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Vyse, T. J.

Publications and source records attributed to Vyse, T. J..

2 recordsLinked to original sources

De Novo Mutations Implicate Novel Genes With Burden Of Rare Variants In Systemic Lupus Erythematosus

The omnigenic model of complex diseases 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 (WES), high-density imputation, and in vitro cellular assays, we identify three 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 (GWAS). 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. 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). Both genes are functional candidates and significantly constrained against missense mutations in gene-level analyses, along with C1QTNF4. We further characterise the TNF-dependent functions of candidate gene C1QTNF4 on NF-{kappa}B activation and apoptosis, which are inhibited by the p.His198Gln DNM. Our results support extreme-phenotype sampling and DNM gene discovery to aid the search for core disease genes implicated through rare variation.\n\nSignificance StatementRare variants, present in <1% in population, are expected to explain little of the heritability of complex diseases, such as Systemic Lupus Erythematosus (SLE), yet are likely to identify core genes crucial to disease mechanisms. Their rarity, however, limits the power to show their statistical association with disease. Through sequencing the exomes of SLE patients and their parents, we identified non-inherited de novo mutations in 14 genes and hypothesised that these are prime candidates for harbouring additional disease-associated rare variants. We demonstrate that two of these genes also carry a significant excess of rare variants in an independent, large cohort of SLE patients. Our findings will influence future study designs in the search for the missing heritability of complex diseases.

genetics

Leveraging The Resolution Of RNA-Seq Markedly Increases The Number Of Causal eQTLs And Candidate Genes In Human 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. As an example, 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. BANK1, UBE2L3, IKZF2, TYK2, LYST). By leveraging RNA-Seq, we were able to isolate the specific transcripts, exons, junctions, and introns modulated by the cis-eQTL - which supports the targeted design of follow-up functional studies involving alternative splicing. Causal cis-eQTLs detected at different quantification types were also found to localise to discrete epigenetic annotations. We provide our findings from all twenty autoimmune diseases as a web resource.\n\nAuthor SummaryIt is well acknowledged that non-coding genetic variants contribute to disease susceptibility through alteration of gene expression levels (known as eQTLs). Identifying the variants that are causal to both disease risk and changes to expression levels has not been easy and we believe this is in part due to how expression is quantified using RNA-Sequencing (RNA-Seq). Whole-gene expression, where abundance is estimated by culminating expression of all transcripts or exons of the same gene, is conventionally used in eQTL analysis. This low resolution may conceal subtle isoform switches and expression variation in independent exons. Using isoform-, exon-, and junction-level quantification can not only point to the candidate genes involved, but also the specific transcripts implicated. We make use of existing RNA-Seq expression data profiled at gene-, isoform-, exon-, junction-, and intron-level, and perform eQTL analysis using association data from twenty autoimmune diseases. We find exon-, and junction-level thoroughly outperform gene-level analysis, and by leveraging all five quantification types, we find >20% of autoimmune loci share a single genetic effect with gene expression. We highlight that existing and new eQTL cohorts using RNA-Seq should profile expression at multiple resolutions to maximise the ability to detect causal eQTLs and candidate genes.

genetics