Biology subjects

Barrett, J. C.

Publications and source records attributed to Barrett, J. C..

10 recordsLinked to original sources

bioRxiv2018

HLA-DQA1*05 is associated with the development of antibodies to anti-TNF therapy

BackgroundAnti-tumour necrosis factor (anti-TNF) therapies are the most widely used biologic therapies for treating immune-mediated diseases. Their efficacy is significantly reduced by the development of anti-drug antibodies which can lead to treatment failure and adverse reactions. The biological mechanisms underlying antibody development are unknown but the ability to identify subjects at higher risk would have significant clinical benefits.\n\nMethodsThe PANTS cohort consists of Crohns disease patients recruited prior to first administration of anti-TNF, with serial measurements of anti-drug antibody titres. We performed a genome-wide association study across 1240 individuals from this cohort to identify genetic variants associated with anti-drug antibody development.\n\nFindingsThe Human Leukocyte Antigen allele, HLA-DQA1*05, carried by approximately 40% of Europeans, significantly increased the rate of anti-drug antibody development (hazard ratio [HR], 1.90; 95% confidence interval [CI], 1.60 to 2.25; P=5.88x10-13). This association was consistent for patients treated with adalimumab (HR, 1.89; 95% CI, 1.32 to 2.70) and infliximab (HR, 1.92; 95% CI, 1.57 to 2.33), and for patients treated with mono-(HR, 1.75; 95% CI, 1.37 to 2.22) or combination therapy with immunomodulators (HR, 2.0; 95% CI, 1.57 to 2.58).\n\nInterpretationHLA-DQA1*05 is significantly associated with an increased rate of anti-drug antibody formation in patients with Crohns disease treated with infliximab and adalimumab. Pre-treatment HLA-DQA1*05 genetic testing may help personalise the choice of anti-TNF therapy and allow the targeted use of immunomodulator therapy to minimise risk and maximise response.

genomics↗

bioRxiv2018

Common genetic variants contribute to risk of rare severe neurodevelopmental disorders

There are thousands of rare human disorders caused by a single deleterious, protein-coding genetic variant 1. However, patients with the same genetic defect can have different clinical presentation 2-4, and some individuals carrying known disease-causing variants can appear unaffected 5. What explains these differences? Here, we show in a cohort of 6,987 children with heterogeneous severe neurodevelopmental disorders expected to be almost entirely monogenic that 7.7% of variance in risk is attributable to inherited common genetic variation. We replicated this genome wide common variant burden by showing that it is over-transmitted from parents to children in an independent sample of 728 trios from the same cohort. Our common variant signal is significantly positively correlated with genetic predisposition to fewer years of schooling, decreased intelligence, and risk of schizophrenia. We found that common variant risk was not significantly different between individuals with and without a known protein-coding diagnostic variant, suggesting that common variant risk is not confined to patients without a monogenic diagnosis. In addition, previously published common variant scores for autism, height, birth weight, and intracranial volume were all correlated with those traits within our cohort, suggesting that phenotypic expression in individuals with monogenic disorders is affected by the same variants as the general population. Our results demonstrate that common genetic variation affects both overall risk and clinical presentation in disorders typically considered to be monogenic.

genetics↗

bioRxiv2018

Mutational origins and pathogenic consequences of multinucleotide mutations in 6,688 trios with developmental disorders

De novo mutations (DNMs) in protein-coding genes are a well-established cause of developmental disorders (DD). However, known DD-associated genes only account for a minority of the observed excess of such DNMs. To identify novel DD-associated genes, we integrated healthcare and research exome sequences on 31,058 DD parent-offspring trios, and developed a simulation-based statistical test to identify gene-specific enrichments of DNMs. We identified 299 significantly DD-associated genes, including 49 not previously robustly associated with DDs. Despite detecting more DD-associated genes than in any previous study, much of the excess of DNMs of protein-coding genes remains unaccounted for. Modelling suggests that over 500 novel DD-associated genes await discovery, many of which are likely to be less penetrant than the currently known genes. Research access to clinical diagnostic datasets will be critical for completing the map of dominant DDs.

genomics↗

bioRxiv2018

The contribution of non-canonical splicing mutations to severe dominant developmental disorders

Mutations which perturb normal pre-mRNA splicing are significant contributors to human disease. We used exome sequencing data from 7,833 probands with developmental disorders (DD) and their unaffected parents, as well as >60,000 aggregated exomes from the Exome Aggregation Consortium, to investigate selection around the splice site, and quantify the contribution of splicing mutations to DDs. Patterns of purifying selection, a deficit of variants in highly constrained genes in healthy subjects and excess de novo mutations in patients highlighted particular positions within and around the consensus splice site of greater functional relevance. Using mutational burden analyses in this large cohort of proband-parent trios, we could estimate in an unbiased manner the relative contributions of mutations at canonical dinucleotides (73%) and flanking non-canonical positions (27%), and calculated the positive predictive value of pathogenicity for different classes of mutations. We identified 18 patients with likely diagnostic de novo mutations in dominant DD-associated genes at non-canonical positions in splice sites. We estimate 35-40% of pathogenic variants in non-canonical splice site positions are missing from public databases.

genetics↗

bioRxiv2017

Autosomal recessive coding variants explain only a small proportion of undiagnosed developmental disorders in the British Isles

Large exome-sequencing datasets offer an unprecedented opportunity to understand the genetic architecture of rare diseases, informing clinical genetics counseling and optimal study designs for disease gene identification. We analyzed 7,448 exome-sequenced families from the Deciphering Developmental Disorders study, and, for the first time, estimated the causal contribution of recessive coding variation exome-wide. We found that the proportion of cases attributable to recessive coding variants is surprisingly low in patients of European ancestry, at only 3.6%, versus 50% of cases explained by de novo coding mutations. Surprisingly, we found that, even in European probands with affected siblings, recessive coding variants are only likely to explain ~12% of cases. In contrast, they account for 31% of probands with Pakistani ancestry due to elevated autozygosity. We tested every gene for an excess of damaging homozygous or compound heterozygous genotypes and found three genes that passed stringent Bonferroni correction: EIF3F, KDM5B, and THOC6. EIF3F is a novel disease gene, and KDM5B has previously been reported as a dominant disease gene. KDM5B appears to follow a complex mode of inheritance, in which heterozygous loss-of-function variants (LoFs) show incomplete penetrance and biallelic LoFs are fully penetrant. Our results suggest that a large proportion of undiagnosed developmental disorders remain to be explained by other factors, such as noncoding variants and polygenic risk.

genetics↗

bioRxiv2017

Detecting T cell receptor rearrangements in silico from non-targeted DNA-sequencing (WGS/WES)

To better understand the composition of heterogeneous tissue samples used in generating large genomic datasets, we developed a method for estimating the abundance of T cells within the cellular population. Somatic recombination of chromosomal DNA in T cells creates a vast repertoire of structurally divergent T cell receptors (TCRs) that recognize an array of non-self proteins. It also generates a genomic signature by which TCR sequences can be distinguished from other cell types in non-targeted NGS genomic data. Here we leverage this signature to extract reads with rearranged TCR sequences from a non-targeted population, such as whole genome sequencing (WGS) or whole exome sequencing (WES) datasets. We isolate and confirm T cell rearranged reads from the remainder of the genome (99.9%), accurately estimate relative T cell abundance within a cellular population, and provide a snapshot of the T cell receptor repertoire. This approach is unique from available TCR software options that focus on examining the overall diversity of the TCR repertoire and require prior amplification or selection of this region before sequencing, and has particular utility in immunoscoring clinical patient samples in situations where genomic data exists and other approaches are unavailable.

bioinformatics↗

bioRxiv2017

Designing an intuitive web application for drug discovery scientists

Although a scientific web application that is intuitive can help scientists utilize data more easily and advance their research, there is little guidance on how to design such an application in the academic literature. We discuss how we designed an intuitive application for bench scientists working in drug discovery following an approach that can be applied to the design and development of scientific resources in a broad range of disciplines.

bioinformatics↗

bioRxiv2017

De novo mutations in regulatory elements cause neurodevelopmental disorders

SummaryDe novo mutations in hundreds of different genes collectively cause 25-42% of severe developmental disorders (DD). The cause in the remaining cases is largely unknown. The role of de novo mutations in regulatory elements affecting known DD associated genes or other genes is essentially unexplored. We identified de novo mutations in three classes of putative regulatory elements in almost 8,000 DD patients. Here we show that de novo mutations in highly conserved fetal-brain active elements are significantly and specifically enriched in neurodevelopmental disorders. We identified a significant two-fold enrichment of recurrently mutated elements. We estimate that, genome-wide, de novo mutations in fetaLbrain active elements are likely to be causal for 1-3% of patients without a diagnostic coding variant and that only a small fraction (<2%) of de novo mutations in these elements are pathogenic. Our findings represent a robust estimate of the contribution of de novo mutations in regulatory elements to this genetically heterogeneous set of disorders, and emphasise the importance of combining functional and evolutionary evidence to delineate regulatory causes of genetic disorders.

genetics↗

bioRxiv2017

New mutations, old statistical challenges

Based on targeted sequencing of 208 genes in 11,730 neurodevelopmental disorder cases, Stessman et al. report the identification of 91 genes associated (at a False Discovery Rate [FDR] of 0.1) with autism spectrum disorders (ASD), intellectual disability (ID), and developmental delay (DD)--including what they characterize as 38 novel genes, not previously reported as connected with these diseases1.\n\nIf true, this would represent a substantial step forward. Unfortunately, each of the two discovery analyses (1. De novo mutation analysis and, 2. a comparison of private mutations with public control data) contain critical statistical flaws. When one accounts for these problems, fewer than half of the genes--and very few, if any, of the novel findings--survive. These errors have implications for how future analyses should be conducted, for understanding the genetic basis of these disorders, and for genomic medicine.\n\nWe discuss the two main ana ...

genetics↗

bioRxiv2016

Prioritisation of Structural Variant Calls in Cancer Genomes

Sensitivity of short read DNA-sequencing for gene fusion detection is improving, but is hampered by the significant amount of noise composed of uninteresting or false positive hits in the data. In this paper we describe a tiered prioritisation approach to extract high impact gene fusion events. Using cell line and patient DNA sequence data we improve the annotation and interpretation of structural variant calls to best highlight likely cancer driving fusions. We also considerably improve on the automated visualisation of the high impact structural variants to highlight the effects of the variants on the resulting transcripts. The resulting framework greatly improves on readily detecting clinically actionable structural variants.

genomics↗