Biology subjects

Olde Loohuis, L.

Publications and source records attributed to Olde Loohuis, L..

4 recordsLinked to original sources

bioRxiv2021

Powerful eQTL mapping through low coverage RNA sequencing

Mapping genetic variants that regulate gene expression (eQTL mapping) in large-scale RNA sequencing (RNA-seq) studies is often employed to understand functional consequences of regulatory variants. However, the high cost of RNA-Seq limits sample size, sequencing depth, and therefore, discovery power. In this work, we demonstrate that, given a fixed budget, eQTL discovery power can be increased by lowering the sequencing depth per sample and increasing the number of individuals sequenced in the assay. We perform RNA-Seq of whole blood tissue across 1490 individuals at low-coverage (5.9 million reads/sample) and show that the effective power is higher than that of an RNA-Seq study of 570 individuals at high-coverage (13.9 million reads/sample). Next, we leverage synthetic datasets derived from real RNA-Seq data to explore the interplay of coverage and number individuals in eQTL studies, and show that a 10-fold reduction in coverage leads to only a 2.5-fold reduction in statistical power. Our study suggests that lowering coverage while increasing the number of individuals is an effective approach to increase discovery power in RNA-Seq studies.

genomics↗

bioRxiv2019

Distinct and shared contributions of diagnosis and symptom domains to cognitive performance in severe mental illness

BackgroundSevere mental illness (SMI) diagnoses display overlapping symptomatology and shared genetic risk, motivating trans-diagnostic investigations of disease-relevant quantitative measures. We analyzed relationships between neurocognitive performance, symptom domains, and diagnoses, in a large sample of SMI cases (ascertained agnostic to diagnosis) and healthy controls from a single, homogeneous population. Methods2,406 participants (1,689 cases, 717 controls; mean age 39 years, 64% female) were assessed for speed and accuracy using the Penn Computerized Neurocognitive Battery (CNB). Cases carried structured-interview based diagnoses of schizophrenia (SCZ, n=160), bipolar-I (BP-I, n=519), bipolar-II (BP-II, n=204) and major depressive disorder (MDD, n=806). Linear mixed models, using CNB tests as repeated measures, modeled neurocognition as a function of diagnosis, sex, and all interactions. Follow-up analyses, in cases, included symptom factor scores obtained from exploratory factor analysis of symptom data, as main effects. FindingsBP-I and SCZ displayed nearly identical impairments in accuracy and speed, across cognitive domains. BP-II and MDD performed similarly to controls, with subtle deficits in executive and social cognition. A three-factor model (psychosis, mania, and depression) best represented symptom data. Controlling for diagnosis, premorbid IQ, and disease severity, high lifetime psychosis scores were associated with reduced accuracy and speed across cognitive domains, while high depression scores were associated with increased social cognition accuracy. InterpretationTrans-diagnostic investigations demonstrated that neurocognitive function in SMI is characterized by two distinct profiles (BP-I/SCZ and BP-II/MDD), and is associated with specific symptom domains. These results suggest the utility of this design for elucidating SMI causes and trajectories.

neuroscience↗

bioRxiv2018

Transient patterns of functional dysconnectivity in youth with psychosis spectrum symptoms

Psychosis spectrum disorders are conceptualized as neurodevelopmental disorders accompanied by disruption of large-scale functional brain networks. Both static and dynamic dysconnectivity have been described in patients with schizophrenia and, more recently, in help-seeking individuals at clinical high-risk for psychosis. Less is known, however, about developmental aspects of dynamic functional network connectivity (FNC) associated with psychotic symptoms (PS) in the general population. Here, we investigate resting state fMRI data using established dynamic FNC methods in the Philadelphia Neurodevelopmental Cohort (ages 8-22), including 129 participants experiencing PS and 452 participants without PS (non-PS).\n\nApplying a sliding window approach and k-means clustering, 5 dynamic states with distinct whole-brain connectivity patterns were identified. PS-associated dysconnectivity was most prominent in states characterized by synchronization or antagonism of the default mode network (DMN) and cognitive control (CC) domains. Hyperconnectivity between DMN, salience, and CC domains in PS youth only occurred in a state characterized by synchronization of the DMN and CC domains, a state that also becomes less frequent with age. However, dysconnectivity of the sensorimotor and visual systems in PS youth was revealed in other transient states completing the picture of whole-brain dysconnectivity patterns associated with PS.\n\nOverall, state-dependent dysconnectivity was observed in PS youth, providing the first evidence that disruptions of dynamic functional connectivity are present across a broader psychosis continuum.

neuroscience↗

bioRxiv2018

Preoperative predictions of in-hospital mortality using electronic medical record data

BackgroundPredicting preoperative in-hospital mortality using readily-available electronic medical record (EMR) data can aid clinicians in accurately and rapidly determining surgical risk. While previous work has shown that the American Society of Anesthesiologists (ASA) Physical Status Classification is a useful, though subjective, feature for predicting surgical outcomes, obtaining this classification requires a clinician to review the patients medical records. Our goal here is to create an improved risk score using electronic medical records and demonstrate its utility in predicting in-hospital mortality without requiring clinician-derived ASA scores.\n\nMethodsData from 49,513 surgical patients were used to train logistic regression, random forest, and gradient boosted tree classifiers for predicting in-hospital mortality. The features used are readily available before surgery from EMR databases. A gradient boosted tree regression model was trained to impute the ASA Physical Status Classification, and this new, imputed score was included as an additional feature to preoperatively predict in-hospital post-surgical mortality. The preoperative risk prediction was then used as an input feature to a deep neural network (DNN), along with intraoperative features, to predict postoperative in-hospital mortality risk. Performance was measured using the area under the receiver operating characteristic (ROC) curve (AUC).\n\nResultsWe found that the random forest classifier (AUC 0.921, 95%CI 0.908-0.934) outperforms logistic regression (AUC 0.871, 95%CI 0.841-0.900) and gradient boosted trees (AUC 0.897, 95%CI 0.881-0.912) in predicting in-hospital post-surgical mortality. Using logistic regression, the ASA Physical Status Classification score alone had an AUC of 0.865 (95%CI 0.848-0.882). Adding preoperative features to the ASA Physical Status Classification improved the random forest AUC to 0.929 (95%CI 0.915-0.943). Using only automatically obtained preoperative features with no clinician intervention, we found that the random forest model achieved an AUC of 0.921 (95%CI 0.908-0.934). Integrating the preoperative risk prediction into the DNN for postoperative risk prediction results in an AUC of 0.924 (95%CI 0.905-0.941), and with both a preoperative and postoperative risk score for each patient, we were able to show that the mortality risk changes over time.\n\nConclusionsFeatures easily extracted from EMR data can be used to preoperatively predict the risk of in-hospital post-surgical mortality in a fully automated fashion, with accuracy comparable to models trained on features that require clinical expertise. This preoperative risk score can then be compared to the postoperative risk score to show that the risk changes, and therefore should be monitored longitudinally over time.\n\nAuthor summaryRapid, preoperative identification of those patients at highest risk for medical complications is necessary to ensure that limited infrastructure and human resources are directed towards those most likely to benefit. Existing risk scores either lack specificity at the patient level, or utilize the American Society of Anesthesiologists (ASA) physical status classification, which requires a clinician to review the chart. In this manuscript we report on using machine-learning algorithms, specifically random forest, to create a fully automated score that predicts preoperative in-hospital mortality based solely on structured data available at the time of surgery. This score has a higher AUC than both the ASA physical status score and the Charlson comorbidity score. Additionally, we integrate this score with a previously published postoperative score to demonstrate the extent to which patient risk changes during the perioperative period.

bioinformatics↗