Recent advances in polygenic scores: translation, equitability, methods and FAIR tools

Polygenic scores (PGS) can be used for risk stratification by quantifying individuals' genetic predisposition to disease, and many potentially clinically useful applications have been proposed. Here, we review the latest potential benefits of PGS in the clinic and challenges to implementation. PGS could augment risk stratification through combined use with traditional risk factors (demographics, disease-specific risk factors, family history, etc.), to support diagnostic pathways, to predict groups with therapeutic benefits, and to increase the efficiency of clinical trials. However, there exist challenges to maximizing the clinical utility of PGS, including FAIR (Findable, Accessible, Interoperable, and Reusable) use and standardized sharing of the genomic data needed to develop and recalculate PGS, the equitable performance of PGS across populations and ancestries, the generation of robust and reproducible PGS calculations, and the responsible communication and interpretation of results. We outline how these challenges may be overcome analytically and with more diverse data as well as highlight sustained community efforts to achieve equitable, impactful, and responsible use of PGS in healthcare.

What makes a good life: using theatrical performance to enhance communication about polygenic risk scores research in patient and public involvement

The aim of this patient and public involvement and engagement (PPIE) work was to explore improvised theatre as a tool for facilitating bi-directional dialogue between researchers and patients/members of the public on the topic of polygenic risk scores (PRS) use within primary or secondary care. PRS are a tool to quantify genetic risk for a heritable disease or trait and may be used to predict future health outcomes. In the United Kingdom (UK), they are often cited as a next-in-line public health tool to be implemented, and their use in consumer genetic testing as well as patient-facing settings is increasing. Despite their potential clinical utility, broader themes about how they might influence an individual's perception of disease risk and decision-making are an active area of research; however, this has mostly been in the setting of return of results to patients. We worked with a youth theatre group and patients involved in a PPIE group to develop two short plays about public perceptions of genetic risk information that could be captured by PRS. These plays were shared in a workshop with patients/members of the public to facilitate discussions about PRS and their perceived benefits, concerns and emotional reactions. Discussions with both performers and patients/public raised three key questions: (1) can the data be trusted?; (2) does knowing genetic risk actually help the patient?; and (3) what makes a life worthwhile? Creating and watching fictional narratives helped all participants explore the potential use of PRS in a clinical setting, informing future research considerations and improving communication between the researchers and lay members of the PPIE group.

An atlas of genetic scores to predict multi-omic traits

The use of omic modalities to dissect the molecular underpinnings of common diseases and traits is becoming increasingly common. But multi-omic traits can be genetically predicted, which enables highly cost-effective and powerful analyses for studies that do not have multi-omics1. Here we examine a large cohort (the INTERVAL study2; n = 50,000 participants) with extensive multi-omic data for plasma proteomics (SomaScan, n = 3,175; Olink, n = 4,822), plasma metabolomics (Metabolon HD4, n = 8,153), serum metabolomics (Nightingale, n = 37,359) and whole-blood Illumina RNA sequencing (n = 4,136), and use machine learning to train genetic scores for 17,227 molecular traits, including 10,521 that reach Bonferroni-adjusted significance. We evaluate the performance of genetic scores through external validation across cohorts of individuals of European, Asian and African American ancestries. In addition, we show the utility of these multi-omic genetic scores by quantifying the genetic control of biological pathways and by generating a synthetic multi-omic dataset of the UK Biobank3 to identify disease associations using a phenome-wide scan. We highlight a series of biological insights with regard to genetic mechanisms in metabolism and canonical pathway associations with disease; for example, JAK-STAT signalling and coronary atherosclerosis. Finally, we develop a portal ( https://www.omicspred.org/ ) to facilitate public access to all genetic scores and validation results, as well as to serve as a platform for future extensions and enhancements of multi-omic genetic scores.

Quality control and removal of technical variation of NMR metabolic biomarker data in ~120,000 UK Biobank participants

Metabolic biomarker data quantified by nuclear magnetic resonance (NMR) spectroscopy in approximately 121,000 UK Biobank participants has recently been released as a community resource, comprising absolute concentrations and ratios of 249 circulating metabolites, lipids, and lipoprotein sub-fractions. Here we identify and characterise additional sources of unwanted technical variation influencing individual biomarkers in the data available to download from UK Biobank. These included sample preparation time, shipping plate well, spectrometer batch effects, drift over time within spectrometer, and outlier shipping plates. We developed a procedure for removing this unwanted technical variation, and demonstrate that it increases signal for genetic and epidemiological studies of the NMR metabolic biomarker data in UK Biobank. We subsequently developed an R package, ukbnmr, which we make available to the wider research community to enhance the utility of the UK Biobank NMR metabolic biomarker data and to facilitate rapid analysis.

Genetically personalised organ-specific metabolic models in health and disease

Understanding how genetic variants influence disease risk and complex traits (variant-to-function) is one of the major challenges in human genetics. Here we present a model-driven framework to leverage human genome-scale metabolic networks to define how genetic variants affect biochemical reaction fluxes across major human tissues, including skeletal muscle, adipose, liver, brain and heart. As proof of concept, we build personalised organ-specific metabolic flux models for 524,615 individuals of the INTERVAL and UK Biobank cohorts and perform a fluxome-wide association study (FWAS) to identify 4312 associations between personalised flux values and the concentration of metabolites in blood. Furthermore, we apply FWAS to identify 92 metabolic fluxes associated with the risk of developing coronary artery disease, many of which are linked to processes previously described to play in role in the disease. Our work demonstrates that genetically personalised metabolic models can elucidate the downstream effects of genetic variants on biochemical reactions involved in common human diseases.

Transferability of genetic loci and polygenic scores for cardiometabolic traits in British Pakistani and Bangladeshi individuals

Individuals with South Asian ancestry have a higher risk of heart disease than other groups but have been largely excluded from genetic research. Using data from 22,000 British Pakistani and Bangladeshi individuals with linked electronic health records from the Genes & Health cohort, we conducted genome-wide association studies of coronary artery disease and its key risk factors. Using power-adjusted transferability ratios, we found evidence for transferability for the majority of cardiometabolic loci powered to replicate. The performance of polygenic scores was high for lipids and blood pressure, but lower for BMI and coronary artery disease. Adding a polygenic score for coronary artery disease to clinical risk factors showed significant improvement in reclassification. In Mendelian randomisation using transferable loci as instruments, our findings were consistent with results in European-ancestry individuals. Taken together, trait-specific transferability of trait loci between populations is an important consideration with implications for risk prediction and causal inference.

Integrative analysis of the plasma proteome and polygenic risk of cardiometabolic diseases

Cardiometabolic diseases are frequently polygenic in architecture, comprising a large number of risk alleles with small effects spread across the genome1-3. Polygenic scores (PGS) aggregate these into a metric representing an individual's genetic predisposition to disease. PGS have shown promise for early risk prediction4-7 and there is an open question as to whether PGS can also be used to understand disease biology8. Here, we demonstrate that cardiometabolic disease PGS can be used to elucidate the proteins underlying disease pathogenesis. In 3,087 healthy individuals, we found that PGS for coronary artery disease, type 2 diabetes, chronic kidney disease and ischaemic stroke are associated with the levels of 49 plasma proteins. Associations were polygenic in architecture, largely independent of cis and trans protein quantitative trait loci and present for proteins without quantitative trait loci. Over a follow-up of 7.7 years, 28 of these proteins associated with future myocardial infarction or type 2 diabetes events, 16 of which were mediators between polygenic risk and incident disease. Twelve of these were druggable targets with therapeutic potential. Our results demonstrate the potential for PGS to uncover causal disease biology and targets with therapeutic potential, including those that may be missed by approaches utilizing information at a single locus.

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.

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.

Towards clinical utility of polygenic risk scores

Prediction of disease risk is an essential part of preventative medicine, often guiding clinical management. Risk prediction typically includes risk factors such as age, sex, family history of disease and lifestyle (e.g. smoking status); however, in recent years, there has been increasing interest to include genomic information into risk models. Polygenic risk scores (PRS) aggregate the effects of many genetic variants across the human genome into a single score and have recently been shown to have predictive value for multiple common diseases. In this review, we summarize the potential use cases for seven common diseases (breast cancer, prostate cancer, coronary artery disease, obesity, type 1 diabetes, type 2 diabetes and Alzheimer’s disease) where PRS has or could have clinical utility. PRS analysis for these diseases frequently revolved around (i) risk prediction performance of a PRS alone and in combination with other non-genetic risk factors, (ii) estimation of lifetime risk trajectories, (iii) the independent information of PRS and family history of disease or monogenic mutations and (iv) estimation of the value of adding a PRS to specific clinical risk prediction scenarios. We summarize open questions regarding PRS usability, ancestry bias and transferability, emphasizing the need for the next wave of studies to focus on the implementation and health-economic value of PRS testing. In conclusion, it is becoming clear that PRS have value in disease risk prediction and there are multiple areas where this may have clinical utility.

Similarity regression predicts evolution of transcription factor sequence specificity

Transcription factor (TF) binding specificities (motifs) are essential for the analysis of gene regulation. Accurate prediction of TF motifs is critical, because it is infeasible to assay all TFs in all sequenced eukaryotic genomes. There is ongoing controversy regarding the degree of motif diversification among related species that is, in part, because of uncertainty in motif prediction methods. Here we describe similarity regression, a significantly improved method for predicting motifs, which we use to update and expand the Cis-BP database. Similarity regression inherently quantifies TF motif evolution, and shows that previous claims of near-complete conservation of motifs between human and Drosophila are inflated, with nearly half of the motifs in each species absent from the other, largely due to extensive divergence in C2H2 zinc finger proteins. We conclude that diversification in DNA-binding motifs is pervasive, and present a new tool and updated resource to study TF diversity and gene regulation across eukaryotes.

The Human Transcription Factors

Transcription factors (TFs) recognize specific DNA sequences to control chromatin and transcription, forming a complex system that guides expression of the genome. Despite keen interest in understanding how TFs control gene expression, it remains challenging to determine how the precise genomic binding sites of TFs are specified and how TF binding ultimately relates to regulation of transcription. This review considers how TFs are identified and functionally characterized, principally through the lens of a catalog of over 1,600 likely human TFs and binding motifs for two-thirds of them. Major classes of human TFs differ markedly in their evolutionary trajectories and expression patterns, underscoring distinct functions. TFs likewise underlie many different aspects of human physiology, disease, and variation, highlighting the importance of continued effort to understand TF-mediated gene regulation.

Breast magnetic resonance elastography: a review of clinical work and future perspectives

This review on magnetic resonance elastography (MRE) of the breast provides an overview of available literature and describes current developments in the field of breast MRE, including new transducer technology for data acquisition and multi-frequency-derived power-law behaviour of tissue. Moreover, we discuss the future potential of breast MRE, which goes beyond its original application as an additional tool in differentiating benign from malignant breast lesions. These areas of ongoing and future research include MRE for pre-operative tumour delineation, staging, monitoring and predicting response to treatment, as well as prediction of the metastatic potential of primary tumours.

Simultaneous MR quantification of hepatic fat content, fatty acid composition, transverse relaxation time and magnetic susceptibility for the diagnosis of non-alcoholic steatohepatitis

Non-alcoholic steatohepatitis (NASH) is characterized at histology by steatosis, hepatocyte ballooning and inflammatory infiltrates, with or without fibrosis. Although diamagnetic material in fibrosis and inflammation can be detected with quantitative susceptibility imaging, fatty acid composition changes in NASH relative to simple steatosis have also been reported. Therefore, our aim was to develop a single magnetic resonance (MR) acquisition and post-processing scheme for the diagnosis of steatohepatitis by the simultaneous quantification of hepatic fat content, fatty acid composition, T₂ * transverse relaxation time and magnetic susceptibility in patients with non-alcoholic fatty liver disease. MR acquisition was performed at 3.0 T using a three-dimensional, multi-echo, spoiled gradient echo sequence. Phase images were unwrapped to compute the B₀ field inhomogeneity (ΔB₀ ) map. The ΔB₀ -demodulated real part images were used for fat-water separation, T₂ * and fatty acid composition quantification. The external and internal fields were separated with the projection onto dipole field method. Susceptibility maps were obtained after dipole inversion from the internal field map with single-orientation Bayesian regularization including spatial priors. Method validation was performed in 32 patients with biopsy-proven, non-alcoholic fatty liver disease from which 12 had simple steatosis and 20 NASH. Liver fat fraction and T₂ * did not change significantly between patients with simple steatosis and NASH. In contrast, the saturated fatty acid fraction increased in patients with NASH relative to patients with simple steatosis (48 ± 2% versus 44 ± 4%; p < 0.05) and the magnetic susceptibility decreased (-0.30 ± 0.27 ppm versus 0.10 ± 0.14 ppm; p < 0.001). The area under the receiver operating characteristic curve for magnetic susceptibility as NASH marker was 0.91 (95% CI: 0.79-1.0). Simultaneous MR quantification of fat content, fatty acid composition, T₂ * and magnetic susceptibility is feasible in the liver. Our preliminary results suggest that quantitative susceptibility imaging has a high diagnostic performance for the diagnosis of NASH.

Transcription factors read epigenetics

Many homeodomain transcription factors can bind methylated DNA

Comparison of viscoelastic property characterization of plastisol phantoms with magnetic resonance elastography and high-frequency rheometry

This study aims at evaluating Magnetic Resonance Elastography (MRE) as a reliable technique for the characterization of viscoelastic properties of soft tissues. Three phantoms with different concentrations of plastisol and softener were prepared in order to mechanically mimic a broad panel of healthy and pathological soft tissues. Once placed in a MRI device, each sample was excited by a homemade external driver, inducing shear waves within the medium. The storage (G') and loss (G") moduli of each phantom were then reconstructed from MRE acquisitions over a frequency range from 300 to 1,000 Hz, by applying a 2D Helmholtz inversion algorithm. At the same time, mechanical tests were performed on four samples of each phantom with a High-Frequency piezo-Rheometer (HFR) over an overlapping frequency range (from 160 to 630 Hz) with the same test conditions (temperature, ageing). The comparison between both techniques shows a good agreement in the measurement of the storage and loss moduli, underlying the capability of MRE to noninvasively assess the complex shear modulus G* of a medium and its interest for investigating the viscoelastic properties of living tissues. Moreover, the phantoms with varying concentrations of plastisol used in this study show interesting rheological properties, which make them good candidates to simulate the broad variety of viscoelastic behaviors of healthy and pathological soft tissues.

Motif comparison based on similarity of binding affinity profiles

Measuring motif similarity is essential for identifying functionally related transcription factors (TFs) and RNA-binding proteins, and for annotating de novo motifs. Here, we describe Motif Similarity Based on Affinity of Targets (MoSBAT), an approach for measuring the similarity of motifs by computing their affinity profiles across a large number of random sequences. We show that MoSBAT successfully associates de novo ChIP-seq motifs with their respective TFs, accurately identifies motifs that are obtained from the same TF in different in vitro assays, and quantitatively reflects the similarity of in vitro binding preferences for pairs of TFs.

AVAILABILITY AND IMPLEMENTATION

MoSBAT is available as a webserver at mosbat.ccbr.utoronto.ca, and for download at github.com/csglab/MoSBAT.

CONTACT

t.hughes@utoronto.ca or hamed.najafabadi@mcgill.caSupplementary information: Supplementary data are available at Bioinformatics online.

Mapping and analysis of Caenorhabditis elegans transcription factor sequence specificities

Caenorhabditis elegans is a powerful model for studying gene regulation, as it has a compact genome and a wealth of genomic tools. However, identification of regulatory elements has been limited, as DNA-binding motifs are known for only 71 of the estimated 763 sequence-specific transcription factors (TFs). To address this problem, we performed protein binding microarray experiments on representatives of canonical TF families in C. elegans, obtaining motifs for 129 TFs. Additionally, we predict motifs for many TFs that have DNA-binding domains similar to those already characterized, increasing coverage of binding specificities to 292 C. elegans TFs (∼40%). These data highlight the diversification of binding motifs for the nuclear hormone receptor and C2H2 zinc finger families and reveal unexpected diversity of motifs for T-box and DM families. Motif enrichment in promoters of functionally related genes is consistent with known biology and also identifies putative regulatory roles for unstudied TFs.

DOI:http://dx.doi.org/10.7554/eLife.06967.001

Many scientists use ‘model’ species—such as the fruit fly or a nematode worm called Caenorhabditis elegans—in their research because these organisms have useful features that make it easier to carry out many experiments. For example, C. elegans has a smaller genome compared to many other animals, which is useful for studying the roles of individual genes or stretches of DNA.

Transcription factors are a type of protein that can bind to specific stretches of DNA and help to switch certain genes on or off. These ‘motifs’ may be close to the gene or further away in the genome, and therefore, must stand out amongst the rest of the DNA, like lights on a landing strip. However, the motifs for only 10% of the estimated 763 transcription factors in C. elegans have been identified so far.

In this study, Narasimhan, Lambert, Yang et al. used a technique called a ‘protein binding microarray’ to identify the motifs for many more of the C. elegans transcription factors. These findings were then used to predict motifs for other transcription factors. Together, these methods increased the proportion of C. elegans transcription factors with known DNA-binding motifs from 10% to around 40%.

Now that more DNA motifs have been identified, it is possible to look for similarities and differences between them. For example, Narasimhan, Lambert, Yang et al. found that transcription factors with similar sequences can bind to very varied motifs. On the other hand, some transcription factors that are very different are able to recognize very similar motifs. The experiments also indicate that motifs found very close to genes—in sequences known as ‘promoters’—may be able to interact with many proteins to influence the activity of genes.

Narasimhan, Lambert, Yang et al.'s findings increase the number of C. elegans transcription factors with a motif, bringing the knowledge of these proteins more in line with the better-studied transcription factors of humans and fruit flies. The next challenge is to identify DNA motifs for the remaining 60% of transcription factors.

DOI:http://dx.doi.org/10.7554/eLife.06967.002

Determination and inference of eukaryotic transcription factor sequence specificity

Transcription factor (TF) DNA sequence preferences direct their regulatory activity, but are currently known for only ∼1% of eukaryotic TFs. Broadly sampling DNA-binding domain (DBD) types from multiple eukaryotic clades, we determined DNA sequence preferences for >1,000 TFs encompassing 54 different DBD classes from 131 diverse eukaryotes. We find that closely related DBDs almost always have very similar DNA sequence preferences, enabling inference of motifs for ∼34% of the ∼170,000 known or predicted eukaryotic TFs. Sequences matching both measured and inferred motifs are enriched in chromatin immunoprecipitation sequencing (ChIP-seq) peaks and upstream of transcription start sites in diverse eukaryotic lineages. SNPs defining expression quantitative trait loci in Arabidopsis promoters are also enriched for predicted TF binding sites. Importantly, our motif "library" can be used to identify specific TFs whose binding may be altered by human disease risk alleles. These data present a powerful resource for mapping transcriptional networks across eukaryotes.

The effects of hypnosis/guided imagery on the postoperative course of children

Hypnosis, guided imagery, and relaxation have been shown to improve the postoperative course of adult surgical patients. Children have successfully used hypnosis/guided imagery to significantly reduce the pain associated with invasive procedures and to improve selected medical conditions. The purpose of this study was to examine the effect of hypnosis/guided imagery on the postoperative course of pediatric surgical patients. Fifty-two children (matched for sex, age, and diagnosis) were randomly assigned to an experimental or control group. The experimental group was taught guided imagery by the investigator. Practice of the imagery technique included suggestions for a favorable postoperative course. Significantly lower postoperative pain ratings and shorter hospital stays occurred for children in the experimental group. State anxiety was decreased for the guided imagery group and increased postoperatively for the control group. This study demonstrates the positive effects of hypnosis/guided imagery for the pediatric surgical patient.

Therapeutic relations decision making: the Rainbow Framework

The giving and receiving of gifts, funeral attendance, post-discharge babysitting, usurping of parental roles, indiscriminate sharing of personal information, and other issues prompted a pediatric tertiary care facility in the Midwest to examine intricacies involved in the promotion and maintenance of therapeutic relationships. A multidisciplinary group developed a therapeutic relations decision-making framework to (a) foster a proactive process of conscious deliberation regarding nurse/patient interactions, and (b) afford a nonthreatening mechanism for retrospective review of apparently non-therapeutic relationships. The framework, based upon an "act utilitarian" approach to moral reasoning, promotes the clarification of personal philosophy and values. The framework provides a basis for deliberate, consistent, and confident nursing care delivery and the impetus for developing of new patient and family care initiatives.

Easing the wait: development of a pager program for families

Waiting is inevitable for patients and families seeking health care services. Anxiety, frustration, and stress are exacerbated. A pager program, where pagers are loaned free of charge to families, uses a family-centered approach to counteract the aggravation of waiting. Patients and families can leave the hospital room or clinic setting and then be paged to return as needed. In any clinical setting, having choices and control will enhance coping and satisfaction for both patients and families.

Variables that affect the school-age child's reaction to hospitalization and surgery: a review of the literature

The purpose of this paper has been to identify the variables documented in the literature that affect the school-age child's reaction to hospitalization and surgery. The variables were categorized as pertaining to the child, the parents, or the hospital. Variables in the category pertaining to the child that affect the child's reaction to hospitalization and surgery are developmental level and mechanisms used to cope with hospitalization and surgery. In the parent category, presence of the mother, preparation of the mother, support of the child by the mother, parental values, and socioeconomic status were considered variables that affect the child's reaction to hospitalization and surgery. The hospital environment is generally considered to be threatening to school-age children. The major variables identified in the hospital category that affect the child's reaction to hospitalization and surgery were specific events, length of hospitalization, nature and degree of illness, type of procedure, and method of preparation for the hospital experience.