Modification of coronary artery disease clinical risk factors by coronary artery disease polygenic risk score.

BACKGROUND: The extent to which the relationships between clinical risk factors and coronary artery disease (CAD) are altered by CAD polygenic risk score (PRS) is not well understood. Here, we determine whether the interactions between clinical risk factors and CAD PRS further explain risk for incident CAD. METHODS: Participants were of European ancestry from the UK Biobank without prevalent CAD. An externally trained genome-wide CAD PRS was generated and then applied. Clinical risk factors were ascertained at baseline. Cox proportional hazards models were fitted to examine the incident CAD effects of CAD PRS, risk factors, and their interactions. Next, the PRS and risk factors were stratified to investigate the attributable risk of clinical risk factors. FINDINGS: A total of 357,144 individuals of European ancestry without prevalent CAD were included. During a median of 11.1 years of follow-up (interquartile range 10.4-14.1 years), CAD PRS was associated with 1.35-fold (95% confidence interval [CI] 1.332-1.368) risk per SD for incident CAD. The prognostic relevance of the following risk factors was relatively diminished for those with high CAD PRS on a continuous scale: type 2 diabetes (hazard ratio [HR]interaction 0.91, 95% CIinteraction 0.88-0.94), increased body mass index (HRinteraction 0.97, 95% CIinteraction 0.96-0.98), and increased C-reactive protein (HRinteraction 0.98, 95% CIinteraction 0.96-0.99). However, a high CAD PRS yielded joint risk increases with low-density lipoprotein cholesterol (HRinteraction 1.05, 95% CIinteraction 1.04-1.06) and total cholesterol (HRinteraction 1.05, 95% CIinteraction 1.03-1.06). CONCLUSION: The CAD PRS is associated with incident CAD, and its application improves the prognostic relevance of several clinical risk factors. FUNDING: P.N. (R01HL127564, R01HL151152, and U01HG011719) is supported by the National Institutes of Health.

Integrative polygenic risk score improves the prediction accuracy of complex traits and diseases.

Polygenic risk scores (PRSs) are an emerging tool to predict the clinical phenotypes and outcomes of individuals. We propose PRSmix, a framework that leverages the PRS corpus of a target trait to improve prediction accuracy, and PRSmix+, which incorporates genetically correlated traits to better capture the human genetic architecture for 47 and 32 diseases/traits in European and South Asian ancestries, respectively. PRSmix demonstrated a mean prediction accuracy improvement of 1.20-fold (95% confidence interval [CI], [1.10; 1.3]; p = 9.17 × 10-5) and 1.19-fold (95% CI, [1.11; 1.27]; p = 1.92 × 10-6), and PRSmix+ improved the prediction accuracy by 1.72-fold (95% CI, [1.40; 2.04]; p = 7.58 × 10-6) and 1.42-fold (95% CI, [1.25; 1.59]; p = 8.01 × 10-7) in European and South Asian ancestries, respectively. Compared to the previously cross-trait-combination methods with scores from pre-defined correlated traits, we demonstrated that our method improved prediction accuracy for coronary artery disease up to 3.27-fold (95% CI, [2.1; 4.44]; p value after false discovery rate (FDR) correction = 2.6 × 10-4). Our method provides a comprehensive framework to benchmark and leverage the combined power of PRS for maximal performance in a desired target population.

Polygenic Scores and Preclinical Cardiovascular Disease in Individuals With HIV: Insights From the REPRIEVE Trial.

BACKGROUND: Coronary artery disease (CAD) is a leading cause of death among the 38.4 million people with HIV globally. The extent to which cardiovascular polygenic risk scores (PRSs) derived in non-HIV populations generalize to people with HIV is not well understood. METHODS AND RESULTS: PRSs for CAD (GPSMult) and lipid traits were calculated in a global cohort of people with HIV treated with antiretroviral therapy with low-to-moderate atherosclerotic cardiovascular disease risk enrolled in REPRIEVE (Randomized Trial to Prevent Vascular Events in HIV). The PRSs were associated with baseline lipid traits in 4495 genotyped participants, and with subclinical CAD in a subset of 662 who underwent coronary computed tomography angiography. Among participants who underwent coronary computed tomography angiography (mean age, 50.9 [SD, 5.8] years; 16.1% women; 41.8% African, 57.3% European, 1.1% Asian), GPSMult was associated with plaque presence with odds ratio (OR) per SD in GPSMult of 1.42 (95% CI, 1.20-1.68; P=3.8×10-5), stenosis >50% (OR, 2.39 [95% CI, 1.48-3.85]; P=3.4×10-4), and noncalcified/vulnerable plaque (OR, 1.45 [95% CI, 1.23-1.72]; P=9.6×10-6). Effects were consistent in subgroups of age, sex, 10-year atherosclerotic cardiovascular disease risk, ancestry, and CD4 count. Adding GPSMult to established risk factors increased the C-statistic for predicting plaque presence from 0.718 to 0.734 (P=0.02). Furthermore, a PRS for low-density lipoprotein cholesterol was associated with plaque presence with OR of 1.21 (95% CI, 1.01-1.44; P=0.04), and partially calcified plaque with OR of 1.21 (95% CI, 1.01-1.45; P=0.04) per SD. CONCLUSIONS: Among people with HIV treated with antiretroviral therapy without documented atherosclerotic cardiovascular disease and at low-to-moderate calculated risk in REPRIEVE, an externally developed CAD PRS was predictive of subclinical atherosclerosis. PRS for low-density lipoprotein cholesterol was also associated with subclinical atherosclerosis, supporting a role for low-density lipoprotein cholesterol in HIV-associated CAD. REGISTRATION: URL: https://www.reprievetrial.org; Unique identifier: NCT02344290.

Novel Polygenic Risk Score and Established Clinical Risk Factors for Risk Estimation of Aortic Stenosis.

Importance: Polygenic risk scores (PRSs) have proven to be as strong as or stronger than established clinical risk factors for many cardiovascular phenotypes. Whether this is true for aortic stenosis remains unknown. Objective: To develop a novel aortic stenosis PRS and compare its aortic stenosis risk estimation to established clinical risk factors. Design, Setting, and Participants: This was a longitudinal cohort study using data from the Million Veteran Program (MVP; 2011-2020), UK Biobank (2006-2010), and 6 Thrombolysis in Myocardial Infarction (TIMI) trials, including DECLARE-TIMI 58 (2013-2018), FOURIER (TIMI 59; 2013-2017), PEGASUS-TIMI 54 (2010-2014), SAVOR-TIMI 53 (2010-2013), SOLID-TIMI 52 (2009-2014), and ENGAGE AF-TIMI 48 (2008-2013), which were a mix of population-based and randomized clinical trials. Individuals from UK Biobank and the MVP meeting a previously validated case/control definition for aortic stenosis were included. All individuals from TIMI trials were included unless they had a documented preexisting aortic valve replacement. Analysis took place from January 2022 to December 2023. Exposures: PRS for aortic stenosis (developed using data from MVP and validated in UK Biobank) and other previously validated cardiovascular PRSs, defined either as a continuous variable or as low (bottom 20%), intermediate, and high (top 20%), and clinical risk factors. Main Outcomes: Aortic stenosis (defined using International Classification of Diseases or Current Procedural Terminology codes in UK Biobank and MVP or safety event data in the TIMI trials). Results: The median (IQR) age in MVP was 67 (57-73) years, and 135 140 of 147 104 participants (92%) were male. The median (IQR) age in the TIMI trials was 66 (54-78) years, and 45 524 of 59 866 participants (71%) were male. The best aortic stenosis PRS incorporated 5 170 041 single-nucleotide variants and was associated with aortic stenosis in both the MVP testing sample (odds ratio, 1.41; 95% CI, 1.37-1.45 per 1 SD PRS; P = 4.6 × 10-116) and TIMI trials (hazard ratio, 1.44; 95% CI, 1.27-1.62 per 1 SD PRS; P = 3.2 × 10-9). Among genetic and clinical risk factors, the aortic stenosis PRS performed comparably to most risk factors besides age, and within a given age range, the combination of clinical and genetic risk factors was additive, providing a 3- to 4-fold increased gradient of risk of aortic stenosis. However, the addition of the aortic stenosis PRS to a model including clinical risk factors only improved risk discrimination of aortic stenosis by 0.01 to 0.02 (C index in MVP: 0.78 with clinical risk factors, 0.79 with risk factors and aortic stenosis PRS; C index in TIMI: 0.71 with clinical risk factors, 0.73 with risk factors and aortic stenosis PRS). Conclusions: This study developed and validated 1 of the first aortic stenosis PRSs. While aortic stenosis genetic risk was independent from clinical risk factors and performed comparably to all other risk factors besides age, genetic risk resulted in only a small improvement in overall aortic stenosis risk discrimination beyond age and clinical risk factors. This work sets the stage for further development of an aortic stenosis PRS.

Genetic Associations of Circulating Cardiovascular Proteins With Gestational Hypertension and Preeclampsia.

Importance: Hypertensive disorders of pregnancy (HDPs), including gestational hypertension and preeclampsia, are important contributors to maternal morbidity and mortality worldwide. In addition, women with HDPs face an elevated long-term risk of cardiovascular disease. Objective: To identify proteins in the circulation associated with HDPs. Design, Setting, and Participants: Two-sample mendelian randomization (MR) tested the associations of genetic instruments for cardiovascular disease-related proteins with gestational hypertension and preeclampsia. In downstream analyses, a systematic review of observational data was conducted to evaluate the identified proteins' dynamics across gestation in hypertensive vs normotensive pregnancies, and phenome-wide MR analyses were performed to identify potential non-HDP-related effects associated with the prioritized proteins. Genetic association data for cardiovascular disease-related proteins were obtained from the Systematic and Combined Analysis of Olink Proteins (SCALLOP) consortium. Genetic association data for the HDPs were obtained from recent European-ancestry genome-wide association study meta-analyses for gestational hypertension and preeclampsia. Study data were analyzed October 2022 to October 2023. Exposures: Genetic instruments for 90 candidate proteins implicated in cardiovascular diseases, constructed using cis-protein quantitative trait loci (cis-pQTLs). Main Outcomes and Measures: Gestational hypertension and preeclampsia. Results: Genetic association data for cardiovascular disease-related proteins were obtained from 21 758 participants from the SCALLOP consortium. Genetic association data for the HDPs were obtained from 393 238 female individuals (8636 cases and 384 602 controls) for gestational hypertension and 606 903 female individuals (16 032 cases and 590 871 controls) for preeclampsia. Seventy-five of 90 proteins (83.3%) had at least 1 valid cis-pQTL. Of those, 10 proteins (13.3%) were significantly associated with HDPs. Four were robust to sensitivity analyses for gestational hypertension (cluster of differentiation 40, eosinophil cationic protein [ECP], galectin 3, N-terminal pro-brain natriuretic peptide [NT-proBNP]), and 2 were robust for preeclampsia (cystatin B, heat shock protein 27 [HSP27]). Consistent with the MR findings, observational data revealed that lower NT-proBNP (0.76- to 0.88-fold difference vs no HDPs) and higher HSP27 (2.40-fold difference vs no HDPs) levels during the first trimester of pregnancy were associated with increased risk of HDPs, as were higher levels of ECP (1.60-fold difference vs no HDPs). Phenome-wide MR analyses identified 37 unique non-HDP-related protein-disease associations, suggesting potential on-target effects associated with interventions lowering HDP risk through the identified proteins. Conclusions and Relevance: Study findings suggest genetic associations of 4 cardiovascular disease-related proteins with gestational hypertension and 2 associated with preeclampsia. Future studies are required to test the efficacy of targeting the corresponding pathways to reduce HDP risk.

Leveraging single-cell ATAC-seq and RNA-seq to identify disease-critical fetal and adult brain cell types.

Prioritizing disease-critical cell types by integrating genome-wide association studies (GWAS) with functional data is a fundamental goal. Single-cell chromatin accessibility (scATAC-seq) and gene expression (scRNA-seq) have characterized cell types at high resolution, and studies integrating GWAS with scRNA-seq have shown promise, but studies integrating GWAS with scATAC-seq have been limited. Here, we identify disease-critical fetal and adult brain cell types by integrating GWAS summary statistics from 28 brain-related diseases/traits (average N = 298 K) with 3.2 million scATAC-seq and scRNA-seq profiles from 83 cell types. We identified disease-critical fetal (respectively adult) brain cell types for 22 (respectively 23) of 28 traits using scATAC-seq, and for 8 (respectively 17) of 28 traits using scRNA-seq. Significant scATAC-seq enrichments included fetal photoreceptor cells for major depressive disorder, fetal ganglion cells for BMI, fetal astrocytes for ADHD, and adult VGLUT2 excitatory neurons for schizophrenia. Our findings improve our understanding of brain-related diseases/traits and inform future analyses.

Dynamic Importance of Genomic and Clinical Risk for Coronary Artery Disease Over the Life Course.

Importance: Earlier identification of high coronary artery disease (CAD) risk individuals may enable more effective prevention strategies. However, existing 10-year risk frameworks are ineffective at earlier identification. Understanding the variable importance of genomic and clinical factors across life stages may significantly improve lifelong CAD event prediction. Objective: To assess the time-varying significance of genomic and clinical risk factors in CAD risk estimation across various age groups. Design Setting and Participants: A longitudinal study was performed using data from two cohort studies: the Framingham Offspring Study (FOS) with 3,588 participants aged 19-57 years and the UK Biobank (UKB) with 327,837 participants aged 40-70 years. A total of 134,765 and 3,831,734 person-time years were observed in FOS and UKB, respectively. Main Outcomes and Measures: Hazard ratios (HR) for CAD were calculated for polygenic risk scores (PRS) and clinical risk factors at each age of enrollment. The relative importance of PRS and Pooled Cohort Equations (PCE) in predicting CAD events was also evaluated by age groups. Results: The importance of CAD PRS diminished over the life course, with an HR of 3.58 (95% CI 1.39-9.19) at age 19 in FOS and an HR of 1.51 (95% CI 1.48-1.54) by age 70 in UKB. Clinical risk factors exhibited similar age-dependent trends. PRS significantly outperformed PCE in identifying subsequent CAD events in the 40-45-year age group, with 3.2-fold more appropriately identified events. The mean age of CAD events occurred 1.8 years earlier for those at high genomic risk but 9.6 years later for those at high clinical risk (p<0.001). Overall, adding PRS improved the area under the receiving operating curve of the PCE by an average of +5.1% (95% CI 4.9-5.2%) across all age groups; among individuals <55 years, PRS augmented the AUC-ROC of the PCE by 6.5% (95% CI 5.5-7.5%, p<0.001). Conclusions and Relevance: Genomic and clinical risk factors for CAD display time-varying importance across the lifespan. The study underscores the added value of CAD PRS, particularly among individuals younger than 55 years, for enhancing early risk prediction and prevention strategies.

Bayesian multivariate genetic analysis improves translational insights.

While lipid traits are known essential mediators of cardiovascular disease, few approaches have taken advantage of their shared genetic effects. We apply a Bayesian multivariate size estimator, mash, to GWAS of four lipid traits in the Million Veterans Program (MVP) and provide posterior mean and local false sign rates for all effects. These estimates borrow information across traits to improve effect size accuracy. We show that controlling local false sign rates accurately and powerfully identifies replicable genetic associations and that multivariate control furthers the ability to explain complex diseases. Our application yields high concordance between independent datasets, more accurately prioritizes causal genes, and significantly improves polygenic prediction beyond state-of-the-art methods by up to 59% for lipid traits. The use of Bayesian multivariate genetic shrinkage has yet to be applied to human quantitative trait GWAS results, and we present a staged approach to prediction on a polygenic scale.

Representation of Race and Ethnicity in the Contemporary US Health Cohort All of Us Research Program.

Importance: To address systemic disparities in biomedical research, the All of Us (AoU) Research Program was created to identify the root causes and consequences of health outcomes in the US. However, the extent of AoU's racial and ethnic diversity is unknown. Objective: To quantify representation of key racial and ethnic groups in the accruing AoU nationwide health cohort and compare with their actual representation in the US. Design, Setting, and Participants: This cohort study compared the AoU program from May 2017 to June 2022 for individuals 18 years and older with the Decennial Survey 2020 (DEC) collected by the US Census Bureau. Exposures: Representation of non-Hispanic Asian, non-Hispanic Black or African American, Hispanic or Latino, non-Hispanic White, and uncategorized or multiple races in AoU. Main Outcomes and Measures: The extent of underrepresentation or overrepresentation of each racial group in the AoU program at both nationwide and state-level relative to DEC. Results: Of the 358 705 US adults in the AoU to date, individuals identified with the following race and ethnicity categories: 12 710 non-Hispanic Asian (3.5%), 73 348 non-Hispanic Black or African American (20.5%), 58 488 Hispanic or Latino (16.3%), 205 457 non-Hispanic White (57.3%), and 8702 uncategorized or reporting multiple categories (2.4%). Of 355 413 participants with available sex at birth and age data, 218 981 (61.6%) were female and had a mean (SD) age of 53.1 (17.0) years, 136 037 (38.28%) were male and had a mean (SD) age of 56.7 (17.0) years, and 395 reported nonbinary sex (0.1%), with a mean (SD) age of 55.4 (15.8) years. Compared with the referent US, non-Hispanic Black or African American individuals were overrepresented in the AoU by 8.73% (AoU, 20.5% [73 348 of 358 705] vs DEC, 11.7% [30 266 080 of 258 343 281]) and by relative scale, 1.94-fold. Non-Hispanic White individuals accounted for the greatest participation in the AoU with generally consistent dominance across all regions yet numerically underrepresented by absolute difference of -3.54% (95% CI, -3.70 to -3.38). Uncategorized or multiracial group in the AoU (2.4% [8702 of 358 705]) was 0.43-fold likely to be represented relative to the DEC (4.6% [11 922 096 of 258 343 281]) with an absolute difference of -2.19% (95% CI, -2.24 to -2.14). Moreover, non-Hispanic Asian individuals were underrepresented by -2.54% (95% CI, -2.60 to -2.48) prominently in most states. Individuals identifying as Hispanic or Latino were nominally underrepresented by -0.46% (95% CI, -0.58 to -0.34) (AoU, 16.3% [58 488 of 358 705] vs DEC, 16.8% [43 322 792 of 258 343 281]). Conclusions and Relevance: Recruitment trends for the ongoing AoU show relatively improved representation of some major race groups with geographic trends. These findings underscore the need to further tailor and augment recruitment and participation initiatives for diverse populations.

Genetic Associations with Dynamic Placental Proteins Identify Causal Biomarkers for Hypertension in Pregnancy.

Preeclampsia (PE), a gestational hypertensive disorder, ranks as the second leading cause of maternal mortality worldwide. While PE is considered a multifactorial disease, placental insufficiency is believed to drive its progression. To noninvasively study placental physiology related to adverse pregnancy outcomes (APOs) and predict these outcomes before symptom onset, we measured nine placental protein levels in first- and second-trimester serum samples from 2,352 nulliparous pregnant women in the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers- to-Be (nuMoM2b) study. The proteins analyzed include VEGF, PlGF, ENG, sFlt-1, ADAM-12, PAPP-A, fßHCG, INHA, and AFP. Currently, little is known about the genetic variants contributing to the heritability of these proteins during pregnancy, and no studies have explored the causal relationships between early pregnancy proteins and gestational hypertensive disorders. Our study has three objectives. First, we conducted genome-wide association study (GWAS) of nine placental proteins in maternal serum during the first and second trimesters and the difference between time points to understand how genetics may influence placental proteins in early pregnancy. Second, we examined whether early pregnancy placental proteins are causal factors for PE and gestational hypertension (gHTN). Lastly, we investigated the causal relationship between PE/gHTN and long-term HTN. In conclusion, our study discovered significant genetic associations with placental proteins ADAM-12, VEGF, and sFlt-1, offering insights into their regulation during pregnancy. Mendelian randomization (MR) analyses demonstrated evidence of causal relationships between placental proteins, particularly ADAM-12, and gHTN, potentially informing prevention and treatment strategies. Our findings suggest that placental proteins like ADAM-12 could serve as biomarkers for postpartum HTN risk.

An effective hyper-parameter can increase the prediction accuracy in a single-step genetic evaluation.

The H-matrix best linear unbiased prediction (HBLUP) method has been widely used in livestock breeding programs. It can integrate all information, including pedigree, genotypes, and phenotypes on both genotyped and non-genotyped individuals into one single evaluation that can provide reliable predictions of breeding values. The existing HBLUP method requires hyper-parameters that should be adequately optimised as otherwise the genomic prediction accuracy may decrease. In this study, we assess the performance of HBLUP using various hyper-parameters such as blending, tuning, and scale factor in simulated and real data on Hanwoo cattle. In both simulated and cattle data, we show that blending is not necessary, indicating that the prediction accuracy decreases when using a blending hyper-parameter <1. The tuning process (adjusting genomic relationships accounting for base allele frequencies) improves prediction accuracy in the simulated data, confirming previous studies, although the improvement is not statistically significant in the Hanwoo cattle data. We also demonstrate that a scale factor, α, which determines the relationship between allele frequency and per-allele effect size, can improve the HBLUP accuracy in both simulated and real data. Our findings suggest that an optimal scale factor should be considered to increase prediction accuracy, in addition to blending and tuning processes, when using HBLUP.

Polygenic prediction of preeclampsia and gestational hypertension.

Preeclampsia and gestational hypertension are common pregnancy complications associated with adverse maternal and child outcomes. Current tools for prediction, prevention and treatment are limited. Here we tested the association of maternal DNA sequence variants with preeclampsia in 20,064 cases and 703,117 control individuals and with gestational hypertension in 11,027 cases and 412,788 control individuals across discovery and follow-up cohorts using multi-ancestry meta-analysis. Altogether, we identified 18 independent loci associated with preeclampsia/eclampsia and/or gestational hypertension, 12 of which are new (for example, MTHFR-CLCN6, WNT3A, NPR3, PGR and RGL3), including two loci (PLCE1 and FURIN) identified in the multitrait analysis. Identified loci highlight the role of natriuretic peptide signaling, angiogenesis, renal glomerular function, trophoblast development and immune dysregulation. We derived genome-wide polygenic risk scores that predicted preeclampsia/eclampsia and gestational hypertension in external cohorts, independent of clinical risk factors, and reclassified eligibility for low-dose aspirin to prevent preeclampsia. Collectively, these findings provide mechanistic insights into the hypertensive disorders of pregnancy and have the potential to advance pregnancy risk stratification.

Integrative polygenic risk score improves the prediction accuracy of complex traits and diseases.

Polygenic risk scores (PRS) are an emerging tool to predict the clinical phenotypes and outcomes of individuals. Validation and transferability of existing PRS across independent datasets and diverse ancestries are limited, which hinders the practical utility and exacerbates health disparities. We propose PRSmix, a framework that evaluates and leverages the PRS corpus of a target trait to improve prediction accuracy, and PRSmix+, which incorporates genetically correlated traits to better capture the human genetic architecture. We applied PRSmix to 47 and 32 diseases/traits in European and South Asian ancestries, respectively. PRSmix demonstrated a mean prediction accuracy improvement of 1.20-fold (95% CI: [1.10; 1.3]; P-value = 9.17 × 10-5) and 1.19-fold (95% CI: [1.11; 1.27]; P-value = 1.92 × 10-6), and PRSmix+ improved the prediction accuracy by 1.72-fold (95% CI: [1.40; 2.04]; P-value = 7.58 × 10-6) and 1.42-fold (95% CI: [1.25; 1.59]; P-value = 8.01 × 10-7) in European and South Asian ancestries, respectively. Compared to the previously established cross-trait-combination method with scores from pre-defined correlated traits, we demonstrated that our method can improve prediction accuracy for coronary artery disease up to 3.27-fold (95% CI: [2.1; 4.44]; P-value after FDR correction = 2.6 × 10-4). Our method provides a comprehensive framework to benchmark and leverage the combined power of PRS for maximal performance in a desired target population.

A method for an unbiased estimate of cross-ancestry genetic correlation using individual-level data.

Cross-ancestry genetic correlation is an important parameter to understand the genetic relationship between two ancestry groups. However, existing methods cannot properly account for ancestry-specific genetic architecture, which is diverse across ancestries, producing biased estimates of cross-ancestry genetic correlation. Here, we present a method to construct a genomic relationship matrix (GRM) that can correctly account for the relationship between ancestry-specific allele frequencies and ancestry-specific allelic effects. Through comprehensive simulations, we show that the proposed method outperforms existing methods in the estimations of SNP-based heritability and cross-ancestry genetic correlation. The proposed method is further applied to anthropometric and other complex traits from the UK Biobank data across ancestry groups. For obesity, the estimated genetic correlation between African and European ancestry cohorts is significantly different from unity, suggesting that obesity is genetically heterogenous between these two ancestries.

Clonal Hematopoiesis and Risk of Incident Lung Cancer.

PURPOSE: To prospectively examine the association between clonal hematopoiesis (CH) and subsequent risk of lung cancer. METHODS: Among 200,629 UK Biobank (UKBB) participants with whole-exome sequencing, CH was identified in a nested case-control study of 832 incident lung cancer cases and 3,951 controls (2006-2019) matched on age and year at blood draw, sex, race, and smoking status. A similar nested case-control study (141 cases/652 controls) was conducted among 27,975 participants with whole-exome sequencing in the Mass General Brigham Biobank (MGBB, 2010-2021). In parallel, we compared CH frequency in published data from 5,003 patients with solid tumor (2,279 lung cancer) who had pretreatment blood sequencing performed through Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets. RESULTS: In UKBB, the presence of CH was associated with increased risk of lung cancer (cases: 12.5% v controls: 8.7%; multivariable-adjusted odds ratio [OR], 1.36; 95% CI, 1.06 to 1.74). The association remained robust after excluding participants with chronic obstructive pulmonary disease. No significant interactions with known risk factors, including polygenic risk score and C-reactive protein, were identified. In MGBB, we observed similar enrichment of CH in lung cancer (cases: 15.6% v controls: 12.7%). The meta-analyzed OR (95% CI) of UKBB and MGBB was 1.35 (1.08 to 1.68) for CH overall and 1.61 (1.19 to 2.18) for variant allele frequencies ≥ 10%. In Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets, CH with a variant allele frequency ≥ 10% was enriched in pretreatment lung cancer compared with other tumors after adjusting for age, sex, and smoking (OR for lung v breast cancer: 1.61; 95% CI, 1.03 to 2.53). CONCLUSION: Independent of known risk factors, CH is associated with increased risk of lung cancer.

Assessing polygenic risk score models for applications in populations with under-represented genomics data: an example of Vietnam.

Most polygenic risk score (PRS)models have been based on data from populations of European origins (accounting for the majority of the large genomics datasets, e.g. >78% in the UK Biobank and >85% in the GTEx project). Although several large-scale Asian biobanks were initiated (e.g. Japanese, Korean, Han Chinese biobanks), most other Asian countries have little or near-zero genomics data. To implement PRS models for under-represented populations, we explored transfer learning approaches, assuming that information from existing large datasets can compensate for the small sample size that can be feasibly obtained in developing countries, like Vietnam. Here, we benchmark 13 common PRS methods in meta-population strategy (combining individual genotype data from multiple populations) and multi-population strategy (combining summary statistics from multiple populations). Our results highlight the complementarity of different populations and the choice of methods should depend on the target population. Based on these results, we discussed a set of guidelines to help users select the best method for their datasets. We developed a robust and comprehensive software to allow for benchmarking comparisons between methods and proposed a computational framework for improving PRS performance in a dataset with a small sample size. This work is expected to inform the development of genomics applications in under-represented populations. PRSUP framework is available at: https://github.com/BiomedicalMachineLearning/VGP.

First Clinical Experience With Ophthalmic e-Device for Unaided Patient Self-Examination During COVID-19 Lockdown.

PURPOSE: The aim of this study was to describe a new type of medical device that allows for internet-enabled patient self-screening, without the aid of an ophthalmic professional, through biomicroscopy self-imaging and self-measurement of the best-corrected visual acuity (BCVA). METHODS: In this prospective nonrandomized comparative study, 56 patients were instructed to screen their own eyes using a custom-built e-Device containing miniaturized slitlamp optics and a visual acuity Snellen chart virtually projected at 20 ft. BCVA measurements were recorded, and biomicroscopic videos were scored for image quality of the anterior segment status on a scale from 1 to 5 (1 = poor and 5 = excellent) by a blinded observer. RESULTS: After a short instruction, all patients were able to self-image their eyes and perform a self-BCVA measurement using the e-Device. Patient self-image quality with the e-Device scored on average 3.3 (±0.8) for videos (n = 76) and 3.6 (±0.6) for photographs (n = 49). Self-BCVA measurement was within 1 Snellen line from routine BCVA levels in 66 of 72 eyes (92%). When compared with conventional biomicroscopy, patient self-biomicroscopy allowed for recognition of the relevant pathology (or absence thereof) in 26 of 35 eyes (74%); 9 cases showed insufficient image quality attributed to device operating error (n = 6) and mild corneal edema and/or scarring (n = 3). Patient satisfaction with the device was 4.4 (±0.9). CONCLUSIONS: An e-Device for combined BCVA self-measurement and biomicroscopy self-imaging may have potential as an aid in remote ophthalmic examination in the absence of an ophthalmic professional and may be considered for patients who are unable to visit an ophthalmic clinic for routine follow-up.

Uncovering the roles of microRNAs/lncRNAs in characterising breast cancer subtypes and prognosis.

BACKGROUND: Accurate prognosis and identification of cancer subtypes at molecular level are important steps towards effective and personalised treatments of breast cancer. To this end, many computational methods have been developed to use gene (mRNA) expression data for breast cancer subtyping and prognosis. Meanwhile, microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) have been extensively studied in the last 2 decades and their associations with breast cancer subtypes and prognosis have been evidenced. However, it is not clear whether using miRNA and/or lncRNA expression data helps improve the performance of gene expression based subtyping and prognosis methods, and this raises challenges as to how and when to use these data and methods in practice. RESULTS: In this paper, we conduct a comparative study of 35 methods, including 12 breast cancer subtyping methods and 23 breast cancer prognosis methods, on a collection of 19 independent breast cancer datasets. We aim to uncover the roles of miRNAs and lncRNAs in breast cancer subtyping and prognosis from the systematic comparison. In addition, we created an R package, CancerSubtypesPrognosis, including all the 35 methods to facilitate the reproducibility of the methods and streamline the evaluation. CONCLUSIONS: The experimental results show that integrating miRNA expression data helps improve the performance of the mRNA-based cancer subtyping methods. However, miRNA signatures are not as good as mRNA signatures for breast cancer prognosis. In general, lncRNA expression data does not help improve the mRNA-based methods in both cancer subtyping and cancer prognosis. These results suggest that the prognostic roles of miRNA/lncRNA signatures in the improvement of breast cancer prognosis needs to be further verified.

The winning methods for predicting cellular position in the DREAM single-cell transcriptomics challenge.

MOTIVATION: Predicting cell locations is important since with the understanding of cell locations, we may estimate the function of cells and their integration with the spatial environment. Thus, the DREAM challenge on single-cell transcriptomics required participants to predict the locations of single cells in the Drosophila embryo using single-cell transcriptomic data. RESULTS: We have developed over 50 pipelines by combining different ways of preprocessing the RNA-seq data, selecting the genes, predicting the cell locations and validating predicted cell locations, resulting in the winning methods which were ranked second in sub-challenge 1, first in sub-challenge 2 and third in sub-challenge 3. In this paper, we present an R package, SCTCwhatateam, which includes all the methods we developed and the Shiny web application to facilitate the research on single-cell spatial reconstruction. All the data and the example use cases are available in the Supplementary data.

PAN: Personalized Annotation-Based Networks for the Prediction of Breast Cancer Relapse.

The classification of clinical samples based on gene expression data is an important part of precision medicine. In this manuscript, we show how transforming gene expression data into a set of personalized (sample-specific) networks can allow us to harness existing graph-based methods to improve classifier performance. Existing approaches to personalized gene networks have the limitation that they depend on other samples in the data and must get re-computed whenever a new sample is introduced. Here, we propose a novel method, called Personalized Annotation-based Networks (PAN), that avoids this limitation by using curated annotation databases to transform gene expression data into a graph. Unlike competing methods, PANs are calculated for each sample independent of the population, making it a more efficient way to obtain single-sample networks. Using three breast cancer datasets as a case study, we show that PAN classifiers not only predict cancer relapse better than gene features alone, but also outperform PPI (protein-protein interactions) and population-level graph-based classifiers. This work demonstrates the practical advantages of graph-based classification for high-dimensional genomic data, while offering a new approach to making sample-specific networks. Supplementary information: PAN and the baselines are implemented in Python. Source code and data are available at https://github.com/thinng/PAN.