ATAV: a comprehensive platform for population-scale genomic analyses

Background A common approach for sequencing studies is to do joint-calling and store variants of all samples in a single file. If new samples are continually added or controls are re-used for several studies, the cost and time required to perform joint-calling for each analysis can become prohibitive. Results We present ATAV, an analysis platform for large-scale whole-exome and whole-genome sequencing projects. ATAV stores variant and per site coverage data for all samples in a centralized database, which is efficiently queried by ATAV to support diagnostic analyses for trios and singletons, as well as rare-variant collapsing analyses for finding disease associations in complex diseases. Runtime logs ensure full reproducibility and the modularized ATAV framework makes it extensible to continuous development. Besides helping with the identification of disease-causing variants for a range of diseases, ATAV has also enabled the discovery of disease-genes by rare-variant collapsing on datasets containing more than 20,000 samples. Analyses to date have been performed on data of more than 110,000 individuals demonstrating the scalability of the framework. To allow users to easily access variant-level data directly from the database, we provide a web-based interface, the ATAV data browser ( http://atavdb.org/ ). Through this browser, summary-level data for more than 40,000 samples can be queried by the general public representing a mix of cases and controls of diverse ancestries. Users have access to phenotype categories of variant carriers, as well as predicted ancestry, gender, and quality metrics. In contrast to many other platforms, the data browser is able to show data of newly-added samples in real-time and therefore evolves rapidly as more and more samples are sequenced. Conclusions Through ATAV, users have public access to one of the largest variant databases for patients sequenced at a tertiary care center and can look up any genes or variants of interest. Additionally, since the entire code is freely available on GitHub, ATAV can easily be deployed by other groups that wish to build their own platform, database, and user interface

Family reunion via error correction: an efficient analysis of duplex sequencing data

Background Duplex sequencing is the most accurate approach for identification of sequence variants present at very low frequencies. Its power comes from pooling together multiple descendants of both strands of original DNA molecules, which allows distinguishing true nucleotide substitutions from PCR amplification and sequencing artifacts. This strategy comes at a cost—sequencing the same molecule multiple times increases dynamic range but significantly diminishes coverage, making whole genome duplex sequencing prohibitively expensive. Furthermore, every duplex experiment produces a substantial proportion of singleton reads that cannot be used in the analysis and are thrown away. Results In this paper we demonstrate that a significant fraction of these reads contains PCR or sequencing errors within duplex tags. Correction of such errors allows “reuniting” these reads with their respective families increasing the output of the method and making it more cost effective. Conclusions We combine an error correction strategy with a number of algorithmic improvements in a new version of the duplex analysis software, Du Novo 2.0. It is written in Python, C, AWK, and Bash. It is open source and readily available through Galaxy, Bioconda, and Github: https://github.com/galaxyproject/dunovo

Exome-wide association study to identify rare variants influencing COVID-19 outcomes : Results from the Host Genetics Initiative

Publisher Copyright: Copyright: © 2022 Butler-Laporte et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Host genetics is a key determinant of COVID-19 outcomes. Previously, the COVID-19 Host Genetics Initiative genome-wide association study used common variants to identify multiple loci associated with COVID-19 outcomes. However, variants with the largest impact on COVID-19 outcomes are expected to be rare in the population. Hence, studying rare variants may provide additional insights into disease susceptibility and pathogenesis, thereby informing therapeutics development. Here, we combined whole-exome and whole-genome sequencing from 21 cohorts across 12 countries and performed rare variant exome-wide burden analyses for COVID-19 outcomes. In an analysis of 5,085 severe disease cases and 571,737 controls, we observed that carrying a rare deleterious variant in the SARS-CoV-2 sensor toll-like receptor TLR7 (on chromosome X) was associated with a 5.3-fold increase in severe disease (95% CI: 2.75–10.05, p = 5.41x10-7). This association was consistent across sexes. These results further support TLR7 as a genetic determinant of severe disease and suggest that larger studies on rare variants influencing COVID-19 outcomes could provide additional insights.Peer reviewe

Genome-wide identification and phenotypic characterization of seizure-associated copy number variations in 741,075 individuals

Copy number variants (CNV) are established risk factors for neurodevelopmental disorders with seizures or epilepsy. With the hypothesis that seizure disorders share genetic risk factors, we pooled CNV data from 10,590 individuals with seizure disorders, 16,109 individuals with clinically validated epilepsy, and 492,324 population controls and identified 25 genome-wide significant loci, 22 of which are novel for seizure disorders, such as deletions at 1p36.33, 1q44, 2p21-p16.3, 3q29, 8p23.3-p23.2, 9p24.3, 10q26.3, 15q11.2, 15q12-q13.1, 16p12.2, 17q21.31, duplications at 2q13, 9q34.3, 16p13.3, 17q12, 19p13.3, 20q13.33, and reciprocal CNVs at 16p11.2, and 22q11.21. Using genetic data from additional 248,751 individuals with 23 neuropsychiatric phenotypes, we explored the pleiotropy of these 25 loci. Finally, in a subset of individuals with epilepsy and detailed clinical data available, we performed phenome-wide association analyses between individual CNVs and clinical annotations categorized through the Human Phenotype Ontology (HPO). For six CNVs, we identified 19 significant associations with specific HPO terms and generated, for all CNVs, phenotype signatures across 17 clinical categories relevant for epileptologists. This is the most comprehensive investigation of CNVs in epilepsy and related seizure disorders, with potential implications for clinical practice

Analyzing NGS data with machine learning : from IBD segments to copy number variations

The rise of Next Generation Sequencing (NGS) techniques has enabled the production of large amounts of sequencing data in shorter time and with lower costs than previously. However, equally powerful bioinformatic tools are needed to analyze the data in order to fully exploit the information that is encoded in the sequenced DNA. Segments of DNA, that are identical by descent (IBD) in two or more individuals because they were inherited from a common ancestor, can be used to uncover relationships from Neandertals to present day families. In this thesis the recently developed IBD detection methods HapFABIA and HapRFN were applied to whole genome sequencing (WGS) data from the 1000 Genomes Project to uncover relationships between and within populations as well as with Neandertals and Denisovans. We extracted two types of very old IBD segments that are shared with Neandertals/Denisovans: (1) longer segments primarily found in East Asians, South Asians, and Europeans that confirm already reported introgression events outside of Africa; (2) shorter segments mainly shared by Africans that may indicate events involving ancestors of humans and other ancient hominins within Africa. In clinical diagnostics, NGS techniques, especially targeted NGS panels, have largely replaced Sanger sequencing for the detection of single-nucleotide variants and small insertions/deletions. However, for the detection of copy-number variations (CNVs), previous computational methods had shortcomings regarding accuracy, quality control (QC), incidental findings, and user-friendliness. With the aim to address all these shortcomings, panelcn.MOPS was developed as part of this thesis. panelcn.MOPS is built upon the successful cn.MOPS model, which was adapted for targeted NGS panel data and especially for the usage in a clinical diagnostic setting. In addition to an increase in sensitivity, the extension includes the implementation of QC criteria for samples and genetic regions of interest (ROIs) and a filter for user-selected genes to avoid incidental findings. Furthermore, panelcn.MOPS was made freely available as R package and standalone software with graphical user interface that is easy to use for clinical geneticists without any programming experience. This thesis demonstrates the value of bioinformatics, and especially of machine learning methods, not only for gaining new insights into human history, but also for facilitating routine clinical genetic diagnostics.Der Aufstieg von Next Generation Sequencing (NGS) Techniken ermöglicht die Produktion von großen Mengen an Sequenzdaten in kürzerer Zeit und mit geringeren Kosten als bisher. Allerdings sind ebenso leistungsstarke Bioinformatik-Werkzeuge für zur Analyse der Daten erforderlich, damit die Informationen, die in der sequenzierten DNA codiert sind, voll ausgeschöpft werden können. DNA Segmente, die durch Abstammung in zwei oder mehr Individuen identisch sind, weil sie von einem gemeinsamen Vorfahren geerbt wurden (identity by descent - IBD), können verwendet werden, um Beziehungen von Neandertalern bis hin zu heutigen Familien aufzudecken. In dieser Arbeit wurden die neu entwickelten IBD-Detektionsmethoden HapFABIA und HapRFN auf Whole Genome Sequencing (WGS) Daten des 1000 Genomes Project angewendet, um Beziehungen zwischen und innerhalb von Populationen sowie mit Neandertalern und Denisovas aufzudecken. Wir haben zwei Arten von sehr alten IBD-Segmenten extrahiert, die mit Neandertalern / Denisovas geteilt werden: (1) längere Segmente, die vor allem in Ostasiaten, Südasiaten und Europäern gefunden wurden und die bereits berichtete genetische Vermischungen außerhalb Afrikas bestätigen; (2) kürzere Segmente, die hauptsächlich von Afrikanern geteilt werden und die auf Ereignisse mit Vorfahren von Menschen und anderen alten Homininen in Afrika hinweisen könnten. In der klinischen Diagnostik haben NGS-Techniken, insbesondere sogenannte targeted NGS Panels, die Sanger-Sequenzierung für den Nachweis von Single-Nukleotid-Varianten und kleinen Insertionen / Deletionen weitgehend ersetzt. Für die Erkennung von Kopienzahlvariationen (copy number variations - CNVs) hatten bisherige Nachweismethoden jedoch Mängel hinsichtlich Genauigkeit, Qualitätskontrollen, Zufallsbefunden und Benutzerfreundlichkeit. Mit dem Ziel, alle diese Probleme zu lösen, wurde panelcn.MOPS als Teil dieser Arbeit entwickelt. panelcn.MOPS basiert auf dem erfolgreichen cn.MOPS-Modell, das für targeted NGS Panel-Daten und insbesondere für den Einsatz in der klinischen Diagnostik angepasst wurde. Zusätzlich zu einer Erhöhung der Sensitivität umfasst die Erweiterung die Implementierung von Qualitätskriterien für Proben und genetische Regions-of-Interest (ROIs) und einen Filter für benutzerselektierte Gene, um Zufallsbefunde zu vermeiden. Darüber hinaus wird panelcn.MOPS frei als R-Paket und eigenständige Software mit grafischer Benutzeroberfläche zur Verfügung gestellt, die für klinische Genetiker ohne Programmierkenntnisse einfach zu bedienen ist. Diese Arbeit zeigt den Wert der Bioinformatik und insbesondere der Machine Learning Methoden, nicht nur für neue Einblicke in die menschliche Geschichte, sondern auch für die Erleichterung der routinemäßigen klinischen genetischen Diagnostik.submitted by Gundula PovysilZusammenfassung in deutscher SpracheUniversität Linz, Dissertation, 2017OeBB(VLID)224632

The impact of poly-A microsatellite heterologies in meiotic recombination

Meiotic recombination has strong, but poorly understood effects on short tandem repeat (STR) instability. Here, we screened thousands of single recombinant products with sperm typing to characterize the role of polymorphic poly-A repeats at a human recombination hotspot in terms of hotspot activity and STR evolution. We show that the length asymmetry between heterozygous poly-A’s strongly influences the recombination outcome: a heterology of 10 A’s (9A/19A) reduces the number of crossovers and elevates the frequency of non-crossovers, complex recombination products, and long conversion tracts. Moreover, the length of the heterology also influences the STR transmission during meiotic repair with a strong and significant insertion bias for the short heterology (6A/7A) and a deletion bias for the long heterology (9A/19A). In spite of this opposing insertion-/deletion-biased gene conversion, we find that poly-A’s are enriched at human recombination hotspots that could have important consequences in hotspot activation

Prevalence and significance of clonal hematopoiesis of indeterminate potential in lung transplant recipients

Abstract Background Clonal hematopoiesis of indeterminate potential (CHIP), the age-related acquisition of somatic mutations that leads to an expanded blood cell clone, has been associated with development of a pro-inflammatory state. An enhanced or dysregulated inflammatory response may contribute to rejection after lung transplantation, however the prevalence of CHIP in lung recipients and influence of CHIP on allograft outcomes is unknown. Methods We analyzed whole-exome sequencing data in 279 lung recipients to detect CHIP, defined by pre-specified somatic mutations in 74 genes known to promote clonal expansion of hematopoietic stem cells. We compared the burden of acute rejection (AR) over the first post-transplant year in lung recipients with vs. without CHIP using multivariable ordinal regression. Multivariate Cox proportional hazards models were used to assess the association between CHIP and CLAD-free survival. An exploratory analysis evaluated the association between the number of CHIP-associated variants and chronic lung allograft dysfunction (CLAD)-free survival. Results We detected 64 CHIP-associated mutations in 45 individuals (15.7%), most commonly in TET2 (10.8%), DNMT3A (9.2%), and U2AF1 (9.2%). Patients with CHIP tended to be older but did not significantly differ from patients without CHIP in terms of race or native lung disease. Patients with CHIP did not have a higher incidence of AR over the first post-transplant year (p = 0.45) or a significantly increased risk of death or CLAD (adjusted HR 1.25, 95% CI 0.88–1.78). We did observe a significant association between the number of CHIP variants and CLAD-free survival, specifically patients with 2 or more CHIP-associated variants had an increased risk for death or CLAD (adjusted HR 3.79, 95% CI 1.98–7.27). Conclusions Lung recipients have a higher prevalence of CHIP and a larger variety of genes with CHIP-associated mutations compared with previous reports for the general population. CHIP did not increase the risk of AR, CLAD, or death in lung recipients