Benjamin Goudey

Plasma Lipidomic Analysis of Stable and Unstable Coronary Artery Disease

Objective—Traditional risk factors for coronary artery disease (CAD) fail to adequately distinguish patients who have atherosclerotic plaques susceptible to instability from those who have more benign forms. Using plasma lipid profiling, this study aimed to provide insight into disease pathogenesis and evaluate the potential of lipid profiles to assess risk of future plaque instability. Methods and Results—Plasma lipid profiles containing 305 lipids were measured on 220 individuals (matched healthy controls, n=80; stable angina, n=60; unstable coronary syndrome, n=80) using electrospray-ionisation tandem mass spectrometry. ReliefF feature selection coupled with an L2-regularized logistic regression based classifier was used to create multivariate classification models which were verified via 3-fold cross-validation (1000 repeats). Models incorporating both lipids and traditional risk factors provided improved classification of unstable CAD from stable CAD (C-statistic=0.875, 95% CI 0.874–0.877) compared with models containing only traditional risk factors (C-statistic=0.796, 95% CI 0.795–0.798). Many of the lipids identified as discriminatory for unstable CAD displayed an association with disease acuity (severity), suggesting that they are antecedents to the onset of acute coronary syndrome. Conclusion—Plasma lipid profiling may contribute to a new approach to risk stratification for unstable CAD.

GWIS FI : A universal GPU interface for exhaustive search of pairwise interactions in case-control GWAS in minutes

Epistatic interactions between genes are believed to be a critical component in the genetic architecture of complex diseases. Genome Wide Association Studies (GWAS) may be able to detect such genetic interactions indirectly, via the identification of associated SNP markers. Major obstacles to progress in this area are: the unknown nature of epistatic interactions, little understanding of the capabilities of different filtering methods, and the computational difficulties for exhaustive analysis. A common platform enabling various detection methods is needed to avoid practical issues such as software compatibility and portability, incompatible input and output formats and varying demands on computational resources. We developed a highly optimised GPU system capable of exhaustively analysing all SNP-pairs in typical GWAS data (0.5M SNPs, 5K samples) in a few minutes on a standard desktop computer. A number of programming elements provided by a functional interface can be used to construct user-defined statistical tests to efficiently score every SNP pair. As a proof of principle, we have implemented 8 methods from the literature via our interface. We have applied all of them using a single GPU to exhaustively scan the 7 popular WTCCC case-control GWAS datasets. We present timing results for these methods, both in their original software implementations and using our platform. Significant improvements in timing are observed, up to 10000 times for CPU implementations of the popular FastEpistasis in PLINK and up to 2 orders of magnitude for some GPU implementations in the literature. As an initial discovery we show plots for overlaps of list of selected pairs by 8 algorithms for Type 2 Diabetes, WTCCC data.

Supercomputing enabling exhaustive statistical analysis of genome wide association study data: Preliminary results

Most published GWAS do not examine SNP interactions due to the high computational complexity of computing p-values for the interaction terms. Our aim is to utilize supercomputing resources to apply complex statistical techniques to the worlds accumulating GWAS, epidemiology, survival and pathology data to uncover more information about genetic and environmental risk, biology and aetiology. We performed the Bayesian Posterior Probability test on a pseudo data set with 500,000 single nucleotide polymorphism and 100 samples as proof of principle. We carried out strong scaling simulations on 2 to 4,096 processing cores with factor 2 increments in partition size. On two processing cores, the run time is 317h, i.e. almost two weeks, compared to less than 10 minutes on 4,096 processing cores. The speedup factor is 2,020 that is very close to the theoretical value of 2,048. This work demonstrates the feasibility of performing exhaustive higher order analysis of GWAS studies using independence testing for contingency tables. We are now in a position to employ supercomputers with hundreds of thousands of threads for higher order analysis of GWAS data using complex statistics.

Replication of epistatic DNA loci in two case-control GWAS studies using OPE algorithm

Background One of the limiting factors of current genome-wide association studies (GWAS) is the inability of current methods to comprehensively examine SNP interactions for a reasonable sized dataset. It is hypothesised that this limitation is one of the reasons that GWAS studies have not been able to have a greater impact [1,2]. Many current methods for handling interactions are computationally expensive and do not scale to entire studies. Those methods that do scale often achieve this by pruning their datasets in some manner. This is commonly done by considering only those SNPs that show strong marginal effects, despite the fact that a strongly interacting pair may consist of SNPs with low effects individually.

Stability of Bivariate GWAS Biomarker Detection

Given the difficulty and effort required to confirm candidate causal SNPs detected in genome-wide association studies (GWAS), there is no practical way to definitively filter false positives. Recent advances in algorithmics and statistics have enabled repeated exhaustive search for bivariate features in a practical amount of time using standard computational resources, allowing us to use cross-validation to evaluate the stability. We performed 10 trials of 2-fold cross-validation of exhaustive bivariate analysis on seven Wellcome–Trust Case–Control Consortium GWAS datasets, comparing the traditional test for association, the high-performance GBOOST method and the recently proposed GSS statistic (Available at http://bioinformatics.research.nicta.com.au/software/gwis/). We use Spearmans correlation to measure the similarity between the folds of cross validation. To compare incomplete lists of ranks we propose an extension to Spearmans correlation. The extension allows us to consider a natural threshold for feature selection where the correlation is zero. This is the first reported cross-validation study of exhaustive bivariate GWAS feature selection. We found that stability between ranked lists from different cross-validation folds was higher for GSS in the majority of diseases. A thorough analysis of the correlation between SNP-frequency and univariate score demonstrated that the test for association is highly confounded by main effects: SNPs with high univariate significance replicably dominate the ranked results. We show that removal of the univariately significant SNPs improves replicability but risks filtering pairs involving SNPs with univariate effects. We empirically confirm that the stability of GSS and GBOOST were not affected by removal of univariately significant SNPs. These results suggest that the GSS and GBOOST tests are successfully targeting bivariate association with phenotype and that GSS is able to reliably detect a larger set of SNP-pairs than GBOOST in the majority of the data we analysed. However, the test for association was confounded by main effects.

Interactions within the MHC contribute to the genetic architecture of celiac disease

Interaction analysis of GWAS can detect signal that would be ignored by single variant analysis, yet few robust interactions in humans have been detected. Recent work has highlighted interactions in the MHC region between known HLA risk haplotypes for various autoimmune diseases. To better understand the genetic interactions underlying celiac disease (CD), we have conducted exhaustive genome-wide scans for pairwise interactions in five independent CD case-control studies, using a rapid model-free approach to examine over 500 billion SNP pairs in total. We found 14 independent interaction signals within the MHC region that achieved stringent replication criteria across multiple studies and were independent of known CD risk HLA haplotypes. The strongest independent CD interaction signal corresponded to genes in the HLA class III region, in particular PRRC2A and GPANK1/C6orf47, which are known to contain variants for non-Hodgkins lymphoma and early menopause, co-morbidities of celiac disease. Replicable evidence for statistical interaction outside the MHC was not observed. Both within and between European populations, we observed striking consistency of two-locus models and model distribution. Within the UK population, models of CD based on both interactions and additive single-SNP effects increased explained CD variance by approximately 1% over those of single SNPs. The interactions signal detected across the five cohorts indicates the presence of novel associations in the MHC region that cannot be detected using additive models. Our findings have implications for the determination of genetic architecture and, by extension, the use of human genetics for validation of therapeutic targets.

Epistasis within the MHC contributes to the genetic architecture of celiac disease

Epistasis has long been thought to contribute to the genetic aetiology of complex diseases, yet few robust epistatic interactions in humans have been detected. We have conducted exhaustive genome-wide scans for pairwise epistasis in five independent celiac disease (CD) case-control studies, using a rapid model-free approach to examine over 500 billion SNP pairs in total. We found 20 significant epistatic signals within the HLA region which achieved stringent replication criteria across multiple studies and were independent of known CD risk HLA haplotypes. The strongest independent CD epistatic signal corresponded to genes in the HLA class III region, in particular PRRC2A and GPANK1/C6orf47, which are known to contain variants for non-Hodgkin’s lymphoma and early menopause, co-morbidities of celiac disease. Replicable evidence for epistatic variants outside the MHC was not observed. Both within and between European populations, we observed striking consistency of epistatic models and epistatic model distribution. Within the UK population, models of CD based on both epistatic and additive single-SNP effects increased explained CD variance by approximately 1% over those of single SNPs. Models of only epistatic pairs or additive single-SNPs showed similar levels of CD variance explained, indicating the existence of a substantial overlap of additive and epistatic components. Our findings have implications for the determination of genetic architecture and, by extension, the use of human genetics for validation of therapeutic targets.

A hybrid approach for automated mutation annotation of the extended human mutation landscape in scientific literature

As the cost of DNA sequencing continues to fall, an increasing amount of information on human genetic variation is being produced that could help progress precision medicine. However, information about such mutations is typically first made available in the scientific literature, and is then later manually curated into more standardized genomic databases. This curation process is expensive, time-consuming and many variants do not end up being fully curated, if at all. Detecting mutations in the literature is the first key step towards automating this process. However, most of the current methods have focused on identifying mutations that follow existing nomenclatures. In this work, we show that there is a large number of mutations that are missed by using this standard approach. Furthermore, we implement the first mutation annotator to cover an extended mutation landscape, and we show that its F1 performance is the same performance as human annotation (F1 78.29 for manual annotation vs F1 79.56 for automatic annotation).

Cross-ethnicity tagging SNPs for HLA alleles associated with adverse drug reaction

Reduction of adverse drug reaction (ADR) incidence through screening of predisposing human leucocyte antigen (HLA) alleles is a promising approach for many widely used drugs. However, application of these associations has been limited by the cost burden of HLA genotyping. Use of single nucleotide polymorphisms (SNPs) that can approximate (‘tag’) HLA alleles of interest has been proposed as a cost-effective and simple alternative to conventional genotyping. However, most reported SNP tags have not been validated and there is concern regarding clinical utility of this approach due to tagging inconsistency across different populations. We assess the ability of 67 previously reported and 378 novel tagging SNPs, identified here in 5 HLA reference panels, to tag 15 ADR-associated HLA alleles in a panel of 955 ethnically diverse samples. Tags for 8 HLA alleles of interest were identified with 100% sensitivity and >95% specificity. These SNPs may act as a reliable genotyping approach for the routine screening of patients, without the need to account for patient ethnicity.

A BLOOD-BASED SIGNATURE OF CEREBRAL SPINAL FLUID Aβ1-42 STATUS

Background:Early detection of molecular changes in Alzheimer’s disease is likely to play a key role in the success of interventions aimed at slowing down rates of cognitive decline. Recent evidence indicates that of the two established methods for measuring amyloid, decreases in cerebral spinal fluid (CSF) amyloid (A) levels may be an earlier indicator of Alzheimer’s disease risk than measures of amyloid obtained from Positron Emission Topography (PET). However, CSF collection is highly invasive and expensive. In contrast, blood collection is routinely performed, minimally invasive and cheap. In this work, we develop a blood-based signature that can provide a cheap and minimally invasive estimation of an individual’s CSF amyloid status. Methods:We make use of 57 cognitively normal (CN) and 186 mild cognitively impaired (MCI) individuals from the ADNI dataset, who have measures of CSF A, 149 protein (P) and 140 metabolites (M) levels measured in blood and age and APOE4 status (B) at baseline. A random forest approach in 10 repetitions of 10-fold cross validation is used to get an unbiased estimate of the model performance. An independent 210 MCI individuals without CSF measures are used to validate our model’s performance by examining the difference in rates of conversion to AD between the predicted abnormal/normal CSF A strata. Results: We show that a Random Forest model derived from age, APOE and proteins levels (BP) can accurately predict pre-clinical subjects as having abnormal (low) CSFA levels indicative of AD risk (Fig 1: 0.80 AUC, 0.69 sensitivity, and 0.75 specificity). Only 3 analytes are required to achieve similar high levels of accuracy (BP5, 0.78 AUC). Furthermore, we show across an independent validation cohort that individuals with predicted abnormal CSF A levels transitioned to an AD diagnosis over 120 months significantly faster than those predicted with normal CSF A levels (Fig 2: P< 2.5’10). Conclusions: This is the first study to show that a plasma protein signature, together with age and APOE4 genotype, can predict CSFA status, the earliest risk indicator for AD, with high accuracy, further highlighting the potential for developing a blood-based signature for improved AD screening.