Genomewide Association Study of Severe Covid-19 with Respiratory Failure.

BACKGROUND: There is considerable variation in disease behavior among patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease 2019 (Covid-19). Genomewide association analysis may allow for the identification of potential genetic factors involved in the development of Covid-19. METHODS: We conducted a genomewide association study involving 1980 patients with Covid-19 and severe disease (defined as respiratory failure) at seven hospitals in the Italian and Spanish epicenters of the SARS-CoV-2 pandemic in Europe. After quality control and the exclusion of population outliers, 835 patients and 1255 control participants from Italy and 775 patients and 950 control participants from Spain were included in the final analysis. In total, we analyzed 8,582,968 single-nucleotide polymorphisms and conducted a meta-analysis of the two case-control panels. RESULTS: We detected cross-replicating associations with rs11385942 at locus 3p21.31 and with rs657152 at locus 9q34.2, which were significant at the genomewide level (P<5×10-8) in the meta-analysis of the two case-control panels (odds ratio, 1.77; 95% confidence interval [CI], 1.48 to 2.11; P = 1.15×10-10; and odds ratio, 1.32; 95% CI, 1.20 to 1.47; P = 4.95×10-8, respectively). At locus 3p21.31, the association signal spanned the genes SLC6A20, LZTFL1, CCR9, FYCO1, CXCR6 and XCR1. The association signal at locus 9q34.2 coincided with the ABO blood group locus; in this cohort, a blood-group-specific analysis showed a higher risk in blood group A than in other blood groups (odds ratio, 1.45; 95% CI, 1.20 to 1.75; P = 1.48×10-4) and a protective effect in blood group O as compared with other blood groups (odds ratio, 0.65; 95% CI, 0.53 to 0.79; P = 1.06×10-5). CONCLUSIONS: We identified a 3p21.31 gene cluster as a genetic susceptibility locus in patients with Covid-19 with respiratory failure and confirmed a potential involvement of the ABO blood-group system. (Funded by Stein Erik Hagen and others.).

Protein-coding variants contribute to the risk of atopic dermatitis and skin-specific gene expression.

BACKGROUND: Fifteen percent of atopic dermatitis (AD) liability-scale heritability could be attributed to 31 susceptibility loci identified by using genome-wide association studies, with only 3 of them (IL13, IL-6 receptor [IL6R], and filaggrin [FLG]) resolved to protein-coding variants. OBJECTIVE: We examined whether a significant portion of unexplained AD heritability is further explained by low-frequency and rare variants in the gene-coding sequence. METHODS: We evaluated common, low-frequency, and rare protein-coding variants using exome chip and replication genotype data of 15,574 patients and 377,839 control subjects combined with whole-transcriptome data on lesional, nonlesional, and healthy skin samples of 27 patients and 38 control subjects. RESULTS: An additional 12.56% (SE, 0.74%) of AD heritability is explained by rare protein-coding variation. We identified docking protein 2 (DOK2) and CD200 receptor 1 (CD200R1) as novel genome-wide significant susceptibility genes. Rare coding variants associated with AD are further enriched in 5 genes (IL-4 receptor [IL4R], IL13, Janus kinase 1 [JAK1], JAK2, and tyrosine kinase 2 [TYK2]) of the IL13 pathway, all of which are targets for novel systemic AD therapeutics. Multiomics-based network and RNA sequencing analysis revealed DOK2 as a central hub interacting with, among others, CD200R1, IL6R, and signal transducer and activator of transcription 3 (STAT3). Multitissue gene expression profile analysis for 53 tissue types from the Genotype-Tissue Expression project showed that disease-associated protein-coding variants exert their greatest effect in skin tissues. CONCLUSION: Our discoveries highlight a major role of rare coding variants in AD acting independently of common variants. Further extensive functional studies are required to detect all potential causal variants and to specify the contribution of the novel susceptibility genes DOK2 and CD200R1 to overall disease susceptibility.

Development of a high-resolution NGS-based HLA-typing and analysis pipeline.

The human leukocyte antigen (HLA) complex contains the most polymorphic genes in the human genome. The classical HLA class I and II genes define the specificity of adaptive immune responses. Genetic variation at the HLA genes is associated with susceptibility to autoimmune and infectious diseases and plays a major role in transplantation medicine and immunology. Currently, the HLA genes are characterized using Sanger- or next-generation sequencing (NGS) of a limited amplicon repertoire or labeled oligonucleotides for allele-specific sequences. High-quality NGS-based methods are in proprietary use and not publicly available. Here, we introduce the first highly automated open-kit/open-source HLA-typing method for NGS. The method employs in-solution targeted capturing of the classical class I (HLA-A, HLA-B, HLA-C) and class II HLA genes (HLA-DRB1, HLA-DQA1, HLA-DQB1, HLA-DPA1, HLA-DPB1). The calling algorithm allows for highly confident allele-calling to three-field resolution (cDNA nucleotide variants). The method was validated on 357 commercially available DNA samples with known HLA alleles obtained by classical typing. Our results showed on average an accurate allele call rate of 0.99 in a fully automated manner, identifying also errors in the reference data. Finally, our method provides the flexibility to add further enrichment target regions.

The Gene Expression Classifier ALLCatchR Identifies B-cell Precursor ALL Subtypes and Underlying Developmental Trajectories Across Age.

Current classifications (World Health Organization-HAEM5/ICC) define up to 26 molecular B-cell precursor acute lymphoblastic leukemia (BCP-ALL) disease subtypes by genomic driver aberrations and corresponding gene expression signatures. Identification of driver aberrations by transcriptome sequencing (RNA-Seq) is well established, while systematic approaches for gene expression analysis are less advanced. Therefore, we developed ALLCatchR, a machine learning-based classifier using RNA-Seq gene expression data to allocate BCP-ALL samples to all 21 gene expression-defined molecular subtypes. Trained on n = 1869 transcriptome profiles with established subtype definitions (4 cohorts; 55% pediatric / 45% adult), ALLCatchR allowed subtype allocation in 3 independent hold-out cohorts (n = 1018; 75% pediatric / 25% adult) with 95.7% accuracy (averaged sensitivity across subtypes: 91.1% / specificity: 99.8%). High-confidence predictions were achieved in 83.7% of samples with 98.9% accuracy. Only 1.2% of samples remained unclassified. ALLCatchR outperformed existing tools and identified novel driver candidates in previously unassigned samples. Additional modules provided predictions of samples blast counts, patient's sex, and immunophenotype, allowing the imputation in cases where these information are missing. We established a novel RNA-Seq reference of human B-lymphopoiesis using 7 FACS-sorted progenitor stages from healthy bone marrow donors. Implementation in ALLCatchR enabled projection of BCP-ALL samples to this trajectory. This identified shared proximity patterns of BCP-ALL subtypes to normal lymphopoiesis stages, extending immunophenotypic classifications with a novel framework for developmental comparisons of BCP-ALL. ALLCatchR enables RNA-Seq routine application for BCP-ALL diagnostics with systematic gene expression analysis for accurate subtype allocation and novel insights into underlying developmental trajectories.

BIGwas: Single-command quality control and association testing for multi-cohort and biobank-scale GWAS/PheWAS data.

BACKGROUND: Genome-wide association studies (GWAS) and phenome-wide association studies (PheWAS) involving 1 million GWAS samples from dozens of population-based biobanks present a considerable computational challenge and are carried out by large scientific groups under great expenditure of time and personnel. Automating these processes requires highly efficient and scalable methods and software, but so far there is no workflow solution to easily process 1 million GWAS samples. RESULTS: Here we present BIGwas, a portable, fully automated quality control and association testing pipeline for large-scale binary and quantitative trait GWAS data provided by biobank resources. By using Nextflow workflow and Singularity software container technology, BIGwas performs resource-efficient and reproducible analyses on a local computer or any high-performance compute (HPC) system with just 1 command, with no need to manually install a software execution environment or various software packages. For a single-command GWAS analysis with 974,818 individuals and 92 million genetic markers, BIGwas takes ∼16 days on a small HPC system with only 7 compute nodes to perform a complete GWAS QC and association analysis protocol. Our dynamic parallelization approach enables shorter runtimes for large HPCs. CONCLUSIONS: Researchers without extensive bioinformatics knowledge and with few computer resources can use BIGwas to perform multi-cohort GWAS with 1 million GWAS samples and, if desired, use it to build their own (genome-wide) PheWAS resource. BIGwas is freely available for download from http://github.com/ikmb/gwas-qc and http://github.com/ikmb/gwas-assoc.

SNPInt-GPU: Tool for Epistasis Testing with Multiple Methods and GPU Acceleration.

We present SNPInt-GPU, a software providing several methods for statistical epistasis testing. SNPInt-GPU supports GPU acceleration using the Nvidia CUDA framework, but can also be used without GPU hardware. The software implements logistic regression (as in PLINK epistasis testing), BOOST, log-linear regression, mutual information (MI), and information gain (IG) for pairwise testing as well as mutual information and information gain for third-order tests. Optionally, r2 scores for testing for linkage disequilibrium (LD) can be calculated on-the-fly. SNPInt-GPU is publicly available at GitHub. The software requires a Linux-based operating system and CUDA libraries. This chapter describes detailed installation and usage instructions as well as examples for basic preliminary quality control and analysis of results.

Genome-wide study of a Neolithic Wartberg grave community reveals distinct HLA variation and hunter-gatherer ancestry.

The Wartberg culture (WBC, 3500-2800 BCE) dates to the Late Neolithic period, a time of important demographic and cultural transformations in western Europe. We performed genome-wide analyses of 42 individuals who were interred in a WBC collective burial in Niedertiefenbach, Germany (3300-3200 cal. BCE). The results showed that the farming population of Niedertiefenbach carried a surprisingly large hunter-gatherer ancestry component (34-58%). This component was most likely introduced during the cultural transformation that led to the WBC. In addition, the Niedertiefenbach individuals exhibited a distinct human leukocyte antigen gene pool, possibly reflecting an immune response that was geared towards detecting viral infections.

Genome-wide association study in 8,956 German individuals identifies influence of ABO histo-blood groups on gut microbiome.

The intestinal microbiome is implicated as an important modulating factor in multiple inflammatory1,2, neurologic3 and neoplastic diseases4. Recent genome-wide association studies yielded inconsistent, underpowered and rarely replicated results such that the role of human host genetics as a contributing factor to microbiome assembly and structure remains uncertain5-11. Nevertheless, twin studies clearly suggest host genetics as a driver of microbiome composition11. In a genome-wide association analysis of 8,956 German individuals, we identified 38 genetic loci to be associated with single bacteria and overall microbiome composition. Further analyses confirm the identified associations of ABO histo-blood groups and FUT2 secretor status with Bacteroides and Faecalibacterium spp. Mendelian randomization analysis suggests causative and protective effects of gut microbes, with clade-specific effects on inflammatory bowel disease. This holistic investigative approach of the host, its genetics and its associated microbial communities as a 'metaorganism' broaden our understanding of disease etiology, and emphasize the potential for implementing microbiota in disease treatment and management.

Parallelizing Epistasis Detection in GWAS on FPGA and GPU-Accelerated Computing Systems.

High-throughput genotyping technologies (such as SNP-arrays) allow the rapid collection of up to a few million genetic markers of an individual. Detecting epistasis (based on 2-SNP interactions) in Genome-Wide Association Studies is an important but time consuming operation since statistical computations have to be performed for each pair of measured markers. Computational methods to detect epistasis therefore suffer from prohibitively long runtimes; e.g., processing a moderately-sized dataset consisting of about 500,000 SNPs and 5,000 samples requires several days using state-of-the-art tools on a standard 3 GHz CPU. In this paper, we demonstrate how this task can be accelerated using a combination of fine-grained and coarse-grained parallelism on two different computing systems. The first architecture is based on reconfigurable hardware (FPGAs) while the second architecture uses multiple GPUs connected to the same host. We show that both systems can achieve speedups of around four orders-of-magnitude compared to the sequential implementation. This significantly reduces the runtimes for detecting epistasis to only a few minutes for moderately-sized datasets and to a few hours for large-scale datasets.