of hospital data going unused, according to one reference. Despite these challenges, there are promising efforts to make clinical-genomic data accessible and useful without compromising security. Specifically, federated data platforms are emerging as key resources to facilitate secure data sharing without having to physically move the data from outside of its organizational or jurisdictional boundaries. In this perspective, we summarize the overarching progress in establishing federated data platforms, and highlight critical considerations on how they should be managed to ensure patient and public trust. These platforms are enabling global collaboration and improving representation of underrepresented groups, since sequencing efforts have not prioritized diverse population representation until recently. Federated data platforms, when combined with advances in no-code technology, can be accessible to the diverse end-users that make up the genomics workforce, and we discuss potential strategies to develop sustainable business models so that the platforms can continue to enable research long term. Although these platforms must be carefully managed to ensure appropriate and ethical use, they are democratizing access and insights to clinical-genomic data that will progress research and enable impactful therapeutic findings.

Estimating clinical risk in gene regions from population sequencing cohort data

While pathogenic variants significantly increase disease risk in many genes, it is still challenging to estimate the clinical impact of rare missense variants more generally. Even in genes such as BRCA2 or PALB2 , large cohort studies find no significant association between breast cancer and rare germline missense variants collectively. Here we introduce REGatta, a method to improve the estimation of clinical risk in gene segments. We define gene regions using the density of pathogenic diagnostic reports, and then calculate the relative risk in each of these regions using 109,581 exome sequences from women in the UK Biobank. We apply this method in seven established breast cancer genes, and identify regions in each gene with statistically significant differences in breast cancer incidence for rare missense carriers. Even in genes with no significant difference at the gene level, this approach significantly separates rare missense variant carriers at higher or lower risk ( BRCA2 regional model OR=1.46 [1.12, 1.79], p=0.0036 vs. BRCA2 gene model OR=0.96 [0.85,1.07] p=0.4171). We find high concordance between these regional risk estimates and high-throughput functional assays of variant impact. We compare with existing methods and the use of protein domains (Pfam) as regions, and find REGatta better identifies individuals at elevated or reduced risk. These regions provide useful priors which can potentially be used to improve risk assessment and clinical management.

Joint estimation and imputation of variant functional effects using high throughput assay data

Deep mutational scanning assays enable the functional assessment of variants in high throughput. Phenotypic measurements from these assays are broadly concordant with clinical outcomes but are prone to noise at the individual variant level. We develop a framework to exploit related measurements within and across experimental assays to jointly estimate variant impact. Drawing from a large corpus of deep mutational scanning data, we collectively estimate the mean functional effect per AA residue position within each gene, normalize observed functional effects by substitution type, and make estimates for individual allelic variants with a pipeline called FUSE (Functional Substitution Estimation). FUSE improves the correlation of functional screening datasets covering the same variants, better separates estimated functional impacts for known pathogenic and benign variants (ClinVar BRCA1, p=2.24×10-51), and increases the number of variants for which predictions can be made (2,741 to 10,347) by inferring additional variant effects for substitutions not experimentally screened. For UK Biobank patients who carry a rare variant in TP53, FUSE significantly improves the separation of patients who develop cancer syndromes from those without cancer (p=1.77×10-6). These approaches promise to improve estimates of variant impact and broaden the utility of screening data generated from functional assays.

Genotype first: Clinical genomics research through a reverse phenotyping approach

Although genomic research has predominantly relied on phenotypic ascertainment of individuals affected with heritable disease, the falling costs of sequencing allow consideration of genomic ascertainment and reverse phenotyping (the ascertainment of individuals with specific genomic variants and subsequent evaluation of physical characteristics). In this research modality, the scientific question is inverted: investigators gather individuals with a genomic variant and test the hypothesis that there is an associated phenotype via targeted phenotypic evaluations. Genomic ascertainment research is thus a model of predictive genomic medicine and genomic screening. Here, we provide our experience implementing this research method. We describe the infrastructure we developed to perform reverse phenotyping studies, including aggregating a super-cohort of sequenced individuals who consented to recontact for genomic ascertainment research. We assessed 13 studies completed at the National Institutes of Health (NIH) that piloted our reverse phenotyping approach. The studies can be broadly categorized as (1) facilitating novel genotype-disease associations, (2) expanding the phenotypic spectra, or (3) demonstrating ex vivo functional mechanisms of disease. We highlight three examples of reverse phenotyping studies in detail and describe how using a targeted reverse phenotyping approach (as opposed to phenotypic ascertainment or clinical informatics approaches) was crucial to the conclusions reached. Finally, we propose a framework and address challenges to building collaborative genomic ascertainment research programs at other institutions. Our goal is for more researchers to take advantage of this approach, which will expand our understanding of the predictive capability of genomic medicine and increase the opportunity to mitigate genomic disease.

Informing variant assessment using structured evidence from prior classifications (PS1, PM5, and PVS1 sequence variant interpretation criteria)

PURPOSE

This study aimed to explore whether evidence of pathogenicity from prior variant classifications in ClinVar could be used to inform variant interpretation using the American College of Medical Genetics and Genomics/Association for Molecular Pathology clinical guidelines.

METHODS

We identified distinct single-nucleotide variants (SNVs) that are either similar in location or in functional consequence to pathogenic variants in ClinVar and analyzed evidence in support of pathogenicity using 3 interpretation criteria.

RESULTS

Thousands of variants, including many in clinically actionable disease genes (American College of Medical Genetics and Genomics secondary findings v3.0), have evidence of pathogenicity from existing variant classifications, accounting for 2.5% of nonsynonymous SNVs within ClinVar. Notably, there are many variants with uncertain or conflicting classifications that cause the same amino acid substitution as other pathogenic variants (PS1, N = 323), variants that are predicted to cause different amino acid substitutions in the same codon as pathogenic variants (PM5, N = 7692), and loss-of-function variants that are present in genes in which many loss-of-function variants are classified as pathogenic (PVS1, N = 3635). Most of these variants have similar computational predictions of pathogenicity and splicing effect as their associated pathogenic variants.

CONCLUSION

Broadly, for >1.4 million SNVs exome wide, information from previously classified variants could be used to provide evidence of pathogenicity. We have developed a pipeline to identify variants meeting these criteria that may inform interpretation efforts.

Bioinformatics Analysis of miRNA Sequencing Data

The bioinformatics analysis of miRNA is a complicated task with multiple operations and steps involved from processing of raw sequence data to finally identifying accurate microRNAs associated with the phenotypes of interest. A complete analysis process demands a high level of technical expertise in programming, statistics, and data management. The goal of this chapter is to reduce the burden of technical expertise and provide readers the opportunity to understand crucial steps involved in the analysis of miRNA sequencing data.In this chapter, we describe methods and tools employed in processing of miRNA reads, quality control, alignment, quantification, and differential expression analysis.

Addressing diversity and inclusion challenges in global neuro-psychiatric and behavioral genomics research

Advancements in neuro-psychiatric and behavioral genomics offer significant opportunities for better understanding the human brain, behavior and associated disorders. Such advancements may help us prevent, manage and/or cure complex conditions. The serious challenge confronted by these disciplines however is diversity. Both fields lack diversity in terms of genomic reference datasets needed for discovery research, engagement of diverse communities in translational research and in terms of diverse and multidisciplinary scientific teams. This is a challenge because diversity is needed on all levels in order to increase representation and inclusion of all populations across the globe as we move research activities forward. The lack of diversity can translate to an inability to use scientific innovations from these fields for the benefit of all people everywhere and signifies a missed opportunity to address pervasive global health inequities. In this commentary we identify three persistent barriers to reaching diversity targets while focusing on discovery and translational science. Additionally, we propose four suggestions on how to advance efforts and rapidly move towards achieving diversity and inclusion in neuro-psychiatric and behavioral genomics. Without systematically addressing the diversity gap within these fields, the benefits of the science may not be relevant and accessible to all people.

Understanding changes in genetic literacy over time and in genetic research participants

As genomic and personalized medicine becomes mainstream, assessing and understanding the public's genetic literacy is paramount. Because genetic research drives innovation and involves much of the public, it is equally important to assess its impact on genetic literacy. We designed a survey to assess genetic literacy in three ways (familiarity, knowledge, and skills) and distributed it to two distinct samples: 2,050 members of the general population and 2,023 individuals currently enrolled in a large-scale genetic research study. We compared these data to a similar survey implemented in 2013. The results indicate that familiarity with basic genetic terms in 2021 (M = 5.36 [range 1-7], p < 0.001) and knowledge of genetic concepts in 2021 (M = 9.06 [56.6% correct], p = 0.002) are significantly higher compared to 2013 (familiarity: M = 5.08 [range 1-7]; knowledge: M = 8.72 [54.5% correct]). Those currently enrolled in a genetic study were also significantly more familiar with genetic terms (M = 5.79 [range 1-7], p < 0.001) and more knowledgeable of genetic concepts (M = 10.57 [66.1% correct], p < 0.001), and they scored higher in skills (M = 3.57 [59.5% correct], p < 0.001) than the general population (M = 5.36 [range 1-7]; M = 9.06 [56.6% correct]; M = 2.65 [44.2% correct]). The results suggest that genetic literacy is improving over time, with room for improvement. We conclude that educational interventions are needed to ensure familiarity with and comprehension of basic genetic concepts and suggest further exploration of the impact of genetic research participation on genetic literacy to determine mechanisms for potential interventions.

Revised eutherian gene collections

Objectives

The most recent research projects in scientific field of eutherian comparative genomics included intentions to sequence every extant eutherian species genome in foreseeable future, so that future revisions and updates of eutherian gene data sets were expected.

Data description

Using 35 public eutherian reference genomic sequence assemblies and free available software, the eutherian comparative genomic analysis protocol RRID:SCR_014401 was published as guidance against potential genomic sequence errors. The protocol curated 14 eutherian third-party data gene data sets, including, in aggregate, 2615 complete coding sequences that were deposited in European Nucleotide Archive. The published eutherian gene collections were used in revisions and updates of eutherian gene data set classifications and nomenclatures that included gene annotations, phylogenetic analyses and protein molecular evolution analyses.

The Penn Medicine BioBank: Towards a Genomics-Enabled Learning Healthcare System to Accelerate Precision Medicine in a Diverse Population

The Penn Medicine BioBank (PMBB) is an electronic health record (EHR)-linked biobank at the University of Pennsylvania (Penn Medicine). A large variety of health-related information, ranging from diagnosis codes to laboratory measurements, imaging data and lifestyle information, is integrated with genomic and biomarker data in the PMBB to facilitate discoveries and translational science. To date, 174,712 participants have been enrolled into the PMBB, including approximately 30% of participants of non-European ancestry, making it one of the most diverse medical biobanks. There is a median of seven years of longitudinal data in the EHR available on participants, who also consent to permission to recontact. Herein, we describe the operations and infrastructure of the PMBB, summarize the phenotypic architecture of the enrolled participants, and use body mass index (BMI) as a proof-of-concept quantitative phenotype for PheWAS, LabWAS, and GWAS. The major representation of African-American participants in the PMBB addresses the essential need to expand the diversity in genetic and translational research. There is a critical need for a "medical biobank consortium" to facilitate replication, increase power for rare phenotypes and variants, and promote harmonized collaboration to optimize the potential for biological discovery and precision medicine.

Medicine and health of 21st Century: Not just a high biotech-driven solution

Many biotechnological innovations have shaped the contemporary healthcare system (CHS) with significant progress to treat or cure several acute conditions and diseases of known causes (particularly infectious, trauma). Some have been successful while others have created additional health care challenges. For example, a reliance on drugs has not been a panacea to meet the challenges related to multifactorial noncommunicable diseases (NCDs)-the main health burden of the 21st century. In contrast, the advent of omics-based and big data technologies has raised global hope to predict, treat, and/or cure NCDs, effectively fight even the current COVID-19 pandemic, and improve overall healthcare outcomes. Although this digital revolution has introduced extensive changes on all aspects of contemporary society, economy, firms, job market, and healthcare management, it is facing and will face several intrinsic and extrinsic challenges, impacting precision medicine implementation, costs, possible outcomes, and managing expectations. With all of biotechnology's exciting promises, biological systems' complexity, unfortunately, continues to be underestimated since it cannot readily be compartmentalized as an independent and segregated set of problems, and therefore is, in a number of situations, not readily mimicable by the current algorithm-building proficiency tools. Although the potential of biotechnology is motivating, we should not lose sight of approaches that may not seem as glamorous but can have large impacts on the healthcare of many and across disparate population groups. A balanced approach of "omics and big data" solution in CHS along with a large scale, simpler, and suitable strategies should be defined with expectations properly managed.

Artificial intelligence for precision medicine in autoimmune liver disease

Autoimmune liver diseases (AiLDs) are rare autoimmune conditions of the liver and the biliary tree with unknown etiology and limited treatment options. AiLDs are inherently characterized by a high degree of complexity, which poses great challenges in understanding their etiopathogenesis, developing novel biomarkers and risk-stratification tools, and, eventually, generating new drugs. Artificial intelligence (AI) is considered one of the best candidates to support researchers and clinicians in making sense of biological complexity. In this review, we offer a primer on AI and machine learning for clinicians, and discuss recent available literature on its applications in medicine and more specifically how it can help to tackle major unmet needs in AiLDs.

Impact of integrating genomic data into the electronic health record on genetics care delivery

PURPOSE

Integrating genomic data into the electronic health record (EHR) is key for optimally delivering genomic medicine.

METHODS

The PennChart Genomics Initiative (PGI) at the University of Pennsylvania is a multidisciplinary collaborative that has successfully linked orders and results from genetic testing laboratories with discrete genetic data in the EHR. We quantified the use of the genomic data within the EHR, performed a time study with genetic counselors, and conducted key informant interviews with PGI members to evaluate the effect of the PGI's efforts on genetics care delivery.

RESULTS

The PGI has interfaced with 4 genetic testing laboratories, resulting in the creation of 420 unique computerized genetic testing orders that have been used 4073 times to date. In a time study of 96 genetic testing activities, EHR use was associated with significant reductions in time spent ordering (2 vs 8 minutes, P < .001) and managing (1 vs 5 minutes, P < .001) genetic results compared with the use of online laboratory-specific portals. In key informant interviews, multidisciplinary collaboration and institutional buy-in were identified as key ingredients for the PGI's success.

CONCLUSION

The PGI's efforts to integrate genomic medicine into the EHR have substantially streamlined the delivery of genomic medicine.

Knowledge and attitudes of incoming pharmacy students toward pharmacogenomics and survey reliability

Aims: To assess knowledge and attitudes toward pharmacogenomics (PGx) of incoming doctoral pharmacy students, to evaluate the internal structure and reliability of the PGx survey and to identify variables associated with the different responses. Methods: A PGx survey based on the core pharmacist competencies in PGx was created. Results: Of 83.2% analyzable responses, 91% believed PGx is a useful tool and relevant to future practice but over 70% stated they lack confidence in clinical PGx knowledge. This 38-item PGx survey included three factors showing high reliability. Prior genetic/PGx testing and unsatisfactory medication experiences were associated with a more positive attitude toward PGx. Conclusion: The majority of students have positive attitudes toward PGx, but lack knowledge in genetic concepts and clinical PGx.

Incremental Value of Polygenic Risk Scores in Primary Prevention of Coronary Heart Disease: A Review

IMPORTANCE

Risk prediction for coronary heart disease (CHD) is a cornerstone of primary prevention strategies. Polygenic risk scores (PRSs) have emerged as a new approach to predict risk in asymptomatic people. Polygenic risk scores for CHD have been studied in several populations, but there is lack of agreement about the incremental value of PRS beyond traditional risk factor scores in the primary prevention of CHD.

OBSERVATIONS

This narrative review critically appraised the 5 most highly cited studies published through 2021 that also included a large number (>45 000) of single-nucleotide variations (formerly single-nucleotide polymorphisms) and evaluated the incremental value of PRS in CHD risk prediction according to published PRS reporting standards. The cohorts studied included the Atherosclerosis Risk in Communities Study, FINRISK, the Framingham Heart Study, the Multi-Ethnic Study of Atherosclerosis, and the UK Biobank. All of the studies focused predominantly on populations of European ancestry. The hazard ratio per standard deviation of PRS ranged from 1.24 (95% CI, 1.15-1.34) to 1.74 (95% CI, 1.61-1.86). The C statistic for PRS alone ranged from 0.549 to 0.623. The change in C statistic when PRS was added to a standard risk factor model ranged between -0.001 to +0.021. Net reclassification index was reported in 4 of the 5 studies and varied from 0.001 to 0.097. At a sensitivity (true-positive rate) of 90%, positive predictive values ranged from 1.8% to 16.6%, and false-positive rates ranged from 77.1% to 85.7%.

CONCLUSIONS AND RELEVANCE

In this review, PRS was significantly associated with CHD risk in all studies. The degree of improvement in C statistic and the net reclassification indexes when PRS was added to traditional risk scores ranged from negligible to modest. Based on established metrics to assess risk prediction scores, the addition of PRS to traditional risk scores does not appear to provide meaningful improvements in clinical decision-making in primary prevention populations.

Cost-Effectiveness of Polygenic Risk Scores to Guide Statin Therapy for Cardiovascular Disease Prevention

BACKGROUND

Atherosclerotic cardiovascular diseases (CVDs) are leading causes of death despite effective therapies and result in unnecessary morbidity and mortality throughout the world. We aimed to investigate the cost-effectiveness of polygenic risk scores (PRS) to guide statin therapy for Canadians with intermediate CVD risk and model its economic outlook.

METHODS

This cost-utility analysis was conducted using UK Biobank prospective cohort study participants, with recruitment from 2006 to 2010, and at least 10 years of follow-up. We included nonrelated white British-descent participants (n=96 116) at intermediate CVD risk with no prior lipid lowering medication or statin-indicated conditions. A coronary artery disease PRS was used to inform decision to use statins. The effects of statin therapy with and without PRS, as well as CVD events were modelled to determine the incremental cost-effectiveness ratio from a Canadian public health care perspective. We discounted future costs and quality-adjusted life-years by 1.5% annually.

RESULTS

The optimal economic strategy was when intermediate risk individuals with a PRS in the top 70% are eligible for statins while the lowest 1% are excluded. Base-case analysis at a genotyping cost of $70 produced an incremental cost-effectiveness ratio of $172 906 (143 685 USD) per quality-adjusted life-year. In the probabilistic sensitivity analysis, the intervention has approximately a 50% probability of being cost-effective at $179 100 (148 749 USD) per quality-adjusted life-year. At a $0 genotyping cost, representing individuals with existing genotyping information, PRS-guided strategies dominated standard care when 12% of the lowest PRS individuals were withheld from statins. With improved PRS predictive performance and lower genotyping costs, the incremental cost-effectiveness ratio demonstrates possible cost-effectiveness under thresholds of $150 000 and possibly $50 000 per quality-adjusted life-year.

CONCLUSIONS

This study suggests that using PRS alongside existing guidelines might be cost-effective for CVD. Stronger predictiveness combined with decreased cost of PRS could further improve cost-effectiveness, providing an economic basis for its inclusion into clinical care.

From diagnostic testing to precision medicine: the evolving role of genomics in cardiac channelopathies and cardiomyopathies in children

Pediatric sudden cardiac death (SCD) is the sudden unexpected death of a child or adolescent due to a presumed cardiac etiology. Heritable causes of pediatric SCD are predominantly cardiomyopathies and cardiac ion channelopathies. This review illustrates recent advances in determining the genetic cause of established and emerging channelopathies and cardiomyopathies, and how broader genomic sequencing is uncovering complex interactions between genetic architecture and disease manifestation. We discuss innovative models and experimental platforms for resolving the variant of uncertain significance as both the variants and genes associated with disease continue to evolve. Finally, we highlight the growing problem of incidentally identified variants in cardiovascular disease-causing genes and review innovative methods to determining whether these variants may ultimately result in penetrant disease. Overall, we seek to illustrate both the promise and inherent challenges in bridging the traditional role for genetics in diagnosing cardiomyopathies and channelopathies to one of true risk-predictive precision medicine.

Educational considerations based on medical student use of polygenic risk information and apparent race in a simulated consultation

PURPOSE

To craft evidence-based educational approaches related to polygenic risk score (PRS) implementation, it is crucial to forecast issues and biases that may arise when PRS are introduced in clinical care.

METHODS

Medical students (N = 84) were randomized to a simulated primary care encounter with a Black or White virtual reality-based patient and received either a direct-to-consumer-style PRS report for 5 common complex conditions or control information. The virtual patient inquired about 2 health concerns and her genetic report in the encounter. Data sources included participants' verbalizations in the simulation, care plan recommendations, and self-report outcomes.

RESULTS

When medical students received PRSs, they rated the patient as less healthy and requiring more strict advice. Patterns suggest that PRSs influenced specific medical recommendations related to the patient's concerns, despite student reports that participants did not use it for that purpose. We observed complex patterns regarding the effect of patient race on recommendations and behaviors.

CONCLUSION

Educational approaches should consider potential unintentional influences of PRSs on decision-making and evaluate ways that they may be applied inconsistently across patients from different racial groups.

Nurses’ Knowledge, Attitudes, Confidence, and Practices with Genetics and Genomics: A Theory-Informed Integrative Review Protocol

Introduction: As key healthcare providers, nurses require genomic competency to fulfil their professional obligations in the genomic era. Prior research suggests that nurses have limited competency with genomics-informed practice. Concepts in the Rogers’ Diffusion of Innovation (DOI) theory (i.e., knowledge, attitudes, and attributes of innovation adopters) provide a framework to understand the process of adoption of innovations, such as genomics, across organizations. We aim to synthesize what is known about the adoption of genomics across nursing within the DOI framework to identify gaps and opportunities to enact sustained adoption of genomics in nursing. Methods and analysis: An integrative literature review, following Whittemore and Knafl’s five steps, will be conducted to evaluate qualitative, quantitative, and mixed-method primary studies that meet inclusion and exclusion criteria. The MEDLINE, PsychINFO, CINAHL, Cochrane, and Sociological Abstracts electronic databases will be searched in addition to the ancestry search method. Two researchers will perform independent screening of studies, quality appraisal using the Mixed-Methods Appraisal Tool, and data analysis using the narrative synthesis method. Disagreements will be resolved by a third reviewer. Findings in this review could be used to develop theory- and evidence-informed strategies to support the sustained adoption of genomics in nursing.

Beyond race: Recruitment of diverse participants in clinical genomics research for rare disease

Purpose: Despite recent attention to increasing diversity in clinical genomics research, researchers still struggle to recruit participants from varied sociodemographic backgrounds. We examined the experiences of parents from diverse backgrounds with enrolling their children in clinical genomics research on rare diseases. We explored the barriers and facilitators parents encountered and possible impacts of sociodemographic factors on their access to research.

Methods: We utilized semi-structured interviews with parents of children participating in the Undiagnosed Diseases Network. Interview data were analyzed using comparative content analysis.

Results: We interviewed 13 Hispanic, 11 non-Hispanic White, four Asian, and two biracial parents. Participants discussed different pathways to clinical genomics research for rare disease as well as how sociodemographic factors shaped families’ access. Themes focused on variation in: 1) reliance on providers to access research; 2) cultural norms around health communication; 3) the role of social capital in streamlining access; and 4) the importance of language-concordant research engagement.

Conclusion: Our findings suggest that variables beyond race/ethnicity may influence access in clinical genomics research. Future efforts to diversify research participation should consider utilizing varied recruitment strategies to reach participants with diverse sociodemographic characteristics.

Estimating the number of diseases - the concept of rare, ultra-rare, and hyper-rare

At the dawn of the personalized medicine era, the number of rare diseases has been estimated at 10,000. By considering the influence of environmental factors together with genetic variations and our improved diagnostic capabilities, an assessment suggests a considerably larger number. The majority would be extremely rare, and hence, we introduce the term "hyper-rare," defined as affecting <1/108 individuals. Such disorders would potentially outnumber all currently known rare diseases. Because autosomal recessive disorders are likely concentrated in consanguineous populations, and rare toxicities in rural areas, establishing their existence necessitates a greater reach than is currently viable. Moreover, the randomness of X-linked and gain-of-function mutations greatly compound this challenge. However, whether concurrent diseases actually cause a distinct illness will depend on if their pathological mechanisms interact (phenotype conversion) or not (phenotype maintenance). The hyper-rare disease concept will be important in precision medicine with improved diagnosis and treatment of rare disease patients.

A Mixed-Methods Protocol to Identify Best Practices for Implementing Pharmacogenetic Testing in Clinical Settings

Using a patient's genetic information to inform medication prescriptions can be clinically effective; however, the practice has not been widely implemented. Health systems need guidance on how to engage with providers to improve pharmacogenetic test utilization. Approaches from the field of implementation science may shed light on the complex factors affecting pharmacogenetic test use in real-world settings and areas to target to improve utilization. This paper presents an approach to studying the application of precision medicine that utilizes mixed qualitative and quantitative methods and implementation science frameworks to understand which factors or combinations consistently account for high versus low utilization of pharmocogenetic testing. This approach involves two phases: (1) collection of qualitative and quantitative data from providers-the cases-at four clinical institutions about their experiences with, and utilization of, pharmacogenetic testing to identify salient factors; and (2) analysis using a Configurational Comparative Method (CCM), using a mathematical algorithm to identify the minimally necessary and sufficient factors that distinguish providers who have higher utilization from those with low utilization. Advantages of this approach are that it can be used for small to moderate sample sizes, and it accounts for conditions found in real-world settings by demonstrating how they coincide to affect utilization.

Importance of Including Non-European Populations in Large Human Genetic Studies to Enhance Precision Medicine

One goal of genomic medicine is to uncover an individual's genetic risk for disease, which generally requires data connecting genotype to phenotype, as done in genome-wide association studies (GWAS). While there may be clinical promise to employing prediction tools such as polygenic risk scores (PRS), it currently stands that individuals of non-European ancestry may not reap the benefits of genomic medicine because of underrepresentation in large-scale genetics studies. Here, we discuss why this inequity poses a problem for genomic medicine and the reasons for the low transferability of PRS across populations. We also survey the ancestry representation of published GWAS and investigate how estimates of ancestry diversity in GWASparticipants might be biased. We highlight the importance of expanding genetic research in Africa, one of the most underrepresented regions in human genomics research, and discuss issues of ethics, resources, and technology for equitable advancement of genomic medicine.