On the utilization of polygenic risk scores for therapeutic targeting

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.

Pruning and thresholding approach for methylation risk scores in multi-ancestry populations

Recent efforts have focused on developing methylation risk scores (MRS), a weighted sum of the individual's DNA methylation (DNAm) values of pre-selected CpG sites. Most of the current MRS approaches that utilize Epigenome-wide association studies (EWAS) summary statistics only include genome-wide significant CpG sites and do not consider co-methylation. New methods that relax the p-value threshold to include more CpG sites and account for the inter-correlation of DNAm might improve the predictive performance of MRS. We paired informed co-methylation pruning with P-value thresholding to generate pruning and thresholding (P+T) MRS and evaluated its performance among multi-ancestry populations. Through simulation studies and real data analyses, we demonstrated that pruning provides an improvement over simple thresholding methods for prediction of phenotypes. We demonstrated that European-derived summary statistics can be used to develop P+T MRS among other populations such as African populations. However, the prediction accuracy of P+T MRS may differ across multi-ancestry population due to environmental/cultural/social differences.

mtPGS: Leverage multiple correlated traits for accurate polygenic score construction

Accurate polygenic scores (PGSs) facilitate the genetic prediction of complex traits and aid in the development of personalized medicine. Here, we develop a statistical method called multi-trait assisted PGS (mtPGS), which can construct accurate PGSs for a target trait of interest by leveraging multiple traits relevant to the target trait. Specifically, mtPGS borrows SNP effect size similarity information between the target trait and its relevant traits to improve the effect size estimation on the target trait, thus achieving accurate PGSs. In the process, mtPGS flexibly models the shared genetic architecture between the target and the relevant traits to achieve robust performance, while explicitly accounting for the environmental covariance among them to accommodate different study designs with various sample overlap patterns. In addition, mtPGS uses only summary statistics as input and relies on a deterministic algorithm with several algebraic techniques for scalable computation. We evaluate the performance of mtPGS through comprehensive simulations and applications to 25 traits in the UK Biobank, where in the real data mtPGS achieves an average of 0.90%-52.91% accuracy gain compared to the state-of-the-art PGS methods. Overall, mtPGS represents an accurate, fast, and robust solution for PGS construction in biobank-scale datasets.

Genome-wide polygenic risk scores for hypertensive disease during pregnancy can also predict the risk for long-term cardiovascular disease

BACKGROUND

Hypertensive disorders during pregnancy are associated with the risk of long-term cardiovascular disease after pregnancy, but it has not yet been determined whether genetic predisposition for hypertensive disorders during pregnancy can predict the risk for long-term cardiovascular disease.

OBJECTIVE

This study aimed to evaluate the risk for long-term atherosclerotic cardiovascular disease according to polygenic risk scores for hypertensive disorders during pregnancy.

STUDY DESIGN

Among UK Biobank participants, we included European-descent women (n=164,575) with at least 1 live birth. Participants were divided according to genetic risk categorized by polygenic risk scores for hypertensive disorders during pregnancy (low risk, score ≤25th percentile; medium risk, score 25th∼75th percentile; high risk, score >75th percentile), and were evaluated for incident atherosclerotic cardiovascular disease, defined as the new occurrence of one of the following: coronary artery disease, myocardial infarction, ischemic stroke, or peripheral artery disease.

RESULTS

Among the study population, 2427 (1.5%) had a history of hypertensive disorders during pregnancy, and 8942 (5.6%) developed incident atherosclerotic cardiovascular disease after enrollment. Women with high genetic risk for hypertensive disorders during pregnancy had a higher prevalence of hypertension at enrollment. After enrollment, women with high genetic risk for hypertensive disorders during pregnancy had an increased risk for incident atherosclerotic cardiovascular disease, including coronary artery disease, myocardial infarction, and peripheral artery disease, compared with those with low genetic risk, even after adjustment for history of hypertensive disorders during pregnancy.

CONCLUSION

High genetic risk for hypertensive disorders during pregnancy was associated with increased risk for atherosclerotic cardiovascular disease. This study provides evidence on the informative value of polygenic risk scores for hypertensive disorders during pregnancy in prediction of long-term cardiovascular outcomes later in life.

Can Pharmacogenetic Variants in TPMT, MTHFR and SLCO1B1 Genes Be Used as Potential Markers of Outcome Prediction in Systemic Sclerosis Patients?

Systemic sclerosis (SSc) is a rare connective tissue disorder with highest morbidity and mortality among rheumatologic diseases. Disease progression is highly heterogeneous between patients, implying a strong need for individualization of therapy. Four pharmacogenetic variants, namely TPMT rs1800460, TPMT rs1142345, MTHFR rs1801133 and SLCO1B1 rs4149056 were tested for association with severe disease outcomes in 102 patients with SSc from Serbia treated either with immunosuppressants azathioprine (AZA) and methotrexate (MTX) or with other types of medications. Genotyping was performed using PCR-RFLP and direct Sanger sequencing. R software was used for statistical analysis and development of polygenic risk score (PRS) model. Association was found between MTHFR rs1801133 and higher risk for elevated systolic pressure in all patients except those prescribed with MTX, and higher risk for kidney insufficiency in patients prescribed with other types of drugs. In patients treated with MTX, variant SLCO1B1 rs4149056 was protective against kidney insufficiency. For patients receiving MTX a trend was shown for having a higher PRS rank and elevated systolic pressure. Our results open a door wide for more extensive research on pharmacogenomics markers in patients with SSc. Altogether, pharmacogenomics markers could predict the outcome of patients with SSc and help in prevention of adverse drug reactions.

iPSC-derived organ-on-a-chip models for personalized human genetics and pharmacogenomics studies

Genome-wide association studies (GWAS) have now correlated hundreds of genetic variants with complex genetic diseases and drug efficacy. Functional characterization of these factors remains challenging, particularly because of the lack of human model systems. Molecular and nanotechnological advances, in particular the ability to generate patient-specific PSC lines, differentiate them into diverse cell types, and seed and combine them on microfluidic chips, have led to the establishment of organ-on-a-chip (OoC) platforms that recapitulate organ biology. OoC technology thus provides unique personalized platforms for studying the effects of host genetics and environmental factors on organ physiology. In this review we describe the technology and provide examples of how OoCs may be used for disease modeling and pharmacogenetic research.

Does Therapeutic Repurposing in Cancer Meet the Expectations of Having Drugs at a Lower Price?

Therapeutic repurposing emerged as an alternative to the traditional drug discovery and development model (DDD) of new molecular entities (NMEs). It was anticipated that by being faster, safer, and cheaper, the development would result in lower-cost drugs. As defined in this work, a repurposed cancer drug is one approved by a health regulatory authority against a non-cancer indication that then gains new approval for cancer. With this definition, only three drugs are repurposed for cancer: Bacillus Calmette-Guerin (BCG) vaccine (superficial bladder cancer, thalidomide [multiple myeloma], and propranolol [infantile hemangioma]). Each of these has a different history regarding price and affordability, and it is not yet possible to generalize the impact of drug repurposing on the final price to the patient. However, the development, including the price, does not differ significantly from an NME. For the end consumer, the product's price is unrelated to whether it followed the classical development or repurposing. Economic constraints for clinical development, and drug prescription biases for repurposing drugs, are barriers yet to be overcome. The affordability of cancer drugs is a complex issue that varies from country to country. Many alternatives for having affordable drugs have been put forward, however these measures have thus far failed and are, at best, palliative. There are no immediate solutions to the problem of access to cancer drugs. It is necessary to critically analyze the impact of the current drug development model and be creative in implementing new models that genuinely benefit society.

Profiling the peripheral immune response to ex vivo TNF stimulation in untreated juvenile idiopathic arthritis using single cell RNA sequencing

BACKGROUND

Juvenile Idiopathic Arthritis (JIA) is an autoimmune disease with a heterogenous clinical presentation and unpredictable response to available therapies. This personalized transcriptomics study sought proof-of-concept for single-cell RNA sequencing to characterize patient-specific immune profiles.

METHODS

Whole blood samples from six untreated children, newly diagnosed with JIA, and two healthy controls were cultured for 24 h with or without ex vivo TNF stimulation and subjected to scRNAseq to examine cellular populations and transcript expression in PBMCs. A novel analytical pipeline, scPool, was developed wherein cells are first pooled into pseudocells prior to expression analysis, facilitating variance partitioning of the effects of TNF stimulus, JIA disease status, and individual donor.

RESULTS

Seventeen robust immune cell-types were identified, the abundance of which was significantly affected by TNF stimulus, which resulted in notable elevation of memory CD8 + T-cells and NK56 cells, but down-regulation of naïve B-cell proportions. Memory CD8 + and CD4 + T-cells were also both reduced in the JIA cases relative to two controls. Significant differential expression responses to TNF stimulus were also characterized, with monocytes showing more transcriptional shifts than T-lymphocyte subsets, while the B-cell response was more limited. We also show that donor variability exceeds the small degree of possible intrinsic differentiation between JIA and control profiles. An incidental finding of interest was association of HLA-DQA2 and HLA-DRB5 expression with JIA status.

CONCLUSIONS

These results support the development of personalized immune-profiling combined with ex-vivo immune stimulation for evaluation of patient-specific modes of immune cell activity in autoimmune rheumatic disease.

Osteoporosis: An Update on Screening, Diagnosis, Evaluation, and Treatment

Osteoporosis screening, diagnosis, and treatment have gained much attention in the health care community over the past 2 decades. During this time, creation of multispecialty awareness programs (eg, "Own the Bone," American Orthopedic Association; "Capture the Fracture," International Osteoporosis Foundation) and improvements in diagnostic protocols have been evident. Significant advances in technology have elucidated elements of genetic predisposition for decreased bone mineral density in the aging population. Additionally, several novel drug therapies have entered the market and provide more options for primary care and osteoporosis specialists to medically manage patients at risk for fragility fractures. Despite this, adherence to osteoporosis screening and treatment protocols has been surprisingly low by health care practitioners, including orthopedic surgeons. Continued awareness and education of this skeletal disorder is crucial to effectively care for our aging population. [Orthopedics. 2023;46(1):e20-e26.].

Polygenic risk scores for predicting outcomes and treatment response in psychiatry: hope or hype?

Over the last years, the decreased costs and enhanced accessibility to large genome-wide association studies datasets have laid the foundations for the development of polygenic risk scores (PRSs). A PRS is calculated on the weighted sum of single nucleotide polymorphisms and measures the individual genetic predisposition to develop a certain phenotype. An increasing number of studies have attempted to utilize the PRSs for risk stratification and prognostic evaluation. The present narrative review aims to discuss the potential clinical utility of PRSs in predicting outcomes and treatment response in psychiatry. After summarizing the evidence on major mental disorders, we have discussed the advantages and limitations of currently available PRSs. Although PRSs represent stable trait features with a normal distribution in the general population and can be relatively easily calculated in terms of time and costs, their real-world applicability is reduced by several limitations, such as low predictive power and lack of population diversity. Even with the rapid expansion of the psychiatric genetic knowledge base, pure genetic prediction in clinical psychiatry appears to be out of reach in the near future. Therefore, combining genomic and exposomic vulnerabilities for mental disorders with a detailed clinical characterization is needed to personalize care.

Genetic and epigenetic mechanisms influencing acute to chronic postsurgical pain transitions in pediatrics: Preclinical to clinical evidence

Background

Chronic postsurgical pain (CPSP) in children remains an important problem with no effective preventive or therapeutic strategies. Recently, genomic underpinnings explaining additional interindividual risk beyond psychological factors have been proposed.

Aims

We present a comprehensive review of current preclinical and clinical evidence for genetic and epigenetic mechanisms relevant to pediatric CPSP.

Methods

Narrative review.

Results

Animal models are relevant to translational research for unraveling genomic mechanisms. For example, Cacng2, p2rx7, and bdnf mutant mice show altered mechanical hypersensitivity to injury, and variants of the same genes have been associated with CPSP susceptibility in humans; similarly, differential DNA methylation (H1SP) and miRNAs (miR-96/7a) have shown translational implications. Animal studies also suggest that crosstalk between neurons and immune cells may be involved in nociceptive priming observed in neonates. In children, differential DNA methylation in regulatory genomic regions enriching GABAergic, dopaminergic, and immune pathways, as well as polygenic risk scores for enhanced prediction of CPSP, have been described. Genome-wide studies in pediatric CPSP are scarce, but pathways identified by adult gene association studies point to potential common mechanisms.

Conclusions

Bench-to-bedside genomics research in pediatric CPSP is currently limited. Reverse translational approaches, use of other -omics, and inclusion of pediatric/CPSP endophenotypes in large-scale biobanks may be potential solutions. Time of developmental vulnerability and longitudinal genomic changes after surgery warrant further investigation. Emergence of promising precision pain management strategies based on gene editing and epigenetic programing emphasize need for further research in pediatric CPSP-related genomics.

Are polygenic risk scores for systolic blood pressure and LDL-cholesterol associated with treatment effectiveness, and clinical outcomes among those on treatment?

AIMS

Many studies have investigated associations between polygenic risk scores (PRS) and the incidence of cardiovascular disease (CVD); few have examined whether risk factor-related PRS predict CVD outcomes among adults treated with risk-modifying therapies. We assessed whether PRS for systolic blood pressure (PRSSBP) and for low-density lipoprotein cholesterol (PRSLDL-C) were associated with achieving SBP and LDL-C-related targets, and with major adverse cardiovascular events (MACE: non-fatal stroke or myocardial infarction, CVD death, and revascularization procedures).

METHODS AND RESULTS

Using observational data from the UK Biobank (UKB), we calculated PRSSBP and PRSLDL-C and constructed two sub-cohorts of unrelated adults of White British ancestry aged 40-69 years and with no history of CVD, who reported taking medications used in the treatment of hypertension or hypercholesterolaemia. Treatment effectiveness in achieving adequate risk factor control was ascertained using on-treatment blood pressure (BP) or LDL-C levels measured at enrolment (uncontrolled hypertension: BP ≥ 140/90 mmHg; uncontrolled hypercholesterolaemia: LDL-C ≥ 3 mmol/L). We conducted multivariable logistic and Cox regression modelling for incident events, adjusting for socioeconomic characteristics, and CVD risk factors. There were 55 439 participants using BP lowering therapies (51.0% male, mean age 61.0 years, median follow-up 11.5 years) and 33 787 using LDL-C lowering therapies (58.5% male, mean age 61.7 years, median follow-up 11.4 years). PRSSBP was associated with uncontrolled hypertension (odds ratio 1.70; 95% confidence interval: 1.60-1.80) top vs. bottom quintile, equivalent to a 5.4 mmHg difference in SBP, and with MACE [hazard ratio (HR) 1.13; 1.04-1.23]. PRSLDL-C was associated with uncontrolled hypercholesterolaemia (HR 2.78; 2.58-3.00) but was not associated with subsequent MACE.

CONCLUSION

We extend previous findings in the UKB cohort to examine PRSSBP and PRSLDL-C with treatment effectiveness. Our results indicate that both PRSSBP and PRSLDL-C can help identify individuals who, despite being on treatment, have inadequately controlled SBP and LDL-C, and for SBP are at higher risk for CVD events. This extends the potential role of PRS in clinical practice from identifying patients who may need these interventions to identifying patients who may need more intensive intervention.

Genetics of osteoarthritis

Osteoarthritis genetics has been transformed in the past decade through the application of large-scale genome-wide association scans. So far, over 100 polymorphic DNA variants have been associated with this common and complex disease. These genetic risk variants account for over 20% of osteoarthritis heritability and the vast majority map to non-protein coding regions of the genome where they are presumed to act by regulating the expression of target genes. Statistical fine mapping, in silico analyses of genomics data, and laboratory-based functional studies have enabled the identification of some of these targets, which encode proteins with diverse roles, including extracellular signaling molecules, intracellular enzymes, transcription factors, and cytoskeletal proteins. A large number of the risk variants correlate with epigenetic factors, in particular cartilage DNA methylation changes in cis, implying that epigenetics may be a conduit through which genetic effects on gene expression are mediated. Some of the variants also appear to have been selected as humans adapted to bipedalism, suggesting that a proportion of osteoarthritis genetic susceptibility results from antagonistic pleiotropy, with risk variants having a positive role in joint formation but a negative role in the long-term health of the joint. Although data from an osteoarthritis genetic study has not yet directly led to a novel treatment, some of the osteoarthritis associated genes code for proteins that have available therapeutics. Genetic investigations are therefore revealing fascinating fundamental insights into osteoarthritis and can expose options for translational intervention.

Stability of polygenic scores across discovery genome-wide association studies

Polygenic scores (PGS) are commonly evaluated in terms of their predictive accuracy at the population level by the proportion of phenotypic variance they explain. To be useful for precision medicine applications, they also need to be evaluated at the individual level when phenotypes are not necessarily already known. We investigated the stability of PGS in European American (EUR) and African American (AFR)-ancestry individuals from the Philadelphia Neurodevelopmental Cohort and the Adolescent Brain Cognitive Development study using different discovery genome-wide association study (GWAS) results for post-traumatic stress disorder (PTSD), type 2 diabetes (T2D), and height. We found that pairs of EUR-ancestry GWAS for the same trait had genetic correlations >0.92. However, PGS calculated from pairs of same-ancestry and different-ancestry GWAS had correlations that ranged from <0.01 to 0.74. PGS stability was greater for height than for PTSD or T2D. A series of height GWAS in the UK Biobank suggested that correlation between PGS is strongly dependent on the extent of sample overlap between the discovery GWAS. Focusing on the upper end of the PGS distribution, different discovery GWAS do not consistently identify the same individuals in the upper quantiles, with the best case being 60% of individuals above the 80th percentile of PGS overlapping from one height GWAS to another. The degree of overlap decreases sharply as higher quantiles, less heritable traits, and different-ancestry GWAS are considered. PGS computed from different discovery GWAS have only modest correlation at the individual level, underscoring the need to proceed cautiously with integrating PGS into precision medicine applications.

Pharmacogenetics: A Precision Medicine Approach to Combatting the Opioid Epidemic

Ineffective pain control is the most commonly cited reason for misuse of prescription opioids and is influenced by genetics. In particular, the gene encoding the CYP2D6 enzyme, which metabolizes some of the most commonly prescribed opioids (e.g., tramadol, hydrocodone) to their more potent forms, is highly polymorphic and can lead to reduced concentrations of the active metabolites and decreased opioid effectiveness. Consideration of the CYP2D6 genotype may allow for predicting opioid response and identifying patients who are likely to respond well to lower potency opioids as well as those who may derive greater pain relief from non-opioid analgesics versus certain opioids. There is emerging evidence that a CYP2D6-guided approach to pain management improves pain control and reduces opioid consumption and thus may be a promising means for combating opioid misuse. Clinical practice guidelines are available for select opioids and other analgesics to support medication and dose selection based on pharmacogenetic data. This article describes the evidence supporting genotype-guided pain management as a means of improving pain control and reducing opioid misuse and clinical recommendations for genotype-guided analgesic prescribing. In addition, a "how to" guide using patient case examples is provided to demystify the process for implementing pharmacogenetics-guided pain management in order to optimize analgesia and minimize adverse effects. Optimizing pain management through genotype-guided approaches may ultimately provide safer and more effective therapy for pain control while decreasing the risk for opioid misuse.

Polygenic risk scores across the extended psychosis spectrum

As early detection of symptoms in the subclinical to clinical psychosis spectrum may improve health outcomes, knowing the probabilistic susceptibility of developing a disorder could guide mitigation measures and clinical intervention. In this context, polygenic risk scores (PRSs) quantifying the additive effects of multiple common genetic variants hold the potential to predict complex diseases and index severity gradients. PRSs for schizophrenia (SZ) and bipolar disorder (BD) were computed using Bayesian regression and continuous shrinkage priors based on the latest SZ and BD genome-wide association studies (Psychiatric Genomics Consortium, third release). Eight well-phenotyped groups (n = 1580; 56% males) were assessed: control (n = 305), lower (n = 117) and higher (n = 113) schizotypy (both groups of healthy individuals), at-risk for psychosis (n = 120), BD type-I (n = 359), BD type-II (n = 96), schizoaffective disorder (n = 86), and SZ groups (n = 384). PRS differences were investigated for binary traits and the quantitative Positive and Negative Syndrome Scale. Both BD-PRS and SZ-PRS significantly differentiated controls from at-risk and clinical groups (Nagelkerke's pseudo-R2: 1.3-7.7%), except for BD type-II for SZ-PRS. Out of 28 pairwise comparisons for SZ-PRS and BD-PRS, 9 and 12, respectively, reached the Bonferroni-corrected significance. BD-PRS differed between control and at-risk groups, but not between at-risk and BD type-I groups. There was no difference between controls and schizotypy. SZ-PRSs, but not BD-PRSs, were positively associated with transdiagnostic symptomology. Overall, PRSs support the continuum model across the psychosis spectrum at the genomic level with possible irregularities for schizotypy. The at-risk state demands heightened clinical attention and research addressing symptom course specifiers. Continued efforts are needed to refine the diagnostic and prognostic accuracy of PRSs in mental healthcare.

Genetic prediction of complex traits with polygenic scores: a statistical review

Accurate genetic prediction of complex traits can facilitate disease screening, improve early intervention, and aid in the development of personalized medicine. Genetic prediction of complex traits requires the development of statistical methods that can properly model polygenic architecture and construct a polygenic score (PGS). We present a comprehensive review of 46 methods for PGS construction. We connect the majority of these methods through a multiple linear regression framework which can be instrumental for understanding their prediction performance for traits with distinct genetic architectures. We discuss the practical considerations of PGS analysis as well as challenges and future directions of PGS method development. We hope our review serves as a useful reference both for statistical geneticists who develop PGS methods and for data analysts who perform PGS analysis.

Breast cancer risk stratification for mammographic screening: A nation-wide screening cohort of 24,431 women in Singapore

Background

Breast cancer incidence is increasing in Asia. However, few women in Singapore attend routine mammography screening. We aim to identify women at high risk of breast cancer who will benefit most from regular screening using the Gail model and information from their first screen (recall status and mammographic density).

Methods

In 24,431 Asian women (50–69 years) who attended screening between 1994 and 1997, 117 developed breast cancer within 5 years of screening. Cox proportional hazard models were used to study the associations between risk classifiers (Gail model 5‐year absolute risk, recall status, mammographic density), and breast cancer occurrence. The efficacy of risk stratification was evaluated by considering sensitivity, specificity, and the proportion of cancers identified.

Results

Adjusting for information from first screen attenuated the hazard ratios (HR) associated with 5‐year absolute risk (continuous, unadjusted HR [95% confidence interval]: 2.3 [1.8–3.1], adjusted HR: 1.9 [1.4–2.6]), but improved the discriminatory ability of the model (unadjusted AUC: 0.615 [0.559–0.670], adjusted AUC: 0.703 [0.653–0.753]). The sensitivity and specificity of the adjusted model were 0.709 and 0.622, respectively. Thirty‐eight percent of all breast cancers were detected in 12% of the study population considered high risk (top five percentile of the Gail model 5‐year absolute risk [absolute risk ≥1.43%], were recalled, and/or mammographic density ≥50%).

Conclusion

The Gail model is able to stratify women based on their individual breast cancer risk in this population. Including information from the first screen can improve prediction in the 5 years after screening. Risk stratification has the potential to pick up more cancers.

Utility of polygenic embryo screening for disease depends on the selection strategy

Polygenic risk scores (PRSs) have been offered since 2019 to screen in vitro fertilization embryos for genetic liability to adult diseases, despite a lack of comprehensive modeling of expected outcomes. Here we predict, based on the liability threshold model, the expected reduction in complex disease risk following polygenic embryo screening for a single disease. A strong determinant of the potential utility of such screening is the selection strategy, a factor that has not been previously studied. When only embryos with a very high PRS are excluded, the achieved risk reduction is minimal. In contrast, selecting the embryo with the lowest PRS can lead to substantial relative risk reductions, given a sufficient number of viable embryos. We systematically examine the impact of several factors on the utility of screening, including: variance explained by the PRS, number of embryos, disease prevalence, parental PRSs, and parental disease status. We consider both relative and absolute risk reductions, as well as population-averaged and per-couple risk reductions, and also examine the risk of pleiotropic effects. Finally, we confirm our theoretical predictions by simulating 'virtual' couples and offspring based on real genomes from schizophrenia and Crohn's disease case-control studies. We discuss the assumptions and limitations of our model, as well as the potential emerging ethical concerns.

Controlling for background genetic effects using polygenic scores improves the power of genome-wide association studies

Ongoing increases in the size of human genotype and phenotype collections offer the promise of improved understanding of the genetics of complex diseases. In addition to the biological insights that can be gained from the nature of the variants that contribute to the genetic component of complex trait variability, these data bring forward the prospect of predicting complex traits and the risk of complex genetic diseases from genotype data. Here we show that advances in phenotype prediction can be applied to improve the power of genome-wide association studies. We demonstrate a simple and efficient method to model genetic background effects using polygenic scores derived from SNPs that are not on the same chromosome as the target SNP. Using simulated and real data we found that this can result in a substantial increase in the number of variants passing genome-wide significance thresholds. This increase in power to detect trait-associated variants also translates into an increase in the accuracy with which the resulting polygenic score predicts the phenotype from genotype data. Our results suggest that advances in methods for phenotype prediction can be exploited to improve the control of background genetic effects, leading to more accurate GWAS results and further improvements in phenotype prediction.

Stratification of risk of progression to colectomy in ulcerative colitis via measured and predicted gene expression

An important goal of clinical genomics is to be able to estimate the risk of adverse disease outcomes. Between 5% and 10% of individuals with ulcerative colitis (UC) require colectomy within 5 years of diagnosis, but polygenic risk scores (PRSs) utilizing findings from genome-wide association studies (GWASs) are unable to provide meaningful prediction of this adverse status. By contrast, in Crohn disease, gene expression profiling of GWAS-significant genes does provide some stratification of risk of progression to complicated disease in the form of a transcriptional risk score (TRS). Here, we demonstrate that a measured TRS based on bulk rectal gene expression in the PROTECT inception cohort study has a positive predictive value approaching 50% for colectomy. Single-cell profiling demonstrates that the genes are active in multiple diverse cell types from both the epithelial and immune compartments. Expression quantitative trait locus (QTL) analysis identifies genes with differential effects at baseline and week 52 follow-up, but for the most part, differential expression associated with colectomy risk is independent of local genetic regulation. Nevertheless, a predicted polygenic transcriptional risk score (PPTRS) derived by summation of transcriptome-wide association study (TWAS) effects identifies UC-affected individuals at 5-fold elevated risk of colectomy with data from the UK Biobank population cohort studies, independently replicated in an NIDDK-IBDGC dataset. Prediction of gene expression from relatively small transcriptome datasets can thus be used in conjunction with TWASs for stratification of risk of disease complications.

Polygenic risk scores predict diabetes complications and their response to intensive blood pressure and glucose control

AIMS/HYPOTHESIS

Type 2 diabetes increases the risk of cardiovascular and renal complications, but early risk prediction could lead to timely intervention and better outcomes. Genetic information can be used to enable early detection of risk.

METHODS

We developed a multi-polygenic risk score (multiPRS) that combines ten weighted PRSs (10 wPRS) composed of 598 SNPs associated with main risk factors and outcomes of type 2 diabetes, derived from summary statistics data of genome-wide association studies. The 10 wPRS, first principal component of ethnicity, sex, age at onset and diabetes duration were included into one logistic regression model to predict micro- and macrovascular outcomes in 4098 participants in the ADVANCE study and 17,604 individuals with type 2 diabetes in the UK Biobank study.

RESULTS

The model showed a similar predictive performance for cardiovascular and renal complications in different cohorts. It identified the top 30% of ADVANCE participants with a mean of 3.1-fold increased risk of major micro- and macrovascular events (p = 6.3 × 10^-21 and p = 9.6 × 10^-31, respectively) and a 4.4-fold (p = 6.8 × 10^-33) higher risk of cardiovascular death. While in ADVANCE overall, combined intensive blood pressure and glucose control decreased cardiovascular death by 24%, the model identified a high-risk group in whom it decreased the mortality rate by 47%, and a low-risk group in whom it had no discernible effect. High-risk individuals had the greatest absolute risk reduction with a number needed to treat of 12 to prevent one cardiovascular death over 5 years.

CONCLUSIONS/INTERPRETATION

This novel multiPRS model stratified individuals with type 2 diabetes according to risk of complications and helped to target earlier those who would receive greater benefit from intensive therapy.

Improved genetic prediction of complex traits from individual-level data or summary statistics

Most existing tools for constructing genetic prediction models begin with the assumption that all genetic variants contribute equally towards the phenotype. However, this represents a suboptimal model for how heritability is distributed across the genome. Therefore, we develop prediction tools that allow the user to specify the heritability model. We compare individual-level data prediction tools using 14 UK Biobank phenotypes; our new tool LDAK-Bolt-Predict outperforms the existing tools Lasso, BLUP, Bolt-LMM and BayesR for all 14 phenotypes. We compare summary statistic prediction tools using 225 UK Biobank phenotypes; our new tool LDAK-BayesR-SS outperforms the existing tools lassosum, sBLUP, LDpred and SBayesR for 223 of the 225 phenotypes. When we improve the heritability model, the proportion of phenotypic variance explained increases by on average 14%, which is equivalent to increasing the sample size by a quarter.

Perspectives on Drug Repurposing

Complex common diseases are a significant burden for our societies and demand not only preventive measures but also more effective, safer, and more affordable treatments. The whole process of the current model of drug discovery and development implies a high investment by the pharmaceutical industry, which ultimately impact in high drug prices. In this sense, drug repurposing would help meet the needs of patients to access useful and novel treatments. Unlike the traditional approach, drug repurposing enters both the preclinical evaluation and clinical trials of the compound of interest faster, budgeting research and development costs, and limiting potential biosafety risks. The participation of government, society, and private investors is needed to secure the funds for experimental design and clinical development of repurposing candidates to have affordable, effective, and safe repurposed drugs. Moreover, extensive advertising of repurposing as a concept in the health community, could reduce prescribing bias when enough clinical evidence exists, which will support the employment of cheaper and more accessible repurposed compounds for common conditions.

Advances in Genomic Discovery and Implications for Personalized Prevention and Medicine: Estonia as Example

The current paradigm of personalized medicine envisages the use of genomic data to provide predictive information on the health course of an individual with the aim of prevention and individualized care. However, substantial efforts are required to realize the concept: enhanced genetic discoveries, translation into intervention strategies, and a systematic implementation in healthcare. Here we review how further genetic discoveries are improving personalized prediction and advance functional insights into the link between genetics and disease. In the second part we give our perspective on the way these advances in genomic research will transform the future of personalized prevention and medicine using Estonia as a primer.

Novel approaches to develop biomarkers predicting treatment responses to TNF-blockers

Introduction: Chronic inflammatory diseases (CIDs) cause significant morbidity and are a considerable burden for the patients in terms of pain, impaired function, and diminished quality of life. Important progress in CID treatment has been obtained with biological therapies, such as tumor-necrosis-factor blockers. However, more than a third of the patients fail to respond to these inhibitors and are exposed to the side effects of treatment, without the benefits. Therefore, there is a strong interest in developing tools to predict response of patients to biologics. Areas covered: The authors searched PubMed for recent studies on biomarkers for disease assessment and prediction of therapeutic responses, focusing on the effect of TNF blockers on immune responses in spondyloarthritis (SpA), and other CID, in particular rheumatoid arthritis and inflammatory bowel disease. Conclusions will be drawn about the possible development of predictive biomarkers for response to treatment. Expert opinion: No validated biomarker is currently available to predict treatment response in CID. New insight could be generated through the development of new bioinformatic modeling approaches to combine multidimensional biomarkers that explain the different genetic, immunological and environmental determinants of therapeutic responses.

Systems Biology Guided Gene Enrichment Approaches Improve Prediction of Chronic Post-surgical Pain After Spine Fusion

Objectives

Incorporation of genetic factors in psychosocial/perioperative models for predicting chronic postsurgical pain (CPSP) is key for personalization of analgesia. However, single variant associations with CPSP have small effect sizes, making polygenic risk assessment important. Unfortunately, pediatric CPSP studies are not sufficiently powered for unbiased genome wide association (GWAS). We previously leveraged systems biology to identify candidate genes associated with CPSP. The goal of this study was to use systems biology prioritized gene enrichment to generate polygenic risk scores (PRS) for improved prediction of CPSP in a prospectively enrolled clinical cohort.

Methods

In a prospectively recruited cohort of 171 adolescents (14.5 ± 1.8 years, 75.4% female) undergoing spine fusion, we collected data about anesthesia/surgical factors, childhood anxiety sensitivity (CASI), acute pain/opioid use, pain outcomes 6-12 months post-surgery and blood (for DNA extraction/genotyping). We previously prioritized candidate genes using computational approaches based on similarity for functional annotations with a literature-derived "training set." In this study, we tested ranked deciles of 1336 prioritized genes for increased representation of variants associated with CPSP, compared to 10,000 randomly selected control sets. Penalized regression (LASSO) was used to select final variants from enriched variant sets for calculation of PRS. PRS incorporated regression models were compared with previously published non-genetic models for predictive accuracy.

Results

Incidence of CPSP in the prospective cohort was 40.4%. 33,104 case and 252,590 control variants were included for association analyses. The smallest gene set enriched for CPSP had 80/1010 variants associated with CPSP (p < 0.05), significantly higher than in 10,000 randomly selected control sets (p = 0.0004). LASSO selected 20 variants for calculating weighted PRS. Model adjusted for covariates including PRS had AUROC of 0.96 (95% CI: 0.92-0.99) for CPSP prediction, compared to 0.70 (95% CI: 0.59-0.82) for non-genetic model (p < 0.001). Odds ratios and positive regression coefficients for the final model were internally validated using bootstrapping: PRS [OR 1.98 (95% CI: 1.21-3.22); β 0.68 (95% CI: 0.19-0.74)] and CASI [OR 1.33 (95% CI: 1.03-1.72); β 0.29 (0.03-0.38)].

Discussion

Systems biology guided PRS improved predictive accuracy of CPSP risk in a pediatric cohort. They have potential to serve as biomarkers to guide risk stratification and tailored prevention. Findings highlight systems biology approaches for deriving PRS for phenotypes in cohorts less amenable to large scale GWAS.

Polygenic Risk Scores Augment Stroke Subtyping

Objective

To determine whether the polygenic risk score (PRS) derived from MEGASTROKE is associated with ischemic stroke (IS) and its subtypes in an independent tertiary health care system and to identify the PRS derived from gene sets of known biological pathways associated with IS.

Methods

Controls (n = 19,806/7,484, age ≥69/79 years) and cases (n = 1,184/951 for discovery/replication) of acute IS with European ancestry and clinical risk factors were identified by leveraging the Geisinger Electronic Health Record and chart review confirmation. All Geisinger MyCode patients with age ≥69/79 years and without any stroke-related diagnostic codes were included as low risk control. Genetic heritability and genetic correlation between Geisinger and MEGASTROKE (EUR) were calculated using the summary statistics of the genome-wide association study by linkage disequilibrium score regression. All PRS for any stroke (AS), any ischemic stroke (AIS), large artery stroke (LAS), cardioembolic stroke (CES), and small vessel stroke (SVS) were constructed by PRSice-2.

Results

A moderate heritability (10%–20%) for Geisinger sample as well as the genetic correlation between MEGASTROKE and the Geisinger cohort was identified. Variation of all 5 PRS significantly explained some of the phenotypic variations of Geisinger IS, and the R² increased by raising the cutoff for the age of controls. PRSLAS, PRSCES, and PRSSVS derived from low-frequency common variants provided the best fit for modeling (R² = 0.015 for PRSLAS). Gene sets analyses highlighted the association of PRS with Gene Ontology terms (vascular endothelial growth factor, amyloid precursor protein, and atherosclerosis). The PRSLAS, PRSCES, and PRSSVS explained the most variance of the corresponding subtypes of Geisinger IS suggesting shared etiologies and corroborated Geisinger TOAST subtyping.

Conclusions

We provide the first evidence that PRSs derived from MEGASTROKE have value in identifying shared etiologies and determining stroke subtypes.

GABAergic polygenic risk for cocaine use disorder is negatively correlated with precuneus activity during cognitive control in African American individuals

Impaired cognitive control has been implicated in cocaine use disorder (CUD). GABAergic treatments have been proposed for CUD. Here we examined relationships between GABAergic genes and neural correlates of cognitive control in CUD. We analyzed two independent African American cohorts: one of >3000 genomewide-genotyped subjects with substance dependence and another of 40 CUD and 22 healthy control (HC) subjects who were exome-array genotyped and completed an fMRI Stroop task. We used five association thresholds to select variants of GABAergic genes in the reference cohort, yielding five polygenic risk scores (i.e., CUD-GABA-PRSs) for the fMRI cohort. At p < 0.005, the CUD-GABA-PRSs, which aggregated relative risks of CUD from 89 variants harboring in 16 genes, differed between CUD and HC individuals in the fMRI sample (p = 0.013). This CUD-GABA-PRS correlated inversely with Stroop-related activity in the left precuneus in CUD (r = -80.58, p_FWE < 0.05) but not HC participants. Post-hoc seed-based connectivity analysis of the left precuneus identified reduced functional connectivity to the posterior cingulate cortex (PCC) in CUD compared to HC subjects (p = 0.0062) and the degree of connectivity correlated with CUD-GABA-PRSs in CUD individuals (r = 0.287, p = 0.036). Our findings suggest that the GABAergic genetic risk of CUD in African Americans relates to precuneus/PCC functional connectivity during cognitive control. Identification of these GABAergic processes may be relevant targets in CUD treatment. The novel identification of 16 GABAergic genes may be investigated further to inform treatment development efforts for this condition that currently has no medication with a formal indication for its treatment.

Highly elevated polygenic risk scores are better predictors of myocardial infarction risk early in life than later

Background

Several polygenic risk scores (PRS) have been developed for cardiovascular risk prediction, but the additive value of including PRS together with conventional risk factors for risk prediction is questionable. This study assesses the clinical utility of including four PRS generated from 194, 46K, 1.5M, and 6M SNPs, along with conventional risk factors, to predict risk of ischemic heart disease (IHD), myocardial infarction (MI), and first MI event on or before age 50 (early MI).

Methods

A cross-validated logistic regression (LR) algorithm was trained either on ~ 440K European ancestry individuals from the UK Biobank (UKB), or the full UKB population, including as features different combinations of conventional established-at-birth risk factors (ancestry, sex) and risk factors that are non-fixed over an individual’s lifespan (age, BMI, hypertension, hyperlipidemia, diabetes, smoking, family history), with and without also including PRS. The algorithm was trained separately with IHD, MI, and early MI as prediction labels.

Results

When LR was trained using risk factors established-at-birth, adding the four PRS significantly improved the area under the curve (AUC) for IHD (0.62 to 0.67) and MI (0.67 to 0.73), as well as for early MI (0.70 to 0.79). When LR was trained using all risk factors, adding the four PRS only resulted in a significantly higher disease prevalence in the 98th and 99th percentiles of both the IHD and MI scores.

Conclusions

PRS improve cardiovascular risk stratification early in life when knowledge of later-life risk factors is unavailable. However, by middle age, when many risk factors are known, the improvement attributed to PRS is marginal for the general population.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-021-00828-8.

An Academic Clinician's Road Map to Hypertension Genomics: Recent Advances and Future Directions MMXX

At the dawn of the new decade, it is judicious to reflect on the boom of knowledge about polygenic risk for essential hypertension supplied by the wealth of genome-wide association studies. Hypertension continues to account for significant cardiovascular morbidity and mortality, with increasing prevalence anticipated. Here, we overview recent advances in the use of big data to understand polygenic hypertension, as well as opportunities for future innovation to translate this windfall of knowledge into clinical benefit.

Cannabis Use in People With Obsessive-Compulsive Symptomatology: Results From a Mexican Epidemiological Sample

Recent studies suggest that the endocannabinoid system could play an important role in the physiopathology of obsessive-compulsive disorder (OCD). There are reports of effective treatment with derivatives of tetrahydrocannabinol (THC). The study of the genetic factor associated with psychiatric disorders has made possible an exploration of its contribution to the pharmacological response. However, very little is known about the genetic factor or the prevalence of cannabis use in the Mexican population with OCD. The objective of this study is to compare the prevalence of use and dependence on cannabis in individuals with obsessive-compulsive symptomatology (OCS) with that of individuals with other psychiatric symptoms (psychosis, depression, and anxiety), and to explore the association between genetic risk and use. The study includes a total of 13,130 individuals evaluated in the second stage of the 2016 National Survey of Drug, Alcohol, and Tobacco Use (Encodat 2016), with genetic analysis (polygenic risk scoring) of a subsample of 3,521 individuals. Obsessive symptomatology had a prevalence of 7.2% and compulsive symptomatology a prevalence of 8.6%. The proportion of individuals with OCS who had ever used cannabis was 23.4%, and of those with cannabis dependency was 2.7%, the latter figure higher than that in individuals with other psychiatric symptoms (hypomania, 2.6%; anxiety, 2.8%; depression, 2.3%), except psychosis (5.9%). Individuals with OCS who reported using cannabis had an increased genetic risk for cannabis dependence but not for OCD. We thus cannot know how the increased genetic risk of cannabis dependence in people with OCD is influenced by their pharmacological response to derivatives of THC. The results, however, suggest paths for future studies.

Using common genetic variation to examine phenotypic expression and risk prediction in 22q11.2 deletion syndrome

The 22q11.2 deletion syndrome (22q11DS) is associated with a 20-25% risk of schizophrenia. In a cohort of 962 individuals with 22q11DS, we examined the shared genetic basis between schizophrenia and schizophrenia-related early trajectory phenotypes: sub-threshold symptoms of psychosis, low baseline intellectual functioning and cognitive decline. We studied the association of these phenotypes with two polygenic scores, derived for schizophrenia and intelligence, and evaluated their use for individual risk prediction in 22q11DS. Polygenic scores were not only associated with schizophrenia and baseline intelligence quotient (IQ), respectively, but schizophrenia polygenic score was also significantly associated with cognitive (verbal IQ) decline and nominally associated with sub-threshold psychosis. Furthermore, in comparing the tail-end deciles of the schizophrenia and IQ polygenic score distributions, 33% versus 9% of individuals with 22q11DS had schizophrenia, and 63% versus 24% of individuals had intellectual disability. Collectively, these data show a shared genetic basis for schizophrenia and schizophrenia-related phenotypes and also highlight the future potential of polygenic scores for risk stratification among individuals with highly, but incompletely, penetrant genetic variants.

The Differential Influence of Immune, Endocytotic, and Lipid Metabolism Genes on Amyloid Deposition and Neurodegeneration in Subjects at Risk of Alzheimer's Disease

BACKGROUND

Over 20 single-nucleotide polymorphisms (SNPs) are associated with increased risk of Alzheimer's disease (AD). We categorized these loci into immunity, lipid metabolism, and endocytosis pathways, and associated the polygenic risk scores (PRS) calculated, with AD biomarkers in mild cognitive impairment (MCI) subjects.

OBJECTIVE

The aim of this study was to identify associations between pathway-specific PRS and AD biomarkers in patients with MCI and healthy controls.

METHODS

AD biomarkers ([18F]Florbetapir-PET SUVR, FDG-PET SUVR, hippocampal volume, CSF tau and amyloid-β levels) and neurocognitive tests scores were obtained in 258 healthy controls and 451 MCI subjects from the ADNI dataset at baseline and at 24-month follow up. Pathway-related (immunity, lipid metabolism, and endocytosis) and total polygenic risk scores were calculated from 20 SNPs. Multiple linear regression analysis was used to test predictive value of the polygenic risk scores over longitudinal biomarker and cognitive changes.

RESULTS

Higher immune risk score was associated with worse cognitive measures and reduced glucose metabolism. Higher lipid risk score was associated with increased amyloid deposition and cortical hypometabolism. Total, immune, and lipid scores were associated with significant changes in cognitive measures, amyloid deposition, and brain metabolism.

CONCLUSION

Polygenic risk scores highlights the influence of specific genes on amyloid-dependent and independent pathways; and these pathways could be differentially influenced by lipid and immune scores respectively.

Genome Wide Epistasis Study of On-Statin Cardiovascular Events with Iterative Feature Reduction and Selection

Predicting risk for major adverse cardiovascular events (MACE) is an evidence-based practice that incorporates lifestyle, history, and other risk factors. Statins reduce risk for MACE by decreasing lipids, but it is difficult to stratify risk following initiation of a statin. Genetic risk determinants for on-statin MACE are low-effect size and impossible to generalize. Our objective was to determine high-level epistatic risk factors for on-statin MACE with GWAS-scale data. Controlled-access data for 5890 subjects taking a statin collected from Vanderbilt University Medical Center's BioVU were obtained from dbGaP. We used Random Forest Iterative Feature Reduction and Selection (RF-IFRS) to select highly informative genetic and environmental features from a GWAS-scale dataset of patients taking statin medications. Variant-pairs were distilled into overlapping networks and assembled into individual decision trees to provide an interpretable set of variants and associated risk. 1718 cases who suffered MACE and 4172 controls were obtained from dbGaP. Pathway analysis showed that variants in genes related to vasculogenesis (FDR = 0.024), angiogenesis (FDR = 0.019), and carotid artery disease (FDR = 0.034) were related to risk for on-statin MACE. We identified six gene-variant networks that predicted odds of on-statin MACE. The most elevated risk was found in a small subset of patients carrying variants in COL4A2, TMEM178B, SZT2, and TBXAS1 (OR = 4.53, p < 0.001). The RF-IFRS method is a viable method for interpreting complex "black-box" findings from machine-learning. In this study, it identified epistatic networks that could be applied to risk estimation for on-statin MACE. Further study will seek to replicate these findings in other populations.

The pharmacogenetics of opioid treatment for pain management

BACKGROUND

Opioids are widely used as an analgesic for the treatment of moderate to severe pain. However, there are interindividual variabilities in opioid response. Current evidence suggests that these variabilities can be attributed to single nucleotide polymorphisms in genes involved in opioid pharmacodynamics and pharmacokinetics. Knowledge of these genetic factors through pharamacogenetic (PGx) testing can help clinicians to more consistently prescribe opioids that can provide patients with maximal clinical benefit and minimal risk of adverse effects.

AIM

The research outlined in this literature review identifies variants involved in opioid PGx, which may be an important tool to achieving the goal of personalized pain management.

RESULTS

Cytochrome P450 (CYP) 2D6, CYP3A4, CYP3A5, catechol-o-methyltransferase (COMT), adenosine triphosphate binding cassette transporter B1 (ABCB1), opioid receptor mu 1 (OPRM1), and opioid receptor delta 1 (OPRD1) are all important genes involved in opioid drug response, side effect profile and risk of dependence; these are important genetic factors that should be included in potential opioid PGx tests for pain management.

CONCLUSIONS

Employing a PGx-guided strategy for prescribing opioids can improve response rate, reduce side effects and increase adherence to treatment plans for pain; more research is needed to explore opioid-related PGx factors for the development and validation of an opioid genetic panel. Optimal prescriptions could also provide healthcare payers with beneficial savings, while reducing the risk of propagating the current opioid crisis.

Polygenic Score for β-Blocker Survival Benefit in European Ancestry Patients With Reduced Ejection Fraction Heart Failure

BACKGROUND

β-Blockers (BBs) are mainstay therapy for heart failure with reduced ejection fraction. However, individual patient responses to BB vary, which may be partially due to genetic variation. The goal of this study was to derive and validate the first polygenic response predictor (PRP) for BB survival benefit in heart failure with reduced ejection fraction patients.

METHODS

Derivation and validation analyses were performed in n=1436 total HF patients of European descent and with ejection fraction <50%. The PRP was derived in a random subset of the Henry Ford Heart Failure Pharmacogenomic Registry (n=248) and then validated in a meta-analysis of the remaining patients from Henry Ford Heart Failure Pharmacogenomic Registry (n=247), the TIME-CHF (Trial of Intensified Versus Standard Medical Therapy in Elderly Patients With Congestive Heart Failure; n=431), and HF-ACTION trial (Heart Failure: a Controlled Trial Investigating Outcomes of Exercise Training; n=510). The PRP was constructed from a genome-wide analysis of BB×genotype interaction predicting time to all-cause mortality, adjusted for Meta-Analysis Global Group in Chronic Heart Failure score, genotype, level of BB exposure, and BB propensity score.

RESULTS

Five-fold cross-validation summaries out to 1000 single-nucleotide polymorphisms identified optimal prediction with a 44 single-nucleotide polymorphism score and cutoff at the 30th percentile. In validation testing (n=1188), greater BB exposure was associated with reduced all-cause mortality in patients with low PRP score (n=251; hazard ratio, 0.19 [95% CI, 0.04-0.51]; P=0.0075) but not high PRP score (n=937; hazard ratio, 0.84 [95% CI, 0.53-1.3]; P=0.448)-a difference that was statistically significant (P interaction, 0.0235). Results were consistent regardless of atrial fibrillation, ejection fraction (≤40% versus 41%-50%), or when examining cardiovascular death.

CONCLUSIONS

Among patients of European ancestry with heart failure with reduced ejection fraction, a PRP distinguished patients who derived substantial survival benefit from BB exposure from a larger group that did not. Additional work is needed to prospectively test clinical utility and to develop PRPs for other population groups and other medications.

Forecasting Opioid Use Disorder at 25 Years of Age in 16-Year-Old Adolescents

OBJECTIVE

To evaluate the accuracy of detecting 16-year-old male (n = 465) and female (n = 162) youths who subsequently manifest opioid use disorder (OUD) at 25 years of age. We hypothesized that the combined measures of 2 components of etiology, heritable risk, and substance use, accurately detect youths who develop OUD.

STUDY DESIGN

Heritable risk was measured by the transmissible liability index (TLI). Severity of the prodrome presaging OUD was quantified by the revised Drug Use Screening Inventory containing the consumption frequency index (CFI) documenting substance use events during the past month and the overall problem density (OPD) score indicating co-occurring biopsychosocial problems. Diagnosis of OUD was formulated by a clinical committee based on results of the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition in conjunction with medical and social history records.

RESULTS

Bivariate analysis shows that the TLI, CFI, and OPD scores at 16 years of age predict OUD at 25 years. Multivariate modeling indicates that the TLI combined with the CFI predict OUD with 86% accuracy (sensitivity = 87%; specificity = 62%). The TLI and CFI at 16 years of age mediate the association between parental substance use disorder and OUD in offspring at 25 years of age, indicating that these measures respectively evaluate risk and prodrome.

CONCLUSIONS

These results demonstrate the feasibility of identifying youths requiring intervention to prevent OUD.

A single nucleotide polymorphism genetic risk score to aid diagnosis of coeliac disease: a pilot study in clinical care

BACKGROUND

Single nucleotide polymorphism-based genetic risk scores (GRS) model genetic risk as a continuum and can discriminate coeliac disease but have not been validated in clinic. Human leukocyte antigen (HLA) DQ gene testing is available in clinic but does not include non-HLA attributed risk and is limited by discrete risk stratification.

AIMS

To accurately characterise both HLA and non-HLA coeliac disease genetic risk as a single nucleotide polymorphism-based GRS and evaluate diagnostic utility.

METHODS

We developed a 42 single nucleotide polymorphism coeliac disease GRS from a European case-control study (12 041 cases vs 12 228 controls) using HLA-DQ imputation and published genome-wide association studies. We validated the GRS in UK Biobank (1237 cases) and developed direct genotyping assays. We tested the coeliac disease GRS in a pilot clinical cohort of 128 children presenting with suspected coeliac disease.

RESULTS

The GRS was more discriminative of coeliac disease than HLA-DQ stratification in UK Biobank (receiver operating characteristic area under the curve [ROC-AUC] = 0.88 [95% CIs: 0.87-0.89] vs 0.82 [95% CIs: 0.80-0.83]). We demonstrated similar discrimination in the pilot clinical cohort (114 cases vs 40 controls, ROC-AUC = 0.84 [95% CIs: 0.76-0.91]). As a rule-out test, no children with coeliac disease in the clinical cohort had a GRS below 38th population centile.

CONCLUSIONS

A single nucleotide polymorphism-based GRS may offer more effective and cost-efficient testing of coeliac disease genetic risk in comparison to HLA-DQ stratification. As a comparatively inexpensive test it could facilitate non-invasive coeliac disease diagnosis but needs detailed assessment in the context of other diagnostic tests and against current diagnostic algorithms.

Tutorial: a guide to performing polygenic risk score analyses

A polygenic score (PGS) or polygenic risk score (PRS) is an estimate of an individual's genetic liability to a trait or disease, calculated according to their genotype profile and relevant genome-wide association study (GWAS) data. While present PRSs typically explain only a small fraction of trait variance, their correlation with the single largest contributor to phenotypic variation-genetic liability-has led to the routine application of PRSs across biomedical research. Among a range of applications, PRSs are exploited to assess shared etiology between phenotypes, to evaluate the clinical utility of genetic data for complex disease and as part of experimental studies in which, for example, experiments are performed that compare outcomes (e.g., gene expression and cellular response to treatment) between individuals with low and high PRS values. As GWAS sample sizes increase and PRSs become more powerful, PRSs are set to play a key role in research and stratified medicine. However, despite the importance and growing application of PRSs, there are limited guidelines for performing PRS analyses, which can lead to inconsistency between studies and misinterpretation of results. Here, we provide detailed guidelines for performing and interpreting PRS analyses. We outline standard quality control steps, discuss different methods for the calculation of PRSs, provide an introductory online tutorial, highlight common misconceptions relating to PRS results, offer recommendations for best practice and discuss future challenges.

Challenges and Opportunities in Clinical Applications of Blood-Based Proteomics in Cancer

Simple Summary

The traditional approach in identifying cancer related protein biomarkers has focused on evaluation of a single peptide/protein in tissue or circulation. At best, this approach has had limited success for clinical applications, since multiple pathological tumor pathways may be involved during initiation or progression of cancer which diminishes the significance of a single candidate protein/peptide. Emerging sensitive proteomic based technologies like liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics can provide a platform for evaluating serial serum or plasma samples to interrogate secreted products of tumor–host interactions, thereby revealing a more “complete” repertoire of biological variables encompassing heterogeneous tumor biology. However, several challenges need to be met for successful application of serum/plasma based proteomics. These include uniform pre-analyte processing of specimens, sensitive and specific proteomic analytical platforms and adequate attention to study design during discovery phase followed by validation of discovery-level signatures for prognostic, predictive, and diagnostic cancer biomarker applications.

Abstract

Blood is a readily accessible biofluid containing a plethora of important proteins, nucleic acids, and metabolites that can be used as clinical diagnostic tools in diseases, including cancer. Like the on-going efforts for cancer biomarker discovery using the liquid biopsy detection of circulating cell-free and cell-based tumor nucleic acids, the circulatory proteome has been underexplored for clinical cancer biomarker applications. A comprehensive proteome analysis of human serum/plasma with high-quality data and compelling interpretation can potentially provide opportunities for understanding disease mechanisms, although several challenges will have to be met. Serum/plasma proteome biomarkers are present in very low abundance, and there is high complexity involved due to the heterogeneity of cancers, for which there is a compelling need to develop sensitive and specific proteomic technologies and analytical platforms. To date, liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics has been a dominant analytical workflow to discover new potential cancer biomarkers in serum/plasma. This review will summarize the opportunities of serum proteomics for clinical applications; the challenges in the discovery of novel biomarkers in serum/plasma; and current proteomic strategies in cancer research for the application of serum/plasma proteomics for clinical prognostic, predictive, and diagnostic applications, as well as for monitoring minimal residual disease after treatments. We will highlight some of the recent advances in MS-based proteomics technologies with appropriate sample collection, processing uniformity, study design, and data analysis, focusing on how these integrated workflows can identify novel potential cancer biomarkers for clinical applications.

[Assessing the quality of digital health services: How can informed decisions be promoted?]

An important prerequisite for the success of the digitisation of the healthcare system are risk-literate users. Risk literacy means the ability to weigh potential benefits and harms of digital technologies and information, to use digital services critically, and to understand statistical evidence. How do people find reliable and comprehensible health information on the Internet? How can they better assess the quality of algorithmic decision systems? This narrative contribution describes two approaches that show how the competence to make informed decisions can be promoted.Evidence-based and reliable health information exists on the Internet but must be distinguished from a large amount of unreliable information. Various institutions in the German-speaking world have therefore provided guidance to help laypersons make informed decisions. The Harding Center for Risk Literacy in Potsdam, for example, has developed a decision tree ("fast-and-frugal tree"). When dealing with algorithms, natural frequency trees (NFTs) can help to assess the quality and fairness of an algorithmic decision system.Independent of reliable and comprehensible digital health services, further tools for laypersons to assess information and algorithms should be developed and provided. These tools can also be included in institutional training programmes for the promotion of digital literacy. This would be an important step towards the success of digitisation in prevention and health promotion.

The contribution of non-coding regulatory elements to cardiovascular disease

Cardiovascular disease collectively accounts for a quarter of deaths worldwide. Genome-wide association studies across a range of cardiovascular traits and pathologies have highlighted the prevalence of common non-coding genetic variants within candidate loci. Here, we review genetic, epigenomic and molecular approaches to investigate the contribution of non-coding regulatory elements in cardiovascular biology. We then discuss recent insights on the emerging role of non-coding variation in predisposition to cardiovascular disease, with a focus on novel mechanistic examples from functional genomics studies. Lastly, we consider the clinical significance of these findings at present, and some of the current challenges facing the field.

Genetic susceptibility, plant-based dietary patterns, and risk of cardiovascular disease

BACKGROUND

Plant-based dietary patterns may be related to better cardiovascular profiles. Whether a healthy plant-based dietary index is predictive of future cardiovascular disease (CVD) across people with different genetic susceptibility remains uncertain.

OBJECTIVE

We investigated associations of adherence to healthy plant-based diets with the incidence of CVD considering the genetic susceptibility.

METHODS

This prospective cohort study included a total of 156,148 adults initially free of CVD and cancer. We calculated a healthful plant-based diet index (healthful-PDI) in which healthy plant foods received positive scores, and less healthy plant foods and animal foods received reverse scores. Genetic risk scores (GRSs) for myocardial infarction (MI) and stroke were calculated to assess interactions between healthful-PDI and GRSs.

RESULTS

During 5 y of follow-up, we observed 1812 incident cases of CVD. Higher healthful-PDI was associated with a lower CVD risk [HR per 10-unit increment: 0.87 (95% CI: 0.81, 0.93) after adjusting for demographic, lifestyle, and other dietary factors (model 1); HR 0.90 (0.84, 0.97) after further adjusting for obesity and metabolic factors (model 2)]. The risk of CVD was gradually decreased in association with higher adherence to healthful-PDI, regardless of genetic susceptibility. The inverse associations of healthful-PDI with CVD were consistently observed in people with low GRS-MI [HR 0.85 (95% CI: 0.76, 0.94) in model 1; HR 0.88 (0.79, 0.97) in model 2] and those with high GRS-MI [HR 0.91 (0.82, 0.99) in model 1; HR 0.94 (0.86, 1.04) in model 2], without significant interactions (Pinteraction = 0.59 in model 1; Pinteraction = 0.66 in model 2). Similarly, higher healthful-PDI was related to a lower risk of CVD, regardless of low/high GRS-stroke.

CONCLUSION

Adherence to healthy plant-based diets may be associated with a decreased incidence of CVD in the entire population, suggesting that plant-based dietary patterns may modify the risk of CVD, regardless of genetic susceptibility.

Importance of Genetic Studies of Cardiometabolic Disease in Diverse Populations

Genome-wide association studies have revolutionized our understanding of the genetic underpinnings of cardiometabolic disease. Yet, the inadequate representation of individuals of diverse ancestral backgrounds in these studies may undercut their ultimate potential for both public health and precision medicine. The goal of this review is to describe the imperativeness of studying the populations who are most affected by cardiometabolic disease, to the aim of better understanding the genetic underpinnings of the disease. We support this premise by describing the current variation in the global burden of cardiometabolic disease and emphasize the importance of building a globally and ancestrally representative genetics evidence base for the identification of population-specific variants, fine-mapping, and polygenic risk score estimation. We discuss the important ethical, legal, and social implications of increasing ancestral diversity in genetic studies of cardiometabolic disease and the challenges that arise from the (1) lack of diversity in current reference populations and available analytic samples and the (2) unequal generation of health-associated genomic data and their prediction accuracies. Despite these challenges, we conclude that additional, unprecedented opportunities lie ahead for public health genomics and the realization of precision medicine, provided that the gap in diversity can be systematically addressed. Achieving this goal will require concerted efforts by social, academic, professional and regulatory stakeholders and communities, and these efforts must be based on principles of equity and social justice.

Polygenic risk scores: from research tools to clinical instruments

Genome-wide association studies have shown unequivocally that common complex disorders have a polygenic genetic architecture and have enabled researchers to identify genetic variants associated with diseases. These variants can be combined into a polygenic risk score that captures part of an individual's susceptibility to diseases. Polygenic risk scores have been widely applied in research studies, confirming the association between the scores and disease status, but their clinical utility has yet to be established. Polygenic risk scores may be used to estimate an individual's lifetime genetic risk of disease, but the current discriminative ability is low in the general population. Clinical implementation of polygenic risk score (PRS) may be useful in cohorts where there is a higher prior probability of disease, for example, in early stages of diseases to assist in diagnosis or to inform treatment choices. Important considerations are the weaker evidence base in application to non-European ancestry and the challenges in translating an individual's PRS from a percentile of a normal distribution to a lifetime disease risk. In this review, we consider how PRS may be informative at different points in the disease trajectory giving examples of progress in the field and discussing obstacles that need to be addressed before clinical implementation.

Accurate and Scalable Construction of Polygenic Scores in Large Biobank Data Sets

Accurate construction of polygenic scores (PGS) can enable early diagnosis of diseases and facilitate the development of personalized medicine. Accurate PGS construction requires prediction models that are both adaptive to different genetic architectures and scalable to biobank scale datasets with millions of individuals and tens of millions of genetic variants. Here, we develop such a method called Deterministic Bayesian Sparse Linear Mixed Model (DBSLMM). DBSLMM relies on a flexible modeling assumption on the effect size distribution to achieve robust and accurate prediction performance across a range of genetic architectures. DBSLMM also relies on a simple deterministic search algorithm to yield an approximate analytic estimation solution using summary statistics only. The deterministic search algorithm, when paired with further algebraic innovations, results in substantial computational savings. With simulations, we show that DBSLMM achieves scalable and accurate prediction performance across a range of realistic genetic architectures. We then apply DBSLMM to analyze 25 traits in UK Biobank. For these traits, compared to existing approaches, DBSLMM achieves an average of 2.03%-101.09% accuracy gain in internal cross-validations. In external validations on two separate datasets, including one from BioBank Japan, DBSLMM achieves an average of 14.74%-522.74% accuracy gain. In these real data applications, DBSLMM is 1.03-28.11 times faster and uses only 7.4%-24.8% of physical memory as compared to other multiple regression-based PGS methods. Overall, DBSLMM represents an accurate and scalable method for constructing PGS in biobank scale datasets.

Brain Connectome Mapping of Complex Human Traits and Their Polygenic Architecture Using Machine Learning

BACKGROUND

Mental disorders and individual characteristics such as intelligence and personality are complex traits sharing a largely unknown neuronal basis. Their genetic architectures are highly polygenic and overlapping, which is supported by heterogeneous phenotypic expression and substantial clinical overlap. Brain network analysis provides a noninvasive means of dissecting biological heterogeneity, yet its sensitivity, specificity, and validity in assessing individual characteristics relevant for brain function and mental health and their genetic underpinnings in clinical applications remain a challenge.

METHODS

In a machine learning approach, we predicted individual scores for educational attainment, fluid intelligence and dimensional measures of depression, anxiety, and neuroticism using functional magnetic resonance imaging-based static and dynamic temporal synchronization between large-scale brain network nodes in 10,343 healthy individuals from the UK Biobank. In addition to using age and sex to serve as our reference point, we also predicted individual polygenic scores for related phenotypes and 13 different neuroticism traits and schizophrenia.

RESULTS

Beyond high accuracy for age and sex, supporting the biological sensitivity of the connectome-based features, permutation tests revealed above chance-level prediction accuracy for trait-level educational attainment and fluid intelligence. Educational attainment and fluid intelligence were mainly negatively associated with static brain connectivity in frontal and default mode networks, whereas age showed positive correlations with a more widespread pattern. In contrast, prediction accuracy was at chance level for depression, anxiety, neuroticism, and polygenic scores across traits.

CONCLUSIONS

These novel findings provide a benchmark for future studies linking the genetic architecture of individual and mental health traits with functional magnetic resonance imaging-based brain connectomics.