@article {131, title = {Graphical analysis for phenome-wide causal discovery in genotyped population-scale biobanks.}, journal = {Nat Commun}, volume = {12}, year = {2021}, month = {2021 01 13}, pages = {350}, abstract = {

Causal inference via Mendelian randomization requires making strong assumptions about horizontal pleiotropy, where genetic instruments are connected to the outcome not only through the exposure. Here, we present causal Graphical Analysis Using Genetics (cGAUGE), a pipeline that overcomes these limitations using instrument filters with provable properties. This is achievable by identifying conditional independencies while examining multiple traits. cGAUGE also uses ExSep (Exposure-based Separation), a novel test for the existence of causal pathways that does not require selecting instruments. In simulated data we illustrate how cGAUGE can reduce the empirical false discovery rate by up to 30\%, while retaining the majority of true discoveries. On 96 complex traits from 337,198 subjects from the UK Biobank, our results cover expected causal links and many new ones that were previously suggested by correlation-based observational studies. Notably, we identify multiple risk factors for cardiovascular disease, including red blood cell distribution width.

}, keywords = {Biological Specimen Banks, Cardiovascular Diseases, Causality, Computer Simulation, Gene Regulatory Networks, Genetic Pleiotropy, Genetic Variation, Genome-Wide Association Study, Genotype, Humans, Mendelian Randomization Analysis, Models, Theoretical, Multifactorial Inheritance, Phenotype, Risk Factors}, issn = {2041-1723}, doi = {10.1038/s41467-020-20516-2}, author = {Amar, David and Sinnott-Armstrong, Nasa and Ashley, Euan A and Rivas, Manuel A} } @article {116, title = {Epidemiology of Functional Seizures Among Adults Treated at a University Hospital.}, journal = {JAMA Netw Open}, volume = {3}, year = {2020}, month = {2020 12 01}, pages = {e2027920}, abstract = {

Importance: Functional seizures (formerly psychogenic nonepileptic seizures), paroxysmal episodes that are often similar to epileptic seizures in their clinical presentation and display no aberrant brain electrical patterns, are understudied. Patients experience a long diagnostic delay, few treatment modalities, a high rate of comorbidities, and significant stigma due to the lack of knowledge about functional seizures.

Objective: To characterize the clinical epidemiology of a population of patients with functional seizures observed at Vanderbilt University Medical Center (VUMC).

Design, Setting, and Participants: This case-control study included patients with functional seizures identified in the VUMC electronic health record (VUMC-EHR) system from October 1989 to October 2018. Patients with epilepsy were excluded from the study and all remaining patients in the VUMC medical center system were used as controls. In total, the study included 1431 patients diagnosed with functional seizures, 2251 with epilepsy and functional seizures, 4715 with epilepsy without functional seizures, and 502 200 control patients who received treatment at VUMC for a minimum of a 3 years. Data were analyzed from November 2018 to March 2020.

Exposure: Diagnosis of functional seizures, as identified from the VUMC-EHR system by an automated phenotyping algorithm that incorporated International Classification of Diseases, Ninth Revision (ICD-9) codes, International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes, Current Procedural Terminology codes, and natural language processing.

Main Outcomes and Measures: Associations of functional seizures with comorbidities and risk factors, measured in odds ratios (ORs).

Results: Of 2 346 808 total patients in the VUMC-EHR aged 18 years or older, 3341 patients with functional seizures were identified (period prevalence, 0.14\%), 1062 (74.2\%) of whom were women and for which the median (interquartile range) age was 49.3 (39.4-59.9) years. This assessment replicated previously reported associations with psychiatric disorders including posttraumatic stress disorder (PTSD) (OR, 1.22; 95\% CI, 1.21-1.24; P < 3.02 {\texttimes} 10-5), anxiety (OR, 1.14; 95\% CI, 1.13-1.15; P < 3.02 {\texttimes} 10-5), and depression (OR, 1.14; 95\% CI, 1.13-1.15; P < 3.02 {\texttimes} 10-5), and identified novel associations with cerebrovascular disease (OR, 1.08; 95\% CI, 1.06-1.09; P < 3.02 {\texttimes} 10-5). An association was found between functional seizures and the known risk factor sexual assault trauma (OR, 10.26; 95\% CI, 10.09-10.44; P < 3.02 {\texttimes} 10-5), and sexual assault trauma was found to mediate nearly a quarter of the association between female sex and functional seizures in the VUMC-EHR.

Conclusions and Relevance: This case-control study found evidence to support previously reported associations, discovered new associations between functional seizures and PTSD, anxiety, and depression. An association between cerebrovascular disease and functional seizures was also found. Results suggested that sexual trauma may be a mediating factor in the association between female sex and functional seizures.

}, keywords = {Adult, Anxiety, Case-Control Studies, Comorbidity, Delayed Diagnosis, Depression, Female, Hospitals, University, Humans, Male, Mental Disorders, Middle Aged, Odds Ratio, Risk Factors, Seizures, Stress Disorders, Post-Traumatic}, issn = {2574-3805}, doi = {10.1001/jamanetworkopen.2020.27920}, author = {Goleva, Slavina B and Lake, Allison M and Torstenson, Eric S and Haas, Kevin F and Davis, Lea K} } @article {64, title = {A Bayesian framework that integrates multi-omics data and gene networks predicts risk genes from schizophrenia GWAS data.}, journal = {Nat Neurosci}, volume = {22}, year = {2019}, month = {2019 05}, pages = {691-699}, abstract = {

Genome-wide association studies (GWAS) have identified more than 100 schizophrenia (SCZ)-associated loci, but using these findings to illuminate disease biology remains a challenge. Here we present integrative risk gene selector (iRIGS), a Bayesian framework that integrates multi-omics data and gene networks to infer risk genes in GWAS loci. By applying iRIGS to SCZ GWAS data, we predicted a set of high-confidence risk genes, most of which are not the nearest genes to the GWAS index variants. High-confidence risk genes account for a significantly enriched heritability, as estimated by stratified linkage disequilibrium score regression. Moreover, high-confidence risk genes are predominantly expressed in brain tissues, especially prenatally, and are enriched for targets of approved drugs, suggesting opportunities to reposition existing drugs for SCZ. Thus, iRIGS can leverage accumulating functional genomics and GWAS data to advance our understanding of SCZ etiology and potential therapeutics.

}, keywords = {Animals, Bayes Theorem, Disease Models, Animal, Gene Regulatory Networks, Genetic Predisposition to Disease, Genome-Wide Association Study, Genomics, Humans, Mice, Risk Factors, Schizophrenia}, issn = {1546-1726}, doi = {10.1038/s41593-019-0382-7}, author = {Wang, Quan and Chen, Rui and Cheng, Feixiong and Wei, Qiang and Ji, Ying and Yang, Hai and Zhong, Xue and Tao, Ran and Wen, Zhexing and Sutcliffe, James S and Liu, Chunyu and Cook, Edwin H and Cox, Nancy J and Li, Bingshan} } @article {29, title = {Phenotype risk scores identify patients with unrecognized Mendelian disease patterns.}, journal = {Science}, volume = {359}, year = {2018}, month = {2018 Mar 16}, pages = {1233-1239}, abstract = {

Genetic association studies often examine features independently, potentially missing subpopulations with multiple phenotypes that share a single cause. We describe an approach that aggregates phenotypes on the basis of patterns described by Mendelian diseases. We mapped the clinical features of 1204 Mendelian diseases into phenotypes captured from the electronic health record (EHR) and summarized this evidence as phenotype risk scores (PheRSs). In an initial validation, PheRS distinguished cases and controls of five Mendelian diseases. Applying PheRS to 21,701 genotyped individuals uncovered 18 associations between rare variants and phenotypes consistent with Mendelian diseases. In 16 patients, the rare genetic variants were associated with severe outcomes such as organ transplants. PheRS can augment rare-variant interpretation and may identify subsets of patients with distinct genetic causes for common diseases.

}, keywords = {Databases, Genetic, DNA Mutational Analysis, Electronic Health Records, Exome, Genetic Association Studies, Genetic Diseases, Inborn, Genetic Predisposition to Disease, Genetic Variation, Humans, Phenotype, Risk Factors}, issn = {1095-9203}, doi = {10.1126/science.aal4043}, author = {Bastarache, Lisa and Hughey, Jacob J and Hebbring, Scott and Marlo, Joy and Zhao, Wanke and Ho, Wanting T and Van Driest, Sara L and McGregor, Tracy L and Mosley, Jonathan D and Wells, Quinn S and Temple, Michael and Ramirez, Andrea H and Carroll, Robert and Osterman, Travis and Edwards, Todd and Ruderfer, Douglas and Velez Edwards, Digna R and Hamid, Rizwan and Cogan, Joy and Glazer, Andrew and Wei, Wei-Qi and Feng, QiPing and Brilliant, Murray and Zhao, Zhizhuang J and Cox, Nancy J and Roden, Dan M and Denny, Joshua C} } @article {45, title = {Quantifying the Polygenic Contribution to Cutaneous Squamous Cell Carcinoma Risk.}, journal = {J Invest Dermatol}, volume = {138}, year = {2018}, month = {2018 Jul}, pages = {1507-1510}, abstract = {

Genetic factors play an important role in cutaneous squamous cell carcinoma risk. Genome-wide association studies have identified 21 single nucleotide polymorphisms associated with cutaneous squamous cell carcinoma risk. Yet no studies have attempted to quantify the contribution of heritability to cutaneous squamous cell carcinoma risk by calculating the population attributable risk using a combination of all discovered genetic variants. Using an additive multi-locus linear logistic model, we determined the cumulative association of these 21 genetic regions to cutaneous squamous cell carcinoma population attributable risk. We computed a multi-locus population attributable risk of 62\%, suggesting that if the effects of all the risk alleles were removed from a population, the cutaneous squamous cell carcinoma risk would drop by 62\%. Using stratified analysis, we also examined the impact of sex on polygenic risk score, and found that men have an increased relative risk throughout the spectrum of the polygenic risk score. Quantifying the impact of genetic predisposition on the proportion of cancer cases can guide future research decisions and public health policy planning.

}, keywords = {Carcinoma, Squamous Cell, Female, Genetic Loci, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Linear Models, Logistic Models, Male, Models, Biological, Polymorphism, Single Nucleotide, Risk Assessment, Risk Factors, Sex Factors, Skin Neoplasms}, issn = {1523-1747}, doi = {10.1016/j.jid.2018.01.031}, author = {Sordillo, Joanne E and Kraft, Peter and Wu, Ann Chen and Asgari, Maryam M} }