Whole-genome sequencing reveals host factors underlying critical COVID-19.

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2-4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes-including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)-in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease.

Genome-wide assessment of genetic risk for systemic lupus erythematosus and disease severity.

Using three European and two Chinese genome-wide association studies (GWAS), we investigated the performance of genetic risk scores (GRSs) for predicting the susceptibility and severity of systemic lupus erythematosus (SLE), using renal disease as a proxy for severity. We used four GWASs to test the performance of GRS both cross validating within the European population and between European and Chinese populations. The performance of GRS in SLE risk prediction was evaluated by receiver operating characteristic (ROC) curves. We then analyzed the polygenic nature of SLE statistically. We also partitioned patients according to their age-of-onset and evaluated the predictability of GRS in disease severity in each age group. We found consistently that the best GRS in the prediction of SLE used SNPs associated at the level of P < 1e-05 in all GWAS data sets and that SNPs with P-values above 0.2 were inflated for SLE true positive signals. The GRS results in an area under the ROC curve ranging between 0.64 and 0.72, within European and between the European and Chinese populations. We further showed a significant positive correlation between a GRS and renal disease in two independent European GWAS (Pcohort1 = 2.44e-08; Pcohort2 = 0.00205) and a significant negative correlation with age of SLE onset (Pcohort1 = 1.76e-12; Pcohort2 = 0.00384). We found that the GRS performed better in the prediction of renal disease in the 'later onset' compared with the 'earlier onset' group. The GRS predicts SLE in both European and Chinese populations and correlates with poorer prognostic factors: young age-of-onset and lupus nephritis.

De novo mutations implicate novel genes in systemic lupus erythematosus.

The omnigenic model of complex disease stipulates that the majority of the heritability will be explained by the effects of common variation on genes in the periphery of core disease pathways. Rare variant associations, expected to explain far less of the heritability, may be enriched in core disease genes and thus will be instrumental in the understanding of complex disease pathogenesis and their potential therapeutic targets. Here, using complementary whole-exome sequencing, high-density imputation, and in vitro cellular assays, we identify candidate core genes in the pathogenesis of systemic lupus erythematosus (SLE). Using extreme-phenotype sampling, we sequenced the exomes of 30 SLE parent-affected-offspring trios and identified 14 genes with missense de novo mutations (DNM), none of which are within the >80 SLE susceptibility loci implicated through genome-wide association studies. In a follow-up cohort of 10, 995 individuals of matched European ancestry, we imputed genotype data to the density of the combined UK10K-1000 genomes Phase III reference panel across the 14 candidate genes. Gene-level analyses indicate three functional candidates: DNMT3A, PRKCD, and C1QTNF4. We identify a burden of rare variants across PRKCD associated with SLE risk (P = 0.0028), and across DNMT3A associated with two severe disease prognosis sub-phenotypes (P = 0.0005 and P = 0.0033). We further characterise the TNF-dependent functions of the third candidate gene C1QTNF4 on NF-κB activation and apoptosis, which are inhibited by the p.His198Gln DNM. Our results identify three novel genes in SLE susceptibility and support extreme-phenotype sampling and DNM gene discovery to aid the search for core disease genes implicated through rare variation.

Profiling RNA-Seq at multiple resolutions markedly increases the number of causal eQTLs in autoimmune disease.

Genome-wide association studies have identified hundreds of risk loci for autoimmune disease, yet only a minority (~25%) share genetic effects with changes to gene expression (eQTLs) in immune cells. RNA-Seq based quantification at whole-gene resolution, where abundance is estimated by culminating expression of all transcripts or exons of the same gene, is likely to account for this observed lack of colocalisation as subtle isoform switches and expression variation in independent exons can be concealed. We performed integrative cis-eQTL analysis using association statistics from twenty autoimmune diseases (560 independent loci) and RNA-Seq data from 373 individuals of the Geuvadis cohort profiled at gene-, isoform-, exon-, junction-, and intron-level resolution in lymphoblastoid cell lines. After stringently testing for a shared causal variant using both the Joint Likelihood Mapping and Regulatory Trait Concordance frameworks, we found that gene-level quantification significantly underestimated the number of causal cis-eQTLs. Only 5.0-5.3% of loci were found to share a causal cis-eQTL at gene-level compared to 12.9-18.4% at exon-level and 9.6-10.5% at junction-level. More than a fifth of autoimmune loci shared an underlying causal variant in a single cell type by combining all five quantification types; a marked increase over current estimates of steady-state causal cis-eQTLs. Causal cis-eQTLs detected at different quantification types localised to discrete epigenetic annotations. We applied a linear mixed-effects model to distinguish cis-eQTLs modulating all expression elements of a gene from those where the signal is only evident in a subset of elements. Exon-level analysis detected disease-associated cis-eQTLs that subtly altered transcription globally across the target gene. We dissected in detail the genetic associations of systemic lupus erythematosus and functionally annotated the candidate genes. Many of the known and novel genes were concealed at gene-level (e.g. IKZF2, TYK2, LYST). Our findings are provided as a web resource.

Mapping eQTLs with RNA-seq reveals novel susceptibility genes, non-coding RNAs and alternative-splicing events in systemic lupus erythematosus.

Studies attempting to functionally interpret complex-disease susceptibility loci by GWAS and eQTL integration have predominantly employed microarrays to quantify gene-expression. RNA-Seq has the potential to discover a more comprehensive set of eQTLs and illuminate the underlying molecular consequence. We examine the functional outcome of 39 variants associated with Systemic Lupus Erythematosus (SLE) through the integration of GWAS and eQTL data from the TwinsUK microarray and RNA-Seq cohort in lymphoblastoid cell lines. We use conditional analysis and a Bayesian colocalisation method to provide evidence of a shared causal-variant, then compare the ability of each quantification type to detect disease relevant eQTLs and eGenes. We discovered the greatest frequency of candidate-causal eQTLs using exon-level RNA-Seq, and identified novel SLE susceptibility genes (e.g. NADSYN1 and TCF7) that were concealed using microarrays, including four non-coding RNAs. Many of these eQTLs were found to influence the expression of several genes, supporting the notion that risk haplotypes may harbour multiple functional effects. Novel SLE associated splicing events were identified in the T-reg restricted transcription factor, IKZF2, and other candidate genes (e.g. WDFY4) through asQTL mapping using the Geuvadis cohort. We have significantly increased our understanding of the genetic control of gene-expression in SLE by maximising the leverage of RNA-Seq and performing integrative GWAS-eQTL analysis against gene, exon, and splice-junction quantifications. We conclude that to better understand the true functional consequence of regulatory variants, quantification by RNA-Seq should be performed at the exon-level as a minimum, and run in parallel with gene and splice-junction level quantification.

Large-Scale Pharmacogenomics Analysis of Patients With Cancer Within the 100,000 Genomes Project Combining Whole-Genome Sequencing and Medical Records to Inform Clinical Practice.

PURPOSE: As part of the 100,000 Genomes Project, we set out to assess the potential viability and clinical impact of reporting genetic variants associated with drug-induced toxicity for patients with cancer recruited for whole-genome sequencing (WGS) as part of a genomic medicine service. METHODS: Germline WGS from 76,805 participants was analyzed for pharmacogenetic (PGx) variants in four genes (DPYD, NUDT15, TPMT, UGT1A1) associated with toxicity induced by five drugs used in cancer treatment (capecitabine, fluorouracil, mercaptopurine, thioguanine, irinotecan). Linking genomic data with prescribing and hospital incidence records, a phenome-wide association study (PheWAS) was performed to identify whether phenotypes indicative of adverse drug reactions (ADRs) were enriched in drug-exposed individuals with the relevant PGx variants. In a subset of 7,081 patients with cancer, DPYD variants were reported back to clinicians and outcomes were collected. RESULTS: We identified clinically relevant PGx variants across the four genes in 62.7% of participants in our cohort. Extending this to annual prescription numbers in England for the drugs affected by these PGx variants, approximately 14,540 patients per year could potentially benefit from a reduced dose or alternative drug to reduce the risk of ADRs. Validating PGx associations in a real-world data set, we found a significant association between PGx variants in DPYD and toxicity-related phenotypes in patients treated with capecitabine or fluorouracil. Reported DPYD variants were deemed informative for clinical decision making in a majority of cases. CONCLUSION: Reporting PGx variants from germline WGS relevant to patients with cancer alongside primary findings related to their cancer can be clinically informative, informing prescribing to reduce the risk of ADRs. Extending the range of actionable variants to those found in patients of non-European ancestry is important and will extend the potential clinical impact.

Author Correction: Nuclear-mitochondrial DNA segments resemble paternally inherited mitochondrial DNA in humans.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Nuclear-mitochondrial DNA segments resemble paternally inherited mitochondrial DNA in humans.

Several strands of evidence question the dogma that human mitochondrial DNA (mtDNA) is inherited exclusively down the maternal line, most recently in three families where several individuals harbored a 'heteroplasmic haplotype' consistent with biparental transmission. Here we report a similar genetic signature in 7 of 11,035 trios, with allelic fractions of 5-25%, implying biparental inheritance of mtDNA in 0.06% of offspring. However, analysing the nuclear whole genome sequence, we observe likely large rare or unique nuclear-mitochondrial DNA segments (mega-NUMTs) transmitted from the father in all 7 families. Independently detecting mega-NUMTs in 0.13% of fathers, we see autosomal transmission of the haplotype. Finally, we show the haplotype allele fraction can be explained by complex concatenated mtDNA-derived sequences rearranged within the nuclear genome. We conclude that rare cryptic mega-NUMTs can resemble paternally mtDNA heteroplasmy, but find no evidence of paternal transmission of mtDNA in humans.

Interferon inducible X-linked gene CXorf21 may contribute to sexual dimorphism in Systemic Lupus Erythematosus.

Systemic lupus erythematosus (SLE) is an autoimmune disease, characterised by increased expression of type I interferon (IFN)-regulated genes and a striking sex imbalance towards females. Through combined genetic, in silico, in vitro, and ex vivo approaches, we define CXorf21, a gene of hitherto unknown function, which escapes X-chromosome inactivation, as a candidate underlying the Xp21.2 SLE association. We demonstrate that CXorf21 is an IFN-response gene and that the sexual dimorphism in expression is magnified by immunological challenge. Fine-mapping reveals a single haplotype as a potential causal cis-eQTL for CXorf21. We propose that expression is amplified through modification of promoter and 3'-UTR chromatin interactions. Finally, we show that the CXORF21 protein colocalises with TLR7, a pathway implicated in SLE pathogenesis. Our study reveals modulation in gene expression affected by the combination of two hallmarks of SLE: CXorf21 expression increases in a both an IFN-inducible and sex-specific manner.