An ensemble penalized regression method for multi-ancestry polygenic risk prediction.

Great efforts are being made to develop advanced polygenic risk scores (PRS) to improve the prediction of complex traits and diseases. However, most existing PRS are primarily trained on European ancestry populations, limiting their transferability to non-European populations. In this article, we propose a novel method for generating multi-ancestry Polygenic Risk scOres based on enSemble of PEnalized Regression models (PROSPER). PROSPER integrates genome-wide association studies (GWAS) summary statistics from diverse populations to develop ancestry-specific PRS with improved predictive power for minority populations. The method uses a combination of L 1 (lasso) and L 2 (ridge) penalty functions, a parsimonious specification of the penalty parameters across populations, and an ensemble step to combine PRS generated across different penalty parameters. We evaluate the performance of PROSPER and other existing methods on large-scale simulated and real datasets, including those from 23andMe Inc., the Global Lipids Genetics Consortium, and All of Us. Results show that PROSPER can substantially improve multi-ancestry polygenic prediction compared to alternative methods across a wide variety of genetic architectures. In real data analyses, for example, PROSPER increased out-of-sample prediction R2 for continuous traits by an average of 70% compared to a state-of-the-art Bayesian method (PRS-CSx) in the African ancestry population. Further, PROSPER is computationally highly scalable for the analysis of large SNP contents and many diverse populations.

MUSSEL: Enhanced Bayesian polygenic risk prediction leveraging information across multiple ancestry groups.

Polygenic risk scores (PRSs) are now showing promising predictive performance on a wide variety of complex traits and diseases, but there exists a substantial performance gap across populations. We propose MUSSEL, a method for ancestry-specific polygenic prediction that borrows information in summary statistics from genome-wide association studies (GWASs) across multiple ancestry groups via Bayesian hierarchical modeling and ensemble learning. In our simulation studies and data analyses across four distinct studies, totaling 5.7 million participants with a substantial ancestral diversity, MUSSEL shows promising performance compared to alternatives. For example, MUSSEL has an average gain in prediction R2 across 11 continuous traits of 40.2% and 49.3% compared to PRS-CSx and CT-SLEB, respectively, in the African ancestry population. The best-performing method, however, varies by GWAS sample size, target ancestry, trait architecture, and linkage disequilibrium reference samples; thus, ultimately a combination of methods may be needed to generate the most robust PRSs across diverse populations.

An Ensemble Penalized Regression Method for Multi-ancestry Polygenic Risk Prediction.

Great efforts are being made to develop advanced polygenic risk scores (PRS) to improve the prediction of complex traits and diseases. However, most existing PRS are primarily trained on European ancestry populations, limiting their transferability to non-European populations. In this article, we propose a novel method for generating multi-ancestry Polygenic Risk scOres based on enSemble of PEnalized Regression models (PROSPER). PROSPER integrates genome-wide association studies (GWAS) summary statistics from diverse populations to develop ancestry-specific PRS with improved predictive power for minority populations. The method uses a combination of ℒ1 (lasso) and ℒ2 (ridge) penalty functions, a parsimonious specification of the penalty parameters across populations, and an ensemble step to combine PRS generated across different penalty parameters. We evaluate the performance of PROSPER and other existing methods on large-scale simulated and real datasets, including those from 23andMe Inc., the Global Lipids Genetics Consortium, and All of Us. Results show that PROSPER can substantially improve multi-ancestry polygenic prediction compared to alternative methods across a wide variety of genetic architectures. In real data analyses, for example, PROSPER increased out-of-sample prediction R2 for continuous traits by an average of 70% compared to a state-of-the-art Bayesian method (PRS-CSx) in the African ancestry population. Further, PROSPER is computationally highly scalable for the analysis of large SNP contents and many diverse populations.

A new method for multiancestry polygenic prediction improves performance across diverse populations.

Polygenic risk scores (PRSs) increasingly predict complex traits; however, suboptimal performance in non-European populations raise concerns about clinical applications and health inequities. We developed CT-SLEB, a powerful and scalable method to calculate PRSs, using ancestry-specific genome-wide association study summary statistics from multiancestry training samples, integrating clumping and thresholding, empirical Bayes and superlearning. We evaluated CT-SLEB and nine alternative methods with large-scale simulated genome-wide association studies (~19 million common variants) and datasets from 23andMe, Inc., the Global Lipids Genetics Consortium, All of Us and UK Biobank, involving 5.1 million individuals of diverse ancestry, with 1.18 million individuals from four non-European populations across 13 complex traits. Results demonstrated that CT-SLEB significantly improves PRS performance in non-European populations compared with simple alternatives, with comparable or superior performance to a recent, computationally intensive method. Moreover, our simulation studies offered insights into sample size requirements and SNP density effects on multiancestry risk prediction.

MUSSEL: Enhanced Bayesian Polygenic Risk Prediction Leveraging Information across Multiple Ancestry Groups.

Polygenic risk scores (PRS) are now showing promising predictive performance on a wide variety of complex traits and diseases, but there exists a substantial performance gap across different populations. We propose MUSSEL, a method for ancestry-specific polygenic prediction that borrows information in the summary statistics from genome-wide association studies (GWAS) across multiple ancestry groups. MUSSEL conducts Bayesian hierarchical modeling under a MUltivariate Spike-and-Slab model for effect-size distribution and incorporates an Ensemble Learning step using super learner to combine information across different tuning parameter settings and ancestry groups. In our simulation studies and data analyses of 16 traits across four distinct studies, totaling 5.7 million participants with a substantial ancestral diversity, MUSSEL shows promising performance compared to alternatives. The method, for example, has an average gain in prediction R2 across 11 continuous traits of 40.2% and 49.3% compared to PRS-CSx and CT-SLEB, respectively, in the African Ancestry population. The best-performing method, however, varies by GWAS sample size, target ancestry, underlying trait architecture, and the choice of reference samples for LD estimation, and thus ultimately, a combination of methods may be needed to generate the most robust PRS across diverse populations.

Reliability of Ancestry-specific Prostate Cancer Genetic Risk Score in Four Racial and Ethnic Populations.

Background: Reliability of prostate cancer (PCa) genetic risk score (GRS), that is, the concordance between its estimated risk and observed risk, is required for genetic testing at the individual level. Reliability data are lacking for non-European racial/ethnic populations, which hinders its clinical use and exacerbates racial disparity. Objective: To calibrate PCa ancestry-specific GRS in four racial/ethnic populations. Design setting and participants: PCa ancestry-specific GRSs, calculated from published risk-associated single-nucleotide polymorphisms in corresponding racial/ethnic populations, were evaluated in men who participated in 23andMe, Inc. genetic testing and consented for research, including 888 086 of European (EUR), 81 109 of Hispanic (HIS), 30 472 of African (AFR), and 13 985 of East Asian (EAS) ancestry, as classified by 23andMe's ancestry composition algorithm. Outcome measurements and statistical analysis: The concordance between the observed and estimated PCa risks at ten ancestry-specific GRS deciles was measured primarily by using the calibration slope (ß), where 1 represents a perfect calibration. Platt scaling was used to correct the systematic bias of GRS. Results and limitations: A linear trend of an increased observed PCa prevalence in men with higher ancestry-specific GRS deciles was found in each racial population (all p -trend < 0.001). A calibration analysis revealed a systematic bias of GRS; ß was considerably lower than 1 (0.73, 0.64, 0.66, and 0.75 in EUR, HIS, AFR, and EAS ancestries, respectively). This bias was reduced after the Platt scaling correction: ß for scaled GRS in the testing dataset (40% of individuals) approximated 1 for all groups (0.95, 1.05, 1.02, and 1.01 in EUR, HIS, AFR, and EAS populations, respectively). The generalizability of the Platt correction needs to be validated in independent cohorts. Conclusions: A systematic bias of ancestry-specific GRS in the direction of an overestimated risk for men in the highest decile was found in EUR and non-EUR populations. GRS is well calibrated after correction and is appropriate for genetic testing at the individual level for personalized PCa screening. Patient summary: A corrected genetic risk score is more reliable (supported by the observed prostate cancer [PCa] risk) and appropriate for genetic testing for personalized PCa screening.

A Polygenic Score for Type 2 Diabetes Improves Risk Stratification Beyond Current Clinical Screening Factors in an Ancestrally Diverse Sample.

A substantial proportion of the adult United States population with type 2 diabetes (T2D) are undiagnosed, calling into question the comprehensiveness of current screening practices, which primarily rely on age, family history, and body mass index (BMI). We hypothesized that a polygenic score (PGS) may serve as a complementary tool to identify high-risk individuals. The T2D polygenic score maintained predictive utility after adjusting for family history and combining genetics with family history led to even more improved disease risk prediction. We observed that the PGS was meaningfully related to age of onset with implications for screening practices: there was a linear and statistically significant relationship between the PGS and T2D onset (-1.3 years per standard deviation of the PGS). Evaluation of U.S. Preventive Task Force and a simplified version of American Diabetes Association screening guidelines showed that addition of a screening criterion for those above the 90th percentile of the PGS provided a small increase the sensitivity of the screening algorithm. Among T2D-negative individuals, the T2D PGS was associated with prediabetes, where each standard deviation increase of the PGS was associated with a 23% increase in the odds of prediabetes diagnosis. Additionally, each standard deviation increase in the PGS corresponded to a 43% increase in the odds of incident T2D at one-year follow-up. Using complications and forms of clinical intervention (i.e., lifestyle modification, metformin treatment, or insulin treatment) as proxies for advanced illness we also found statistically significant associations between the T2D PGS and insulin treatment and diabetic neuropathy. Importantly, we were able to replicate many findings in a Hispanic/Latino cohort from our database, highlighting the value of the T2D PGS as a clinical tool for individuals with ancestry other than European. In this group, the T2D PGS provided additional disease risk information beyond that offered by traditional screening methodologies. The T2D PGS also had predictive value for the age of onset and for prediabetes among T2D-negative Hispanic/Latino participants. These findings strengthen the notion that a T2D PGS could play a role in the clinical setting across multiple ancestries, potentially improving T2D screening practices, risk stratification, and disease management.

Identifying Ashkenazi Jewish BRCA1/2 founder variants in individuals who do not self-report Jewish ancestry.

Current guidelines recommend BRCA1 and BRCA2 genetic testing for individuals with a personal or family history of certain cancers. Three BRCA1/2 founder variants - 185delAG (c.68_69delAG), 5382insC (c.5266dupC), and 6174delT (c.5946delT) - are common in the Ashkenazi Jewish population. We characterized a cohort of more than 2,800 research participants in the 23andMe database who carry one or more of the three Ashkenazi Jewish founder variants, evaluating two characteristics that are typically used to recommend individuals for BRCA testing: self-reported Jewish ancestry and family history of breast, ovarian, prostate, or pancreatic cancer. Of the 1,967 carriers who provided self-reported ancestry information, 21% did not self-report Jewish ancestry; of these individuals, more than half (62%) do have detectable Ashkenazi Jewish genetic ancestry. In addition, of the 343 carriers who provided both ancestry and family history information, 44% did not have a first-degree family history of a BRCA-related cancer and, in the absence of a personal history of cancer, would therefore be unlikely to qualify for clinical genetic testing. These findings may help inform the discussion around broader access to BRCA genetic testing.

Imaging Renal Urea Handling in Rats at Millimeter Resolution using Hyperpolarized Magnetic Resonance Relaxometry.

In vivo spin spin relaxation time (T2) heterogeneity of hyperpolarized [13C,15N2]urea in the rat kidney was investigated. Selective quenching of the vascular hyperpolarized 13C signal with a macromolecular relaxation agent revealed that a long-T2 component of the [13C,15N2]urea signal originated from the renal extravascular space, thus allowing the vascular and renal filtrate contrast agent pools of the [13C,15N2]urea to be distinguished via multi-exponential analysis. The T2 response to induced diuresis and antidiuresis was performed with two imaging agents: hyperpolarized [13C,15N2]urea and a control agent hyperpolarized bis-1,1-(hydroxymethyl)-1-13C-cyclopropane-2H8. Large T2 increases in the inner-medullar and papilla were observed with the former agent and not the latter during antidiuresis. Therefore, [13C,15N2]urea relaxometry is sensitive to two steps of the renal urea handling process: glomerular filtration and the inner-medullary urea transporter (UT)-A1 and UT-A3 mediated urea concentrating process. Simple motion correction and subspace denoising algorithms are presented to aid in the multi exponential data analysis. Furthermore, a T2-edited, ultra long echo time sequence was developed for sub-2 mm3 resolution 3D encoding of urea by exploiting relaxation differences in the vascular and filtrate pools.

Separation of extra- and intracellular metabolites using hyperpolarized (13)C diffusion weighted MR.

This work demonstrates the separation of extra- and intracellular components of glycolytic metabolites with diffusion weighted hyperpolarized (13)C magnetic resonance spectroscopy. Using b-values of up to 15,000smm(-2), a multi-exponential signal response was measured for hyperpolarized [1-(13)C] pyruvate and lactate. By fitting the fast and slow asymptotes of these curves, their extra- and intracellular weighted diffusion coefficients were determined in cells perfused in a MR compatible bioreactor. In addition to measuring intracellular weighted diffusion, extra- and intracellular weighted hyperpolarized (13)C metabolites pools are assessed in real-time, including their modulation with inhibition of monocarboxylate transporters. These studies demonstrate the ability to simultaneously assess membrane transport in addition to enzymatic activity with the use of diffusion weighted hyperpolarized (13)C MR. This technique could be an indispensible tool to evaluate the impact of microenvironment on the presence, aggressiveness and metastatic potential of a variety of cancers.

Metabolic response of prostate cancer to nicotinamide phophoribosyltransferase inhibition in a hyperpolarized MR/PET compatible bioreactor.

BACKGROUND: Metabolic shifts in disease are of great interest for the development of novel therapeutics. In cancer treatment, these therapies exploit the metabolic phenotype associated with oncogenesis and cancer progression. One recent strategy involves the depletion of the cofactors needed to maintain the high rate of glycolysis seen with the Warburg effect. Specifically, blocking nicotinamide adenine dinucleotide (NAD) biosynthesis via nicotinamide phosphoribosyltransferase (NAMPT) inhibition depletes cancer cells of the NAD needed for glycolysis. To characterize this metabolic phenotype in vivo and describe changes in flux with treatment, non-invasive biomarkers are necessary. One such biomarker is hyperpolarized (HP) [1-(13) C] pyruvate, a clinically translatable probe that allows real-time assessment of metabolism. METHODS: We therefore developed a cell perfusion system compatible with HP magnetic resonance (MR) and positron emission tomography (PET) to develop translatable biomarkers of response to NAMPT inhibition in reduced volume cell cultures. RESULTS: Using this platform, we observed a reduction in pyruvate flux through lactate dehydrogenase with NAMPT inhibition in prostate cancer cells, and showed that both HP lactate and 2-[(18) F] fluoro-2-deoxy-D-glucose (FDG) can be used as biomarkers for treatment response of such targeted agents. Moreover, we observed dynamic flux changes whereby HP pyruvate was re-routed to alanine, providing both positive and negative indicators of treatment response. CONCLUSIONS: This study demonstrated the feasibility of a MR/PET compatible bioreactor approach to efficiently explore cell and tissue metabolism, the understanding of which is critical for developing clinically translatable biomarkers of disease states and responses to therapeutics.

Rapid in vivo apparent diffusion coefficient mapping of hyperpolarized (13) C metabolites.

PURPOSE: Hyperpolarized (13) C magnetic resonance allows for the study of real-time metabolism in vivo, including significant hyperpolarized (13) C lactate production in many tumors. Other studies have shown that aggressive and highly metastatic tumors rapidly transport lactate out of cells. Thus, the ability to not only measure the production of hyperpolarized (13) C lactate but also understand its compartmentalization using diffusion-weighted MR will provide unique information for improved tumor characterization. METHODS: We used a bipolar, pulsed-gradient, double spin echo imaging sequence to rapidly generate diffusion-weighted images of hyperpolarized (13) C metabolites. Our methodology included a simultaneously acquired B1 map to improve apparent diffusion coefficient (ADC) accuracy and a diffusion-compensated variable flip angle scheme to improve ADC precision. RESULTS: We validated this sequence and methodology in hyperpolarized (13) C phantoms. Next, we generated ADC maps of several hyperpolarized (13) C metabolites in a normal rat, rat brain tumor, and prostate cancer mouse model using both preclinical and clinical trial-ready hardware. CONCLUSION: ADC maps of hyperpolarized (13) C metabolites provide information about the localization of these molecules in the tissue microenvironment. The methodology presented here allows for further studies to investigate ADC changes due to disease state that may provide unique information about cancer aggressiveness and metastatic potential.

High resolution (13)C MRI with hyperpolarized urea: in vivo T(2) mapping and (15)N labeling effects.

(13)C steady state free precession (SSFP) magnetic resonance imaging and effective spin-spin relaxation time (T2) mapping were performed using hyperpolarized [(13)C] urea and [(13) C,(15)N2] urea injected intravenously in rats. (15)N labeling gave large T2 increases both in solution and in vivo due to the elimination of a strong scalar relaxation pathway. The T2 increase was pronounced in the kidney, with [(13) C,(15) N2] urea giving T2 values of 6.3±1.3 s in the cortex and medulla, and 11±2 s in the renal pelvis. The measured T2 in the aorta was 1.3±0.3 s. [(13)C] urea showed shortened T2 values in the kidney of 0.23±0.03 s compared to 0.28±0.03 s measured in the aorta. The enhanced T2 of [(13)C,(15)N2] urea was utilized to generate large signal enhancement by SSFP acquisitions with flip angles approaching the fully refocused regime. Projection images at 0.94 mm in-plane resolution were acquired with both urea isotopes, with [(13)C,(15) N2] urea giving a greater than four-fold increase in signal-to-noise ratio over [(13)C] urea.

Diffusion MR of hyperpolarized 13C molecules in solution.

We combined the high MR signal enhancement achieved using dissolution dynamic nuclear polarization (DNP) with a pulsed gradient double spin echo diffusion MR sequence to rapidly and accurately measure the diffusion coefficients of various hyperpolarized (13)C molecules in solution. Furthermore, with a diffusion-weighted imaging sequence we generate diffusion coefficient maps of multiple hyperpolarized metabolites simultaneously. While hyperpolarized experiments can measure rapid, non-equilibrium processes by avoiding signal averaging, continuous signal loss due to longitudinal relaxation (T(1)) complicates quantitation. By correcting for this signal loss, we demonstrate the feasibility of using hyperpolarized (13)C diffusion-weighted MR to accurately measure real-time (seconds) molecular transport phenomena. Potential applications include rapidly measuring molecular binding, cellular membrane transport, in vivo metabolite distribution and establishing a magnetic field independent hyperpolarized parameter.

Hyperpolarized 13C-pyruvate magnetic resonance reveals rapid lactate export in metastatic renal cell carcinomas.

Renal cell carcinomas (RCC) are a heterogeneous group of tumors with a wide range of aggressiveness. Noninvasive methods to confidently predict the tumor biologic behavior and select appropriate treatment are lacking. Here, we investigate the dynamic metabolic flux in living RCC cells using hyperpolarized (13)C-pyruvate magnetic resonance spectroscopy (MRS) combined with a bioreactor platform and interrogated the biochemical basis of the MRS data with respect to cancer aggressiveness. RCC cells have significantly higher pyruvate-to-lactate flux than the normal renal tubule cells. Furthermore, a key feature distinguishing the localized from the metastatic RCC cells is the lactate efflux rate, mediated by the monocarboxylate transporter 4 (MCT4). The metastatic RCC cells have significantly higher MCT4 expression and corresponding higher lactate efflux, which is essential for maintaining a high rate of glycolysis. We show that such differential cellular transporter expression and associated metabolic phenotype can be noninvasively assessed via real-time monitoring of hyperpolarized (13)C-pyruvate-to-lactate flux.