A framework to score the effects of structural variants in health and disease.

Although technological advances improved the identification of structural variants (SVs) in the human genome, their interpretation remains challenging. Several methods utilize individual mechanistic principles like the deletion of coding sequence or 3D genome architecture disruptions. However, a comprehensive tool using the broad spectrum of available annotations is missing. Here, we describe CADD-SV, a method to retrieve and integrate a wide set of annotations to predict the effects of SVs. Previously, supervised learning approaches were limited due to a small number and biased set of annotated pathogenic or benign SVs. We overcome this problem by using a surrogate training objective, the Combined Annotation Dependent Depletion (CADD) of functional variants. We use human- and chimpanzee-derived SVs as proxy-neutral and contrast them with matched simulated variants as proxy-deleterious, an approach that has proven powerful for short sequence variants. Our tool computes summary statistics over diverse variant annotations and uses random forest models to prioritize deleterious structural variants. The resulting CADD-SV scores correlate with known pathogenic and rare population variants. We further show that we can prioritize somatic cancer variants as well as noncoding variants known to affect gene expression. We provide a website and offline-scoring tool for easy application of CADD-SV.

[Foundations for the scientific use of extensive health care data in Germany-results of the Data Sharing working group of the Medical Informatics Initiative]. / Grundlagen für die wissenschaftliche Nutzung umfangreicher Versorgungsdaten in Deutschland ­ Ergebnisse der AG Data Sharing der Medizininformatik-Initiative.

Healthcare data are an important resource in applied medical research. They are available multicentrically. However, it remains a challenge to enable standardized data exchange processes between federal states and their individual laws and regulations. The Medical Informatics Initiative (MII) was founded in 2016 to implement processes that enable cross-clinic access to healthcare data in Germany. Several working groups (WGs) have been set up to coordinate standardized data structures (WG Interoperability), patient information and declarations of consent (WG Consent), and regulations on data exchange (WG Data Sharing). Here we present the most important results of the Data Sharing working group, which include agreed terms of use, legal regulations, and data access processes. They are already being implemented by the established Data Integration Centers (DIZ) and Use and Access Committees (UACs). We describe the services that are necessary to provide researchers with standardized data access. They are implemented with the Research Data Portal for Health, among others. Since the pilot phase, the processes of 385 active researchers have been used on this basis, which, as of April 2024, has resulted in 19 registered projects and 31 submitted research applications.

HemoMIPs-Automated analysis and result reporting pipeline for targeted sequencing data.

Targeted sequencing of genomic regions is a cost- and time-efficient approach for screening patient cohorts. We present a fast and efficient workflow to analyze highly imbalanced, targeted next-generation sequencing data generated using molecular inversion probe (MIP) capture. Our Snakemake pipeline performs sample demultiplexing, overlap paired-end merging, alignment, MIP-arm trimming, variant calling, coverage analysis and report generation. Further, we support the analysis of probes specifically designed to capture certain structural variants and can assign sex using Y-chromosome-unique probes. In a user-friendly HTML report, we summarize all these results including covered, incomplete or missing regions, called variants and their predicted effects. We developed and tested our pipeline using the hemophilia A & B MIP design from the "My Life, Our Future" initiative. HemoMIPs is available as an open-source tool on GitHub at: https://github.com/kircherlab/hemoMIPs.

Germany's Approach for the Secondary Use of Federated Real-World Data in the German Portal for Medical Research Data.

In the light of big data driven clinical research, fair access to real world clinical health data enables evidence to improve patient care. Germany's healthcare system provides an abundant data resource but unique challenges due to its federated nature, heterogeneity and high data-protection standards. The Medical Informatics Initiative (MII) developed concepts that are being implemented in the German Portal for Medical Research Data (FDPG) to grant access to distributed data-sources across state borders. The portal currently provides access to more than 10 million patient resources containing hundreds of millions of laboratory parameters, diagnostic reports, administered medications, procedures and specimens. Upcoming datasets include among others oncological data, molecular analysis results and microbiological findings. Here, we describe the philosophy, implementation and experience behind the framework: standardized access processes, interoperable fair data, software for in depth feasibility requests, tools to support researchers and hospital stakeholders alike as well as transparency measures to provide data use information for patients. Challenges remain to improve data quality and automatization of technical and organizational processes.

Towards a National Portal for Medical Research Data (FDPG): Vision, Status, and Lessons Learned.

Harmonizing medical data sharing frameworks is challenging. Data collection and formats follow local solutions in individual hospitals; thus, interoperability is not guaranteed. The German Medical Informatics Initiative (MII) aims to provide a Germany-wide, federated, large-scale data sharing network. In the last five years, numerous efforts have been successfully completed to implement the regulatory framework and software components for securely interacting with decentralized and centralized data sharing processes. 31 German university hospitals have today established local data integration centers that are connected to the central German Portal for Medical Research Data (FDPG). Here, we present milestones and associated major achievements of various MII working groups and subprojects which led to the current status. Further, we describe major obstacles and the lessons learned during its routine application in the last six months.

Signatures of Long-Term Balancing Selection in Human Genomes.

Balancing selection maintains advantageous diversity in populations through various mechanisms. Although extensively explored from a theoretical perspective, an empirical understanding of its prevalence and targets lags behind our knowledge of positive selection. Here, we describe the Non-central Deviation (NCD), a simple yet powerful statistic to detect long-term balancing selection (LTBS) that quantifies how close frequencies are to expectations under LTBS, and provides the basis for a neutrality test. NCD can be applied to a single locus or genomic data, and can be implemented considering only polymorphisms (NCD1) or also considering fixed differences with respect to an outgroup (NCD2) species. Incorporating fixed differences improves power, and NCD2 has higher power to detect LTBS in humans under different frequencies of the balanced allele(s) than other available methods. Applied to genome-wide data from African and European human populations, in both cases using chimpanzee as an outgroup, NCD2 shows that, albeit not prevalent, LTBS affects a sizable portion of the genome: ∼0.6% of analyzed genomic windows and 0.8% of analyzed positions. Significant windows (P < 0.0001) contain 1.6% of SNPs in the genome, which disproportionally fall within exons and change protein sequence, but are not enriched in putatively regulatory sites. These windows overlap ∼8% of the protein-coding genes, and these have larger number of transcripts than expected by chance even after controlling for gene length. Our catalog includes known targets of LTBS but a majority of them (90%) are novel. As expected, immune-related genes are among those with the strongest signatures, although most candidates are involved in other biological functions, suggesting that LTBS potentially influences diverse human phenotypes.