Biology subjects

Dennis, J.

Publications and source records attributed to Dennis, J..

4 recordsLinked to original sources

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↗