A pleiotropy-informed Bayesian false discovery rate adapted to a shared control design finds new disease associations from GWAS summary statistics.

Genome-wide association studies (GWAS) have been successful in identifying single nucleotide polymorphisms (SNPs) associated with many traits and diseases. However, at existing sample sizes, these variants explain only part of the estimated heritability. Leverage of GWAS results from related phenotypes may improve detection without the need for larger datasets. The Bayesian conditional false discovery rate (cFDR) constitutes an upper bound on the expected false discovery rate (FDR) across a set of SNPs whose p values for two diseases are both less than two disease-specific thresholds. Calculation of the cFDR requires only summary statistics and have several advantages over traditional GWAS analysis. However, existing methods require distinct control samples between studies. Here, we extend the technique to allow for some or all controls to be shared, increasing applicability. Several different SNP sets can be defined with the same cFDR value, and we show that the expected FDR across the union of these sets may exceed expected FDR in any single set. We describe a procedure to establish an upper bound for the expected FDR among the union of such sets of SNPs. We apply our technique to pairwise analysis of p values from ten autoimmune diseases with variable sharing of controls, enabling discovery of 59 SNP-disease associations which do not reach GWAS significance after genomic control in individual datasets. Most of the SNPs we highlight have previously been confirmed using replication studies or larger GWAS, a useful validation of our technique; we report eight SNP-disease associations across five diseases not previously declared. Our technique extends and strengthens the previous algorithm, and establishes robust limits on the expected FDR. This approach can improve SNP detection in GWAS, and give insight into shared aetiology between phenotypically related conditions.This work was funded by the JDRF (9-2011-253), the Wellcome Trust (061858 and 091157) and the NIHR Cambridge Biomedical Research Centre. The research leading to these results has received funding from the European Union's 7th Framework Programme (FP7/2007–2013) under grant agreement no. 241447 (NAIMIT). JL is funded by the NIHR Cambridge Biomedical Research Centre and is on the Wellcome Trust PhD programme in Mathematical Genomics and Medicine at the University of Cambridge. CW is funded by the Wellcome Trust (089989). The Cambridge Institute for Medical Research (CIMR) is in receipt of a Wellcome Trust Strategic Award (100140). ImmunoBase.org is supported by Eli-Lilly and Company. The use of DNA from the UK Blood Services collection of Common Controls (UKBS collection) was funded by the Wellcome Trust grant 076113/C/04/Z, by the Wellcome Trust/JDRF grant 061858, and by the National Institute of Health Research of England. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.This is the final published version of the article. It was originally published in PLOS Genetics (Liley J, Wallace C PLOS Genetics 2015, 11(2): e1004926. doi:10.1371/journal.pgen.1004926) http://dx.doi.org/10.1371/journal.pgen.1004926

Accurate error control in high-dimensional association testing using conditional false discovery rates.

High-dimensional hypothesis testing is ubiquitous in the biomedical sciences, and informative covariates may be employed to improve power. The conditional false discovery rate (cFDR) is a widely used approach suited to the setting where the covariate is a set of p-values for the equivalent hypotheses for a second trait. Although related to the Benjamini-Hochberg procedure, it does not permit any easy control of type-1 error rate and existing methods are over-conservative. We propose a new method for type-1 error rate control based on identifying mappings from the unit square to the unit interval defined by the estimated cFDR and splitting observations so that each map is independent of the observations it is used to test. We also propose an adjustment to the existing cFDR estimator which further improves power. We show by simulation that the new method more than doubles potential improvement in power over unconditional analyses compared to existing methods. We demonstrate our method on transcriptome-wide association studies and show that the method can be used in an iterative way, enabling the use of multiple covariates successively. Our methods substantially improve the power and applicability of cFDR analysis

Statistical co-analysis of high-dimensional association studies

Modern medical practice and science involve complex phenotypic definitions. Understanding patterns of association across this range of phenotypes requires co-analysis of high-dimensional association studies in order to characterise shared and distinct elements. In this thesis I address several problems in this area, with a general linking aim of making more efficient use of available data. The main application of these methods is in the analysis of genome-wide association studies (GWAS) and similar studies. Firstly, I developed methodology for a Bayesian conditional false discovery rate (cFDR) for levering GWAS results using summary statistics from a related disease. I extended an existing method to enable a shared control design, increasing power and applicability, and developed an approximate bound on false-discovery rate (FDR) for the procedure. Using the new method I identified several new variant-disease associations. I then developed a second application of shared control design in the context of study replication, enabling improvement in power at the cost of changing the spectrum of sensitivity to systematic errors in study cohorts. This has application in studies on rare diseases or in between-case analyses. I then developed a method for partially characterising heterogeneity within a disease by modelling the bivariate distribution of case-control and within-case effect sizes. Using an adaptation of a likelihood-ratio test, this allows an assessment to be made of whether disease heterogeneity corresponds to differences in disease pathology. I applied this method to a range of simulated and real datasets, enabling insight into the cause of heterogeneity in autoantibody positivity in type 1 diabetes (T1D). Finally, I investigated the relation of subtypes of juvenile idiopathic arthritis (JIA) to adult diseases, using modified genetic risk scores and linear discriminants in a penalised regression framework. The contribution of this thesis is in a range of methodological developments in the analysis of high-dimensional association study comparison. Methods such as these will have wide application in the analysis of GWAS and similar areas, particularly in the development of stratified medicine.I was supported by a grant from the NIHR Cambridge BR

Accurate error control in high‐dimensional association testing using conditional false discovery rates

Funder: Johnson and Johnson; Id: http://dx.doi.org/10.13039/100004331Abstract: High‐dimensional hypothesis testing is ubiquitous in the biomedical sciences, and informative covariates may be employed to improve power. The conditional false discovery rate (cFDR) is a widely used approach suited to the setting where the covariate is a set of p‐values for the equivalent hypotheses for a second trait. Although related to the Benjamini–Hochberg procedure, it does not permit any easy control of type‐1 error rate and existing methods are over‐conservative. We propose a new method for type‐1 error rate control based on identifying mappings from the unit square to the unit interval defined by the estimated cFDR and splitting observations so that each map is independent of the observations it is used to test. We also propose an adjustment to the existing cFDR estimator which further improves power. We show by simulation that the new method more than doubles potential improvement in power over unconditional analyses compared to existing methods. We demonstrate our method on transcriptome‐wide association studies and show that the method can be used in an iterative way, enabling the use of multiple covariates successively. Our methods substantially improve the power and applicability of cFDR analysis

Accurate error control in high‐dimensional association testing using conditional false discovery rates

Funder: Johnson and Johnson; Id: http://dx.doi.org/10.13039/100004331Abstract: High‐dimensional hypothesis testing is ubiquitous in the biomedical sciences, and informative covariates may be employed to improve power. The conditional false discovery rate (cFDR) is a widely used approach suited to the setting where the covariate is a set of p‐values for the equivalent hypotheses for a second trait. Although related to the Benjamini–Hochberg procedure, it does not permit any easy control of type‐1 error rate and existing methods are over‐conservative. We propose a new method for type‐1 error rate control based on identifying mappings from the unit square to the unit interval defined by the estimated cFDR and splitting observations so that each map is independent of the observations it is used to test. We also propose an adjustment to the existing cFDR estimator which further improves power. We show by simulation that the new method more than doubles potential improvement in power over unconditional analyses compared to existing methods. We demonstrate our method on transcriptome‐wide association studies and show that the method can be used in an iterative way, enabling the use of multiple covariates successively. Our methods substantially improve the power and applicability of cFDR analysis

Model updating after interventions paradoxically introduces bias

Machine learning is increasingly being used to generate prediction models for use in a number of real-world settings, from credit risk assessment to clinical decision support. Recent discussions have highlighted potential problems in the updating of a predictive score for a binary outcome when an existing predictive score forms part of the standard workflow, driving interventions. In this setting, the existing score induces an additional causative pathway which leads to miscalibration when the original score is replaced. We propose a general causal framework to describe and address this problem, and demonstrate an equivalent formulation as a partially observed Markov decision process. We use this model to demonstrate the impact of such `naive updating' when performed repeatedly. Namely, we show that successive predictive scores may converge to a point where they predict their own effect, or may eventually tend toward a stable oscillation between two values, and we argue that neither outcome is desirable. Furthermore, we demonstrate that even if model-fitting procedures improve, actual performance may worsen. We complement these findings with a discussion of several potential routes to overcome these issues.Comment: Sections of this preprint on 'Successive adjuvancy' (section 4, theorem 2, figures 4,5, and associated discussions) were not included in the originally submitted version of this paper due to length. This material does not appear in the published version of this manuscript, and the reader should be aware that these sections did not undergo peer revie

A method for identifying genetic heterogeneity within phenotypically defined disease subgroups.

Many common diseases show wide phenotypic variation. We present a statistical method for determining whether phenotypically defined subgroups of disease cases represent different genetic architectures, in which disease-associated variants have different effect sizes in two subgroups. Our method models the genome-wide distributions of genetic association statistics with mixture Gaussians. We apply a global test without requiring explicit identification of disease-associated variants, thus maximizing power in comparison to standard variant-by-variant subgroup analysis. Where evidence for genetic subgrouping is found, we present methods for post hoc identification of the contributing genetic variants. We demonstrate the method on a range of simulated and test data sets, for which expected results are already known. We investigate subgroups of individuals with type 1 diabetes (T1D) defined by autoantibody positivity, establishing evidence for differential genetic architecture with positivity for thyroid-peroxidase-specific antibody, driven generally by variants in known T1D-associated genomic regions.We acknowledge the help of the Diabetes and Inflammation Laboratory Data Service for access and quality control procedures on the data sets used in this study. The JDRF/Wellcome Trust Diabetes and Inflammation Laboratory is in receipt of a Wellcome Trust Strategic Award (107212; J.A.T.) and receives funding from the NIHR Cambridge Biomedical Research Centre. J.L. is funded by the NIHR Cambridge Biomedical Research Centre and is on the Wellcome Trust PhD program in Mathematical Genomics and Medicine at the University of Cambridge. C.W. is funded by the MRC (grant MC_UP_1302/5). We thank M. Simmonds, S. Gough, J. Franklyn, and O. Brand for sharing their AITD genetic association data set and all patients with AITD and control subjects for participating in this study. The AITD UK national collection was funded by the Wellcome Trust. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Large‐Amplitude Mountain Waves in the Mesosphere Observed on 21 June 2014 During DEEPWAVE: 1.Wave Development, Scales, Momentum Fluxes, and Environmental Sensitivity

A remarkable, large‐amplitude, mountain wave (MW) breaking event was observed on the night of 21 June 2014 by ground‐based optical instruments operated on the New Zealand South Island during the Deep Propagating Gravity Wave Experiment (DEEPWAVE). Concurrent measurements of the MW structures, amplitudes, and background environment were made using an Advanced Mesospheric Temperature Mapper, a Rayleigh Lidar, an All‐Sky Imager, and a Fabry‐Perot Interferometer. The MW event was observed primarily in the OH airglow emission layer at an altitude of ~82 km, over an ~2‐hr interval (~10:30–12:30 UT), during strong eastward winds at the OH altitude and above, which weakened with time. The MWs displayed dominant horizontal wavelengths ranging from ~40 to 70 km and temperature perturbation amplitudes as large as ~35 K. The waves were characterized by an unusual, “saw‐tooth” pattern in the larger‐scale temperature field exhibiting narrow cold phases separating much broader warm phases with increasing temperatures toward the east, indicative of strong overturning and instability development. Estimates of the momentum fluxes during this event revealed a distinct periodicity (~25 min) with three well‐defined peaks ranging from ~600 to 800 m2/s2, among the largest ever inferred at these altitudes. These results suggest that MW forcing at small horizontal scales (km) can play large roles in the momentum budget of the mesopause region when forcing and propagation conditions allow them to reach mesospheric altitudes with large amplitudes. A detailed analysis of the instability dynamics accompanying this breaking MW event is presented in a companion paper, Fritts et al. (2019, https://doi.org/10.1029/2019jd030899)

Effectiveness-implementation hybrid-2 randomised trial of a collaborative Shared Care Model for Detecting Neurodevelopmental Impairments after Critical Illness in Young Children (DAISY): pilot study protocol

INTRODUCTION In Australia, while paediatric intensive care unit (PICU) mortality has dropped to 2.2%, one in three survivors experience long-term neurodevelopmental impairment, limiting their life-course opportunities. Unlike other high-risk paediatric populations, standardised routine neurodevelopmental follow-up of PICU survivors is rare, and there is limited knowledge regarding the best methods. The present study intends to pilot a combined multidisciplinary, online screening platform and general practitioner (GP) shared care neurodevelopmental follow-up model to determine feasibility of a larger, future study. We will also assess the difference between neurodevelopmental vulnerability and parental stress in two intervention groups and the impact of child, parent, sociodemographic and illness/treatment risk factors on child and parent outcomes. METHODS AND ANALYSIS Single-centre randomised effectiveness-implementation (hybrid-2 design) pilot trial for parents of children aged ≥2 months and <4 years discharged from PICU after critical illness or injury. One intervention group will receive 6 months of collaborative shared care follow-up with GPs (supported by online outcome monitoring), and the other will be offered self-directed screening and education about post-intensive care syndrome and child development. Participants will be followed up at 1, 3 and 6 months post-PICU discharge. The primary outcome is feasibility. Secondary outcomes include neurodevelopmental vulnerability and parental stress. An implementation evaluation will analyse barriers to and facilitators of the intervention. ETHICS AND DISSEMINATION The study is expected to lead to a full trial, which will provide much-needed guidance about the clinical effectiveness and implementation of follow-up models of care for children after critical illness or injury. The Children's Health Queensland Human Research Ethics Committee approved this study. Dissemination of the outcomes of the study is expected via publication in a peer-reviewed journal, presentation at relevant conferences, and via social media, podcast presentations and open-access medical education resources. REGISTRATION DETAILS The trial was prospectively registered with the Australian New Zealand Clinical Trials Registry as 'Pilot testing of a collaborative Shared Care Model for Detecting Neurodevelopmental Impairments after Critical Illness in Young Children' (the DAISY Pilot Study). TRIAL REGISTRATION NUMBER ACTRN12621000799853