Integrative statistical methods for the genomic analysis of immune-mediated disease

Genome-wide association studies (GWAS) have proved to be a successful method in cataloguing loci influencing thousands of complex human disease phenotypes. However, elucidating the causal mechanisms underlying such associations has proved challenging due to the regulatory nature of the majority of signals. In Chapters 2 and 3, I hypothesised that promoter-capture Hi-C (PCHi-C) data might have utility in physically linking disease-associated regulatory variants to their target genes, in a tissue-specific manner. To examine the genome-wide enrichment of GWAS summary statistics within PCHi-C chromatin contact maps I developed a novel statistical method, blockshifter. I applied \textit{blockshifter} to a compendium of GWAS summary statistics for 31 traits and PCHi-C data across 17 primary blood tissues, and found convincing evidence for the enrichment of immune-mediated disease (IMD) GWAS signals in lymphocyte-specific chromatin interactions, providing support for the hypothesis. Taking a more gene-centric approach I developed `COGS', a novel method for integrating GWAS and PCHi-C to prioritise specific causal variants, genes and cellular contexts for functional follow up. With a focus on IMD, I prioritised tissue-context specific interactions in CD4$^{+}$ T cells linking putative causal variants for type 1 diabetes, to the promoter of IL2RA. The effect of these variants on IL2RA expression was subsequently validated by allele-specific expression, by a collaborator, supporting the approach. In Chapter 4, I hypothesised that summary statistics from multiple, well-powered GWAS of related diseases might be exploited to provide insight into rarer related diseases or disease subtypes. To investigate this I developed a PCA based framework to generate a lower-dimensional basis, summarising input GWAS traits. I constructed such a basis from ten IMD GWAS studies, excluding variants in the HLA region, and projected on summary GWAS data from multiple sources in order to characterise individual principal components (PCs). By projecting on both summary and individual-level genotype data for juvenile idiopathic disease subtypes, I was able to show that a single PC was able to discriminate enthesitis-related and systemic forms of the disease from other subtypes

VSEAMS: a pipeline for variant set enrichment analysis using summary GWAS data identifies IKZF3, BATF and ESRRA as key transcription factors in type 1 diabetes.

MOTIVATION: Genome-wide association studies (GWAS) have identified many loci implicated in disease susceptibility. Integration of GWAS summary statistics (P-values) and functional genomic datasets should help to elucidate mechanisms. RESULTS: We extended a non-parametric SNP set enrichment method to test for enrichment of GWAS signals in functionally defined loci to a situation where only GWAS P-values are available. The approach is implemented in VSEAMS, a freely available software pipeline. We use VSEAMS to identify enrichment of type 1 diabetes (T1D) GWAS associations near genes that are targets for the transcription factors IKZF3, BATF and ESRRA. IKZF3 lies in a known T1D susceptibility region, while BATF and ESRRA overlap other immune disease susceptibility regions, validating our approach and suggesting novel avenues of research for T1D. AVAILABILITY AND IMPLEMENTATION: VSEAMS is available for download (http://github.com/ollyburren/vseams).This work was funded by the JDRF (9-2011-253), the Wellcome Trust (091157) and the National Institute for Health Research Cambridge Biomedical Research Centre. The research leading to these results has received funding from the European Unions seventh Framework Programme (FP7/2007-2013) under grant agreement no. 241447 (NAIMIT). The Cambridge Institute for Medical Research is in receipt of a Wellcome Trust Strategic Award (100140). C.W. and H.G. are supported by the Wellcome Trust (089989). ImmunoBase.org is supported by Eli Lilly and Company.This is the final published version, also available from OUP at http://bioinformatics.oxfordjournals.org/content/early/2014/09/18/bioinformatics.btu571.short?rss=1

Prioritisation of Candidate Genes Underpinning COVID-19 Host Genetic Traits Based on High-Resolution 3D Chromosomal Topology

Genetic variants showing associations with specific biological traits and diseases detected by genome-wide association studies (GWAS) commonly map to non-coding DNA regulatory regions. Many of these regions are located considerable distances away from the genes they regulate and come into their proximity through 3D chromosomal interactions. We previously developed COGS, a statistical pipeline for linking GWAS variants with their putative target genes based on 3D chromosomal interaction data arising from high-resolution assays such as Promoter Capture Hi-C (PCHi-C). Here, we applied COGS to COVID-19 Host Genetic Consortium (HGI) GWAS meta-analysis data on COVID-19 susceptibility and severity using our previously generated PCHi-C results in 17 human primary cell types and SARS-CoV-2-infected lung carcinoma cells. We prioritise 251 genes putatively associated with these traits, including 16 out of 47 genes highlighted by the GWAS meta-analysis authors. The prioritised genes are expressed in a broad array of tissues, including, but not limited to, blood and brain cells, and are enriched for genes involved in the inflammatory response to viral infection. Our prioritised genes and pathways, in conjunction with results from other prioritisation approaches and targeted validation experiments, will aid in the understanding of COVID-19 pathology, paving the way for novel treatments

Bioclimatic envelope models predict a decrease in tropical forest carbon stocks with climate change in Madagascar

Recent studies have underlined the importance of climatic variables in determining tree height and biomass in tropical forests. Nonetheless, the effects of climate on tropical forest carbon stocks remain uncertain. In particular, the application of process-based dynamic global vegetation models has led to contrasting conclusions regarding the potential impact of climate change on tropical forest carbon storage. Using a correlative approach based on a bioclimatic envelope model and data from 1771 forest plots inventoried during the period 1996–2013 in Madagascar over a large climatic gradient, we show that temperature seasonality, annual precipitation and mean annual temperature are key variables in determining forest above-ground carbon density. Taking into account the explicative climate variables, we obtained an accurate (R2 = 70% and RMSE = 40 Mg ha−1) forest carbon map for Madagascar at 250 m resolution for the year 2010. This national map was more accurate than previously published global carbon maps (R2 ≤ 26% and RMSE ≥ 63 Mg ha−1). Combining our model with the climatic projections for Madagascar from 7 IPCC CMIP5 global climate models following the RCP 8.5, we forecast an average forest carbon stock loss of 17% (range: 7–24%) by the year 2080. For comparison, a spatially homogeneous deforestation of 0.5% per year on the same period would lead to a loss of 30% of the forest carbon stock. Synthesis. Our study shows that climate change is likely to induce a decrease in tropical forest carbon stocks. This loss could be due to a decrease in the average tree size and to shifts in tree species distribution, with the selection of small-statured species. In Madagascar, climate-induced carbon emissions might be, at least, of the same order of magnitude as emissions associated with anthropogenic deforestation

A method for gene-based pathway analysis using genomewide association study summary statistics reveals nine new type 1 diabetes associations.

Pathway analysis can complement point-wise single nucleotide polymorphism (SNP) analysis in exploring genomewide association study (GWAS) data to identify specific disease-associated genes that can be candidate causal genes. We propose a straightforward methodology that can be used for conducting a gene-based pathway analysis using summary GWAS statistics in combination with widely available reference genotype data. We used this method to perform a gene-based pathway analysis of a type 1 diabetes (T1D) meta-analysis GWAS (of 7,514 cases and 9,045 controls). An important feature of the conducted analysis is the removal of the major histocompatibility complex gene region, the major genetic risk factor for T1D. Thirty-one of the 1,583 (2%) tested pathways were identified to be enriched for association with T1D at a 5% false discovery rate. We analyzed these 31 pathways and their genes to identify SNPs in or near these pathway genes that showed potentially novel association with T1D and attempted to replicate the association of 22 SNPs in additional samples. Replication P-values were skewed (P=9.85×10-11) with 12 of the 22 SNPs showing P<0.05. Support, including replication evidence, was obtained for nine T1D associated variants in genes ITGB7 (rs11170466, P=7.86×10-9), NRP1 (rs722988, 4.88×10-8), BAD (rs694739, 2.37×10-7), CTSB (rs1296023, 2.79×10-7), FYN (rs11964650, P=5.60×10-7), UBE2G1 (rs9906760, 5.08×10-7), MAP3K14 (rs17759555, 9.67×10-7), ITGB1 (rs1557150, 1.93×10-6), and IL7R (rs1445898, 2.76×10-6). The proposed methodology can be applied to other GWAS datasets for which only summary level data are available.This is the final version. It was first published by Wiley at http://onlinelibrary.wiley.com/doi/10.1002/gepi.21853/abstract

Resolving mechanisms of immune-mediated disease in primary CD4 T cells

ABSTRACT Deriving mechanisms of immune-mediated disease from GWAS data remains a formidable challenge, with attempts to identify causal variants being frequently hampered by linkage disequilibrium. To determine whether causal variants could be identified via their functional effects, we adapted a massively-parallel reporter assay for use in primary CD4 T-cells, key effectors of many immune-mediated diseases. Using the results to guide further study, we provide a generalisable framework for resolving disease mechanisms from non-coding associations – illustrated by a locus linked to 6 immune-mediated diseases, where the lead functional variant causally disrupts a super-enhancer within an NF-κB-driven regulatory circuit, triggering unrestrained T-cell activation

simGWAS: a fast method for simulation of large scale case-control GWAS summary statistics.

MOTIVATION: Methods for analysis of GWAS summary statistics have encouraged data sharing and democratized the analysis of different diseases. Ideal validation for such methods is application to simulated data, where some 'truth' is known. As GWAS increase in size, so does the computational complexity of such evaluations; standard practice repeatedly simulates and analyses genotype data for all individuals in an example study. RESULTS: We have developed a novel method based on an alternative approach, directly simulating GWAS summary data, without individual data as an intermediate step. We mathematically derive the expected statistics for any set of causal variants and their effect sizes, conditional upon control haplotype frequencies (available from public reference datasets). Simulation of GWAS summary output can be conducted independently of sample size by simulating random variates about these expected values. Across a range of scenarios, our method, produces very similar output to that from simulating individual genotypes with a substantial gain in speed even for modest sample sizes. Fast simulation of GWAS summary statistics will enable more complete and rapid evaluation of summary statistic methods as well as opening new potential avenues of research in fine mapping and gene set enrichment analysis. AVAILABILITY AND IMPLEMENTATION: Our method is available under a GPL license as an R package from http://github.com/chr1swallace/simGWAS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

Atorvastatin added to interferon beta for relapsing multiple sclerosis: a randomized controlled trial

Statins have anti-inflammatory and immunomodulatory properties in addition to lipid-lowering effects. The present study evaluated the effect of atorvastatin added to interferon beta-1b in multiple sclerosis (MS) in a multicenter, randomized, parallel-group, rater-blinded study performed in eight Swiss hospitals. Seventy-seven patients with relapsing-remitting MS started interferon beta-1b every other day. After 3months, they were randomized 1:1 to receive atorvastatin 40mg/day or not in addition to interferon beta-1b until month 15. The primary endpoint was the proportion of patients with new lesions on T2-weighted images at month 15 compared to baseline at month three. At study end, the proportion of patients with new lesions on T2-weighted images was equal in both groups (odds ratio 1.14; 95% CI 0.36-3.56; p=0.81). All predefined secondary endpoints including number of new lesions and total lesion volume on T2-weighted images, total number of new Gd-enhancing lesions on T1-weighted images, total brain volume, volume of grey matter, volume of white matter, EDSS, MSFC, relapse rate, time to first relapse, number of relapse-free patients and neutralizing antibodies did not show any significant differences (all p values >0.1). Transient elevations of liver enzymes were more frequent with atorvastatin (p=0.02). In conclusion, atorvastatin 40mg/day in addition to interferon beta-1b did not have a beneficial effect on relapsing-remitting MS compared to interferon beta-1b monotherapy over a 12-month perio