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 fast permutation entropy for pulse rate variability online analysis with one-sample recursion.

Pulse rate variability (PRV) signals are extracted from pulsation signal can be effectively used for cardiovascular disease monitoring in wearable devices. Permutation entropy (PE) algorithm is an effective index for the analysis of PRV signals. However, PE is computationally intensive and impractical for online PRV processing on wearable devices. Therefore, to overcome this challenge, a fast permutation entropy (FPE) algorithm is proposed based on the microprocessor data updating process in this paper, which can analyze PRV signals with single-sample recursive. The simulation data and PRV signals extracted from pulse signals in "Fantasia database" were utilized to verify the performance and accuracy of the improved methods. The results show that the speed of FPE is 211 times faster than PE and maintain the accuracy of algorithm (Root Mean Squared Error = 0) for simulation data with a length of 10,000 samples and embedded dimension m = 5, time delay τ = 5, buffer length Lw = 512. For the RRV signals with 3000∼5000 samples, the result show that the consumption of FPE is less than 0.2 s, which is 175 times faster than PE. This indicates that FPE has better application performance than PE. Furthermore, a low-cost wearable signal detection system is developed to verify the proposed method, the result show that the proposed method can calculate the FPE of PRV signal online with single-sample recursive calculation. Subsequently, entropy-based features are used to explore the performance of decision trees in identifying life-threatening arrhythmias, and the method resulted in a classification accuracy of 85.43%. It can therefore be inferred that the proposed method has great potential in cardiovascular disease.

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.

Priors, population sizes, and power in genome-wide hypothesis tests.

BACKGROUND: Genome-wide tests, including genome-wide association studies (GWAS) of germ-line genetic variants, driver tests of cancer somatic mutations, and transcriptome-wide association tests of RNAseq data, carry a high multiple testing burden. This burden can be overcome by enrolling larger cohorts or alleviated by using prior biological knowledge to favor some hypotheses over others. Here we compare these two methods in terms of their abilities to boost the power of hypothesis testing. RESULTS: We provide a quantitative estimate for progress in cohort sizes and present a theoretical analysis of the power of oracular hard priors: priors that select a subset of hypotheses for testing, with an oracular guarantee that all true positives are within the tested subset. This theory demonstrates that for GWAS, strong priors that limit testing to 100-1000 genes provide less power than typical annual 20-40% increases in cohort sizes. Furthermore, non-oracular priors that exclude even a small fraction of true positives from the tested set can perform worse than not using a prior at all. CONCLUSION: Our results provide a theoretical explanation for the continued dominance of simple, unbiased univariate hypothesis tests for GWAS: if a statistical question can be answered by larger cohort sizes, it should be answered by larger cohort sizes rather than by more complicated biased methods involving priors. We suggest that priors are better suited for non-statistical aspects of biology, such as pathway structure and causality, that are not yet easily captured by standard hypothesis tests.

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.

CYP2C19 Allele Frequencies in Over 2.2 Million Direct-to-Consumer Genetics Research Participants and the Potential Implication for Prescriptions in a Large Health System.

Understanding the prevalence of clinically relevant pharmacogenetic variants using large unselected populations is critical for gauging the potential clinical impact of widespread preemptive pharmacogenetic testing. To this end, we assessed the frequencies and ethnic distribution of the three most common CYP2C19 alleles (*2, *3, and *17) in 2.29 million direct-to-consumer genetics research participants (23andMe, Sunnyvale, CA). The overall frequencies of *2, *3, and *17 were 15.2%, 0.3%, and 20.4%, respectively, but varied by ethnicity. The most common variant diplotypes were *1/*17 at 26% and *1/*2 at 19.4%. The less common *2/*17, *17/*17, and *2/*2 genotypes occurred at 6.0%, 4.4%, and 2.5%, respectively. Overall, 58.3% of participants had at least one increased-function or no-function CYP2C19 allele. To better understand how this high frequency might impact a real patient population, we examined the prescription rates (Rx) of high-pharmacogenetic-risk medications metabolized by CYP2C19 using the University of California at San Francisco (UCSF) health system's anonymized database of over 1.25 million patients. Between 2012 and 2019, a total of 151,068 UCSF patients (15.8%) representing 5 self-reported ethnicities were prescribed one or more high-pharmacogenetic-risk CYP2C19 medications: proton pump inhibitors (145,243 Rx), three selective serotonin reuptake inhibitor antidepressants (54,463 Rx), clopidogrel (14,376 Rx), and voriconazole (2,303 Rx).

Transcriptome analysis reveals dysregulation of innate immune response genes and neuronal activity-dependent genes in autism.

Recent studies of genomic variation associated with autism have suggested the existence of extreme heterogeneity. Large-scale transcriptomics should complement these results to identify core molecular pathways underlying autism. Here we report results from a large-scale RNA sequencing effort, utilizing region-matched autism and control brains to identify neuronal and microglial genes robustly dysregulated in autism cortical brain. Remarkably, we note that a gene expression module corresponding to M2-activation states in microglia is negatively correlated with a differentially expressed neuronal module, implicating dysregulated microglial responses in concert with altered neuronal activity-dependent genes in autism brains. These observations provide pathways and candidate genes that highlight the interplay between innate immunity and neuronal activity in the aetiology of autism.

Modular multifunctional poly(ethylene glycol) hydrogels for stem cell differentiation.

Synthetic polymers are employed to create highly defined microenvironments with controlled biochemical and biophysical properties for cell culture and tissue engineering. Chemical modification is required to input biological or chemical ligands, which often changes the fundamental structural properties of the material. Here, we report on a simple modular biomaterial design strategy that employs functional cyclodextrin nanobeads threaded onto poly(ethylene glycol) polymer necklaces to form multifunctional hydrogels. Nanobeads with desired chemical or biological functionalities can be simply threaded onto the PEG chains to form hydrogels, creating an accessible platform for users. We describe the design and synthesis of these multifunctional hydrogels, elucidate structure-property relationships, and demonstrate applications ranging from stem cell culture and differentiation to tissue engineering.

Multifunctional aliphatic polyester nanofibers for tissue engineering.

Electrospun fibers based on aliphatic polyesters, such as poly(ε-caprolactone) (PCL), have been widely used in regenerative medicine and drug delivery applications due to their biocompatibility, low cost and ease of fabrication. However, these aliphatic polyester fibers are hydrophobic in nature, resulting in poor wettability, and they lack functional groups for decorating the scaffold with chemical and biological cues. Current strategies employed to overcome these challenges include coating and blending the fibers with bioactive components or chemically modifying the fibers with plasma treatment and reactants. In the present study, we report on designing multifunctional electrospun nanofibers based on the inclusion complex of PCL-α-cyclodextrin (PCL-α-CD), which provides both structural support and multiple functionalities for further conjugation of bioactive components. This strategy is independent of any chemical modification of the PCL main chain, and electrospinning of PCL-α-CD is as easy as electrospinning PCL. Here, we describe synthesis of the PCL-α-CD electrospun nanofibers, elucidate composition and structure, and demonstrate the utility of functional groups on the fibers by conjugating a fluorescent small molecule and a polymeric-nanobead to the nanofibers. Furthermore, we demonstrate the application of PCL-α-CD nanofibers for promoting osteogenic differentiation of human adipose-derived stem cells (hADSCs), which induced a higher level of expression of osteogenic markers and enhanced production of extracellular matrix (ECM) proteins or molecules compared with control PCL fibers.

Autophagy plays a protective role in free cholesterol overload-induced death of smooth muscle cells.

Smooth muscle cells (SMC) make up most of the vascular system. In advanced atherosclerotic plaques, dying SMCs undergo a complex death mode. In the present study, we examined the activation of autophagy in SMCs overloaded with excess free cholesterol (FC) and investigated the possible role which autophagy plays during the FC-induced cell death. After incubation with excess FC, a robust expression of autophagic vacuoles (AV) was detected using both fluorescence microscopy and transmission electron microscopy (TEM). The results revealed that FC induced a time-dependent upregulation of microtubule-associated protein-1 light chain 3-II (LC3-II). Inhibition of autophagy by 3-methyladenine (3-MA) enhanced both cell apoptosis and necrosis, while on the contrary, rapamycin inhibited cell death following cholesterol application. Furthermore, the impact of the colocalization of fragmented mitochondria with AVs was observed after cholesterol treatment. Our results also revealed that the modulation of autophagy directly influenced the cellular organellar stress. In conclusion, our findings demonstrated that excess FC induced the activation of autophagy in SMCs as a cellular defense mechanism, possibly through the degradation of dysfunctional organelles such as mitochondria and endoplasmic reticulum.