SurvivalGWAS_SV: software for the analysis of genome-wide association studies of imputed genotypes with "time-to-event" outcomes

A genome-wide association study on hematopoietic stem cell transplantation reveals novel genomic loci associated with transplant outcomes

Introduction

Data on genomic susceptibility for adverse outcomes after hematopoietic stem cell transplantation (HSCT) for recipients are scarce.

Methods

We performed a genome wide association study (GWAS) to identify genes associated with survival/mortality, relapse, and severe graft-versus-host disease (sGvHD), fitting proportional hazard and subdistributional models to data of n=1,392 recipients of European ancestry from three centres.

Results

The single nucleotide polymorphism (SNP) rs17154454, intronic to the neuronal growth guidant semaphorin 3C gene (SEMA3C), was genome-wide significantly associated with event-free survival (p=7.0x10-8) and sGvHD (p=7.5x10-8). Further associations were detected for SNPs in the Paxillin gene (PXN) with death without prior relapse or sGvHD, as well as for SNPs of the Plasmacytoma Variant Translocation 1 gene (PVT1, a long non-coding RNA gene), the Melanocortin 5 Receptor (MC5R) gene and the WW Domain Containing Oxidoreductase gene (WWOX), all associated with the occurrence of sGvHD. Functional considerations support the observed associations.

Discussion

Thus, new genes were identified, potentially influencing the outcome of HSCT.

Dopamine pathway and Parkinson's risk variants are associated with levodopa-induced dyskinesia

Background

Levodopa-induced dyskinesia (LID) is a common adverse effect of levodopa, one of the main therapeutics used to treat the motor symptoms of Parkinson's disease (PD). Previous evidence suggests a connection between LID and a disruption of the dopaminergic system as well as genes implicated in PD, including GBA1 and LRRK2.

Objectives

To investigate the effects of genetic variants on risk and time to LID.

Methods

We performed a genome-wide association study (GWAS) and analyses focused on GBA1 and LRRK2 variants. We also calculated polygenic risk scores including risk variants for PD and variants in genes involved in the dopaminergic transmission pathway. To test the influence of genetics on LID risk we used logistic regression, and to examine its impact on time to LID we performed Cox regression including 1,612 PD patients with and 3,175 without LID.

Results

We found that GBA1 variants were associated with LID risk (OR=1.65, 95% CI=1.21-2.26, p=0.0017) and LRRK2 variants with reduced time to LID onset (HR=1.42, 95% CI=1.09-1.84, p=0.0098). The fourth quartile of the PD PRS was associated with increased LID risk (ORfourth_quartile=1.27, 95% CI=1.03-1.56, p=0.0210). The third and fourth dopamine pathway PRS quartiles were associated with a reduced time to development of LID (HRthird_quartile=1.38, 95% CI=1.07-1.79, p=0.0128; HRfourth_quartile=1.38, 95% CI=1.06-1.78, p=0.0147).

Conclusions

This study suggests that variants implicated in PD and in the dopaminergic transmission pathway play a role in the risk/time to develop LID. Further studies will be necessary to examine how these findings can inform clinical care.

ADuLT: An efficient and robust time-to-event GWAS

Proportional hazards models have been proposed to analyse time-to-event phenotypes in genome-wide association studies (GWAS). However, little is known about the ability of proportional hazards models to identify genetic associations under different generative models and when ascertainment is present. Here we propose the age-dependent liability threshold (ADuLT) model as an alternative to a Cox regression based GWAS, here represented by SPACox. We compare ADuLT, SPACox, and standard case-control GWAS in simulations under two generative models and with varying degrees of ascertainment as well as in the iPSYCH cohort. We find Cox regression GWAS to be underpowered when cases are strongly ascertained (cases are oversampled by a factor 5), regardless of the generative model used. ADuLT is robust to ascertainment in all simulated scenarios. Then, we analyse four psychiatric disorders in iPSYCH, ADHD, Autism, Depression, and Schizophrenia, with a strong case-ascertainment. Across these psychiatric disorders, ADuLT identifies 20 independent genome-wide significant associations, case-control GWAS finds 17, and SPACox finds 8, which is consistent with simulation results. As more genetic data are being linked to electronic health records, robust GWAS methods that can make use of age-of-onset information will help increase power in analyses for common health outcomes.

Genome-wide Survival Analysis for Macular Neovascularization Development in Central Serous Chorioretinopathy Revealed Shared Genetic Susceptibility with Polypoidal Choroidal Vasculopathy

PURPOSE

To identify susceptibility genes for macular neovascularization (MNV) development in central serous chorioretinopathy (CSC).

DESIGN

Genome-wide survival analysis using a longitudinal cohort study.

PARTICIPANTS

We included 402 and 137 patients with CSC but without MNV at their first visit from the Kyoto CSC Cohort and Kobe CSC dataset, respectively. All patients underwent detailed ophthalmologic examinations, including multimodal imaging, such as fundus autofluorescence, spectral-domain optical coherence tomography, and fluorescein angiography/indocyanine green angiography and/or optimal coherence tomography angiography.

METHODS

We conducted a genome-wide survival analysis using the Kyoto CSC Cohort. We applied the Cox proportional hazard model to adjust for age, sex, and the first principal component. Single nucleotide polymorphisms (SNPs) with P-values <1.0×10^-5 were carried forward to the replication in the Kobe CSC dataset. Moreover, we evaluated the contribution of previously-reported age-related macular degeneration (AMD) susceptibility loci. We used FUMA and ToppFun for the functional enrichment analysis.

MAIN OUTCOME MEASURES

The association between SNPs and MNV development in patients with CSC.

RESULTS

Rs370974631 near ARMS2 displayed a genome-wide significant association in the meta-analysis of discovery and replication result (hazard ratio [HR]_meta = 3.63; P_meta = 5.76×10^-9). Among previously-reported AMD susceptibility loci, we additionally identified CFH rs800292 (HR = 0.39, P = 2.55×10^-4), COL4A3 rs4276018 (HR = 0.26, P = 1.56×10^-3), and B3GALTL rs9564692 (HR = 0.56, P = 8.30×10^-3) as susceptibility loci for MNV development in CSC. The functional enrichment analysis revealed significant enrichment of eight pathways (GO:0051561, GO:0036444, GO:0008282, GO:1990246, GO:0015272, GO:0030955, GO:0031420, and GO:0005242) related to ion transport.

CONCLUSIONS

ARMS2, CFH, COL4A3, and B3GALTL were identified as susceptibility genes for MNV development in CSC. The aforementioned four genes are known as susceptibility genes for AMD, whereas COL4A3 and B3GALTL were previously reported to be polypoidal choroidal vasculopathy (PCV)-specific susceptibility genes. Our findings revealed the shared genetic susceptibility between PCV and MNV secondary to CSC.

Powerful Identification of Large Quantitative Trait Loci Using Genome-wide R/glmnet-Based Regression

R/glmnet has been successfully applied to jointly-mapped multiple quantitative trait loci for linkage analysis, along with statistical inference for quantitative trait loci candidates with non-zero genetic effects using R/lm for normally distributed traits, R/glm for discrete traits, and R/coxph for survival times. In this study, we extended R/glmnet to a genome-wide association study by means of parallel computation. A multi-locus genome-wide association study for high-throughput single nucleotide polymorphisms was implemented in the "Multi-Runking" software written within the R workspace. This software can better detect common and large quantitative trait nucleotides and more accurately estimate than genome-wide mixed model analysis for one single nucleotide polymorphism at a time and linear mixed models-least absolute shrinkage and selection operator. Its applicability and utility were demonstrated by multi-locus genome-wide association studies for the simulated and real traits distributed normally, binary traits, and survival times.

Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis

While recent advancements in computation and modelling have improved the analysis of complex traits, our understanding of the genetic basis of the time at symptom onset remains limited. Here, we develop a Bayesian approach (BayesW) that provides probabilistic inference of the genetic architecture of age-at-onset phenotypes in a sampling scheme that facilitates biobank-scale time-to-event analyses. We show in extensive simulation work the benefits BayesW provides in terms of number of discoveries, model performance and genomic prediction. In the UK Biobank, we find many thousands of common genomic regions underlying the age-at-onset of high blood pressure (HBP), cardiac disease (CAD), and type-2 diabetes (T2D), and for the genetic basis of onset reflecting the underlying genetic liability to disease. Age-at-menopause and age-at-menarche are also highly polygenic, but with higher variance contributed by low frequency variants. Genomic prediction into the Estonian Biobank data shows that BayesW gives higher prediction accuracy than other approaches.

A Fast and Accurate Method for Genome-Wide Time-to-Event Data Analysis and Its Application to UK Biobank

With increasing biobanking efforts connecting electronic health records and national registries to germline genetics, the time-to-event data analysis has attracted increasing attention in the genetics studies of human diseases. In time-to-event data analysis, the Cox proportional hazards (PH) regression model is one of the most used approaches. However, existing methods and tools are not scalable when analyzing a large biobank with hundreds of thousands of samples and endpoints, and they are not accurate when testing low-frequency and rare variants. Here, we propose a scalable and accurate method, SPACox (a saddlepoint approximation implementation based on the Cox PH regression model), that is applicable for genome-wide scale time-to-event data analysis. SPACox requires fitting a Cox PH regression model only once across the genome-wide analysis and then uses a saddlepoint approximation (SPA) to calibrate the test statistics. Simulation studies show that SPACox is 76-252 times faster than other existing alternatives, such as gwasurvivr, 185-511 times faster than the standard Wald test, and more than 6,000 times faster than the Firth correction and can control type I error rates at the genome-wide significance level regardless of minor allele frequencies. Through the analysis of UK Biobank inpatient data of 282,871 white British European ancestry samples, we show that SPACox can efficiently analyze large sample sizes and accurately control type I error rates. We identified 611 loci associated with time-to-event phenotypes of 12 common diseases, of which 38 loci would be missed within a logistic regression framework with a binary phenotype defined as event occurrence status during the follow-up period.

Identifying epigenetic biomarkers of established prognostic factors and survival in a clinical cohort of individuals with oropharyngeal cancer

BACKGROUND

Smoking status, alcohol consumption and HPV infection (acquired through sexual activity) are the predominant risk factors for oropharyngeal cancer and are thought to alter the prognosis of the disease. Here, we conducted single-site and differentially methylated region (DMR) epigenome-wide association studies (EWAS) of these factors, in addition to ∼ 3-year survival, using Illumina Methylation EPIC DNA methylation profiles from whole blood in 409 individuals as part of the Head and Neck 5000 (HN5000) study. Overlapping sites between each factor and survival were then assessed using two-step Mendelian randomization to assess whether methylation at these positions causally affected survival.

RESULTS

Using the MethylationEPIC array in an OPC dataset, we found novel CpG associations with smoking, alcohol consumption and ~ 3-year survival. We found no CpG associations below our multiple testing threshold associated with HPV16 E6 serological response (used as a proxy for HPV infection). CpG site associations below our multiple-testing threshold (PBonferroni < 0.05) for both a prognostic factor and survival were observed at four gene regions: SPEG (smoking), GFI1 (smoking), PPT2 (smoking) and KHDC3L (alcohol consumption). Evidence for a causal effect of DNA methylation on survival was only observed in the SPEG gene region (HR per SD increase in methylation score 1.28, 95% CI 1.14 to 1.43, P 2.12 × 10-05).

CONCLUSIONS

Part of the effect of smoking on survival in those with oropharyngeal cancer may be mediated by methylation at the SPEG gene locus. Replication in data from independent datasets and data from HN5000 with longer follow-up times is needed to confirm these findings.

Cox regression increases power to detect genotype-phenotype associations in genomic studies using the electronic health record

Background

The growth of DNA biobanks linked to data from electronic health records (EHRs) has enabled the discovery of numerous associations between genomic variants and clinical phenotypes. Nonetheless, although clinical data are generally longitudinal, standard approaches for detecting genotype-phenotype associations in such linked data, notably logistic regression, do not naturally account for variation in the period of follow-up or the time at which an event occurs. Here we explored the advantages of quantifying associations using Cox proportional hazards regression, which can account for the age at which a patient first visited the healthcare system (left truncation) and the age at which a patient either last visited the healthcare system or acquired a particular phenotype (right censoring).

Results

In comprehensive simulations, we found that, compared to logistic regression, Cox regression had greater power at equivalent Type I error. We then scanned for genotype-phenotype associations using logistic regression and Cox regression on 50 phenotypes derived from the EHRs of 49,792 genotyped individuals. Consistent with the findings from our simulations, Cox regression had approximately 10% greater relative sensitivity for detecting known associations from the NHGRI-EBI GWAS Catalog. In terms of effect sizes, the hazard ratios estimated by Cox regression were strongly correlated with the odds ratios estimated by logistic regression.

Conclusions

As longitudinal health-related data continue to grow, Cox regression may improve our ability to identify the genetic basis for a wide range of human phenotypes.

gwasurvivr: an R package for genome-wide survival analysis

SUMMARY

To address the limited software options for performing survival analyses with millions of SNPs, we developed gwasurvivr, an R/Bioconductor package with a simple interface for conducting genome-wide survival analyses using VCF (outputted from Michigan or Sanger imputation servers), IMPUTE2 or PLINK files. To decrease the number of iterations needed for convergence when optimizing the parameter estimates in the Cox model, we modified the R package survival; covariates in the model are first fit without the SNP, and those parameter estimates are used as initial points. We benchmarked gwasurvivr with other software capable of conducting genome-wide survival analysis (genipe, SurvivalGWAS_SV and GWASTools). gwasurvivr is significantly faster and shows better scalability as sample size, number of SNPs and number of covariates increases.

AVAILABILITY AND IMPLEMENTATION

gwasurvivr, including source code, documentation and vignette are available at: http://bioconductor.org/packages/gwasurvivr.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.