Scientific Computing and Data

Partnering with researchers to advance scientific discovery

Scientific Computing and Data / High Performance Computing

Top 10 Minerva Users

21 Nov to 27 Nov 2021




Total Hours

Luksza, Marta

Oncological Sciences


Huckins, Laura

Genetics and Genomic Sciences


Roussos, Panos



Klein, Robert

Genetics and Genomic Sciences


Fang, Gang

Genetics and Genomic Sciences


Schlessinger, Avner



Buxbaum, Joseph



Sharp, Andrew

Genetics and Genomic Sciences


Filizola, Marta

Structural and Chemical Biology


Parekh, Samir

Oncological Sciences


Minerva Research Feature: AKI in Hospitalized Patients with COVID-19

Chan L, Chaudhary K, Saha A, Chauhan K, Vaid A, et al. 2021. 


Background: Early reports indicate that AKI is common among patients with coronavirus disease 2019 (COVID-19) and associated with worse outcomes. However, AKI among hospitalized patients with COVID-19 in the United States is not well described.

Methods: This retrospective, observational study involved a review of data from electronic health records of patients aged ≥18 years with laboratory-confirmed COVID-19 admitted to the Mount Sinai Health System from February 27 to May 30, 2020. We describe the frequency of AKI and dialysis requirement, AKI recovery, and adjusted odds ratios (aORs) with mortality.

Results: Of 3993 hospitalized patients with COVID-19, AKI occurred in 1835 (46%) patients; 347 (19%) of the patients with AKI required dialysis. The proportions with stages 1, 2, or 3 AKI were 39%, 19%, and 42%, respectively. A total of 976 (24%) patients were admitted to intensive care, and 745 (76%) experienced AKI. Of the 435 patients with AKI and urine studies, 84% had proteinuria, 81% had hematuria, and 60% had leukocyturia. Independent predictors of severe AKI were CKD, men, and higher serum potassium at admission. In-hospital mortality was 50% among patients with AKI versus 8% among those without AKI (aOR, 9.2; 95% confidence interval, 7.5 to 11.3). Of survivors with AKI who were discharged, 35% had not recovered to baseline kidney function by the time of discharge. An additional 28 of 77 (36%) patients who had not recovered kidney function at discharge did so on posthospital follow-up.

Conclusions: AKI is common among patients hospitalized with COVID-19 and is associated with high mortality. Of all patients with AKI, only 30% survived with recovery of kidney function by the time of discharge.

Minerva Research Feature: Causal Associations Between Modifiable Risk Factors and the Alzheimer's Phenome

Andrews SJ, Fulton-Howard B, O’Reilly P, Marcora E, Goate AM, Consortium cotAsDG. 2021


Objective: The purpose of this study was to infer causal relationships between 22 previously reported risk factors for Alzheimer’s disease (AD) and the “AD phenome”: AD, AD age of onset (AAOS), hippocampal volume, cortical surface area and thickness, cerebrospinal fluid (CSF) levels of amyloid-β (Aβ42 ), tau, and ptau181 , and the neuropathological burden of neuritic plaques, neurofibrillary tangles (NFTs), and vascular brain injury (VBI).

Methods: Polygenic risk scores (PRS) for the 22 risk factors were computed in 26,431 AD cases/controls and the association with AD was evaluated using logistic regression. Two-sample Mendelian randomization (MR) was used to infer the causal effect of risk factors on the AD phenome.

Results: PRS for increased education and diastolic blood pressure were associated with reduced risk for AD. MR indicated that only education was causally associated with reduced risk of AD, delayed AAOS, and increased cortical surface area and thickness. Total- and LDL-cholesterol levels were causally associated with increased neuritic plaque burden, although the effects were driven by single nucleotide polymorphisms (SNPs) within the APOE locus. Diastolic blood pressure and pulse pressure are causally associated with increased risk of VBI. Furthermore, total cholesterol was associated with decreased hippocampal volume; smoking initiation with decreased cortical thickness; type 2 diabetes with an earlier AAOS; and sleep duration with increased cortical thickness.

Interpretation: Our comprehensive examination of the genetic evidence for the causal relationships between previously reported risk factors in AD using PRS and MR supports a causal role for education, blood pressure, cholesterol levels, smoking, and diabetes with the AD phenome. ANN NEUROL 2021;89:54-65.

Minerva Research Feature: Toward a fine-scale population health monitoring system

Belbin GM, Cullina S, Wenric S, Soper ER, Glicksberg BS, et al. 2021.


Understanding population health disparities is an essential component of equitable precision health efforts. Epidemiology research often relies on definitions of race and ethnicity, but these population labels may not adequately capture disease burdens and environmental factors impacting specific sub-populations. Here, we propose a framework for repurposing data from electronic health records (EHRs) in concert with genomic data to explore the demographic ties that can impact disease burdens. Using data from a diverse biobank in New York City, we identified 17 communities sharing recent genetic ancestry. We observed 1,177 health outcomes that were statistically associated with a specific group and demonstrated significant differences in the segregation of genetic variants contributing to Mendelian diseases. We also demonstrated that fine-scale population structure can impact the prediction of complex disease risk within groups. This work reinforces the utility of linking genomic data to EHRs and provides a framework toward fine-scale monitoring of population health.

Technical Specifications

Minerva utilizes 14,304 Intel Gold 8168 24C, 2.7 GHz compute cores (48 cores per node with two sockets in each node), 286 nodes with 192 GB of memory per node, 65.7 terabytes of total memory, 350 terabytes of solid-state storage and nearly 30 petabytes of spinning storage accessed via IBM’s Spectrum Scale/General Parallel File System (GPFS) for a total of 1.2 petaflops of compute power. Read more.

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Mount Sinai Data Warehouse

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Data Ark: Data Commons

The Data Ark consists of seven data sets with the overarching goal to ensure that research data at Mount Sinai are managed, processed and combined in a way that optimizes the power, pace and relevance of our science. Request access to utilize this resource. Learn more about Data Ark Data Sets.

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An acknowledgement of support from the Icahn School of Medicine at Mount Sinai should appear in a publication of any material based on or developed with Mount Sinai-supported computing resource. Click here for acknowledgements to add to your work.

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