Ultra-secure storage and analysis of genetic data for the advancement of precision medicine.

Cloud computing provides the opportunity to store the ever-growing genotype-phenotype data sets needed to achieve the full potential of precision medicine. However, due to the sensitive nature of this data and the patchwork of data privacy laws across states and countries, additional security protections are proving necessary to ensure data privacy and security. Here we present SQUiD, a secure queryable database for storing and analyzing genotype-phenotype data. With SQUiD, genotype-phenotype data can be stored in a low-security, low-cost public cloud in the encrypted form, which researchers can securely query without the public cloud ever being able to decrypt the data. We demonstrate the usability of SQUiD by replicating various commonly used calculations such as polygenic risk scores, cohort creation for GWAS, MAF filtering, and patient similarity analysis both on synthetic and UK Biobank data. Our work represents a new and scalable platform enabling the realization of precision medicine without security and privacy concerns.

Predicting A/B compartments from histone modifications using deep learning.

The three-dimensional organization of genomes plays a crucial role in essential biological processes. The segregation of chromatin into A and B compartments highlights regions of activity and inactivity, providing a window into the genomic activities specific to each cell type. Yet, the steep costs associated with acquiring Hi-C data, necessary for studying this compartmentalization across various cell types, pose a significant barrier in studying cell type specific genome organization. To address this, we present a prediction tool called compartment prediction using recurrent neural networks (CoRNN), which predicts compartmentalization of 3D genome using histone modification enrichment. CoRNN demonstrates robust cross-cell-type prediction of A/B compartments with an average AuROC of 90.9%. Cell-type-specific predictions align well with known functional elements, with H3K27ac and H3K36me3 identified as highly predictive histone marks. We further investigate our mispredictions and found that they are located in regions with ambiguous compartmental status. Furthermore, our model's generalizability is validated by predicting compartments in independent tissue samples, which underscores its broad applicability.

Examining the Generalizability of Pretrained De-identification Transformer Models on Narrative Nursing Notes.

BACKGROUND: Narrative nursing notes are a valuable resource in informatics research with unique predictive signals about patient care. The open sharing of these data, however, is appropriately constrained by rigorous regulations set by the Health Insurance Portability and Accountability Act (HIPAA) for the protection of privacy. Several models have been developed and evaluated on the open-source i2b2 dataset. A focus on the generalizability of these models with respect to nursing notes remains understudied. OBJECTIVES: The study aims to understand the generalizability of pretrained transformer models and investigate the variability of personal protected health information (PHI) distribution patterns between discharge summaries and nursing notes with a goal to inform the future design for model evaluation schema. METHODS: Two pretrained transformer models (RoBERTa, ClinicalBERT) fine-tuned on i2b2 2014 discharge summaries were evaluated on our data inpatient nursing notes and compared with the baseline performance. Statistical testing was deployed to assess differences in PHI distribution across discharge summaries and nursing notes. RESULTS: RoBERTa achieved the optimal performance when tested on an external source of data, with an F1 score of 0.887 across PHI categories and 0.932 in the PHI binary task. Overall, discharge summaries contained a higher number of PHI instances and categories of PHI compared with inpatient nursing notes. CONCLUSION: The study investigated the applicability of two pretrained transformers on inpatient nursing notes and examined the distinctions between nursing notes and discharge summaries concerning the utilization of personal PHI. Discharge summaries presented a greater quantity of PHI instances and types when compared with narrative nursing notes, but narrative nursing notes exhibited more diversity in the types of PHI present, with some pertaining to patient's personal life. The insights obtained from the research help improve the design and selection of algorithms, as well as contribute to the development of suitable performance thresholds for PHI.

Assessing and mitigating privacy risks of sparse, noisy genotypes by local alignment to haplotype databases.

Single nucleotide polymorphisms (SNPs) from omics data create a reidentification risk for individuals and their relatives. Although the ability of thousands of SNPs (especially rare ones) to identify individuals has been repeatedly shown, the availability of small sets of noisy genotypes, from environmental DNA samples or functional genomics data, motivated us to quantify their informativeness. We present a computational tool suite, termed Privacy Leakage by Inference across Genotypic HMM Trajectories (PLIGHT), using population-genetics-based hidden Markov models (HMMs) of recombination and mutation to find piecewise alignment of small, noisy SNP sets to reference haplotype databases. We explore cases in which query individuals are either known to be in the database, or not, and consider several genotype queries, including those from environmental sample swabs from known individuals and from simulated "mosaics" (two-individual composites). Using PLIGHT on a database with ∼5000 haplotypes, we find for common, noise-free SNPs that only ten are sufficient to identify individuals, ∼20 can identify both components in two-individual mosaics, and 20-30 can identify first-order relatives. Using noisy environmental-sample-derived SNPs, PLIGHT identifies individuals in a database using ∼30 SNPs. Even when the individuals are not in the database, local genotype matches allow for some phenotypic information leakage based on coarse-grained SNP imputation. Finally, by quantifying privacy leakage from sparse SNP sets, PLIGHT helps determine the value of selectively sanitizing released SNPs without explicit assumptions about population membership or allele frequency. To make this practical, we provide a sanitization tool to remove the most identifying SNPs from genomic data.

Evaluation of the relationship between anxiety levels and dental appearance.

OBJECTIVE: The aim of this study was to evaluate the relationship between personal traits, dental anxiety level and dental appearance of the individuals. STUDY DESIGN: The study included 431 individuals who completed State Trait Anxiety Inventory-Trait Form (STAI-T) and Corah's Dental Anxiety Scale (CDAS) questionnaires during their first appointment at the orthodontic clinic. The Index of Complexity, Outcome and Need (ICON) index scoring was performed using intraoral frontal photographs by an orthodontist. According to the STAI-T scores, three anxiety groups were formed: mild, moderate, and severe. The Kruskal-Wallis H test was used for intergroup comparisons. Spearman's correlation analysis was performed to evaluate the relationship between STAI-T, CDAS, and ICON scores. RESULTS: It was found that 38.28% of the participants had mild, 34.1% had severe, and 27.62% had moderate anxiety levels. CDAS score was significantly lower in the mild anxiety group (p ≤ 0.0001) compared to the groups showing moderate and severe anxiety. There was no significant difference between the moderate and severe anxiety groups. ICON score was significantly higher in the severe anxiety group (p ≤ 0.0001) than the other groups. It was also significantly higher in the moderate anxiety group (p ≤ 0.0001) than in the mild anxiety group. There was a significant positive correlation between STAI-T and both CDAS and ICON scores. There was no significant correlation between CDAS and ICON scores. CONCLUSION: Dental appearance had a significant effect on the general anxiety of individuals. Improving the dental appearance with orthodontic treatments can have positive effects on reducing anxiety. The low level of dental anxiety in individuals with a high need for treatment will facilitate the work of the orthodontist in the procedures to be applied.

The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models.

Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of 1,635 open-access datasets from four donors (∼30 tissues × âˆ¼15 assays). The datasets are mapped to matched, diploid genomes with long-read phasing and structural variants, instantiating a catalog of >1 million allele-specific loci. These loci exhibit coordinated activity along haplotypes and are less conserved than corresponding, non-allele-specific ones. Surprisingly, a deep-learning transformer model can predict the allele-specific activity based only on local nucleotide-sequence context, highlighting the importance of transcription-factor-binding motifs particularly sensitive to variants. Furthermore, combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci. It also enables models for transferring known eQTLs to difficult-to-profile tissues (e.g., from skin to heart). Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.

LDmat: efficiently queryable compression of linkage disequilibrium matrices.

MOTIVATION: Linkage disequilibrium (LD) matrices derived from large populations are widely used in population genetics in fine-mapping, LD score regression, and linear mixed models for Genome-wide Association Studies (GWAS). However, these matrices can reach large sizes when they are derived from millions of individuals; hence, moving, sharing and extracting granular information from this large amount of data can be cumbersome. RESULTS: We sought to address the need for compressing and easily querying large LD matrices by developing LDmat. LDmat is a standalone tool to compress large LD matrices in an HDF5 file format and query these compressed matrices. It can extract submatrices corresponding to a sub-region of the genome, a list of select loci, and loci within a minor allele frequency range. LDmat can also rebuild the original file formats from the compressed files. AVAILABILITY AND IMPLEMENTATION: LDmat is implemented in python, and can be installed on Unix systems with the command 'pip install ldmat'. It can also be accessed through https://github.com/G2Lab/ldmat and https://pypi.org/project/ldmat/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Privacy-preserving cancer type prediction with homomorphic encryption.

Cancer genomics tailors diagnosis and treatment based on an individual's genetic information and is the crux of precision medicine. However, analysis and maintenance of high volume of genetic mutation data to build a machine learning (ML) model to predict the cancer type is a computationally expensive task and is often outsourced to powerful cloud servers, raising critical privacy concerns for patients' data. Homomorphic encryption (HE) enables computation on encrypted data, thus, providing cryptographic guarantees to protect privacy. But restrictive overheads of encrypted computation deter its usage. In this work, we explore the challenges of privacy preserving cancer type prediction using a dataset consisting of more than 2 million genetic mutations from 2713 patients for several cancer types by building a highly accurate ML model and then implementing its privacy preserving version in HE. Our solution for cancer type inference encodes somatic mutations based on their impact on the cancer genomes into the feature space and then uses statistical tests for feature selection. We propose a fast matrix multiplication algorithm for HE-based model. Our final model achieves 0.98 micro-average area under curve improving accuracy from 70.08 to 83.61% , being 550 times faster than the standard matrix multiplication-based privacy-preserving models. Our tool can be found at https://github.com/momalab/octal-candet .

Recurrent repeat expansions in human cancer genomes.

Expansion of a single repetitive DNA sequence, termed a tandem repeat (TR), is known to cause more than 50 diseases1,2. However, repeat expansions are often not explored beyond neurological and neurodegenerative disorders. In some cancers, mutations accumulate in short tracts of TRs, a phenomenon termed microsatellite instability; however, larger repeat expansions have not been systematically analysed in cancer3-8. Here we identified TR expansions in 2,622 cancer genomes spanning 29 cancer types. In seven cancer types, we found 160 recurrent repeat expansions (rREs), most of which (155/160) were subtype specific. We found that rREs were non-uniformly distributed in the genome with enrichment near candidate cis-regulatory elements, suggesting a potential role in gene regulation. One rRE, a GAAA-repeat expansion, located near a regulatory element in the first intron of UGT2B7 was detected in 34% of renal cell carcinoma samples and was validated by long-read DNA sequencing. Moreover, in preliminary experiments, treating cells that harbour this rRE with a GAAA-targeting molecule led to a dose-dependent decrease in cell proliferation. Overall, our results suggest that rREs may be an important but unexplored source of genetic variation in human cancer, and we provide a comprehensive catalogue for further study.

Session Introduction: TOWARDS ETHICAL BIOMEDICAL INFORMATICS: LEARNING FROM OLELO NOEAU, HAWAIIAN PROVERBS.

Innovations in human-centered biomedical informatics are often developed with the eventual goal of real-world translation. While biomedical research questions are usually answered in terms of how a method performs in a particular context, we argue that it is equally important to consider and formally evaluate the ethical implications of informatics solutions. Several new research paradigms have arisen as a result of the consideration of ethical issues, including but not limited for privacy-preserving computation and fair machine learning. In the spirit of the Pacific Symposium on Biocomputing, we discuss broad and fundamental principles of ethical biomedical informatics in terms of Olelo Noeau, or Hawaiian proverbs and poetical sayings that capture Hawaiian values. While we emphasize issues related to privacy and fairness in particular, there are a multitude of facets to ethical biomedical informatics that can benefit from a critical analysis grounded in ethics.

A generalizable physiological model for detection of Delayed Cerebral Ischemia using Federated Learning.

Delayed cerebral ischemia (DCI) is a complication seen in patients with subarachnoid hemorrhage stroke. It is a major predictor of poor outcomes and is detected late. Machine learning models are shown to be useful for early detection, however training such models suffers from small sample sizes due to rarity of the condition. Here we propose a Federated Learning approach to train a DCI classifier across three institutions to overcome challenges of sharing data across hospitals. We developed a framework for federated feature selection and built a federated ensemble classifier. We compared the performance of FL model to that obtained by training separate models at each site. FL significantly improved performance at only two sites. We found that this was due to feature distribution differences across sites. FL improves performance in sites with similar feature distributions, however, FL can worsen performance in sites with heterogeneous distributions. The results highlight both the benefit of FL and the need to assess dataset distribution similarity before conducting FL.

A new perspective on the relationship between anchorage and palatal morphology: Three-dimensional digital model analysis.

Background: The tooth movements were generally analyzed in two dimensions on cephalometric radiographs. Nowaday, 3D digital model analysis, which does not have any harmful effects on patients, can be used to evaluate the palatal morphology and coronal tooth movements in a very comfortable and easy way. Aims: To investigate the effect of palatal morphology on anchorage reinforcement during intraoral molar distalization with pendulum appliance using 3D model analysis. Materials and Methods: The material consisted of before (T0) and after (T1) dental plaster models of Class II malocclusion patients (17 females, 3 males) treated with pendulum appliance for molar distalization and Nance appliance for anchorage. T0 and T1 digital models were superimposed using the palatal area as a reference via three points and surface-matching software, and the changes in teeth movement were calculated for left and right central incisors, first premolars, and first and second molars. Palatal morphology was evaluated at T0 on digital models as palatal inclination, palatal depth angles, and anterior hard palate area. Wilcoxon test was used to evaluate the treatment results and Spearman's correlation analysis was performed to evaluate the relationship between palatal morphology and dental movement. The upper limit for the level of significance was taken as 0.05. Results: Mesial movement of first premolars and distal movement of first and second molars were found to be statistically significant (P < 0.001). A weak negative correlation was found between the palatal inclination and the movement of first premolars (P < 0.045 and P < 0.003). Palatal depth angles and anterior hard palate area had no correlation with dental movements. Conclusion: Presented results supported that the mesial movement of premolar teeth decreased as the inclination of the palate increased.

Privacy-preserving Model Training for Disease Prediction Using Federated Learning with Differential Privacy.

Machine learning is playing an increasingly critical role in health science with its capability of inferring valuable information from high-dimensional data. More training data provides greater statistical power to generate better models that can help decision-making in healthcare. However, this often requires combining research and patient data across institutions and hospitals, which is not always possible due to privacy considerations. In this paper, we outline a simple federated learning algorithm implementing differential privacy to ensure privacy when training a machine learning model on data spread across different institutions. We tested our model by predicting breast cancer status from gene expression data. Our model achieves a similar level of accuracy and precision as a single-site non-private neural network model when we enforce privacy. This result suggests that our algorithm is an effective method of implementing differential privacy with federated learning, and clinical data scientists can use our general framework to produce differentially private models on federated datasets. Our framework is available at https://github.com/gersteinlab/idash20FL.

Characteristics of Patients Who Underwent a Diagnostic Test for Hereditary Angioedema Admitted Due to Angioedema.

OBJECTIVE: Hereditary angioedema (HAE) is clinically characterized by recurrent attacks of angioedema. This study evaluated the clinical findings and examination results of patients admitted due to angioedema who then underwent a diagnostic test for HAE. The study aimed to assess the contribution of laboratory findings to the diagnostic process and to determine clinicians' level of awareness regarding the differential diagnosis of angioedema and the appropriate laboratory tests. METHODS: Pediatric patients suspected to have HAE based on the presence of angioedema and screened for C1 esterase inhibitor levels and/or function were included in the study. RESULTS: A total of 136 patients were evaluated for a preliminary diagnosis of HAE in the presence of angioedema. Angioedema was accompanied by urticaria in 65 patients (47.7%) and itching in 24 patients (17.6%). Patients were evaluated using laboratory tests, C4 levels were studied in 124 patients (91.1%) and were found to be within normal reference limits. C1 esterase inhibitor levels were studied in all patients and were found to be within normal limits. C1 esterase inhibitor function was also studied in 101 patients (74.2%) and was found to be within normal limits. DISCUSSION: It was concluded that clinicians keep HAE in mind when encountering angioedema, but that increasing their knowledge of clinical findings that assist in differential diagnosis among angioedema types would be useful. The study authors would like to emphasize that this topic should be included in the specialty training curriculum to raise the awareness of clinicians, especially pediatricians, about clinical HAE findings and the algorithmic approach to the differential diagnosis of angioedema.

Storing and analyzing a genome on a blockchain.

There are major efforts underway to make genome sequencing a routine part of clinical practice. A critical barrier to these is achieving practical solutions for data ownership and integrity. Blockchain provides solutions to these challenges in other realms, such as finance. However, its use in genomics is stymied due to the difficulty in storing large-scale data on-chain, slow transaction speeds, and limitations on querying. To overcome these roadblocks, we developed a private blockchain network to store genomic variants and reference-aligned reads on-chain. It uses nested database indexing with an accompanying tool suite to rapidly access and analyze the data.

Genome Privacy and Trust.

Genomics data are important for advancing biomedical research, improving clinical care, and informing other disciplines such as forensics and genealogy. However, privacy concerns arise when genomic data are shared. In particular, the identifying nature of genetic information, its direct relationship to health status, and the potential financial harm and stigmatization posed to individuals and their blood relatives call for a survey of the privacy issues related to sharing genetic and related data and potential solutions to overcome these issues. In this work, we provide an overview of the importance of genomic privacy, the information gleaned from genomics data, the sources of potential private information leakages in genomics, and ways to preserve privacy while utilizing the genetic information in research. We discuss the relationship between trust in the scientific community and protecting privacy, illuminating a future roadmap for data sharing and study participation.

A COVID-19 first evaluation clinic at a university hospital in Turkey.

BACKGROUND: We aimed to analyze the usefulness of such a reserved area for the admission of the patients' symptoms suggesting COVID-19 and compare the demographic and clinical characteristics of the patients with COVID-19 and without COVID-19 who were admitted to C1 during the first month of the COVID-19 outbreak in our hospital. METHODS: A new area was set up in Hacettepe University Adult Hospital to limit the contact of COVID-19 suspicious patients with other patients, which was named as COVID-19 First Evaluation Outpatient Clinic (C1). C1 had eight isolation rooms and two sampling rooms for SARS-CoV-2 polymerase-chain-reaction (PCR). All rooms were negative-pressurized. Patients who had symptoms that were compatible with COVID-19 were referred to C1 from pretriage areas. All staff received training for the appropriate use of personal protective equipment and were visited daily by the Infection Prevention and Control team. RESULTS: One hundred and ninety-eight (29.4%) of 673 patients who were admitted to C1were diagnosed with COVID-19 between March 20, 2020, and April 19, 2020. SARS-CoV-2 PCR was positive in 142 out of 673 patients. Chest computerized tomography (CT) was performed in 421 patients and COVID-19 was diagnosed in 56 of them based on CT findings despite negative PCR. Four hundred and ninety-three patients were tested for other viral and bacterial infections with multiplex real-time reverse-transcriptase PCR (RTPCR). Blood tests that included complete blood count, renal and liver functions, d-dimer levels, ferritin, C- reactive protein, and procalcitonin were performed in 593 patients. Only one out of 44 healthcare workers who worked at C1 was infected by SARS-CoV-2. DISCUSSION: Early diagnosis of infected patients and ensuring adequate isolation are very important to control the spread of COVID-19. The purpose of setting up the COVID-19 first evaluation outpatient clinic was to prevent the overcrowding of ER due to mild or moderate infections, ensure appropriate distancing and isolation, and enable emergency services to serve for real emergencies. A wellplanned outpatient care area and teamwork including internal medicine, microbiology, and radiology specialists under the supervision of infectious diseases specialists allowed adequate management of the mild-to-moderate patients with suspicion of COVID-19.

Privacy-preserving genotype imputation with fully homomorphic encryption.

Genotype imputation is the inference of unknown genotypes using known population structure observed in large genomic datasets; it can further our understanding of phenotype-genotype relationships and is useful for QTL mapping and GWASs. However, the compute-intensive nature of genotype imputation can overwhelm local servers for computation and storage. Hence, many researchers are moving toward using cloud services, raising privacy concerns. We address these concerns by developing an efficient, privacy-preserving algorithm called p-Impute. Our method uses homomorphic encryption, allowing calculations on ciphertext, thereby avoiding the decryption of private genotypes in the cloud. It is similar to k-nearest neighbor approaches, inferring missing genotypes in a genomic block based on the SNP genotypes of genetically related individuals in the same block. Our results demonstrate accuracy in agreement with the state-of-the-art plaintext solutions. Moreover, p-Impute is scalable to real-world applications as its memory and time requirements increase linearly with the increasing number of samples. p-Impute is freely available for download here: https://doi.org/10.5281/zenodo.5542001.

Functional genomics data: privacy risk assessment and technological mitigation.

The generation of functional genomics data by next-generation sequencing has increased greatly in the past decade. Broad sharing of these data is essential for research advancement but poses notable privacy challenges, some of which are analogous to those that occur when sharing genetic variant data. However, there are also unique privacy challenges that arise from cryptic information leakage during the processing and summarization of functional genomics data from raw reads to derived quantities, such as gene expression values. Here, we review these challenges and present potential solutions for mitigating privacy risks while allowing broad data dissemination and analysis.