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.</p

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

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

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

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

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

The Genomic Landscape of Familial Glioma

Glioma is a rare brain tumor with a poor prognosis. Familial glioma is a subset of glioma with a strong genetic predisposition that accounts for approximately 5% of glioma cases. We performed whole-genome sequencing on an exploratory cohort of 203 individuals from 189 families with a history of familial glioma and an additional validation cohort of 122 individuals from 115 families. We found significant enrichment of rare deleterious variants of seven genes in both cohorts, and the most significantly enriched gene was HERC2 (P = 0.0006). Furthermore, we identified rare noncoding variants in both cohorts that were predicted to affect transcription factor binding sites or cause cryptic splicing. Last, we selected a subset of discovered genes for validation by CRISPR knockdown screening and found that DMBT1, HP1BP3, and ZCH7B3 have profound impacts on proliferation. This study performs comprehensive surveillance of the genomic landscape of familial glioma

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 hospitalisation2-4 following SARS-CoV-2 infection. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from critically-ill cases with population controls in order to find underlying disease mechanisms. Here, we use whole genome sequencing in 7,491 critically-ill cases 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 involved in interferon signalling (IL10RB, PLSCR1), leucocyte differentiation (BCL11A), and blood type antigen secretor status (FUT2). Using transcriptome-wide association and colocalisation to infer the effect of gene expression on disease severity, we find evidence implicating multiple genes, including reduced expression of a membrane flippase (ATP11A), and increased mucin expression (MUC1), in critical disease. Mendelian randomisation provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5, CD209) and 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 critically-ill cases and population controls is highly efficient for detection of therapeutically-relevant mechanisms of disease