Discussion of "Gene hunting with hidden Markov model knockoffs'

We congratulate the authors on this interesting paper that tackles the difficult problem of extending the work of Candès et al. (2018) to structured predictors possessing a particular type of dependence. Motivated by the aim of making the knockoff construction applicable to genome-wide association studies, Sesia et al. (2019) propose using a hidden Markov model to generate knockoffs that capture patterns of DNA variation, following the work of Scheet & Stephens (2006)

The chromosome 6q22.33 region is associated with age at diagnosis of type 1 diabetes and disease risk in those diagnosed under 5 years of age

AIMS/HYPOTHESIS: The genetic risk of type 1 diabetes has been extensively studied. However, the genetic determinants of age at diagnosis (AAD) of type 1 diabetes remain relatively unexplained. Identification of AAD genes and pathways could provide insight into the earliest events in the disease process. METHODS: Using ImmunoChip data from 15,696 cases, we aimed to identify regions in the genome associated with AAD. RESULTS: Two regions were convincingly associated with AAD (p  0.001), the SNP most associated with AAD, rs72975913, was associated with susceptibility to type 1 diabetes in those individuals diagnosed at less than 5 years old (p = 2.3 × 10(-9)). CONCLUSION/INTERPRETATION: PTPRK and its neighbour THEMIS are required for early development of the thymus, which we can assume influences the initiation of autoimmunity. Non-HLA genes may only be detectable as risk factors for the disease in individuals diagnosed under the age 5 years because, after that period of immune development, their role in disease susceptibility has become redundant.CW is funded by the Wellcome Trust (WT107881) and the Medical Research Council (MC_UP_1302/5). LB was supported by the Alan Turing Institute under the EPSRC grant EP/N510129/1

New insights into the genetic control of gene expression using a Bayesian multi-tissue approach.

The majority of expression quantitative trait locus (eQTL) studies have been carried out in single tissues or cell types, using methods that ignore information shared across tissues. Although global analysis of RNA expression in multiple tissues is now feasible, few integrated statistical frameworks for joint analysis of gene expression across tissues combined with simultaneous analysis of multiple genetic variants have been developed to date. Here, we propose Sparse Bayesian Regression models for mapping eQTLs within individual tissues and simultaneously across tissues. Testing these on a set of 2,000 genes in four tissues, we demonstrate that our methods are more powerful than traditional approaches in revealing the true complexity of the eQTL landscape at the systems-level. Highlighting the power of our method, we identified a two-eQTL model (cis/trans) for the Hopx gene that was experimentally validated and was not detected by conventional approaches. We showed common genetic regulation of gene expression across four tissues for ∼27% of transcripts, providing >5 fold increase in eQTLs detection when compared with single tissue analyses at 5% FDR level. These findings provide a new opportunity to uncover complex genetic regulatory mechanisms controlling global gene expression while the generality of our modelling approach makes it adaptable to other model systems and humans, with broad application to analysis of multiple intermediate and whole-body phenotypes

High-throughput multivariable Mendelian randomization analysis prioritizes apolipoprotein B as key lipid risk factor for coronary artery disease

Background Genetic variants can be used to prioritize risk factors as potential therapeutic targets via Mendelian randomization (MR). An agnostic statistical framework using Bayesian model averaging (MR-BMA) can disentangle the causal role of correlated risk factors with shared genetic predictors. Here, our objective is to identify lipoprotein measures as mediators between lipid-associated genetic variants and coronary artery disease (CAD) for the purpose of detecting therapeutic targets for CAD. Methods As risk factors we consider 30 lipoprotein measures and metabolites derived from a high-throughput metabolomics study including 24 925 participants. We fit multivariable MR models of genetic associations with CAD estimated in 453 595 participants (including 113 937 cases) regressed on genetic associations with the risk factors. MR-BMA assigns to each combination of risk factors a model score quantifying how well the genetic associations with CAD are explained. Risk factors are ranked by their marginal score and selected using false-discovery rate (FDR) criteria. We perform supplementary and sensitivity analyses varying the dataset for genetic associations with CAD. Results In the main analysis, the top combination of risk factors ranked by the model score contains apolipoprotein B (ApoB) only. ApoB is also the highest ranked risk factor with respect to the marginal score (FDR <0.005). Additionally, ApoB is selected in all sensitivity analyses. No other measure of cholesterol or triglyceride is consistently selected otherwise. Conclusions Our agnostic genetic investigation prioritizes ApoB across all datasets considered, suggesting that ApoB, representing the total number of hepatic-derived lipoprotein particles, is the primary lipid determinant of CAD

Metabolic effects of bezafibrate in mitochondrial disease

Mitochondrial disorders affect 1/5,000 and have no cure. Inducing mitochondrial biogenesis with bezafibrate improves mitochondrial function in animal models, but there are no comparable human studies. We performed an open-label observational experimental medicine study of six patients with mitochondrial myopathy caused by the m.3243A>G MTTL1 mutation. Our primary aim was to determine the effects of bezafibrate on mitochondrial metabolism, whilst providing preliminary evidence of safety and efficacy using biomarkers. The participants received 600-1,200 mg bezafibrate daily for 12 weeks. There were no clinically significant adverse events, and liver function was not affected. We detected a reduction in the number of complex IV-immunodeficient muscle fibres and improved cardiac function. However, this was accompanied by an increase in serum biomarkers of mitochondrial disease, including fibroblast growth factor 21 (FGF-21), growth and differentiation factor 15 (GDF-15), plus dysregulation of fatty acid and amino acid metabolism. Thus, although potentially beneficial in short term, inducing mitochondrial biogenesis with bezafibrate altered the metabolomic signature of mitochondrial disease, raising concerns about long-term sequelae

Expression QTLs Mapping and Analysis: A Bayesian Perspective.

The aim of expression Quantitative Trait Locus (eQTL) mapping is the identification of DNA sequence variants that explain variation in gene expression. Given the recent yield of trait-associated genetic variants identified by large-scale genome-wide association analyses (GWAS), eQTL mapping has become a useful tool to understand the functional context where these variants operate and eventually narrow down functional gene targets for disease. Despite its extensive application to complex (polygenic) traits and disease, the majority of eQTL studies still rely on univariate data modeling strategies, i.e., testing for association of all transcript-marker pairs. However these "one at-a-time" strategies are (1) unable to control the number of false-positives when an intricate Linkage Disequilibrium structure is present and (2) are often underpowered to detect the full spectrum of trans-acting regulatory effects. Here we present our viewpoint on the most recent advances on eQTL mapping approaches, with a focus on Bayesian methodology. We review the advantages of the Bayesian approach over frequentist methods and provide an empirical example of polygenic eQTL mapping to illustrate the different properties of frequentist and Bayesian methods. Finally, we discuss how multivariate eQTL mapping approaches have distinctive features with respect to detection of polygenic effects, accuracy, and interpretability of the results

A Bayesian Approach for Analysis of Whole-Genome Bisulphite Sequencing Data Identifies Disease-Associated Changes in DNA Methylation

DNA methylation is a key epigenetic modification involved in gene regulation whose contribution to disease susceptibility remains to be fully understood. Here, we present a novel Bayesian smoothing approach (called ABBA) to detect differentially methylated regions (DMRs) from whole-genome bisulphite sequencing (WGBS). We also show how this approach can be leveraged to identify disease-associated changes in DNA methylation, suggesting mechanisms through which these alterations might affect disease. From a data modeling perspective, ABBA has the distinctive feature of automatically adapting to different correlation structures in CpG methylation levels across the genome whilst taking into account the distance between CpG sites as a covariate. Our simulation study shows that ABBA has greater power to detect DMRs than existing methods, providing an accurate identification of DMRs in the large majority of simulated cases. To empirically demonstrate the method's efficacy in generating biological hypotheses, we performed WGBS of primary macrophages derived from an experimental rat system of glomerulonephritis and used ABBA to identify >1,000 disease-associated DMRs. Investigation of these DMRs revealed differential DNA methylation localized to a 600bp region in the promoter of the Ifitm3 gene. This was confirmed by ChIP-seq and RNA-seq analyses, showing differential transcription factor binding at the Ifitm3 promoter by JunD (an established determinant of glomerulonephritis) and a consistent change in Ifitm3 expression. Our ABBA analysis allowed us to propose a new role for Ifitm3 in the pathogenesis of glomerulonephritis via a mechanism involving promoter hypermethylation that is associated with Ifitm3 repression in the rat strain susceptible to glomerulonephritis.This research was funded by Engineering and Physical Sciences Research Council Grant EP/K030760/1 (L.B.), The Alan Turing Institute under the EPSRC grant EP/N510129/1 (L.B., P.D.), Royal Society IE110977 (L.B., P.D.), European Union (European Social Fund - ESF), Greek national funds through the Operational Program "Education and Lifelong Learning'' of the National Strategic Reference Framework (NSRF), project ARISTEIA (P.D.), Duke-NUS Medical School and Singapore Ministry of Health (O.J.L.R., E.P.), a Medical Research Council Chain-Florey fellowship (T.O.), the Medical Research Council (MR/M004716/1 to J.B. and E.P.) and by Kidney Research UK - RP9/2013 (J.B.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Systems genetics identifies Sestrin 3 as a regulator of a proconvulsant gene network in human epileptic hippocampus

Gene-regulatory network analysis is a powerful approach to elucidate the molecular processes and pathways underlying complex disease. Here we employ systems genetics approaches to characterize the genetic regulation of pathophysiological pathways in human temporal lobe epilepsy (TLE). Using surgically acquired hippocampi from 129 TLE patients, we identify a gene-regulatory network genetically associated with epilepsy that contains a specialized, highly expressed transcriptional module encoding proconvulsive cytokines and Toll-like receptor signalling genes. RNA sequencing analysis in a mouse model of TLE using 100 epileptic and 100 control hippocampi shows the proconvulsive module is preserved across-species, specific to the epileptic hippocampus and upregulated in chronic epilepsy. In the TLE patients, we map the trans-acting genetic control of this proconvulsive module to Sestrin 3 (SESN3), and demonstrate that SESN3 positively regulates the module in macrophages, microglia and neurons. Morpholino-mediated Sesn3 knockdown in zebrafish confirms the regulation of the transcriptional module, and attenuates chemically induced behavioural seizures in vivo