PheWAS and Beyond: The Landscape of Associations with Medical Diagnoses and Clinical Measures across 38,662 Individuals from Geisinger

PheSeq, a Bayesian deep learning model to enhance and interpret the gene-disease association studies

Despite the abundance of genotype-phenotype association studies, the resulting association outcomes often lack robustness and interpretations. To address these challenges, we introduce PheSeq, a Bayesian deep learning model that enhances and interprets association studies through the integration and perception of phenotype descriptions. By implementing the PheSeq model in three case studies on Alzheimer's disease, breast cancer, and lung cancer, we identify 1024 priority genes for Alzheimer's disease and 818 and 566 genes for breast cancer and lung cancer, respectively. Benefiting from data fusion, these findings represent moderate positive rates, high recall rates, and interpretation in gene-disease association studies.

mixWAS: An efficient distributed algorithm for mixed-outcomes genome-wide association studies

Genome-wide association studies (GWAS) have been instrumental in identifying genetic associations for various diseases and traits. However, uncovering genetic underpinnings among traits beyond univariate phenotype associations remains a challenge. Multi-phenotype associations (MPA), or genetic pleiotropy, offer important insights into shared genes and pathways among traits, enhancing our understanding of genetic architectures of complex diseases. GWAS of biobank-linked electronic health record (EHR) data are increasingly being utilized to identify MPA among various traits and diseases. However, methodologies that can efficiently take advantage of distributed EHR to detect MPA are still lacking. Here, we introduce mixWAS, a novel algorithm that efficiently and losslessly integrates multiple EHRs via summary statistics, allowing the detection of MPA among mixed phenotypes while accounting for heterogeneities across EHRs. Simulations demonstrate that mixWAS outperforms the widely used MPA detection method, Phenome-wide association study (PheWAS), across diverse scenarios. Applying mixWAS to data from seven EHRs in the US, we identified 4,534 MPA among blood lipids, BMI, and circulatory diseases. Validation in an independent EHR data from UK confirmed 97.7% of the associations. mixWAS fundamentally improves the detection of MPA and is available as a free, open-source software.

A phenome-wide comparative analysis of genetic discordance between obesity and type 2 diabetes

Obesity and type 2 diabetes are causally related, yet there is considerable heterogeneity in the consequences of both conditions and the mechanisms of action are poorly defined. Here we show a genetic-driven approach defining two obesity profiles that convey highly concordant and discordant diabetogenic effects. We annotate and then compare association signals for these profiles across clinical and molecular phenotypic layers. Key differences are identified in a wide range of traits, including cardiovascular mortality, fat distribution, liver metabolism, blood pressure, specific lipid fractions and blood levels of proteins involved in extracellular matrix remodelling. We find marginal differences in abundance of Bacteroidetes and Firmicutes bacteria in the gut. Instrumental analyses reveal prominent causal roles for waist-to-hip ratio, blood pressure and cholesterol content of high-density lipoprotein particles in the development of diabetes in obesity. We prioritize 17 genes from the discordant signature that convey protection against type 2 diabetes in obesity, which may represent logical targets for precision medicine approaches.

Large-scale genomic analyses reveal insights into pleiotropy across circulatory system diseases and nervous system disorders

Clinical and epidemiological studies have shown that circulatory system diseases and nervous system disorders often co-occur in patients. However, genetic susceptibility factors shared between these disease categories remain largely unknown. Here, we characterized pleiotropy across 107 circulatory system and 40 nervous system traits using an ensemble of methods in the eMERGE Network and UK Biobank. Using a formal test of pleiotropy, five genomic loci demonstrated statistically significant evidence of pleiotropy. We observed region-specific patterns of direction of genetic effects for the two disease categories, suggesting potential antagonistic and synergistic pleiotropy. Our findings provide insights into the relationship between circulatory system diseases and nervous system disorders which can provide context for future prevention and treatment strategies.

Whole Exome Sequencing in Healthy Individuals of Extreme Constitution Types Reveals Differential Disease Risk: A Novel Approach towards Predictive Medicine

Precision medicine aims to move from traditional reactive medicine to a system where risk groups can be identified before the disease occurs. However, phenotypic heterogeneity amongst the diseased and healthy poses a major challenge for identification markers for risk stratification and early actionable interventions. In Ayurveda, individuals are phenotypically stratified into seven constitution types based on multisystem phenotypes termed “Prakriti”. It enables the prediction of health and disease trajectories and the selection of health interventions. We hypothesize that exome sequencing in healthy individuals of phenotypically homogeneous Prakriti types might enable the identification of functional variations associated with the constitution types. Exomes of 144 healthy Prakriti stratified individuals and controls from two genetically homogeneous cohorts (north and western India) revealed differential risk for diseases/traits like metabolic disorders, liver diseases, and body and hematological measurements amongst healthy individuals. These SNPs differ significantly from the Indo-European background control as well. Amongst these we highlight novel SNPs rs304447 (IFIT5) and rs941590 (SERPINA10) that could explain differential trajectories for immune response, bleeding or thrombosis. Our method demonstrates the requirement of a relatively smaller sample size for a well powered study. This study highlights the potential of integrating a unique phenotyping approach for the identification of predictive markers and the at-risk population amongst the healthy.

Leveraging deep phenotyping from health check-up cohort with 10,000 Korean individuals for phenome-wide association study of 136 traits

The expanding use of the phenome-wide association study (PheWAS) faces challenges in the context of using International Classification of Diseases billing codes for phenotype definition, imbalanced study population ethnicity, and constrained application of the results in research. We performed a PheWAS utilizing 136 deep phenotypes corroborated by comprehensive health check-ups in a Korean population, along with trans-ethnic comparisons through using the UK Biobank and Biobank Japan Project. Meta-analysis with Korean and Japanese population was done. The PheWAS associated 65 phenotypes with 14,101 significant variants (P < 4.92 × 10-10). Network analysis, visualization of cross-phenotype mapping, and causal inference mapping with Mendelian randomization were conducted. Among phenotype pairs from the genotype-driven cross-phenotype associations, we evaluated penetrance in correlation analysis using a clinical database. We focused on the application of PheWAS in order to make it robust and to aid the derivation of biological meaning post-PheWAS. This comprehensive analysis of PheWAS results based on a health check-up database will provide researchers and clinicians with a panoramic overview of the networks among multiple phenotypes and genetic variants, laying groundwork for the practical application of precision medicine.

Maturation and application of phenome-wide association studies

In the past 10 years since its introduction, phenome-wide association studies (PheWAS) have uncovered novel genotype-phenotype relationships. Along the way, PheWAS have evolved in many aspects as a study design with the expanded availability of large data repositories with genome-wide data linked to detailed phenotypic data. Advancement in methods, including algorithms, software, and publicly available integrated resources, makes it feasible to more fully realize the potential of PheWAS, overcoming the previous computational and analytical limitations. We review here the most recent improvements and notable applications of PheWAS since the second half of the decade from its inception. We also note the challenges that remain embedded along the entire PheWAS analytical pipeline that necessitate further development of tools and resources to further advance the understanding of the complex genetic architecture underlying human diseases and traits.

Recent innovations and in-depth aspects of post-genome wide association study (Post-GWAS) to understand the genetic basis of complex phenotypes

In the past decade, the high throughput and low cost of sequencing/genotyping approaches have led to the accumulation of a large amount of data from genome-wide association studies (GWASs). The first aim of this review is to highlight how post-GWAS analysis can be used make sense of the obtained associations. Novel directions for integrating GWAS results with other resources, such as somatic mutation, metabolite-transcript, and transcriptomic data, are also discussed; these approaches can help us move beyond each individual data point and provide valuable information about complex trait genetics. In addition, cross-phenotype association tests, when the loci detected by GWASs have significant associations with multiple traits, are reviewed to provide biologically informative results for use in real-time applications. This review also discusses the challenges of identifying interactions between genetic mutations (epistasis) and mutations of loci affecting more than one trait (pleiotropy) as underlying causes of cross-phenotype associations; these challenges can be overcome using post-GWAS analysis. Genetic similarities between phenotypes that can be revealed using post-GWAS analysis are also discussed. In summary, different methodologies of post-GWAS analysis are now available, enhancing the value of information obtained from GWAS results, and facilitating application in both humans and nonhuman species. However, precise methods still need to be developed to overcome challenges in the field and uncover the genetic underpinnings of complex traits.

A cross-population atlas of genetic associations for 220 human phenotypes

Current genome-wide association studies do not yet capture sufficient diversity in populations and scope of phenotypes. To expand an atlas of genetic associations in non-European populations, we conducted 220 deep-phenotype genome-wide association studies (diseases, biomarkers and medication usage) in BioBank Japan (n = 179,000), by incorporating past medical history and text-mining of electronic medical records. Meta-analyses with the UK Biobank and FinnGen (n_total = 628,000) identified ~5,000 new loci, which improved the resolution of the genomic map of human traits. This atlas elucidated the landscape of pleiotropy as represented by the major histocompatibility complex locus, where we conducted HLA fine-mapping. Finally, we performed statistical decomposition of matrices of phenome-wide summary statistics, and identified latent genetic components, which pinpointed responsible variants and biological mechanisms underlying current disease classifications across populations. The decomposed components enabled genetically informed subtyping of similar diseases (for example, allergic diseases). Our study suggests a potential avenue for hypothesis-free re-investigation of human diseases through genetics.

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 Multiancestry Sex-Stratified Genome-Wide Association Study of Spontaneous Clearance of Hepatitis C Virus

BACKGROUND

Spontaneous clearance of acute hepatitis C virus (HCV) infection is more common in women than in men, independent of known risk factors.

METHODS

To identify sex-specific genetic loci, we studied 4423 HCV-infected individuals (2903 male, 1520 female) of European, African, and Hispanic ancestry. We performed autosomal, and X chromosome sex-stratified and combined association analyses in each ancestry group.

RESULTS

A male-specific region near the adenosine diphosphate-ribosylation factor-like 5B (ARL5B) gene was identified. Individuals with the C allele of rs76398191 were about 30% more likely to have chronic HCV infection than individuals with the T allele (OR, 0.69; P = 1.98 × 10-07), and this was not seen in females. The ARL5B gene encodes an interferon-stimulated gene that inhibits immune response to double-stranded RNA viruses. We also identified suggestive associations near septin 6 and ribosomal protein L39 genes on the X chromosome. In box sexes, allele G of rs12852885 was associated with a 40% increase in HCV clearance compared with the A allele (OR, 1.4; P = 2.46 × 10-06). Septin 6 facilitates HCV replication via interaction with the HCV NS5b protein, and ribosomal protein L39 acts as an HCV core interactor.

CONCLUSIONS

These novel gene associations support differential mechanisms of HCV clearance between the sexes and provide biological targets for treatment or vaccine development.

A Phenome-Wide Association Study (PheWAS) of COVID-19 Outcomes by Race Using the Electronic Health Records Data in Michigan Medicine

Background: We performed a phenome-wide association study to identify pre-existing conditions related to Coronavirus disease 2019 (COVID-19) prognosis across the medical phenome and how they vary by race. Methods: The study is comprised of 53,853 patients who were tested/diagnosed for COVID-19 between 10 March and 2 September 2020 at a large academic medical center. Results: Pre-existing conditions strongly associated with hospitalization were renal failure, pulmonary heart disease, and respiratory failure. Hematopoietic conditions were associated with intensive care unit (ICU) admission/mortality and mental disorders were associated with mortality in non-Hispanic Whites. Circulatory system and genitourinary conditions were associated with ICU admission/mortality in non-Hispanic Blacks. Conclusions: Understanding pre-existing clinical diagnoses related to COVID-19 outcomes informs the need for targeted screening to support specific vulnerable populations to improve disease prevention and healthcare delivery.

A Phenome-Wide Analysis of Healthcare Costs Associated with Inflammatory Bowel Diseases

INTRODUCTION

Crohn's disease (CD) and ulcerative colitis (UC) are associated with considerable direct healthcare costs. There have been few comprehensive analyses of all IBD- and non-IBD comorbidities that determine direct costs in this population.

METHODS

We used data from a validated cohort of patients with inflammatory bowel disease (IBD). Total healthcare costs were estimated as a sum of costs associated with IBD-related hospitalizations and surgery, imaging (CT or MR scans), outpatient visits, endoscopic evaluation, and emergency room (ER) care. All ICD-9 codes were extracted for each patient and clustered into 1804 distinct phecode clusters representing individual phenotypes. A phenome-wide association analysis (PheWAS) was performed using logistic regression to identify predictors of being in the top decile of costs.

RESULTS

Our cohort is comprised of 10,721 patients with IBD among whom 50% had CD. The median age was 46 years. The median total cost per patient is $11,203 (IQR $2396-30,563). The strongest association with total healthcare costs was intestinal obstruction without mention of hernia (p = 5.93 × 10^-156) and other intestinal obstruction (p = 9.24 × 10^-131). In addition, strong associations were observed for symptoms consistent with severity of IBD including the presence of fluid-electrolyte imbalance (p = 1.90 × 10^-130), hypovolemia (p = 1.65 × 10^-114), abdominal pain (p = 7.29 × 10^-60), and anemia (p = 1.90-10^-83). Cardiopulmonary diseases and psychological comorbidity also demonstrated significant associations with total costs with the latter being more strongly associated with ER visit-related costs.

CONCLUSIONS

Surrogate markers suggesting possible irreversible bowel damage and active disease demonstrate the greatest influence on IBD-related healthcare costs.

A phenome-wide association study (PheWAS) of COVID-19 outcomes by race using the electronic health records data in Michigan Medicine

Background:

We perform a phenome-wide scan to identify pre-existing conditions related to COVID-19 susceptibility and prognosis across the medical phenome and how they vary by race.

Methods:

The study is comprised of 53,853 patients who were tested/positive for COVID-19 between March 10 and September 2, 2020 at a large academic medical center.

Results:

Pre-existing conditions strongly associated with hospitalization were renal failure, pulmonary heart disease, and respiratory failure. Hematopoietic conditions were associated with ICU admission/mortality and mental disorders were associated with mortality in non-Hispanic Whites. Circulatory system and genitourinary conditions were associated with ICU admission/mortality in non-Hispanic Blacks.

Conclusions:

Understanding pre-existing clinical diagnoses related to COVID-19 outcomes informs the need for targeted screening to support specific vulnerable populations to improve disease prevention and healthcare delivery.

Detecting differentially methylated regions using a fast wavelet-based approach to functional association analysis

Background

We present here a computational shortcut to improve a powerful wavelet-based method by Shim and Stephens (Ann Appl Stat 9(2):665–686, 2015. 10.1214/14-AOAS776) called WaveQTL that was originally designed to identify DNase I hypersensitivity quantitative trait loci (dsQTL).

Results

WaveQTL relies on permutations to evaluate the significance of an association. We applied a recent method by Zhou and Guan (J Am Stat Assoc 113(523):1362–1371, 2017. 10.1080/01621459.2017.1328361) to boost computational speed, which involves calculating the distribution of Bayes factors and estimating the significance of an association by simulations rather than permutations. We called this simulation-based approach “fast functional wavelet” (FFW), and tested it on a publicly available DNA methylation (DNAm) dataset on colorectal cancer. The simulations confirmed a substantial gain in computational speed compared to the permutation-based approach in WaveQTL. Furthermore, we show that FFW controls the type I error satisfactorily and has good power for detecting differentially methylated regions.

Conclusions

Our approach has broad utility and can be applied to detect associations between different types of functions and phenotypes. As more and more DNAm datasets are being made available through public repositories, an attractive application of FFW would be to re-analyze these data and identify associations that might have been missed by previous efforts. The full R package for FFW is freely available at GitHub https://github.com/william-denault/ffw.

Polygenic Risk of Psychiatric Disorders Exhibits Cross-trait Associations in Electronic Health Record Data From European Ancestry Individuals

BACKGROUND

Prediction of disease risk is a key component of precision medicine. Common traits such as psychiatric disorders have a complex polygenic architecture, making the identification of a single risk predictor difficult. Polygenic risk scores (PRSs) denoting the sum of an individual's genetic liability for a disorder are a promising biomarker for psychiatric disorders, but they require evaluation in a clinical setting.

METHODS

We developed PRSs for 6 psychiatric disorders (schizophrenia, bipolar disorder, major depressive disorder, cross disorder, attention-deficit/hyperactivity disorder, and anorexia nervosa) and 17 nonpsychiatric traits in more than 10,000 individuals from the Penn Medicine Biobank with accompanying electronic health records. We performed phenome-wide association analyses to test their association across disease categories.

RESULTS

Four of the 6 psychiatric PRSs were associated with their primary phenotypes (odds ratios from 1.2 to 1.6). Cross-trait associations were identified both within the psychiatric domain and across trait domains. PRSs for coronary artery disease and years of education were significantly associated with psychiatric disorders, largely driven by an association with tobacco use disorder.

CONCLUSIONS

We demonstrated that the genetic architecture of electronic health record-derived psychiatric diagnoses is similar to ascertained research cohorts from large consortia. Psychiatric PRSs are moderately associated with psychiatric diagnoses but are not yet clinically predictive in naïve patients. Cross-trait associations for these PRSs suggest a broader effect of genetic liability beyond traditional diagnostic boundaries. As identification of genetic markers increases, including PRSs alongside other clinical risk factors may enhance prediction of psychiatric disorders and associated conditions in clinical registries.

Statistical Impact of Sample Size and Imbalance on Multivariate Analysis in silico and A Case Study in the UK Biobank

Large-scale biobank cohorts coupled with electronic health records offer unprecedented opportunities to study genotype-phenotype relationships. Genome-wide association studies uncovered disease-associated loci through univariate methods, with the focus on one trait at a time. With genetic variants being identifiedfor thousands of traits, researchers found that 90% of human genetic loci are associated with more than one trait, highlighting the ubiquity of pleiotropy. Recently, multivariate methods have been proposed to effectively identify pleiotropy. However, the statistical performance in natural biomedical data, which often have unbalanced case-control sample sizes, is largely known. In this work, we designed 21 scenarios of real-data informed simulations to thoroughly evaluate the statistical characteristics of univariate and multivariate methods. Our results can serve as a reference guide for the application of multivariate methods. We also investigated potential pleiotropy across type II diabetes, Alzheimer's disease, atherosclerosis of arteries, depression, and atherosclerotic heart disease in the UK Biobank.

Clinical laboratory test-wide association scan of polygenic scores identifies biomarkers of complex disease

BACKGROUND

Clinical laboratory (lab) tests are used in clinical practice to diagnose, treat, and monitor disease conditions. Test results are stored in electronic health records (EHRs), and a growing number of EHRs are linked to patient DNA, offering unprecedented opportunities to query relationships between genetic risk for complex disease and quantitative physiological measurements collected on large populations.

METHODS

A total of 3075 quantitative lab tests were extracted from Vanderbilt University Medical Center's (VUMC) EHR system and cleaned for population-level analysis according to our QualityLab protocol. Lab values extracted from BioVU were compared with previous population studies using heritability and genetic correlation analyses. We then tested the hypothesis that polygenic risk scores for biomarkers and complex disease are associated with biomarkers of disease extracted from the EHR. In a proof of concept analyses, we focused on lipids and coronary artery disease (CAD). We cleaned lab traits extracted from the EHR performed lab-wide association scans (LabWAS) of the lipids and CAD polygenic risk scores across 315 heritable lab tests then replicated the pipeline and analyses in the Massachusetts General Brigham Biobank.

RESULTS

Heritability estimates of lipid values (after cleaning with QualityLab) were comparable to previous reports and polygenic scores for lipids were strongly associated with their referent lipid in a LabWAS. LabWAS of the polygenic score for CAD recapitulated canonical heart disease biomarker profiles including decreased HDL, increased pre-medication LDL, triglycerides, blood glucose, and glycated hemoglobin (HgbA1C) in European and African descent populations. Notably, many of these associations remained even after adjusting for the presence of cardiovascular disease and were replicated in the MGBB.

CONCLUSIONS

Polygenic risk scores can be used to identify biomarkers of complex disease in large-scale EHR-based genomic analyses, providing new avenues for discovery of novel biomarkers and deeper understanding of disease trajectories in pre-symptomatic individuals. We present two methods and associated software, QualityLab and LabWAS, to clean and analyze EHR labs at scale and perform a Lab-Wide Association Scan.

Lossless integration of multiple electronic health records for identifying pleiotropy using summary statistics

Increasingly, clinical phenotypes with matched genetic data from bio-bank linked electronic health records (EHRs) have been used for pleiotropy analyses. Thus far, pleiotropy analysis using individual-level EHR data has been limited to data from one site. However, it is desirable to integrate EHR data from multiple sites to improve the detection power and generalizability of the results. Due to privacy concerns, individual-level patients' data are not easily shared across institutions. As a result, we introduce Sum-Share, a method designed to efficiently integrate EHR and genetic data from multiple sites to perform pleiotropy analysis. Sum-Share requires only summary-level data and one round of communication from each site, yet it produces identical test statistics compared with that of pooled individual-level data. Consequently, Sum-Share can achieve lossless integration of multiple datasets. Using real EHR data from eMERGE, Sum-Share is able to identify 1734 potential pleiotropic SNPs for five cardiovascular diseases.

LabWAS: Novel findings and study design recommendations from a meta-analysis of clinical labs in two independent biobanks

Phenotypes extracted from Electronic Health Records (EHRs) are increasingly prevalent in genetic studies. EHRs contain hundreds of distinct clinical laboratory test results, providing a trove of health data beyond diagnoses. Such lab data is complex and lacks a ubiquitous coding scheme, making it more challenging than diagnosis data. Here we describe the first large-scale cross-health system genome-wide association study (GWAS) of EHR-based quantitative laboratory-derived phenotypes. We meta-analyzed 70 lab traits matched between the BioVU cohort from the Vanderbilt University Health System and the Michigan Genomics Initiative (MGI) cohort from Michigan Medicine. We show high replication of known association for these traits, validating EHR-based measurements as high-quality phenotypes for genetic analysis. Notably, our analysis provides the first replication for 699 previous GWAS associations across 46 different traits. We discovered 31 novel associations at genome-wide significance for 22 distinct traits, including the first reported associations for two lab-based traits. We replicated 22 of these novel associations in an independent tranche of BioVU samples. The summary statistics for all association tests are freely available to benefit other researchers. Finally, we performed mirrored analyses in BioVU and MGI to assess competing analytic practices for EHR lab traits. We find that using the mean of all available lab measurements provides a robust summary value, but alternate summarizations can improve power in certain circumstances. This study provides a proof-of-principle for cross health system GWAS and is a framework for future studies of quantitative EHR lab traits.

Developing a FHIR-based Framework for Phenome Wide Association Studies: A Case Study with A Pan-Cancer Cohort

Phenome Wide Association Studies (PheWAS) enables phenome-wide scans to discover novel associations between genotype and clinical phenotypes via linking available genomic reports and large-scale Electronic Health Record (EHR). Data heterogeneity from different EHR systems and genetic reports has been a critical challenge that hinders meaningful validation. To address this, we propose an FHIR-based framework to model the PheWAS study in a standard manner. We developed an FHIR-based data model profile to enable the standard representation of data elements from genetic reports and EHR data that are used in the PheWAS study. As a proof-of-concept, we implemented the proposed method using a cohort of 1,595 pan-cancer patients with genetic reports from Foundation Medicine as well as the corresponding lab tests and diagnosis from Mayo EHRs. A PheWAS study is conducted and 81 significant genotype-phenotype associations are identified, in which 36 significant associations for cancers are validated based on a literature review.

A Comprehensive Genome-Wide and Phenome-Wide Examination of BMI and Obesity in a Northern Nevadan Cohort

The aggregation of Electronic Health Records (EHR) and personalized genetics leads to powerful discoveries relevant to population health. Here we perform genome-wide association studies (GWAS) and accompanying phenome-wide association studies (PheWAS) to validate phenotype-genotype associations of BMI, and to a greater extent, severe Class 2 obesity, using comprehensive diagnostic and clinical data from the EHR database of our cohort. Three GWASs of 500,000 variants on the Illumina platform of 6,645 Healthy Nevada participants identified several published and novel variants that affect BMI and obesity. Each GWAS was followed with two independent PheWASs to examine associations between extensive phenotypes (incidence of diagnoses, condition, or disease), significant SNPs, BMI, and incidence of extreme obesity. The first GWAS examines associations with BMI in a cohort with no type 2 diabetics, focusing exclusively on BMI. The second GWAS examines associations with BMI in a cohort that includes type 2 diabetics. In the second GWAS, type 2 diabetes is a comorbidity, and thus becomes a covariate in the statistical model. The intersection of significant variants of these two studies is surprising. The third GWAS is a case vs. control study, with cases defined as extremely obese (Class 2 or 3 obesity), and controls defined as participants with BMI between 18.5 and 25. This last GWAS identifies strong associations with extreme obesity, including established variants in the FTO and NEGR1 genes, as well as loci not yet linked to obesity. The PheWASs validate published associations between BMI and extreme obesity and incidence of specific diagnoses and conditions, yet also highlight novel links. This study emphasizes the importance of our extensive longitudinal EHR database to validate known associations and identify putative novel links with BMI and obesity.

Genome-Wide Association Study of Wood Anatomical and Morphological Traits in Populus trichocarpa

To understand the genetic mechanisms underlying wood anatomical and morphological traits in Populus trichocarpa, we used 869 unrelated genotypes from a common garden in Clatskanie, Oregon that were previously collected from across the distribution range in western North America. Using GEMMA mixed model analysis, we tested for the association of 25 phenotypic traits and nine multitrait combinations with 6.741 million SNPs covering the entire genome. Broad-sense trait heritabilities ranged from 0.117 to 0.477. Most traits were significantly correlated with geoclimatic variables suggesting a role of climate and geography in shaping the variation of this species. Fifty-seven SNPs from single trait GWAS and 11 SNPs from multitrait GWAS passed an FDR threshold of 0.05, leading to the identification of eight and seven nearby candidate genes, respectively. The percentage of phenotypic variance explained (PVE) by the significant SNPs for both single and multitrait GWAS ranged from 0.01% to 6.18%. To further evaluate the potential roles of candidate genes, we used a multi-omic network containing five additional data sets, including leaf and wood metabolite GWAS layers and coexpression and comethylation networks. We also performed a functional enrichment analysis on coexpression nearest neighbors for each gene model identified by the wood anatomical and morphological trait GWAS analyses. Genes affecting cell wall composition and transport related genes were enriched in wood anatomy and stomatal density trait networks. Signaling and metabolism related genes were also common in networks for stomatal density. For leaf morphology traits (leaf dry and wet weight) the networks were significantly enriched for GO terms related to photosynthetic processes as well as cellular homeostasis. The identified genes provide further insights into the genetic control of these traits, which are important determinants of the suitability and sustainability of improved genotypes for lignocellulosic biofuel production.

Rare-variant pathogenicity triage and inclusion of synonymous variants improves analysis of disease associations of orphan G protein-coupled receptors

The pace of deorphanization of G protein-coupled receptors (GPCRs) has slowed, and new approaches are required. Small molecule targeting of orphan GPCRs can potentially be of clinical benefit even if the endogenous receptor ligand has not been identified. Many GPCRs lack common variants that lead to reproducible genome-wide disease associations, and rare-variant approaches have emerged as a viable alternative to identify disease associations for such genes. Therefore, our goal was to prioritize orphan GPCRs by determining their associations with human diseases in a large clinical population. We used sequence kernel association tests to assess the disease associations of 85 orphan or understudied GPCRs in an unselected cohort of 51,289 individuals. Using rare loss-of-function variants, missense variants predicted to be pathogenic or likely pathogenic, and a subset of rare synonymous variants that cause large changes in local codon bias as independent data sets, we found strong, phenome-wide disease associations shared by two or more variant categories for 39% of the GPCRs. To validate the bioinformatics and sequence kernel association test analyses, we functionally characterized rare missense and synonymous variants of GPR39, a family A GPCR, revealing altered expression or Zn²⁺-mediated signaling for members of both variant classes. These results support the utility of rare variant analyses for identifying disease associations for GPCRs that lack impactful common variants. We highlight the importance of rare synonymous variants in human physiology and argue for their routine inclusion in any comprehensive analysis of genomic variants as potential causes of disease.

GWAS and PheWAS of red blood cell components in a Northern Nevadan cohort

In this study, we perform a full genome-wide association study (GWAS) to identify statistically significantly associated single nucleotide polymorphisms (SNPs) with three red blood cell (RBC) components and follow it with two independent PheWASs to examine associations between phenotypic data (case-control status of diagnoses or disease), significant SNPs, and RBC component levels. We first identified associations between the three RBC components: mean platelet volume (MPV), mean corpuscular volume (MCV), and platelet counts (PC), and the genotypes of approximately 500,000 SNPs on the Illumina Infimum DNA Human OmniExpress-24 BeadChip using a single cohort of 4,673 Northern Nevadans. Twenty-one SNPs in five major genomic regions were found to be statistically significantly associated with MPV, two regions with MCV, and one region with PC, with p<5x10^-8. Twenty-nine SNPs and nine chromosomal regions were identified in 30 previous GWASs, with effect sizes of similar magnitude and direction as found in our cohort. The two strongest associations were SNP rs1354034 with MPV (p = 2.4x10^-13) and rs855791 with MCV (p = 5.2x10^-12). We then examined possible associations between these significant SNPs and incidence of 1,488 phenotype groups mapped from International Classification of Disease version 9 and 10 (ICD9 and ICD10) codes collected in the extensive electronic health record (EHR) database associated with Healthy Nevada Project consented participants. Further leveraging data collected in the EHR, we performed an additional PheWAS to identify associations between continuous red blood cell (RBC) component measures and incidence of specific diagnoses. The first PheWAS illuminated whether SNPs associated with RBC components in our cohort were linked with other hematologic phenotypic diagnoses or diagnoses of other nature. Although no SNPs from our GWAS were identified as strongly associated to other phenotypic components, a number of associations were identified with p-values ranging between 1x10^-3 and 1x10^-4 with traits such as respiratory failure, sleep disorders, hypoglycemia, hyperglyceridemia, GERD and IBS. The second PheWAS examined possible phenotypic predictors of abnormal RBC component measures: a number of hematologic phenotypes such as thrombocytopenia, anemias, hemoglobinopathies and pancytopenia were found to be strongly associated to RBC component measures; additional phenotypes such as (morbid) obesity, malaise and fatigue, alcoholism, and cirrhosis were also identified to be possible predictors of RBC component measures.

Systematic characterization of germline variants from the DiscovEHR study endometrial carcinoma population

Background

Endometrial cancer (EMCA) is the fifth most common cancer among women in the world. Identification of potentially pathogenic germline variants from individuals with EMCA will help characterize genetic features that underlie the disease and potentially predispose individuals to its pathogenesis.

Methods

The Geisinger Health System’s (GHS) DiscovEHR cohort includes exome sequencing on over 50,000 consenting patients, 297 of whom have evidence of an EMCA diagnosis in their electronic health record. Here, rare variants were annotated as potentially pathogenic.

Results

Eight genes were identified as having increased burden in the EMCA cohort relative to the non-cancer control cohort. None of the eight genes had an increased burden in the other hormone related cancer cohort from GHS, suggesting they can help characterize the underlying genetic variation that gives rise to EMCA. Comparing GHS to the cancer genome atlas (TCGA) EMCA germline data illustrated 34 genes with potentially pathogenic variation and eight unique potentially pathogenic variants that were present in both studies. Thus, similar germline variation among genes can be observed in unique EMCA cohorts and could help prioritize genes to investigate for future work.

Conclusion

In summary, this systematic characterization of potentially pathogenic germline variants describes the genetic underpinnings of EMCA through the use of data from a single hospital system.

Electronic supplementary material

The online version of this article (10.1186/s12920-019-0504-9) contains supplementary material, which is available to authorized users.

Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery

Electronic Health Record (EHR) systems typically define laboratory test results using the Laboratory Observation Identifier Names and Codes (LOINC) and can transmit them using Fast Healthcare Interoperability Resource (FHIR) standards. LOINC has not yet been semantically integrated with computational resources for phenotype analysis. Here, we provide a method for mapping LOINC-encoded laboratory test results transmitted in FHIR standards to Human Phenotype Ontology (HPO) terms. We annotated the medical implications of 2923 commonly used laboratory tests with HPO terms. Using these annotations, our software assesses laboratory test results and converts each result into an HPO term. We validated our approach with EHR data from 15,681 patients with respiratory complaints and identified known biomarkers for asthma. Finally, we provide a freely available SMART on FHIR application that can be used within EHR systems. Our approach allows readily available laboratory tests in EHR to be reused for deep phenotyping and exploits the hierarchical structure of HPO to integrate distinct tests that have comparable medical interpretations for association studies.

An exploratory phenome wide association study linking asthma and liver disease genetic variants to electronic health records from the Estonian Biobank

The Estonian Biobank, governed by the Institute of Genomics at the University of Tartu (Biobank), has stored genetic material/DNA and continuously collected data since 2002 on a total of 52,274 individuals representing ~5% of the Estonian adult population and is increasing. To explore the utility of data available in the Biobank, we conducted a phenome-wide association study (PheWAS) in two areas of interest to healthcare researchers; asthma and liver disease. We used 11 asthma and 13 liver disease-associated single nucleotide polymorphisms (SNPs), identified from published genome-wide association studies, to test our ability to detect established associations. We confirmed 2 asthma and 5 liver disease associated variants at nominal significance and directionally consistent with published results. We found 2 associations that were opposite to what was published before (rs4374383:AA increases risk of NASH/NAFLD, rs11597086 increases ALT level). Three SNP-diagnosis pairs passed the phenome-wide significance threshold: rs9273349 and E06 (thyroiditis, p = 5.50x10-8); rs9273349 and E10 (type-1 diabetes, p = 2.60x10-7); and rs2281135 and K76 (non-alcoholic liver diseases, including NAFLD, p = 4.10x10-7). We have validated our approach and confirmed the quality of the data for these conditions. Importantly, we demonstrate that the extensive amount of genetic and medical information from the Estonian Biobank can be successfully utilized for scientific research.

Multi-Ancestry Genome-Wide Association Study of Spontaneous Clearance of Hepatitis C Virus

BACKGROUND & AIMS

Spontaneous clearance of hepatitis C virus (HCV) occurs in approximately 30% of infected persons and less often in populations of African ancestry. Variants in major histocompatibility complex (MHC) and in interferon lambda genes are associated with spontaneous HCV clearance, but there have been few studies of these variants in persons of African ancestry. We performed a dense multi-ancestry genome-wide association study of spontaneous clearance of HCV, focusing on individuals of African ancestry.

METHODS

We performed genotype analyses of 4423 people from 3 ancestry groups: 2201 persons of African ancestry (445 with HCV clearance and 1756 with HCV persistence), 1739 persons of European ancestry (701 with HCV clearance and 1036 with HCV persistence), and 486 multi-ancestry Hispanic persons (173 with HCV clearance and 313 with HCV persistence). Samples were genotyped using Illumina (San Diego, CA) arrays and statistically imputed to the 1000 Genomes Project. For each ancestry group, the association of single-nucleotide polymorphisms with HCV clearance was tested by log-additive analysis, and then a meta-analysis was performed.

RESULTS

In the meta-analysis, significant associations with HCV clearance were confirmed at the interferon lambda gene locus IFNL4-IFNL3 (19q13.2) (P = 5.99 × 10-50) and the MHC locus 6p21.32 (P = 1.15 × 10-21). We also associated HCV clearance with polymorphisms in the G-protein-coupled receptor 158 gene (GPR158) at 10p12.1 (P = 1.80 × 10-07). These 3 loci had independent, additive effects of HCV clearance, and account for 6.8% and 5.9% of the variance of HCV clearance in persons of European and African ancestry, respectively. Persons of African or European ancestry carrying all 6 variants were 24-fold and 11-fold, respectively, more likely to clear HCV infection compared with individuals carrying none or 1 of the clearance-associated variants.

CONCLUSIONS

In a meta-analysis of data from 3 studies, we found variants in MHC genes, IFNL4-IFNL3, and GPR158 to increase odds of HCV clearance in patients of European and African ancestry. These findings could increase our understanding of immune response to and clearance of HCV infection.

Using Human Genetics to Drive Drug Discovery: A Perspective

The probability of success of developing medicines to treat human disease can be improved by leveraging human genetics. Different types of genetic data and techniques, including genome-wide association, whole-exome sequencing, and whole-genome sequencing, can be used to gain insight into human disease. Layering different types of genetic evidence from Mendelian disease, coding variants, and common variation can bolster support for a genetic target. Human knockouts offer the potential to perform reverse genetic screens in humans to identify physiologically relevant targets. Other components of a good genetic target include protective loss-of-function mutations, some degree of known biology, tractability, and a clean on-target safety profile. In addition to using human genetics to inspire new drug programs, phenome-wide association studies can be used to identify alternative indications or repurposing opportunities. This information can be combined into a 5-step approach for selecting a genetic target for validation, which is presented in detail in this review. Finally, current challenges in leveraging human genetics are highlighted, including the difficulties translating certain types of genetic data, relatively small number of bona fide disease-associated coding rare variants, and current sample sizes of large well-curated biobanks linked to comprehensive genetic information.

Polygenic risk score identifies associations between sleep duration and diseases determined from an electronic medical record biobank

STUDY OBJECTIVES

We aimed to detect cross-sectional phenotype and polygenic risk score (PRS) associations between sleep duration and prevalent diseases using the Partners Biobank, a hospital-based cohort study linking electronic medical records (EMR) with genetic information.

METHODS

Disease prevalence was determined from EMR, and sleep duration was self-reported. A PRS for sleep duration was derived using 78 previously associated SNPs from genome-wide association studies (GWAS) for self-reported sleep duration. We tested for associations between (1) self-reported sleep duration and 22 prevalent diseases (n = 30 251), (2) the PRS and self-reported sleep duration (n = 6903), and (3) the PRS and the 22 prevalent diseases (n = 16 033). For observed PRS-disease associations, we tested causality using two-sample Mendelian randomization (MR).

RESULTS

In the age-, sex-, and race-adjusted model, U-shaped associations were observed for sleep duration and asthma, depression, hypertension, insomnia, obesity, obstructive sleep apnea, and type 2 diabetes, where both short and long sleepers had higher odds for these diseases than normal sleepers (p < 2.27 × 10-3). Next, we confirmed associations between the PRS and longer sleep duration (0.65 ± 0.19 SD minutes per effect allele; p = 7.32 × 10-04). The PRS collectively explained 1.4% of the phenotypic variance in sleep duration. After adjusting for age, sex, genotyping array, and principal components of ancestry, we observed that the PRS was also associated with congestive heart failure (CHF; p = 0.015), obesity (p = 0.019), hypertension (p = 0.039), restless legs syndrome (RLS; p = 0.041), and insomnia (p = 0.049). Associations were maintained following additional adjustment for obesity status, except for hypertension and insomnia. For all diseases, except RLS, carrying a higher genetic burden of the 78 sleep duration-increasing alleles (i.e. higher sleep duration PRS) associated with lower odds for prevalent disease. In MR, we estimated causal associations between genetically defined longer sleep duration with decreased risk of CHF (inverse variance weighted [IVW] OR per minute of sleep [95% CI] = 0.978 [0.961-0.996]; p = 0.019) and hypertension (IVW OR [95% CI] = 0.993 [0.986-1.000]; p = 0.049), and increased risk of RLS (IVW OR [95% CI] = 1.018 [1.000-1.036]; p = 0.045).

CONCLUSIONS

By validating the PRS for sleep duration and identifying cross-phenotype associations, we lay the groundwork for future investigations on the intersection between sleep, genetics, clinical measures, and diseases using large EMR datasets.

Human-Disease Phenotype Map Derived from PheWAS across 38,682 Individuals

Phenome-wide association studies (PheWASs) have been a useful tool for testing associations between genetic variations and multiple complex traits or diagnoses. Linking PheWAS-based associations between phenotypes and a variant or a genomic region into a network provides a new way to investigate cross-phenotype associations, and it might broaden the understanding of genetic architecture that exists between diagnoses, genes, and pleiotropy. We created a network of associations from one of the largest PheWASs on electronic health record (EHR)-derived phenotypes across 38,682 unrelated samples from the Geisinger's biobank; the samples were genotyped through the DiscovEHR project. We computed associations between 632,574 common variants and 541 diagnosis codes. Using these associations, we constructed a "disease-disease" network (DDN) wherein pairs of diseases were connected on the basis of shared associations with a given genetic variant. The DDN provides a landscape of intra-connections within the same disease classes, as well as inter-connections across disease classes. We identified clusters of diseases with known biological connections, such as autoimmune disorders (type 1 diabetes, rheumatoid arthritis, and multiple sclerosis) and cardiovascular disorders. Previously unreported relationships between multiple diseases were identified on the basis of genetic associations as well. The network approach applied in this study can be used to uncover interactions between diseases as a result of their shared, potentially pleiotropic SNPs. Additionally, this approach might advance clinical research and even clinical practice by accelerating our understanding of disease mechanisms on the basis of similar underlying genetic associations.

Learning Contextual Hierarchical Structure of Medical Concepts with Poincairé Embeddings to Clarify Phenotypes

Biomedical association studies are increasingly done using clinical concepts, and in particular diagnostic codes from clinical data repositories as phenotypes. Clinical concepts can be represented in a meaningful, vector space using word embedding models. These embeddings allow for comparison between clinical concepts or for straightforward input to machine learning models. Using traditional approaches, good representations require high dimensionality, making downstream tasks such as visualization more difficult. We applied Poincaré embeddings in a 2-dimensional hyperbolic space to a large-scale administrative claims database and show performance comparable to 100-dimensional embeddings in a euclidean space. We then examine disease relationships under different disease contexts to better understand potential phenotypes.

Post-GWAS in prostate cancer: from genetic association to biological contribution

Genome-wide association studies (GWAS) have been successful in deciphering the genetic component of predisposition to many human complex diseases including prostate cancer. Germline variants identified by GWAS progressively unravelled the substantial knowledge gap concerning prostate cancer heritability. With the beginning of the post-GWAS era, more and more studies reveal that, in addition to their value as risk markers, germline variants can exert active roles in prostate oncogenesis. Consequently, current research efforts focus on exploring the biological mechanisms underlying specific susceptibility loci known as causal variants by applying novel and precise analytical methods to available GWAS data. Results obtained from these post-GWAS analyses have highlighted the potential of exploiting prostate cancer risk-associated germline variants to identify new gene networks and signalling pathways involved in prostate tumorigenesis. In this Review, we describe the molecular basis of several important prostate cancer-causal variants with an emphasis on using post-GWAS analysis to gain insight into cancer aetiology. In addition to discussing the current status of post-GWAS studies, we also summarize the main molecular mechanisms of potential causal variants at prostate cancer risk loci and explore the major challenges in moving from association to functional studies and their implication in clinical translation.

Detecting potential pleiotropy across cardiovascular and neurological diseases using univariate, bivariate, and multivariate methods on 43,870 individuals from the eMERGE network

The link between cardiovascular diseases and neurological disorders has been widely observed in the aging population. Disease prevention and treatment rely on understanding the potential genetic nexus of multiple diseases in these categories. In this study, we were interested in detecting pleiotropy, or the phenomenon in which a genetic variant influences more than one phenotype. Marker-phenotype association approaches can be grouped into univariate, bivariate, and multivariate categories based on the number of phenotypes considered at one time. Here we applied one statistical method per category followed by an eQTL colocalization analysis to identify potential pleiotropic variants that contribute to the link between cardiovascular and neurological diseases. We performed our analyses on ~530,000 common SNPs coupled with 65 electronic health record (EHR)-based phenotypes in 43,870 unrelated European adults from the Electronic Medical Records and Genomics (eMERGE) network. There were 31 variants identified by all three methods that showed significant associations across late onset cardiac- and neurologic- diseases. We further investigated functional implications of gene expression on the detected "lead SNPs" via colocalization analysis, providing a deeper understanding of the discovered associations. In summary, we present the framework and landscape for detecting potential pleiotropy using univariate, bivariate, multivariate, and colocalization methods. Further exploration of these potentially pleiotropic genetic variants will work toward understanding disease causing mechanisms across cardiovascular and neurological diseases and may assist in considering disease prevention as well as drug repositioning in future research.

Genomic and Phenomic Research in the 21st Century

The field of human genomics has changed dramatically over time. Initial genomic studies were predominantly restricted to rare disorders in small families. Over the past decade, researchers changed course from family-based studies and instead focused on common diseases and traits in populations of unrelated individuals. With further advancements in biobanking, computer science, electronic health record (EHR) data, and more affordable high-throughput genomics, we are experiencing a new paradigm in human genomic research. Rapidly changing technologies and resources now make it possible to study thousands of diseases simultaneously at the genomic level. This review will focus on these advancements as scientists begin to incorporate phenome-wide strategies in human genomic research to understand the etiology of human diseases and develop new drugs to treat them.

DNArCdb: A database of cancer biomarkers in DNA repair genes that includes variants related to multiple cancer phenotypes

Functioning DNA repair capabilities are vital for organisms to ensure that the biological information is preserved and correctly propagated. Disruptions in DNA repair pathways can result in the accumulation of DNA mutations, which may lead to onset of complex disease such as cancer. The discovery and characterization of cancer-related biomarkers may allow early diagnosis and targeted treatment, which could significantly contribute to the survival rates of cancer patients. To this end, we have applied a hypothesis driven bioinformatics approach to identify biomarkers related to 25 different DNA repair enzymes, in combination with structural analysis of six selected missense mutations of newly discovered SNPs that are associated with cancer phenotypes. Our search on 8 distinct cancer databases uncovered 43 missense SNPs that statistically significantly associated at least one phenotype. Moreover, nine of these missense SNPs are statistically significantly associated with two or more cancers. In addition, we have performed classical molecular dynamics to characterize the impact of rs10018786 on POLN, which results in the M310 L Pol ν variant, and rs3218784 on POLI, which results in the I236 M Pol ι. Our results suggest that both of these cancer-associated variants result in noticeable structural and dynamical changes compared with their respective wild-type proteins.