Steven J. Schrodi

A PheWAS approach in studying HLA-DRB1*1501

HLA-DRB1 codes for a major histocompatibility complex class II cell surface receptor. Genetic variants in and around this gene have been linked to numerous autoimmune diseases. Most notably, an association between HLA-DRB1*1501 haplotype and multiple sclerosis (MS) has been defined. Utilizing electronic health records and 4235 individuals within Marshfield Clinic’s Personalized Medicine Research Project, a reverse genetic screen coined phenome-wide association study (PheWAS) tested association of rs3135388 genotype (tagging HLA-DRB1*1501) with 4841 phenotypes. As expected, HLA-DRB1*1501 was associated with MS (International Classification of Disease version 9-CM (ICD9) 340, P=0.023), whereas the strongest association was with alcohol-induced cirrhosis of the liver (ICD9 571.2, P=0.00011). HLA-DRB1*1501 also demonstrated association with erythematous conditions (ICD9 695, P=0.0054) and benign neoplasms of the respiratory and intrathoracic organs (ICD9 212, P=0.042), replicating previous findings. This study not only builds on the feasibility/utility of the PheWAS approach, represents the first external validation of a PheWAS, but may also demonstrate the complex etiologies associated with the HLA-DRB1*1501 loci.

Genetic-based prediction of disease traits: Prediction is very difficult, especially about the future

Translation of results from genetic findings to inform medical practice is a highly anticipated goal of human genetics. The aim of this paper is to review and discuss the role of genetics in medically-relevant prediction. Germline genetics presages disease onset and therefore can contribute prognostic signals that augment laboratory tests and clinical features. As such, the impact of genetic-based predictive models on clinical decisions and therapy choice could be profound. However, given that (i) medical traits result from a complex interplay between genetic and environmental factors, (ii) the underlying genetic architectures for susceptibility to common diseases are not well-understood, and (iii) replicable susceptibility alleles, in combination, account for only a moderate amount of disease heritability, there are substantial challenges to constructing and implementing genetic risk prediction models with high utility. In spite of these challenges, concerted progress has continued in this area with an ongoing accumulation of studies that identify disease predisposing genotypes. Several statistical approaches with the aim of predicting disease have been published. Here we summarize the current state of disease susceptibility mapping and pharmacogenetics efforts for risk prediction, describe methods used to construct and evaluate genetic-based predictive models, and discuss applications.

Changes in Gut and Plasma Microbiome following Exercise Challenge in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS)

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a disease characterized by intense and debilitating fatigue not due to physical activity that has persisted for at least 6 months, post-exertional malaise, unrefreshing sleep, and accompanied by a number of secondary symptoms, including sore throat, memory and concentration impairment, headache, and muscle/joint pain. In patients with post-exertional malaise, significant worsening of symptoms occurs following physical exertion and exercise challenge serves as a useful method for identifying biomarkers for exertion intolerance. Evidence suggests that intestinal dysbiosis and systemic responses to gut microorganisms may play a role in the symptomology of ME/CFS. As such, we hypothesized that post-exertion worsening of ME/CFS symptoms could be due to increased bacterial translocation from the intestine into the systemic circulation. To test this hypothesis, we collected symptom reports and blood and stool samples from ten clinically characterized ME/CFS patients and ten matched healthy controls before and 15 minutes, 48 hours, and 72 hours after a maximal exercise challenge. Microbiomes of blood and stool samples were examined. Stool sample microbiomes differed between ME/CFS patients and healthy controls in the abundance of several major bacterial phyla. Following maximal exercise challenge, there was an increase in relative abundance of 6 of the 9 major bacterial phyla/genera in ME/CFS patients from baseline to 72 hours post-exercise compared to only 2 of the 9 phyla/genera in controls (p = 0.005). There was also a significant difference in clearance of specific bacterial phyla from blood following exercise with high levels of bacterial sequences maintained at 72 hours post-exercise in ME/CFS patients versus clearance in the controls. These results provide evidence for a systemic effect of an altered gut microbiome in ME/CFS patients compared to controls. Upon exercise challenge, there were significant changes in the abundance of major bacterial phyla in the gut in ME/CFS patients not observed in healthy controls. In addition, compared to controls clearance of bacteria from the blood was delayed in ME/CFS patients following exercise. These findings suggest a role for an altered gut microbiome and increased bacterial translocation following exercise in ME/CFS patients that may account for the profound post-exertional malaise experienced by ME/CFS patients.

Phenome-wide association studies (PheWASs) for functional variants

The genome-wide association study (GWAS) is a powerful approach for studying the genetic complexities of human disease. Unfortunately, GWASs often fail to identify clinically significant associations and describing function can be a challenge. GWAS is a phenotype-to-genotype approach. It is now possible to conduct a converse genotype-to-phenotype approach using extensive electronic medical records to define a phenome. This approach associates a single genetic variant with many phenotypes across the phenome and is called a phenome-wide association study (PheWAS). The majority of PheWASs conducted have focused on variants identified previously by GWASs. This approach has been efficient for rediscovering gene–disease associations while also identifying pleiotropic effects for some single-nucleotide polymorphisms (SNPs). However, the use of SNPs identified by GWAS in a PheWAS is limited by the inherent properties of the GWAS SNPs, including weak effect sizes and difficulty when translating discoveries to function. To address these challenges, we conducted a PheWAS on 105 presumed functional stop-gain and stop-loss variants genotyped on 4235 Marshfield Clinic patients. Associations were validated on an additional 10 640 Marshfield Clinic patients. PheWAS results indicate that a nonsense variant in ARMS2 (rs2736911) is associated with age-related macular degeneration (AMD). These results demonstrate that focusing on functional variants may be an effective approach when conducting a PheWAS.

Genome wide association study of SNP-, gene-, and pathway-based approaches to identify genes influencing susceptibility to Staphylococcus aureus infections

Background: We conducted a genome-wide association study (GWAS) to identify specific genetic variants that underlie susceptibility to diseases caused by Staphylococcus aureus in humans. Methods: Cases (n = 309) and controls (n = 2925) were genotyped at 508,921 single nucleotide polymorphisms (SNPs). Cases had at least one laboratory and clinician confirmed disease caused by S. aureus whereas controls did not. R-package (for SNP association), EIGENSOFT (to estimate and adjust for population stratification) and gene- (VEGAS) and pathway-based (DAVID, PANTHER, and Ingenuity Pathway Analysis) analyses were performed. Results: No SNP reached genome-wide significance. Four SNPs exceeded the p < 10−5 threshold including two (rs2455012 and rs7152530) reaching a p-value < 10−7. The nearby genes were PDE4B (rs2455012), TXNRD2 (rs3804047), VRK1 and BCL11B (rs7152530), and PNPLA5 (rs470093). The top two findings from the gene-based analysis were NMRK2 (pgene = 1.20E-05), which codes an integrin binding molecule (focal adhesion), and DAPK3 (pgene = 5.10E-05), a serine/threonine kinase (apoptosis and cytokinesis). The pathway analyses identified epithelial cell responses to mechanical and non-mechanical stress. Conclusion: We identified potential susceptibility genes for S. aureus diseases in this preliminary study but confirmation by other studies is needed. The observed associations could be relevant given the complexity of S. aureus as a pathogen and its ability to exploit multiple biological pathways to cause infections in humans.

Complex host genetic susceptibility to Staphylococcus aureus infections

Understanding of the host genetic susceptibility to carriage of, and infections, due to Staphylococcus aureus, a nosocomial pathogen, is still in its nascent stages. Mouse models show variable susceptibility to S. aureus infections across mouse strains and disease models with roles for signaling pathways involving Toll-like receptors (TLR-1, TLR-2, and TLR-6), interleukins (IL-4, IL-6, IL-10, and IL-13), chemokines [CXC ligand (CXCL)-1 and CXCL-2], and T helper (Th)1/Th2 responses. Genome-wide association studies (GWASs) for carriage in humans identified SNPs in IL4, DEFB1, CRP, and VDR for persistent nasal carriage. NR3C1 haplotypes may either enhance risk or provide protection from colonization. GWASs for all S. aureus diseases have suggested roles for DAPK3, a kinase, and XRN1, a nuclease, while CDON could have a role in complicated bacteremia. More studies are needed to identify host susceptibility genes along with confirmation from functional assays.

Mining Retrospective Data for Virtual Prospective Drug Repurposing: L-DOPA and Age-related Macular Degeneration

BACKGROUND Age-related macular degeneration (AMD) is a leading cause of visual loss among the elderly. A key cell type involved in AMD, the retinal pigment epithelium, expresses a G protein–coupled receptor that, in response to its ligand, L-DOPA, up-regulates pigment epithelia–derived factor, while down-regulating vascular endothelial growth factor. In this study we investigated the potential relationship between L-DOPA and AMD. METHODS We used retrospective analysis to compare the incidence of AMD between patients taking vs not taking L-DOPA. We analyzed 2 separate cohorts of patients with extensive medical records from the Marshfield Clinic (approximately 17,000 and approximately 20,000) and the Truven MarketScan outpatient and databases (approximately 87 million) patients. We used International Classification of Diseases, 9th Revision codes to identify AMD diagnoses and L-DOPA prescriptions to determine the relative risk of developing AMD and age of onset with or without an L-DOPA prescription. RESULTS In the retrospective analysis of patients without an L-DOPA prescription, AMD age of onset was 71.2, 71.3, and 71.3 in 3 independent retrospective cohorts. Age-related macular degeneration occurred significantly later in patients with an L-DOPA prescription, 79.4 in all cohorts. The odds ratio of developing AMD was also significantly negatively correlated by L-DOPA (odds ratio 0.78; confidence interval, 0.76–0.80; P <.001). Similar results were observed for neovascular AMD (P <.001). CONCLUSIONS Exogenous L-DOPA was protective against AMD. L-DOPA is normally produced in pigmented tissues, such as the retinal pigment epithelium, as a byproduct of melanin synthesis by tyrosinase. GPR143 is the only known L-DOPA receptor; it is therefore plausible that GPR143 may be a fruitful target to combat this devastating disease.

SeqHBase: a big data toolset for family based sequencing data analysis

Background Whole-genome sequencing (WGS) and whole-exome sequencing (WES) technologies are increasingly used to identify disease-contributing mutations in human genomic studies. It can be a significant challenge to process such data, especially when a large family or cohort is sequenced. Our objective was to develop a big data toolset to efficiently manipulate genome-wide variants, functional annotations and coverage, together with conducting family based sequencing data analysis. Methods Hadoop is a framework for reliable, scalable, distributed processing of large data sets using MapReduce programming models. Based on Hadoop and HBase, we developed SeqHBase, a big data-based toolset for analysing family based sequencing data to detect de novo, inherited homozygous, or compound heterozygous mutations that may contribute to disease manifestations. SeqHBase takes as input BAM files (for coverage at every site), variant call format (VCF) files (for variant calls) and functional annotations (for variant prioritisation). Results We applied SeqHBase to a 5-member nuclear family and a 10-member 3-generation family with WGS data, as well as a 4-member nuclear family with WES data. Analysis times were almost linearly scalable with number of data nodes. With 20 data nodes, SeqHBase took about 5 secs to analyse WES familial data and approximately 1 min to analyse WGS familial data. Conclusions These results demonstrate SeqHBases high efficiency and scalability, which is necessary as WGS and WES are rapidly becoming standard methods to study the genetics of familial disorders.

Differential Lipid Response to Statins Is Associated With Variants in the BUD13-APOA5 Gene Region.

Abstract: Genetic variants within the BUD13–APOA5 gene region are known to be associated with high-density lipoprotein cholesterol (HDL-C) and triglyceride (TG) levels. Recent studies suggest that single nucleotide polymorphisms (SNPs) within this region affect HDL-C response to statin–fibrate combination therapy and low-density lipoprotein cholesterol (LDL-C) response to statin therapy. We hypothesized that SNPs within the BUD13–APOA5 region are associated with TG, HDL-C, and LDL-C response to statin therapy. We examined 1520 observations for 1086 patients from the Personalized Medicine Research Project, a large biorepository at the Marshfield Clinic Research Foundation, who had received statin therapy and been previously genotyped for polymorphisms in the 11q23 chromosomal region. A significant differential response to statin therapy was observed for 3 SNPs. The minor allele at rs11605293 significantly attenuated TG-lowering response to pravastatin (P = 0.000159), whereas the minor allele at rs12806755 was associated with a similar response to lovastatin (P = 0.000192). Genotypes at rs947990 significantly attenuated LDL-C reduction to atorvastatin therapy (P = 0.000668) with some patients with the minor allele having LDL-C increase after therapy. No SNPs within the BUD13–APOA5 region were associated with a significant effect on HDL-C reduction in response to statin therapy. In conclusion, this study suggests that common SNPs within the BUD13–APOA5 can affect TG and LDL-C response to statin therapy in a North American population.

Genetic evidence of PTPN22 effects on chronic lymphocytic leukemia

To the editor: The recent article by Negro et al represents a thorough investigation into the effect of Lyp, the gene product of PTPN22 , on BCR signaling in the pathogenesis and progression of chronic lymphocytic leukemia (CLL).[1][1] This study shows that increased Lyp enhances Akt, a serine/