Biology subjects

Dennis, J.

Publications and source records attributed to Dennis, J..

18 recordsLinked to original sources

bioRxiv2025

Variant effects depend on polygenic background: experimental, clinical, and evolutionary implications

Both rare and common genetic variants contribute to human disease, and emerging evidence suggests that they combine additively to influence disease liability. However, the non-linear relationship between disease liability and disease prevalence means that risk variants may have more severe phenotypic consequences in high-risk polygenic backgrounds and minimal impact in low-risk backgrounds, resulting in uneven selection across the population. As a result, selection coefficients may be better modeled as distributions that differ across populations, time, environments, and individuals rather than single values. Further, the number of genes contributing to a trait and epistasis between alleles enhance negative selection due to the increased variance pushing more individuals to phenotypic extremes. Because disease-relevant phenotypes may be masked in certain genetic backgrounds, polygenic background should be considered when characterizing the molecular underpinnings of complex traits.

genomics↗

bioRxiv2022

Heparins enhance C1 esterase inhibitor activity: a promising remedy for acute hereditary angioedema

RationaleHereditary angioedema (HAE) is a potentially life-threatening illness most commonly due to deficiency or dysfunction of C1-esterase inhibitor (C1-INH). While specific treatments are available to thwart acute exacerbations, they are extremely costly and some can be associated with rare but serious side effects. The heparins are long known to augment C1-INH activity and case reports / series have documented their efficacy in treating HAE. Objectiveto determine if unfractionated heparin and two low-molecular weight heparins (enoxaparin and nadroparin) can augment C1-INH activity ex vivo in the sera of patients with HAE and in an in vitro biochemical assay. MethodsC1-INH activity in the absence or presence of the heparin formulations were analyzed by two different methods. To measure C1-INH activity ex vivo, a commercially available assay was utilized with patient sera, excess amounts of C1s, and a substrate of C1s which, upon cleavage by C1s, produces a chromogenic product. To determine biochemically the C1-INH activity in vitro, a pharmacologic grade C1-INH, recombinant C1s (C1s-CCP12SP), and a peptide substrate of C1s were employed. Microscale thermophoresis was used to determine whether C1-INH binds to heparin. Main resultsin patient sera, nadroparin was superior to enoxaparin and unfractionated heparin in augmenting C1-INH activity, followed by enoxaparin and then unfractionated heparin. In the in vitro biochemical assay, all three heparins augmented C1-INH-C1s binding linearly in a dose-dependent fashion. Microscale thermophoresis assay demonstrated that nadroparin binds to C1-INH, providing a mechanism by which heparin facilitates the interaction between C1-INH and the proteases known to produce bradykinin, the mediator of HAE. Conclusionlow-molecular weight heparin augments C1-INH activity and should be studied as a potential treatment for acute HAE.

pharmacology and toxicology↗

bioRxiv2021

Rare copy number variants (CNVs) and breast cancer risk

BackgroundCopy number variants (CNVs) are pervasive in the human genome but potential disease associations with rare CNVs have not been comprehensively assessed in large datasets. We analysed rare CNVs in genes and non-coding regions for 86,788 breast cancer cases and 76,122 controls of European ancestry with genome-wide array data. ResultsGene burden tests detected the strongest association for deletions in BRCA1 (P= 3.7E-18). Nine other genes were associated with a p-value < 0.01 including known susceptibility genes CHEK2 (P= 0.0008), ATM (P= 0.002) and BRCA2 (P= 0.008). Outside the known genes we detected associations with p-values < 0.001 for either overall or subtype-specific breast cancer at nine deletion regions and four duplication regions. Three of the deletion regions were in established common susceptibility loci. ConclusionsThis is the first genome-wide analysis of rare CNVs in a large breast cancer case-control dataset. We detected associations with exonic deletions in established breast cancer susceptibility genes. We also detected suggestive associations with non-coding CNVs in known and novel loci with large effects sizes. Larger sample sizes will be required to reach robust levels of statistical significance.

genetics↗

bioRxiv2020

Glioma stem cells invasive phenotype at optimal stiffness is driven by MGAT5 dependent mechanosensing.

Glioblastomas stem-like cells (GSCs) by invading the brain parenchyma escape resection and radiotherapy. GSC invasion is associated with altered N-glycosylation pattern of integrins and other transmembrane proteins resulting in changed mechanosensing but details are elusive. Because the tumour microenvironment has an increased stiffness we studied the interaction between matrix stiffness, N-glycosylation and GSC migration. To mimic the fibrillar microenvironments, we designed 3D-ex-polyacrylonitrile nanofibers scaffolds (NFS) with adjustable stiffnesses by loading multiwall carbon nanotubes (MWCNT). We found that migration of GSCs was maximum at 166 kPa. Migration rate was correlated with cell shape, expression of focal adhesion (FA), Epithelial to Mesenchymal Transition (EMT) proteins and ({beta}1,6) branched N-glycan binding, galectin-3. Mutation of MGAT5 in GSC inhibited N-glycans ({beta}1-6) branching, suppressed the stiffness dependence of FA and EMT protein expression as well as migration on 166kPa NFS; underpinning the role of multibranched N-glycans as a critical regulator of mechanotransduction by GSC. Significance StatementDuring pathological processes in which cell migration is involved, cells undergo important functional changes in protein glycosylation and are responsive to environmental mechanical modifications. We addressed the question of the glycosylation role in mechanotransduction regulation of glioma stem cells. We created a bio-inspired 3D nanofiber scaffold (NFS) loaded with multiwall carbon nanotubes to obtain NFS of adjustable stiffness in physiological and pathological ranges. We highlighted and described a mechanism of fine mechanotransduction leading to a nonlinear migration response regarding to 3D microenvironment stiffness values. We show the importance to develop mechano-pharmacology as new therapeutic target by demonstrating the relationship existing between environmental stiffness and multibranched N-glycans catalysed by the MGAT5 enzyme to optimize directed migration.

cell biology↗

bioRxiv2020

The role of sodium thiocyanate supplementation during dextran sodium sulphate-stimulated experimental colitis

Ulcerative colitis is a condition characterised by the infiltration of leukocytes into the gastrointestinal wall. Leukocyte-MPO catalyses hypochlorous acid (HOCl) and hypothiocyanous acid (HOSCN) formation from chloride (Cl-) and thiocyanous (SCN-) anions, respectively. While HOCl indiscriminately oxidises biomolecules, HOSCN primarily targets low-molecular weight protein thiols. Oxidative damage mediated by HOSCN may be reversible, potentially decreasing MPO-associated host tissue destruction. This study investigated the effect of SCN- supplementation in a model of acute colitis. Female mice were supplemented dextran sodium sulphate (DSS, 3% w/v) in the presence of 10 mM Cl- or SCN- in drinking water ad libitum, or with salts (NaCl and NaSCN only) or water only (controls). Behavioural studies showed mice tolerated NaSCN and NaCl-treated water with water-seeking frequency. Ion-exchange chromatography showed increased fecal and plasma SCN- levels in thiocyanate supplemented mice; plasma SCN- reached similar fold-increase for smokers. Overall there was no difference in weight loss and clinical score, mucin levels, crypt integrity and extent of cellular infiltration between DSS/SCN- and DSS/Cl- groups. Neutrophil recruitment remained unchanged in DSS-treated mice, as assessed by fecal calprotectin levels. Total thiol and tyrosine phosphatase activity remained unchanged between DSS/Cl- and DSS/SCN- groups, however, colonic tissue showed a trend in decreased 3-chlorotyrosine (1.5-fold reduction, p<0.051) and marked increase in colonic GCLC, the rate-limiting enzyme in glutathione synthesis. These data suggest that SCN- administration can modulate MPO activity towards a HOSCN-specific pathway, however, this does not alter the development of colitis within a DSS murine model.HighlightsSodium thiocyanate (SCN-) supplementation increased plasma and fecal SCN- level.Thiocyanate supplementation diverted HOCl-mediated oxidative damage in mice colon.Thiocyanate supplementation stimulated thiol synthesis in the DSS colitis model.Thiocyanate provides no protection in an acute experimental model of UC.Competing Interest StatementThe authors have declared no competing interest.AbbreviationsCDCrohn’s Disease3-Cl-Tyr3-chlorotyrosineDSSDextran Sodium SulphateGAPDHGlyceraldehyde 3-phosphate dehydrogenaseGCLCGlutamate-Cysteine Ligase Catalytic SubunitGIGastrointestinalGSHGlutathioneGSSGGlutathione DisulfideHOBrHypobromous AcidHOClHypochlorous AcidHCN-Hydrogen; CyanideHOSCNHypothiocyanous AcidIBDInflammatory Bowel DiseaseMPOMyeloperoxidaseNrf2Nuclear factor erythroid 2-related Factor 2PMNsPolymorphonuclear NeutrophilsPTPProtein Tyrosine PhosphatasesROSReactive Oxygen SpeciesSCN-ThiocyanateUCUlcerative ColitisView Full Text

pathology↗

bioRxiv2020

Detecting rare copy number variants (CNVs) from Illumina genotyping arrays with the CamCNV pipeline: segmentation of z-scores improves detection and reliability.

BackgroundThe intensities from genotyping array data can be used to detect CNVs but a high level of noise in the data and overlap between different copy-number intensity distributions produces unreliable calls, particularly when only a few probes are covered by the CNV. ResultsWe present a novel pipeline (CamCNV) with a series of steps to reduce noise and detect more reliably rare CNVs covering as few as three probes. The method uses the information from all samples to convert intensities to z-scores, thus adjusting for variance between probes. We tested the sensitivity of our pipeline by looking for known CNVs from the 1000 Genomes project in our genotyping of 1000 Genomes samples. We also compared the CNV calls for 1,661 pairs of genotyped replicate samples. At the chosen mean z-score cut-off, sensitivity to detect the 1000 Genomes CNVs was approximately 85% for deletions and 65% for duplications. From the replicates we estimate the false discovery rate is controlled at [~]10% for deletions (falling to below 3% with more than five probes) and [~]28% for duplications. The pipeline demonstrates improved sensitivity when compared to calling with PennCNV, particularly for short deletions covering only a few probes ConclusionThe CamCNV pipeline provides a reliable method of detecting rare CNVs from Illumina array data and can be used for CNVs that only cover a few probes. For each called CNV the mean z-score is a useful metric for controlling the false discovery rate.

bioinformatics↗

bioRxiv2019

A/T/N polygenic risk score for cognitive decline in old age

INTRODUCTIONWe developed a novel polygenic risk score (PRS) based on the A/T/N (amyloid plaques (A), phosphorylated tau tangles (T), and neurodegeneration (N)) framework and compared a PRS based on clinical AD diagnosis to assess which was a better predictor of cognitive decline. METHODSWe used summary statistics from genome wide association studies of cerebrospinal fluid amyloid-{beta} (A{beta}42) and phosphorylated-tau (ptau181), left hippocampal volume (LHIPV), and late-onset AD dementia to calculate PRS for 1181 participants in the Alzheimers Disease Neuroimaging Initiative (ADNI). Individual PRS were averaged to generate a composite A/T/N PRS. We assessed the association of PRS with baseline and longitudinal cognitive composites of executive function and memory. RESULTSThe A/T/N PRS showed superior predictive performance on AD biomarkers and executive function decline compared to the clinical AD PRS. DISCUSSIONResults suggest that integration of genetic risk across AD biomarkers may improve prediction of disease progression. Research in ContextO_ST_ABSSystematic ReviewC_ST_ABSAuthors reviewed relevant literature using PubMed and Google Scholar. Key studies that generated and validated polygenic risk scores (PRS) for clinical and pathologic AD were cited. PRS scores have been increasingly used in the literature but clinical utility continues to be questioned. InterpretationIn the current research landscape concerning PRS clinical utility in the AD space, there is room for model improvement and our hypothesis was that a PRS with integrated risk for AD biomarkers could yield a better model for cognitive decline. Future DirectionsThis study serves as proof-of-concept that encourages future study of integrated PRS across disease markers and utility in taking an A/T/N (amyloidosis, tauopathy and neurodegeneration) focused approach to genetic risk for cognitive decline and AD.

genomics↗

bioRxiv2019

Genome-wide association study identifies 32 novel breast cancer susceptibility loci from overall and subtype-specific analyses

Breast cancer susceptibility variants frequently show heterogeneity in associations by tumor subtype. To identify novel loci, we performed a genome-wide association study (GWAS) including 133,384 breast cancer cases and 113,789 controls, plus 18,908 BRCA1 mutation carriers (9,414 with breast cancer) of European ancestry, using both standard and novel methodologies that account for underlying tumor heterogeneity by estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status and tumor grade. We identified 32 novel susceptibility loci (P<5.0x10-8), 15 of which showed evidence for associations with at least one tumor feature (false discovery rate <0.05). Five loci showed associations (P<0.05) in opposite directions between luminal- and non-luminal subtypes. In-silico analyses showed these five loci contained cell-specific enhancers that differed between normal luminal and basal mammary cells. The genetic correlations between five intrinsic-like subtypes ranged from 0.35 to 0.80. The proportion of genome-wide chip heritability explained by all known susceptibility loci was 37.6% for triple-negative and 54.2% for luminal A-like disease. These findings provide an improved understanding of genetic predisposition to breast cancer subtypes and will inform the development of subtype-specific polygenic risk scores.

genetics↗

bioRxiv2019

Common breast cancer risk loci predispose to distinct tumor subtypes

BackgroundGenome-wide association studies (GWAS) have identified multiple common breast cancer susceptibility variants. Many of these variants have differential associations by estrogen receptor (ER), but how these variants relate with other tumor features and intrinsic molecular subtypes is unclear. MethodsAmong 106,571 invasive breast cancer cases and 95,762 controls of European ancestry with data on 173 breast cancer variants identified in previous GWAS, we used novel two-stage polytomous logistic regression models to evaluate variants in relation to multiple tumor features (ER, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and grade) adjusting for each other, and to intrinsic-like subtypes. ResultsEighty-five of 173 variants were associated with at least one tumor feature (false discovery rate <5%), most commonly ER and grade, followed by PR and HER2. Models for intrinsic-like subtypes found nearly all of these variants (83 of 85) associated at P<0.05 with risk for at least one luminal-like subtype, and approximately half (41 of 85) of the variants were associated with risk of at least one non-luminal subtype, including 32 variants associated with triple-negative (TN) disease. Ten variants were associated with risk of all subtypes in different magnitude. Five variants were associated with risk of luminal A-like and TN subtypes in opposite directions. ConclusionThis report demonstrates a high level of complexity in the etiology heterogeneity of breast cancer susceptibility variants and can inform investigations of subtype-specific risk prediction.

genetics↗

bioRxiv2019

Assessment of Polygenic Architecture and Risk Prediction based on Common Variants Across Fourteen Cancers

We analyzed summary-level data from genome-wide association studies (GWAS) of European ancestry across fourteen cancer sites to estimate the number of common susceptibility variants (polygenicity) contributing to risk, as well as the distribution of their associated effect sizes. All cancers evaluated showed polygenicity, involving at a minimum thousands of independent susceptibility variants. For some malignancies, particularly chronic lymphoid leukemia (CLL) and testicular cancer, there are a larger proportion of variants with larger effect sizes than those for other cancers. In contrast, most variants for lung and breast cancers have very small associated effect sizes. For different cancer sites, we estimate a wide range of GWAS sample sizes, required to explain 80% of GWAS heritability, varying from 60,000 cases for CLL to over 1,000,000 cases for lung cancer. The maximum relative risk achievable for subjects at the 99th risk percentile of underlying polygenic risk scores, compared to average risk, ranges from 12 for testicular to 2.5 for ovarian cancer. We show that polygenic risk scores have substantial potential for risk stratification for relatively common cancers such as breast, prostate and colon, but limited potential for other cancer sites because of modest heritability and lower disease incidence.

genetics↗

bioRxiv2019

Fine-mapping of 150 breast cancer risk regions identifies 178 high confidence target genes

Genome-wide association studies have identified breast cancer risk variants in over 150 genomic regions, but the mechanisms underlying risk remain largely unknown. These regions were explored by combining association analysis with in silico genomic feature annotations. We defined 205 independent risk-associated signals with the set of credible causal variants (CCVs) in each one. In parallel, we used a Bayesian approach (PAINTOR) that combines genetic association, linkage disequilibrium, and enriched genomic features to determine variants with high posterior probabilities (HPPs) of being causal. Potentially causal variants were significantly over-represented in active gene regulatory regions and transcription factor binding sites. We applied our INQUSIT pipeline for prioritizing genes as targets of potentially causal variants, using gene expression (eQTL), chromatin interaction and functional annotations. Known cancer drivers, transcription factors and genes in the developmental, apoptosis, immune system and DNA integrity checkpoint gene ontology pathways, were over-represented among the 178 highest confidence target genes.

genetics↗

bioRxiv2019

Genetic predisposition to mosaic Y chromosome loss in blood is associated with genomic instability in other tissues and susceptibility to non-haematological cancers

Mosaic loss of chromosome Y (LOY) in circulating white blood cells is the most common form of clonal mosaicism, yet our knowledge of the causes and consequences of this is limited. Using a newly developed approach, we estimate that 20% of the UK Biobank male population (N=205,011) has detectable LOY. We identify 156 autosomal genetic determinants of LOY, which we replicate in 757,114 men of European and Japanese ancestry. These loci highlight genes involved in cell-cycle regulation, cancer susceptibility, somatic drivers of tumour growth and cancer therapy targets. Genetic susceptibility to LOY is associated with non-haematological health outcomes in both men and women, supporting the hypothesis that clonal haematopoiesis is a biomarker of genome instability in other tissues. Single-cell RNA sequencing identifies dysregulated autosomal gene expression in leukocytes with LOY, providing insights into how LOY may confer cellular growth advantage. Collectively, these data highlight the utility of studying clonal mosaicism to uncover fundamental mechanisms underlying cancer and other ageing-related diseases.

genetics↗

bioRxiv2019

Genetic risk for major depressive disorder and loneliness in gender-specific associations with coronary artery disease

ImportanceEpidemiological evidence indicates that major depressive disorder (MDD) and loneliness both reduce life expectancies, but mechanisms underlying the excess morbidity are unclear. Electronic health records (EHRs) linked to genetic data offer new opportunities to address this knowledge gap.\n\nObjectiveTo determine the medical morbidity pattern associated with genetic risk factors for MDD and loneliness, two common psychological traits with adverse health outcomes.\n\nDesignPhenome-wide association study using EHRs spanning 1990 to 2017 from the Vanderbilt University Medical Center biobank, BioVU. Top associations with coronary artery disease (CAD) were replicated in the Atherosclerosis Risk in Communities (ARIC) cohort.\n\nSettingHospital-based EHR study, with replication in a population-based cohort study.\n\nParticipants18,385 genotyped adult patients in BioVU. Replication in ARIC included 7,197 genotyped participants. All participants were of European ancestry.\n\nExposuresPolygenic scores for MDD and loneliness were developed for each individual using previously published meta-GWAS summary statistics.\n\nMain Outcomes and MeasuresThe phenome-wide association study included 882 clinical diagnoses ascertained via billing codes in the EHR. ARIC included 1598 incident CAD cases.\n\nResultsBioVU patients had a median EHR length of 9.91 years. In the phenome-wide association study, polygenic scores for MDD and loneliness were significantly associated with psychiatric and cardiac phenotypes. Targeted analyses of CAD in 3,893 cases and 4,197 controls in BioVU found odds ratios of 1.11 (95% CI, 1.04-1.18; P=8.43x10-4) and 1.13 (95% CI, 1.07-1.20; P=4.51x10-6) per 1-SD increase in the polygenic scores for MDD and loneliness, respectively. Comparable hazard ratios in ARIC were 1.07 (95% CI, 0.99-1.14; P=0.07) and 1.07 (1.01-1.15; P=0.03). Across both studies, the increased risk persisted in women after adjusting for multiple conventional risk factors, a polygenic score for CAD, and psychiatric symptoms (available in BioVU). Controlling for genetic risk factors shared between MDD and loneliness, the polygenic score for loneliness conditioned on MDD remained associated with CAD risk, but the polygenic score for MDD conditioned on loneliness did not.\n\nConclusions and RelevanceGenetic risk factors for MDD and loneliness act pleiotropically to increase CAD risk in women. Continued research into the biological and clinical connections between the heart and mind is warranted.

genetics↗

bioRxiv2018

Phenome-wide investigation of health outcomes associated with genetic predisposition to loneliness

AO_SCPLOWBSTRACTC_SCPLOWHumans are social animals that experience intense suffering when they perceive a lack of social connection. Modern societies are experiencing an epidemic of loneliness. While the experience of loneliness is universally human, some people report experiencing greater loneliness than others. Loneliness is more strongly associated with mortality than obesity, emphasizing the need to understand the nature of the relationship between loneliness and health. While it is intuitive that circumstantial factors such as marital status and age influence loneliness, there is also compelling evidence of a genetic predisposition towards loneliness. To better understand the genetic architecture of loneliness and its relationship with associated outcomes, we conducted a genome-wide association (GWAS) meta-analysis of loneliness (N=475,661), report 12 associated loci (two novel) and significant genetic correlations with 34 other complex traits. The polygenic basis for loneliness was significantly enriched for evolutionary constrained genes and genes expressed in specific brain tissues: (frontal) cortex, cerebellum, anterior cingulate cortex, and substantia nigra. We built polygenic scores based on this GWAS meta-analysis to explore the genetic association between loneliness and health outcomes in an independent sample of 18,498 individuals for whom electronic health records were available. A genetic predisposition towards loneliness predicted cardiovascular, psychiatric, and metabolic disorders, and triglycerides and high-density lipoproteins. Mendelian randomization analyses showed evidence of a causal, increasing, effect of body fat on loneliness, and a similar weaker causal effect of BMI. Our results provide a framework for ongoing studies of the genetic basis of loneliness and its role in mental and physical health.

genetics↗

bioRxiv2018

Shared heritability and functional enrichment across six solid cancers

Quantifying the genetic correlation between cancers can provide important insights into the mechanisms driving cancer etiology. Using genome-wide association study summary statistics across six cancer types based on a total of 296,215 cases and 301,319 controls of European ancestry, we estimate the pair-wise genetic correlations between breast, colorectal, head/neck, lung, ovary and prostate cancer, and between cancers and 38 other diseases. We observed statistically significant genetic correlations between lung and head/neck cancer (rg=0.57, p=4.6x10-8), breast and ovarian cancer (rg=0.24, p=7x10-5), breast and lung cancer (rg=0.18, p=1.5x10-6) and breast and colorectal cancer (rg=0.15, p=1.1x10-4). We also found that multiple cancers are genetically correlated with non-cancer traits including smoking, psychiatric diseases and metabolic characteristics. Functional enrichment analysis revealed a significant excess contribution of conserved and regulatory regions to cancer heritability. Our comprehensive analysis of cross-cancer heritability suggests that solid tumors arising across tissues share in part a common germline genetic basis.

genomics↗

bioRxiv2018

Penetrance and pleiotropy of polygenic risk scores for schizophrenia in 90,000 patients across three healthcare systems

BACKGROUNDIndividuals at high risk schizophrenia may benefit from early intervention but few validated risk predictors are available. Genetic profiling is one approach to risk stratification that has been extensively validated in research cohorts, but its utility in clinical settings remains largely unexplored. Moreover, the broad health consequences of a high genetic risk of schizophrenia are poorly understood, despite being highly relevant to treatment decisions.\n\nMETHODSWe used electronic health records of 91,980 patients from three large healthcare systems to evaluate the penetrance and pleiotropy of genetic risk for schizophrenia. Polygenic risk scores (PRSs) for schizophrenia were calculated from meta-analysis summary statistics and tested for association with schizophrenia diagnostic codes and 1338 code-defined disease categories in a phenome-wide association study. Effect estimates were meta-analyzed across sites, and follow-up analyses evaluated the effect of a schizophrenia diagnosis.\n\nRESULTSPRSs were robustly associated with schizophrenia (odds ratio per standard deviation increase in PRS = 1.65 [95% confidence interval (CI), 1.5-1.8], p = 1.25 x 10-16) and patients in the highest risk decile of the PRS distribution had a four-fold increased odds of schizophrenia compared to those in the bottom decile (95% CI, 2.4-6.5, p = 4.43 x 10-8). PRSs were also associated with other psychiatric phenotypes, including anxiety disorders, bipolar disorder, depression, substance use disorders, personality disorders, and suicidal behavior. Non-psychiatric associations included heart palpitations, urinary syndromes, obesity, and nonspecific somatic symptoms. Most associations remained significant when conditioning on a diagnosis of schizophrenia, indicating genetic pleiotropy.\n\nCONCLUSIONSWe demonstrate that an available measure of genetic risk for schizophrenia is robustly associated with schizophrenia in healthcare settings and has pleiotropic effects on related psychiatric disorders as well as other medical symptoms and syndromes. Our results provide an initial indication of the opportunities and limitations that may arise with the future application of PRS testing in healthcare systems.

genetics↗

bioRxiv2018

Diagnostic algorithms to study post-concussion syndrome using electronic health records: validating a method to capture an important patient population

IntroductionPost-concussion syndrome (PCS) is characterized by persistent cognitive, somatic, and emotional symptoms after a mild traumatic brain injury (mTBI). Genetic and other biological variables may contribute to PCS etiology, and the emergence of biobanks linked to electronic health records (EHR) offers new opportunities for research on PCS. We sought to validate the use of EHR data of PCS patients by comparing two diagnostic algorithms.\n\nMethodsVanderbilt University Medical Center curates a de-identified database of 2.8 million patient EHR. We developed two EHR-based algorithmic approaches that identified individuals with PCS by: (i) natural language processing (NLP) of narrative text in the EHR combined with structured demographic, diagnostic, and encounter data; or (ii) coded billing and procedure data. The predictive value of each algorithm was assessed, and cases and controls identified by each approach were compared on demographic and medical characteristics.\n\nResultsFirst, the NLP algorithm identified 507 cases and 10,857 controls. The positive predictive value (PPV) in the cases was 82% and the negative predictive value in the controls was 78%. Second, the coded algorithm identified 1,142 patients with two or more PCS billing codes and had a PPV of 76%. Comparisons of PCS controls to both case groups recovered known epidemiology of PCS: cases were more likely than controls to be female and to have pre-morbid diagnoses of anxiety, migraine, and PTSD. In contrast, controls and cases were equally likely to have ADHD and learning disabilities, in accordance with the findings of recent systematic reviews of PCS risk factors.\n\nConclusionsEHR are a valuable research tool for PCS. Ascertainment based on coded data alone had a predictive value comparable to an NLP algorithm, recovered known PCS risk factors, and maximized the number of included patients.

epidemiology↗

bioRxiv2017

iSeg: an efficient algorithm for segmentation of genomic and epigenomic data

BackgroundIdentification of functional elements of a genome often requires dividing a sequence of measurements along a genome into segments where adjacent segments have different properties, such as different mean values. This problem is often called the segmentation problem in the field of genomics, and the change-point problem in other scientific disciplines. Despite dozens of algorithms developed to address this problem in genomics research, methods with improved accuracy and speed are still needed to effectively tackle both existing and emerging genomic and epigenomic segmentation problems.\n\nResultsWe designed an efficient algorithm, called iSeg, for segmentation of genomic and epigenomic profiles. iSeg first utilizes dynamic programming to identify candidate segments and test for significance. It then uses a novel data structure based on two coupled balanced binary trees to detect overlapping significant segments and update them simultaneously during searching and refinement stages. Refinement and merging of significant segments are performed at the end to generate the final set of segments. By using an objective function based on the p-values of the segments, the algorithm can serve as a general computational framework to be combined with different assumptions on the distributions of the data. As a general segmentation method, it can segment different types of genomic and epigenomic data, such as DNA copy number variation, nucleosome occupancy, nuclease sensitivity, and differential nuclease sensitivity data. Using simple t-tests to compute p-values across multiple datasets of different types, we evaluate iSeg using both simulated and experimental datasets and show that it performs satisfactorily when compared with some other popular methods, which often employ more sophisticated statistical models. Implemented in C++, iSeg is also very computationally efficient, well suited for large numbers of input profiles and data with very long sequences.\n\nConclusionsWe have developed an effective and efficient general-purpose segmentation tool for sequential data and illustrated its use in segmentation of genomic and epigenomic profiles.

bioinformatics↗