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

Neighborhood-level social determinants of suicidality in youth with schizophrenia: An EHR-based study

BACKGROUND

Suicidal thoughts and behaviors (STB) among youth with schizophrenia represent a significant public health concern. It is well-established that neighborhood-level social determinants of health (SDoHs) can impact health outcomes in individuals with schizophrenia. We aimed to investigate the effects of neighborhood-level social determinants on developing future STB in youth with schizophrenia.

METHODS

We conducted a retrospective cohort study using electronic health records from the INSIGHT Clinical Research Network, which contains >22 million unique patients across five healthcare systems in New York City. Patients' neighborhood-level SDoHs were measured at their residential ZIP Code Tabulation Area using a composite measure, Social Deprivation Index (SDI), as well as specific components derived from the American Community Survey. Survival analysis was used to study the association between neighborhood-level SDoHs and time to STB since the first schizophrenia diagnosis.

RESULTS

Between 10/1/2015 and 10/1/2022, we identified 1209 youth aged between 10 and 25 years with a schizophrenia diagnosis and no prior STB, among whom 176 developed STB during follow-up. SDI quintiles were not associated with the risk of future STB, whereas two specific neighborhood characteristics, Gini index and percentage of residents commuting by car/truck/van, were associated with a decreased risk of STB, after controlling for patients' demographic characteristics.

CONCLUSIONS

Although the overall neighborhood deprivation level was not associated with the risk of STB among youth with schizophrenia, specific neighborhood characteristics were. These findings underscore the need for more targeted community-based suicide prevention strategies. Further research is essential to better understand the underlying mechanism of these associations.

Natural history of rare diseases using natural language processing of narrative unstructured electronic health records: The example of Dravet syndrome

OBJECTIVE

The increasing implementation of electronic health records allows the use of advanced text-mining methods for establishing new patient phenotypes and stratification, and for revealing outcome correlations. In this study, we aimed to explore the electronic narrative clinical reports of a cohort of patients with Dravet syndrome (DS) longitudinally followed at our center, to identify the capacity of this methodology to retrace natural history of DS during the early years.

METHODS

We used a document-based clinical data warehouse employing natural language processing to recognize the phenotype concepts in the narrative medical reports. We included patients with DS who have a medical report produced before the age of 2 years and a follow-up after the age of 3 years ("DS cohort," 56 individuals). We selected two control populations, a "general control cohort" (275 individuals) and a "neurological control cohort" (281 individuals), with similar characteristics in terms of gender, number of reports, and age at last report. To find concepts specifically associated with DS, we performed a phenome-wide association study using Cox regression, comparing the reports of the three cohorts. We then performed a qualitative analysis of the surviving concepts based on their median age at first appearance.

RESULTS

A total of 76 concepts were prevalent in the reports of children with DS. Concepts appearing during the first 2 years were mostly related with the epilepsy features at the onset of DS (convulsive and prolonged seizures triggered by fever, often requiring in-hospital care). Subsequently, concepts related to new types of seizures and to drug resistance appeared. A series of non-seizure-related concepts emerged after the age of 2-3 years, referring to the nonseizure comorbidities classically associated with DS.

SIGNIFICANCE

The extraction of clinical terms by narrative reports of children with DS allows outlining the known natural history of this rare disease in early childhood. This original model of "longitudinal phenotyping" could be applied to other rare and very rare conditions with poor natural history description.

Improving efficiency of fitting Cox proportional hazards models for time-to-event outcomes in genome-wide association studies (GWAS)

Summary

Technologies identifying single nucleotide polymorphisms (SNPs) in DNA sequencing yield an avalanche of data requiring analysis and interpretation. Standard methods may require many weeks of processing time. The use of statistical methods requiring data sorting, matrix inversions of a high-dimension and replication in subsets of the data on multiple outcomes exacerbate these times.A method which reduces the computational time in problems with time-to-event outcomes and hundreds of thousands/millions of SNPs using Cox-Snell residuals after fitting the Cox proportional hazards model (PH) to a fixed set of concomitant variables is proposed. This yields coefficients for SNP effect from a Cox-Snell adjusted Poisson model and shows a high concordance to the adjusted PH model.The method is illustrated with a sample of 10 000 SNPs from a genome-wide association study in a diabetic population. The gain in processing efficiency using the proposed method based on Poisson modelling can be as high as 62%. This could result in saving of over three weeks processing time if 5 million SNPs require analysis. The method involves only a single predictor variable (SNP), offering a simpler, computationally more stable approach to examining and identifying SNP patterns associated with the outcome(s) allowing for a faster development of genetic signatures. Use of deviance residuals from the PH model to screen SNPs demonstrates a large discordance rate at a 0.2% threshold of concordance. This rate is 15 times larger than that based on the Cox-Snell residuals from the Cox-Snell adjusted Poisson model.

Availability and implementation

The method is simple to implement as the procedures are available in most statistical packges. The approach involves obtaining Cox-Snell residuals from a PH model, to a binary time-to-event outcome, for factors which need to be common when assessing each SNP. Each SNP is then fitted as a predictor to the outcome of interest using a Poisson model with the Cox-Snell as the exposure variable.

Local and Bayesian Survival FDR Estimations to Identify Reliable Associations in Whole Genome of Bread Wheat

Estimating the FDR significance threshold in genome-wide association studies remains a major challenge in distinguishing true positive hypotheses from false positive and negative errors. Several comparative methods for multiple testing comparison have been developed to determine the significance threshold; however, these methods may be overly conservative and lead to an increase in false negative results. The local FDR approach is suitable for testing many associations simultaneously based on the empirical Bayes perspective. In the local FDR, the maximum likelihood estimator is sensitive to bias when the GWAS model contains two or more explanatory variables as genetic parameters simultaneously. The main criticism of local FDR is that it focuses only locally on the effects of single nucleotide polymorphism (SNP) in tails of distribution, whereas the signal associations are distributed across the whole genome. The advantage of the Bayesian perspective is that knowledge of prior distribution comes from other genetic parameters included in the GWAS model, such as linkage disequilibrium (LD) analysis, minor allele frequency (MAF) and call rate of significant associations. We also proposed Bayesian survival FDR to solve the multi-collinearity and large-scale problems, respectively, in grain yield (GY) vector in bread wheat with large-scale SNP information. The objective of this study was to obtain a short list of SNPs that are reliably associated with GY under low and high levels of nitrogen (N) in the population. The five top significant SNPs were compared with different Bayesian models. Based on the time to events in the Bayesian survival analysis, the differentiation between minor and major alleles within the association panel can be identified.

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.

The phenotype-genotype reference map: Improving biobank data science through replication

Population-scale biobanks linked to electronic health record data provide vast opportunities to extend our knowledge of human genetics and discover new phenotype-genotype associations. Given their dense phenotype data, biobanks can also facilitate replication studies on a phenome-wide scale. Here, we introduce the phenotype-genotype reference map (PGRM), a set of 5,879 genetic associations from 523 GWAS publications that can be used for high-throughput replication experiments. PGRM phenotypes are standardized as phecodes, ensuring interoperability between biobanks. We applied the PGRM to five ancestry-specific cohorts from four independent biobanks and found evidence of robust replications across a wide array of phenotypes. We show how the PGRM can be used to detect data corruption and to empirically assess parameters for phenome-wide studies. Finally, we use the PGRM to explore factors associated with replicability of GWAS results.

Novel genetic variants linked to prelabor rupture of membranes among Chinese pregnant women

INTRODUCTION

The etiology of prelabor rupture of membranes (PROM), either preterm or term PROM (PPROM or TPROM), remains largely unknown. This study aimed to investigate the association between maternal genetic variants (GVs) and PROM and further establish a GV-based prediction model for PROM.

METHODS

In this case-cohort study (n = 1166), Chinese pregnant women with PPROM (n = 51), TPROM (n = 283) and controls (n = 832) were enrolled. A weighted Cox model was applied to identify the GVs (single nucleotide polymorphisms [SNPs], insertions/deletions, and copy number variants) associated with either PPROM or TPROM. Gene set enrichment analysis (GSEA) was to explore the mechanisms. The suggestively significant GVs were applied to establish a random forest (RF) model.

RESULTS

PTPRT variants (rs117950601, P = 4.37 × 10^-9; rs147178603, P = 8.98 × 10^-9) and SNRNP40 variant (rs117573344, P = 2.13 × 10^-8) were associated with PPROM. STXBP5L variant (rs10511405, P = 4.66 × 10^-8) was associated with TPROM. GSEA results showed that genes associated with PPROM were enriched in cell adhesion, and TPROM in ascorbate and glucuronidation metabolism. The area under the receiver operating characteristic curve of SNP-based RF model for PPROM was 0.961, with a sensitivity of 100.0% and specificity of 83.3%.

DISCUSSION

Maternal GVs in PTPRT and SNRNP40 were associated with PPROM, and GV in STXBP5L was associated with TPROM. Cell adhesion participated in PPROM, while ascorbate and glucuronidation metabolism contributed in TPROM. The PPROM might be well predicted using the SNP-based RF model.

Cox regression is robust to inaccurate EHR-extracted event time: an application to EHR-based GWAS

MOTIVATION

Logistic regression models are used in genomic studies to analyze the genetic data linked to electronic health records (EHRs), and do not take full usage of the time-to-event information available in EHRs. Previous work has shown that Cox regression, which can account for left truncation and right censoring in EHRs, increased the power to detect genotype-phenotype associations compared to logistic regression. We extend this to evaluate the relative performance of Cox regression and various logistic regression models in the presence of positive errors in event time (delayed event time), relating to recorded event time accuracy.

RESULTS

One Cox model and three logistic regression models were considered under different scenarios of delayed event time. Extensive simulations and a genomic study application were used to evaluate the impact of delayed event time. While logistic regression does not model the time-to-event directly, various logistic regression models used in the literature were more sensitive to delayed event time than Cox regression. Results highlighted the importance to identify and exclude the patients diagnosed before entry time. Cox regression had similar or modest improvement in statistical power over various logistic regression models at controlled type I error. This was supported by the empirical data, where the Cox models steadily had the highest sensitivity to detect known genotype-phenotype associations under all scenarios of delayed event time.

AVAILABILITY AND IMPLEMENTATION

Access to individual-level EHR and genotype data is restricted by the IRB. Simulation code and R script for data process are at: https://github.com/QingxiaCindyChen/CoxRobustEHR.git.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Accounting for age of onset and family history improves power in genome-wide association studies

Genome-wide association studies (GWASs) have revolutionized human genetics, allowing researchers to identify thousands of disease-related genes and possible drug targets. However, case-control status does not account for the fact that not all controls may have lived through their period of risk for the disorder of interest. This can be quantified by examining the age-of-onset distribution and the age of the controls or the age of onset for cases. The age-of-onset distribution may also depend on information such as sex and birth year. In addition, family history is not routinely included in the assessment of control status. Here, we present LT-FH++, an extension of the liability threshold model conditioned on family history (LT-FH), which jointly accounts for age of onset and sex as well as family history. Using simulations, we show that, when family history and the age-of-onset distribution are available, the proposed approach yields statistically significant power gains over LT-FH and large power gains over genome-wide association study by proxy (GWAX). We applied our method to four psychiatric disorders available in the iPSYCH data and to mortality in the UK Biobank and found 20 genome-wide significant associations with LT-FH++, compared to ten for LT-FH and eight for a standard case-control GWAS. As more genetic data with linked electronic health records become available to researchers, we expect methods that account for additional health information, such as LT-FH++, to become even more beneficial.

Two-step hypothesis testing to detect gene-environment interactions in a genome-wide scan with a survival endpoint

Defined by their genetic profile, individuals may exhibit differential clinical outcomes due to an environmental exposure. Identifying subgroups based on specific exposure-modifying genes can lead to targeted interventions and focused studies. Genome-wide interaction scans (GWIS) can be performed to identify such genes, but these scans typically suffer from low power due to the large multiple testing burden. We provide a novel framework for powerful two-step hypothesis tests for GWIS with a time-to-event endpoint under the Cox proportional hazards model. In the Cox regression setting, we develop an approach that prioritizes genes for Step-2 G × E testing based on a carefully constructed Step-1 screening procedure. Simulation results demonstrate this two-step approach can lead to substantially higher power for identifying gene-environment ( G × E ) interactions compared to the standard GWIS while preserving the family wise error rate over a range of scenarios. In a taxane-anthracycline chemotherapy study for breast cancer patients, the two-step approach identifies several gene expression by treatment interactions that would not be detected using the standard GWIS.

Mammographic features are associated with cardiometabolic disease risk and mortality

AIMS

In recent years, microcalcifications identified in routine mammograms were found to be associated with cardiometabolic disease in women. Here, we aimed to systematically evaluate the association of microcalcifications and other mammographic features with cardiometabolic disease risk and mortality in a large screening cohort and to understand a potential genetic contribution.

METHODS AND RESULTS

This study included 57 867 women from a prospective mammographic screening cohort in Sweden (KARMA) and 49 583 sisters. Cardiometabolic disease diagnoses and mortality and medication were extracted by linkage to Swedish population registries with virtually no missing data. In the cardiometabolic phenome-wide association study, we found that a higher number of microcalcifications were associated with increased risk for multiple cardiometabolic diseases, particularly in women with pre-existing cardiometabolic diseases. In contrast, dense breasts were associated with a lower incidence of cardiometabolic diseases. Importantly, we observed similar associations in sisters of KARMA women, indicating a potential genetic overlap between mammographic features and cardiometabolic traits. Finally, we observed that the presence of microcalcifications was associated with increased cardiometabolic mortality in women with pre-existing cardiometabolic diseases (hazard ratio and 95% confidence interval: 1.79 [1.24-2.58], P = 0.002) while we did not find such effects in women without cardiometabolic diseases.

CONCLUSIONS

We found that mammographic features are associated with cardiometabolic risk and mortality. Our results strengthen the notion that a combination of mammographic features and other breast cancer risk factors could be a novel and affordable tool to assess cardiometabolic health in women attending mammographic screening.

Genome-wide association studies of low back pain and lumbar spinal disorders using electronic health record data identify a locus associated with lumbar spinal stenosis

ABSTRACT

Identifying genetic risk factors for lumbar spine disorders may lead to knowledge regarding underlying mechanisms and the development of new treatments. We conducted a genome-wide association study involving 100,811 participants with genotypes and longitudinal electronic health record data from the Electronic Medical Records and Genomics Network and Geisinger Health. Cases and controls were defined using validated algorithms and clinical diagnostic codes. Electronic health record-defined phenotypes included low back pain requiring healthcare utilization (LBP-HC), lumbosacral radicular syndrome (LSRS), and lumbar spinal stenosis (LSS). Genome-wide association study used logistic regression with additive genetic effects adjusting for age, sex, site-specific factors, and ancestry (principal components). A fixed-effect inverse-variance weighted meta-analysis was conducted. Genetic variants of genome-wide significance (P < 5 × 10-8) were carried forward for replication in an independent sample from UK Biobank. Phenotype prevalence was 48.8% for LBP-HC, 19.8% for LSRS, and 7.9% for LSS. No variants were significantly associated with LBP-HC. One locus was associated with LSRS (lead variant rs146153280:C>G, odds ratio [OR] = 1.17 for G, P = 2.1 × 10-9), but was not replicated. Another locus on chromosome 2 spanning GFPT1, NFU1, and AAK1 was associated with LSS (lead variant rs13427243:G>A, OR = 1.10 for A, P = 4.3 × 10-8) and replicated in UK Biobank (OR = 1.11, P = 5.4 × 10-5). This was the first genome-wide association study meta-analysis of lumbar spinal disorders using electronic health record data. We identified 2 novel associations with LSRS and LSS; the latter was replicated in an independent sample.

Using Phecodes for Research with the Electronic Health Record: From PheWAS to PheRS

Electronic health records (EHRs) are a rich source of data for researchers, but extracting meaningful information out of this highly complex data source is challenging. Phecodes represent one strategy for defining phenotypes for research using EHR data. They are a high-throughput phenotyping tool based on ICD (International Classification of Diseases) codes that can be used to rapidly define the case/control status of thousands of clinically meaningful diseases and conditions. Phecodes were originally developed to conduct phenome-wide association studies to scan for phenotypic associations with common genetic variants. Since then, phecodes have been used to support a wide range of EHR-based phenotyping methods, including the phenotype risk score. This review aims to comprehensively describe the development, validation, and applications of phecodes and suggest some future directions for phecodes and high-throughput phenotyping.

A novel age-informed approach for genetic association analysis in Alzheimer's disease

BACKGROUND

Many Alzheimer's disease (AD) genetic association studies disregard age or incorrectly account for it, hampering variant discovery.

METHODS

Using simulated data, we compared the statistical power of several models: logistic regression on AD diagnosis adjusted and not adjusted for age; linear regression on a score integrating case-control status and age; and multivariate Cox regression on age-at-onset. We applied these models to real exome-wide data of 11,127 sequenced individuals (54% cases) and replicated suggestive associations in 21,631 genotype-imputed individuals (51% cases).

RESULTS

Modeling variable AD risk across age results in 5-10% statistical power gain compared to logistic regression without age adjustment, while incorrect age adjustment leads to critical power loss. Applying our novel AD-age score and/or Cox regression, we discovered and replicated novel variants associated with AD on KIF21B, USH2A, RAB10, RIN3, and TAOK2 genes.

CONCLUSION

Our AD-age score provides a simple means for statistical power gain and is recommended for future AD studies.

Set-based genetic association and interaction tests for survival outcomes based on weighted V statistics

With advancements in high-throughout technologies, studies have been conducted to investigate the role of massive genetic variants in human diseases. While set-based tests have been developed for binary and continuous disease outcomes, there are few computationally efficient set-based tests available for time-to-event outcomes. To facilitate the genetic association and interaction analyses of time-to-event outcomes, We develop a suite of multivariant tests based on weighted V statistics with or without considering potential genetic heterogeneity. In addition to the computation efficiency and nice asymptotic properties, all the new tests can deal with left truncation and competing risks in the survival data, and adjust for covariates. Simulation studies show that the new tests run faster, are more accurate in small samples, and account for confounding effect better than the existing multivariant survival tests. When the genetic effect is heterogeneous across individuals/subpopulations, the association test considering genetic heterogeneity is more powerful than the existing tests that do not account for genetic heterogeneity. Using the new methods, we perform a genome-wide association analysis of the genotype and age-to-Alzheimer's data from the Rush Memory and Aging Project and the Religious Orders Study. The analysis identifies two genes, APOE and APOC1, associated with age to Alzheimer's disease onset.

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.

Humanizing Big Data: Recognizing the Human Aspect of Big Data

The term “big data” refers broadly to large volumes of data, often gathered from several sources, that are then analyzed, for example, for predictive analytics. Combining and mining genetic data from varied sources including clinical genetic testing, for example, electronic health records, what might be termed as “recreational” genetic testing such as ancestry testing, as well as research studies, provide one type of “big data.” Challenges and cautions in analyzing big data include recognizing the lack of systematic collection of the source data, the variety of assay technologies used, the potential variation in classification and interpretation of genetic variants. While advanced technologies such as microarrays and, more recently, next-generation sequencing, that enable testing an individual's DNA for thousands of genes and variants simultaneously are briefly discussed, attention is focused more closely on challenges to analysis of the massive data generated by these genomic technologies. The main theme of this review is to evaluate challenges associated with big data in general and specifically to bring the sophisticated technology of genetic/genomic testing down to the individual level, keeping in mind the human aspect of the data source and considering where the impact of the data will be translated and applied. Considerations in this “humanizing” process include providing adequate counseling and consent for genetic testing in all settings, as well as understanding the strengths and limitations of assays and their interpretation.

Making the Most of Clumping and Thresholding for Polygenic Scores

Polygenic prediction has the potential to contribute to precision medicine. Clumping and thresholding (C+T) is a widely used method to derive polygenic scores. When using C+T, several p value thresholds are tested to maximize predictive ability of the derived polygenic scores. Along with this p value threshold, we propose to tune three other hyper-parameters for C+T. We implement an efficient way to derive thousands of different C+T scores corresponding to a grid over four hyper-parameters. For example, it takes a few hours to derive 123K different C+T scores for 300K individuals and 1M variants using 16 physical cores. We find that optimizing over these four hyper-parameters improves the predictive performance of C+T in both simulations and real data applications as compared to tuning only the p value threshold. A particularly large increase can be noted when predicting depression status, from an AUC of 0.557 (95% CI: [0.544-0.569]) when tuning only the p value threshold to an AUC of 0.592 (95% CI: [0.580-0.604]) when tuning all four hyper-parameters we propose for C+T. We further propose stacked clumping and thresholding (SCT), a polygenic score that results from stacking all derived C+T scores. Instead of choosing one set of hyper-parameters that maximizes prediction in some training set, SCT learns an optimal linear combination of all C+T scores by using an efficient penalized regression. We apply SCT to eight different case-control diseases in the UK biobank data and find that SCT substantially improves prediction accuracy with an average AUC increase of 0.035 over standard C+T.