Vito A. G. Ricigliano, Renato Umeton, Lorenzo Germinario, Eleonora Alma, Martina Briani, Noemi Di Segni, Dalma Montesanti, Giorgia Pierelli, Fabiana Cancrini, Cristiano Lomonaco, Francesca Grassi, Gabriella Palmieri, and Marco Salvetti,
Struan Frederick Airth Grant, Editor
The factual value of genome-wide association studies (GWAS) for the understanding of multifactorial diseases is a matter of intense debate. Practical consequences for the development of more effective therapies do not seem to be around the corner. Here we propose a pragmatic and objective evaluation of how much new biology is arising from these studies, with particular attention to the information that can help prioritize therapeutic targets. We chose multiple sclerosis (MS) as a paradigm disease and assumed that, in pre-GWAS candidate-gene studies, the knowledge behind the choice of each gene reflected the understanding of the disease prior to the advent of GWAS. Importantly, this knowledge was based mainly on non-genetic, phenotypic grounds. We performed single-gene and pathway-oriented comparisons of old and new knowledge in MS by confronting an unbiased list of candidate genes in pre-GWAS association studies with those genes exceeding the genome-wide significance threshold in GWAS published from 2007 on. At the single gene level, the majority (94 out of 125) of GWAS-discovered variants had never been contemplated as plausible candidates in pre-GWAS association studies. The 31 genes that were present in both pre- and post-GWAS lists may be of particular interest in that they represent disease-associated variants whose pathogenetic relevance is supported at the phenotypic level (i.e. the phenotypic information that steered their selection as candidate genes in pre-GWAS association studies). As such they represent attractive therapeutic targets. Interestingly, our analysis shows that some of these variants are targets of pharmacologically active compounds, including drugs that are already registered for human use. Compared with the above single-gene analysis, at the pathway level GWAS results appear more coherent with previous knowledge, reinforcing some of the current views on MS pathogenesis and related therapeutic research. This study presents a pragmatic approach that helps interpret and exploit GWAS knowledge.
Talk delivered at Warwick Biomedical Engineering Seminar series 27 November 2014. Develops a theme emerging from a review in 2010:
J Watkins, A Marsh, P C Taylor, D R J Singer
Therapeutic Delivery, 2010, 1, 651-665
"Continued adherence to a single-drug single-target paradigm will limit the ability of chemists to contribute to advances in personalized medicine, whether they be in discovery or delivery"
Talk delivered at Warwick Biomedical Engineering Seminar series 27 November 2014. Develops a theme emerging from a review in 2010:
J Watkins, A Marsh, P C Taylor, D R J Singer
Therapeutic Delivery, 2010, 1, 651-665
"Continued adherence to a single-drug single-target paradigm will limit the ability of chemists to contribute to advances in personalized medicine, whether they be in discovery or delivery"
Discuss about Al, machine learning, and the hype cycle
Discuss the knowledge-based classification of proteins
Discuss applications of AI/ML to drug discovery
Optimal drug prediction from personal genomics profilesAarathi Anil
An effective method to choose optimal drugs for cancer patients who show hetrogeneous responses to their medicines.
(slides prepared by referring IEEE paper "Optimal drug prediction from personal genomics profiles" by Jianting Sheng, Fuhai Li, and Stephen T. C. Wong)
Rhetorical moves and audience considerations in the discussion sections of ra...jodischneider
European Conference on Argumentation talk
Jodi Schneider, Graciela Rosemblat, Shabnam Tafreshi and Halil Kilicoglu “Rhetorical moves and audience considerations in the discussion sections of Randomized Controlled Trials of health interventions” [Conference Panel Presentation], 2nd European Conference on Argumentation: Argumentation and Inference, Fribourg, Switzerland, June 20-23
1 of 3 talks in Jodi Schneider and Sally Jackson, organizers, “Innovations in Reasoning and Arguing about Health ”[Conference Panel], 2nd European Conference on Argumentation: Argumentation and Inference, Fribourg, Switzerland, June 20-23.
Citation practices and the construction of scientific fact--ECA-facts-preconf...jodischneider
Citation practices and the construction of scientific fact. Presentation at the European Conference on Argumentation preconference on status, relevance, and authority of facts.
Multiscale integrative data analytics in pharmacogenomicsDr. Gerry Higgins
This strategy for mapping drug networks provides insight into the mechanistic on- and off-target effects, laying a foundation for subsequent preclinical studies.
Unravelling the molecular linkage of co morbid diseaseseSAT Journals
Abstract ABSTRACT : The incidence of Diabetes Mellitus (DM), Hypertension (HTN) and Coronary artery disease (CAD) in the country has increased alarmingly. Since decades DM and HTN have been proved to be independent risk factors for CAD. Gene and its regulatory action through a protein are vital for the normal metabolism. Any abnormality in regulation would lead to a disease. Our study used the principles of network biology to understand the comorbidity of diseases at the molecular level. We have collected disease genes of DM, HTN and CAD from various public databases and extracted genes common to all the three diseases. We constructed a biological network by considering the protein interaction data obtained from Human Protein Reference Database (HPRD).The network was validated using power law distribution and the genes were ranked using Centiscape. Finally we identified the crucial genes with literature validation which could play a major role in causing disease co-morbidity. Keywords –Biological Network, Coronary Artery Disease, Diabetes Mellitus, Hypertension and Systems Biology
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
MINING OF IMPORTANT INFORMATIVE GENES AND CLASSIFIER CONSTRUCTION FOR CANCER ...ijsc
Microarray is a useful technique for measuring expression data of thousands or more of genes
simultaneously. One of challenges in classification of cancer using high-dimensional gene expression data
is to select a minimal number of relevant genes which can maximize classification accuracy. Because of the
distinct characteristics inherent to specific cancerous gene expression profiles, developing flexible and
robust gene identification methods is extremely fundamental. Many gene selection methods as well as their
corresponding classifiers have been proposed. In the proposed method, a single gene with high classdiscrimination
capability is selected and classification rules are generated for cancer based on gene
expression profiles. The method first computes importance factor of each gene of experimental cancer
dataset by counting number of linguistic terms (defined in terms of different discreet quantity) with high
class discrimination capability according to their depended degree of classes. Then initial important genes
are selected according to high importance factor of each gene and form initial reduct. Then traditional kmeans
clustering algorithm is applied on each selected gene of initial reduct and compute missclassification
errors of individual genes. The final reduct is formed by selecting most important genes with
respect to less miss-classification errors. Then a classifier is constructed based on decision rules induced
by selected important genes (single) from training dataset to classify cancerous and non-cancerous samples
of experimental test dataset. The proposed method test on four publicly available cancerous gene
expression test dataset. In most of cases, accurate classifications outcomes are obtained by just using
important (single) genes that are highly correlated with the pathogenesis cancer are identified. Also to
prove the robustness of proposed method compares the outcomes (correctly classified instances) with some
existing well known classifiers.
Discuss about Al, machine learning, and the hype cycle
Discuss the knowledge-based classification of proteins
Discuss applications of AI/ML to drug discovery
Optimal drug prediction from personal genomics profilesAarathi Anil
An effective method to choose optimal drugs for cancer patients who show hetrogeneous responses to their medicines.
(slides prepared by referring IEEE paper "Optimal drug prediction from personal genomics profiles" by Jianting Sheng, Fuhai Li, and Stephen T. C. Wong)
Rhetorical moves and audience considerations in the discussion sections of ra...jodischneider
European Conference on Argumentation talk
Jodi Schneider, Graciela Rosemblat, Shabnam Tafreshi and Halil Kilicoglu “Rhetorical moves and audience considerations in the discussion sections of Randomized Controlled Trials of health interventions” [Conference Panel Presentation], 2nd European Conference on Argumentation: Argumentation and Inference, Fribourg, Switzerland, June 20-23
1 of 3 talks in Jodi Schneider and Sally Jackson, organizers, “Innovations in Reasoning and Arguing about Health ”[Conference Panel], 2nd European Conference on Argumentation: Argumentation and Inference, Fribourg, Switzerland, June 20-23.
Citation practices and the construction of scientific fact--ECA-facts-preconf...jodischneider
Citation practices and the construction of scientific fact. Presentation at the European Conference on Argumentation preconference on status, relevance, and authority of facts.
Multiscale integrative data analytics in pharmacogenomicsDr. Gerry Higgins
This strategy for mapping drug networks provides insight into the mechanistic on- and off-target effects, laying a foundation for subsequent preclinical studies.
Unravelling the molecular linkage of co morbid diseaseseSAT Journals
Abstract ABSTRACT : The incidence of Diabetes Mellitus (DM), Hypertension (HTN) and Coronary artery disease (CAD) in the country has increased alarmingly. Since decades DM and HTN have been proved to be independent risk factors for CAD. Gene and its regulatory action through a protein are vital for the normal metabolism. Any abnormality in regulation would lead to a disease. Our study used the principles of network biology to understand the comorbidity of diseases at the molecular level. We have collected disease genes of DM, HTN and CAD from various public databases and extracted genes common to all the three diseases. We constructed a biological network by considering the protein interaction data obtained from Human Protein Reference Database (HPRD).The network was validated using power law distribution and the genes were ranked using Centiscape. Finally we identified the crucial genes with literature validation which could play a major role in causing disease co-morbidity. Keywords –Biological Network, Coronary Artery Disease, Diabetes Mellitus, Hypertension and Systems Biology
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
MINING OF IMPORTANT INFORMATIVE GENES AND CLASSIFIER CONSTRUCTION FOR CANCER ...ijsc
Microarray is a useful technique for measuring expression data of thousands or more of genes
simultaneously. One of challenges in classification of cancer using high-dimensional gene expression data
is to select a minimal number of relevant genes which can maximize classification accuracy. Because of the
distinct characteristics inherent to specific cancerous gene expression profiles, developing flexible and
robust gene identification methods is extremely fundamental. Many gene selection methods as well as their
corresponding classifiers have been proposed. In the proposed method, a single gene with high classdiscrimination
capability is selected and classification rules are generated for cancer based on gene
expression profiles. The method first computes importance factor of each gene of experimental cancer
dataset by counting number of linguistic terms (defined in terms of different discreet quantity) with high
class discrimination capability according to their depended degree of classes. Then initial important genes
are selected according to high importance factor of each gene and form initial reduct. Then traditional kmeans
clustering algorithm is applied on each selected gene of initial reduct and compute missclassification
errors of individual genes. The final reduct is formed by selecting most important genes with
respect to less miss-classification errors. Then a classifier is constructed based on decision rules induced
by selected important genes (single) from training dataset to classify cancerous and non-cancerous samples
of experimental test dataset. The proposed method test on four publicly available cancerous gene
expression test dataset. In most of cases, accurate classifications outcomes are obtained by just using
important (single) genes that are highly correlated with the pathogenesis cancer are identified. Also to
prove the robustness of proposed method compares the outcomes (correctly classified instances) with some
existing well known classifiers.
We are excited to announce and demonstrate some new and highly requested features in this webcast, including predicting phenotypes by applying existing GBLUP or Bayesian models and meta-analysis for GWAS studies.
Genome-wide association studies (GWAS) have been providing valuable insight to the genetics of common and complex diseases for many years. In this webcast we will walk through one possible workflow for completing GWAS in Golden Helix SNP & Variation Suite (SVS) with special attention paid to adjusting analysis for population stratification.
Professor Michael Levin's presentation at Meningitis Research Foundation's 2013 conference Meningitis & Septicaemia in Children & Adults www.meningitis.org/conference2013
It is widely agreed that complex diseases are typically caused by joint effects of multiple genetic variations, rather than a single genetic variation. Multi-SNP interactions, also known as epistatic interactions, have the potential to provide information about causes of complex diseases, and build on GWAS studies that look at associations between single SNPs and phenotypes. However, epistatic analysis methods are both computationally expensive, and have limited accessibility for biologists wanting to analyse GWAS datasets due to being command line based. Here we present APPistatic, a prototype desktop version of a pipeline for epistatic analysis of GWAS datasets. his application combines ease-of-use, via a GUI, with accelerated implementation of BOOST and FaST-LMM epistatic analysis methods.
The slides of the talk of @PhilippBayer and I gave on the 28th Chaos Communication Congress. Sources can be found here: https://github.com/drsnuggles/opensnp28c3
GWA studies are perhaps most often used for studying the genetic basis of human diseases, but this technology also has great utility for studying the natural variation of other organisms.
In this webcast, Ashley Hintz, Field Application Scientist, will discuss the utility of SVS for analyzing plant GWA data, using publicly available SNP data for Arabidopsis thaliana as a case study. Along the way, Ashley will demonstrate how SVS can be used to manage data, analyze population structure, perform genotype QA and ultimately replicate a published genetic association in A. thaliana using EMMAX regression. She will also address the flexibility of SVS for analyzing the genomes of other plant and animal species.
A lecture for UW EPI 519 providing background for genome-wide association studies, a few examples of recent papers in the CVD GWAS literature, and some lessons and new directions. The talk was originally given in 2008 (in collaboration with a colleagure), this version has been updated slightly for 2010 and includes references for further reading.
Some of the typefaces may have been mangled on conversion; the file download should be more reliable.
Genome-wide association study (GWAS) technology has been a primary method for identifying the genes responsible for diseases and other traits for the past ten years. GWAS continues to be highly relevant as a scientific method. Over 2,000 human GWAS reports now appear in scientific journals. Our free eBook aims to explain the basic steps and concepts to complete a GWAS experiment.
Introduction to association mapping and tutorial using tasselAwais Khan
This presentation introduces association mapping/linkage disequilibrium mapping and also includes a tutorial showing association mapping analysis using TASSEL software.
Comparing Genetic Evolutionary Algorithms on Three Enzymes of HIV-1: Integras...CSCJournals
In this work, we utilized Quantitative Structure-Activity Relationship (QSAR) techniques to develop predictive models for inhibitors of the HIV-1 enzymes Integrase, HIV-Protease, and Reverse Transcriptase. Each predictive model was composed of quantitative drug characteristics that were selected by genetic evolutionary algorithms, such as Genetic Algorithm (GE), Differential Evolutionary Algorithm (DE), Binary Particle Swarm Optimization (BPSO), and Differential Evolution with Binary Particle Swarm Optimization (DE-BPSO). After characteristic selection, each model was tested with machine-learning algorithms such as Multiple Linear Regression (MLR), Support Vector Machine (SVM), and Multi-Layer Perceptron neural networks (MLP/ANN). We found that a combination of DE-BPSO combined with Multi-Layer Perceptron produced the most accurate predictive models as measured by R2, the statistical measure of proportion of variance in prediction values, and root-mean-square-error (RMSE) of prediction values compared to observed values. As for the models themselves: the best predictors for Integrase inhibitor included mass-weighted centred Broto-Moreau autocorrelation values, Moran autocorrelations, and eigenvalues of Burden matrices weighted by I-states; the best predictors for HIV-Protease inhibitors included the second Zagreb index value, the normalized spectral positive sum from Laplace matrix, and the connectivity-like index of order 0 from edge adjacency mat; and the best predictors for Reverse Transcriptase inhibitors included the number of hydrogen atoms, the molecular path count of order 7, the centred Broto-Moreau autocorrelation of lag 2 weighted by Sanderson electronegativity, the P_VSA-like on ionization potential, and the frequency of C – N bonds at topological distance 3.
TIGA: Target Illumination GWAS AnalyticsJeremy Yang
Aggregating and assessing experimental evidence for interpretable, explainable, accountable gene-trait associations. Presentation for NIH IDG Annual Meeting, Feb 9-11, 2021.
Analysis of gene expression microarray data of patients with Spinal Muscular ...Anton Yuryev
By examining experimental gene expression data researchers can identify potential upstream regulatory factors that may control key biological processes. In this paper we examine the effectiveness of two similar approaches to this type of identification using a publicly available data set from research done on Spinal Muscular Atrophy.
The Monarch Initiative: From Model Organism to Precision Medicinemhaendel
NIH BD2K all-hands meeting poster November 12, 2015.
Attempts at correlating phenotypic aspects of disease with causal genetic influences are often confounded by the challenges of interpreting diverse data distributed across numerous resources. New approaches to data modeling, integration, tooling, and community practices are needed to make efficient use of these data. The Monarch Initiative is an international consortium working on the development of shared data, tools, and standards to enable direct translation of integrated genotype, phenotype, and environmental data from human and model organisms to enhance our understanding of human disease. We utilize sophisticated semantic mapping techniques across a diverse set of standardized ontologies to deeply integrate data across species, sources, and modalities. Using phenotype similarity matching algorithms across these data enables disorder prediction, variant prioritization, and patient matching against known diseases and model organisms. These similarity algorithms form the core of several innovative tools. The Exomiser, which enables exome variant prioritization by combining pathogenicity, frequency, inheritance, protein interaction, and cross-species phenotype data. Our Phenotype Sufficiency tool provides clinicians the ability to compare patient phenotypic profiles using the Human Phenotype Ontology to determine uniqueness and specificity in support of variant prioritization. The PhenoGrid visualization widget illustrates phenotype similarity between patients, known diseases, and model organisms. Monarch develops models in collaboration with the community in support of the burgeoning genotype-phenotype disease research community. We have successfully used Exomiser to solve a number of undiagnosed patient cases in collaboration with the NIH Undiagnosed Disease Program. Ongoing development in coordination with the Global Alliance for Genetic Health (GA4GH) and other groups will catalyze the realization of our goal of a vital translational community focused on the collaborative application of integrated genotype, phenotype, and environmental data to human disease.
Objective: The association between telomerase reverse transcriptase (TERT) promoter mutation and outcome of melanoma is unclear and controversial. We aim to conduct a meta-analysis and investigate whether the TERT promoter mutation is a prognostic factor of melanoma.
Study Design: Appropriate studies were searched in 3 databases: PubMed, Web of Science, and Embase. Pooled hazard ratios (HRs) were counted through random effects model.
Results: Heterogeneity was moderate in overall survival (OS) (I2=43.7%, p=0.059) and low in disease-free survival (DFS) (I2=0.0%, p=0.587). Sensitivity analysis indicated that the removal of any of the study did not affect the final results. Evidence for publication bias was not found (Begg’s test, p=0.281; Egger’s test, p=0.078). The pooled OS HRs from combined effects analysis was determined (HR 1.07; 95% CI 0.83–1.39, p=0.585), together with the pooled HRs of DFS (HR 1.65; 95% CI 1.02–2.66, p=0.042). TERT promoter mutation predicted a good outcome in meta-static melanoma patients (HR 0.66; 95% CI 0.46–0.96, p=0.042). The pooled HRs of combined mutation in TERT promoter and BRAF (HR 6.27; 95% CI 2.7–14.58, p=0.000) predicted a bad outcome in melanoma patients.
Conclusion: TERT promoter mutation significantly predicted poor DFS outcome but, on the contrary, predicted a good outcome in metastatic melanoma patients. The combined TERT promoter and BRAF mutation was a significant independent factor of OS in melanoma patients.
Keywords: melanoma; meta-analysis; mutation; prognosis; promoter regions, genetic; skin neoplasms; telomerase; TERT promoter mutation; TERT protein, human
Similar to Contribution of genome-wide association studies to scientific research: a pragmatic approach to evaluate their impact (20)
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
New Drug Discovery and Development .....NEHA GUPTA
The "New Drug Discovery and Development" process involves the identification, design, testing, and manufacturing of novel pharmaceutical compounds with the aim of introducing new and improved treatments for various medical conditions. This comprehensive endeavor encompasses various stages, including target identification, preclinical studies, clinical trials, regulatory approval, and post-market surveillance. It involves multidisciplinary collaboration among scientists, researchers, clinicians, regulatory experts, and pharmaceutical companies to bring innovative therapies to market and address unmet medical needs.
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
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Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdfAnujkumaranit
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
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Transmission of Olfactory Signals:
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Primitive, less old, and new olfactory systems with different path
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Contribution of genome-wide association studies to scientific research: a pragmatic approach to evaluate their impact
1. Contribution of Genome-Wide Association Studies to
Scientific Research: A Pragmatic Approach to Evaluate
Their Impact
Vito A. G. Ricigliano1,2.
, Renato Umeton1
*.
, Lorenzo Germinario2
, Eleonora Alma2
, Martina Briani2
,
Noemi Di Segni2
, Dalma Montesanti2
, Giorgia Pierelli2
, Fabiana Cancrini2
, Cristiano Lomonaco2
,
Francesca Grassi3
, Gabriella Palmieri4
, Marco Salvetti1
*
1 Centre for Experimental Neurological Therapies, (CENTERS) S. Andrea Hospital-site, Department of Neuroscience, Mental Health and Sensory Organs, NESMOS,
‘‘Sapienza’’, University of Rome, Roma, Italy, 2 ‘‘Percorso di Eccellenza’’, Faculty of Medicine and Psychology, ‘‘Sapienza’’, University of Rome, Roma, Italy, 3 Department of
Physiology and Pharmacology, ‘‘Sapienza’’, University of Rome, Roma, Italy, 4 Department of Experimental Medicine, ‘‘Sapienza’’, University of Rome, Roma, Italy
Abstract
The factual value of genome-wide association studies (GWAS) for the understanding of multifactorial diseases is a matter of
intense debate. Practical consequences for the development of more effective therapies do not seem to be around the
corner. Here we propose a pragmatic and objective evaluation of how much new biology is arising from these studies, with
particular attention to the information that can help prioritize therapeutic targets. We chose multiple sclerosis (MS) as a
paradigm disease and assumed that, in pre-GWAS candidate-gene studies, the knowledge behind the choice of each gene
reflected the understanding of the disease prior to the advent of GWAS. Importantly, this knowledge was based mainly on
non-genetic, phenotypic grounds. We performed single-gene and pathway-oriented comparisons of old and new
knowledge in MS by confronting an unbiased list of candidate genes in pre-GWAS association studies with those genes
exceeding the genome-wide significance threshold in GWAS published from 2007 on. At the single gene level, the majority
(94 out of 125) of GWAS-discovered variants had never been contemplated as plausible candidates in pre-GWAS association
studies. The 31 genes that were present in both pre- and post-GWAS lists may be of particular interest in that they represent
disease-associated variants whose pathogenetic relevance is supported at the phenotypic level (i.e. the phenotypic
information that steered their selection as candidate genes in pre-GWAS association studies). As such they represent
attractive therapeutic targets. Interestingly, our analysis shows that some of these variants are targets of pharmacologically
active compounds, including drugs that are already registered for human use. Compared with the above single-gene
analysis, at the pathway level GWAS results appear more coherent with previous knowledge, reinforcing some of the current
views on MS pathogenesis and related therapeutic research. This study presents a pragmatic approach that helps interpret
and exploit GWAS knowledge.
Citation: Ricigliano VAG, Umeton R, Germinario L, Alma E, Briani M, et al. (2013) Contribution of Genome-Wide Association Studies to Scientific Research: A
Pragmatic Approach to Evaluate Their Impact. PLoS ONE 8(8): e71198. doi:10.1371/journal.pone.0071198
Editor: Struan Frederick Airth Grant, The Children’s Hospital of Philadelphia, United States of America
Received February 14, 2013; Accepted June 26, 2013; Published August 14, 2013
Copyright: ß 2013 Ricigliano et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: MS and RU are supported by the Italian Multiple Sclerosis Foundation. The funders had no role in study design, data collection and analysis, decision to
publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: marco.salvetti@uniroma1.it (MS); renato.umeton@uniroma1.it (RU)
. These authors contributed equally to this work.
Introduction
Genome-wide association screenings (GWAS) and, in a
relatively near future, full-genome sequencing of large samples
will substantially deepen our understanding of the etiology of
multifactorial diseases, bringing new hope for the identification of
definitive therapeutic targets. However, in spite of the spectacular
technological progress that is making this happen, difficulties in the
analysis and interpretation of the data are delaying the process [1].
Since the entity of this delay is unpredictable, it would be useful to
look at the available data in a way that may help to set priorities in
certain fields of clinical research.
An obvious strategy to assess the added value of the new
knowledge that is being acquired is to confront it with the old one.
Although successfully accomplished in other areas of bioinfor-
matics [2,3], this knowledge integration process has never been
systematically and objectively attempted for GWAS data since the
vast majority of genetic studies in the pre-GWAS era did not
provide definitive evidence of associations, hence being non
comparable. Nonetheless, being the bulk of the old studies based
on a candidate-gene approach, irrespective of the reliability of
their results the knowledge behind the choice of each gene is a
faithful and thorough representation of pre-GWAS understanding
of the disease.
We evaluated differences between pre- and post-GWAS
knowledge in multiple sclerosis (MS). As first term of comparison,
representing the pre-GWAS knowledge, we used an unbiased list
of those candidate genes (included in GENOTATOR) [4] that
had been considered appropriate choices for genetic studies based
on pre-GWAS candidate-gene approach; as second term, we
PLOS ONE | www.plosone.org 1 August 2013 | Volume 8 | Issue 8 | e71198
2. selected those genes exceeding the genome-wide significance
threshold in GWAS published from 2007 on.
Based on the results of this analysis, performed in a single-gene
and in a pathway-oriented approach, we evaluated the emergence
of ‘‘black swans’’ from the GWAS data and the instances in which
the old and the new knowledge reinforce each other. Importantly,
such cases highlighted a potential coincidence between significant
genetic variants and (endo)phenotypes of possible pathogenetic
relevance, a particularly informative situation in that it tells us that
the genetic association identified by GWAS may be coupled with
pathogenetically relevant phenotypic variation. Being these
variants attractive for pharmaceutical research, we also performed
a survey of drugs that target the products of these genes including
compounds that are already registered for human use and may be
evaluated in proof-of concept clinical trials without further delay.
Methods
To compare pre-GWAS knowledge with GWAS results we used
two independent lists of genes. The first one, that we assume to be
representative of pre-GWAS knowledge, contains all genes chosen
as ‘‘candidate genes’’ for association studies in MS in the pre-
GWAS era (all the studies included in GENOTATOR database
and published up to august 2007). We obtained this list from the
GENOTATOR meta-database [4] (http://GENOTATOR.hms.
harvard.edu ). The second list is made of the genes that are
reported as exceeding the threshold of genome-wide significance
in the 15 GWAS published since 2007 on MS [5–19] (http://
www.genome.gov/gwastudies/).
We compared the single gene composition of the two lists and
then verified whether variations resulted in functional differences
using Ingenuity Pathway Analysis (IPA). IPA settings included (1)
strict experimentally-validated filter in the setting related to source
data quality, (2) inclusion of information coming only from papers
where tissues and cells belong to the following IPA categories:
immune system, nervous system, and cell lines; (3) use only
human-data and discard mouse and rat model data. Statistical
significance was taken at p,0.05 (ie, -log(p). = 1.3); B–H p-values
denote p-values corrected for multiple testing using the Benjamini-
Hochberg procedure (this technique relies on the fact that p-values
are uniformly distributed under the null hypothesis) [20].
In IPA, the p-value associated with a function or a pathway in
Global Functional Analysis (GFA) and Global Canonical Pathways
(GCP) is a measure of the likelihood that the association between a
set of focus genes in the experiment and a given process or
pathway is due to random chance. The p-value is calculated using
the right-tailed Fisher Exact Test. B–H correction method of
accounting for multiple testing is used in this analysis, and enabled
to control the error rate in our results and focused on the most
significant biological functions associated with our genes of
interest. A full mathematical and statistical explanation of the
IPA procedure is available at http://www.ingenuity.com/wp-
content/themes/ingenuitytheme/pdf/ipa/functions-pathways-
pval-whitepaper.pdf.
Finally, we used the IPA software to find out all the molecules
(pharmacologically active substances included) that directly or
indirectly (connection mediated by a common interactor) interact
with the products of the genes that compose our GENOTATOR
and GWAS lists.
The diagram in Figure 1 summarizes the methodology we
designed and followed for our work of knowledge assessment and
comparison.
Results
Our analysis included 522 genes from GENOTATOR and 125
from GWAS, selected according to the parameters described in
the Methods section (see also the diagram in Figure 1 for a
snapshot of the study design). The GENOTATOR-derived panel
can be taken as an unbiased representation of pre-GWAS,
‘‘phenotypic’’ knowledge (the conceptual background behind the
choice of each ‘‘candidate’’ was mainly based on non-genetic
information). The GWAS-derived panel reflects new information
on the genetic variation that influences disease risk. The two
panels were then confronted at the single-gene and at the pathway
level.
As shown in Fig. 2-A (and Table S1), at the single-gene level 31
genes upon the whole (647) could simultaneously be found in both
GENOTATOR and GWAS lists, 491 were exclusive of the
GENOTATOR list and 94 were exclusive of the GWAS list. This
implies that 75.2% (94 out of 125) of the GWAS-discovered genes
had never been considered as plausible candidates for single-gene
association studies in MS. On the other hand the remaining 24.8%
(31 out of 125) of the GWAS-identified genes confirm previous,
phenotypic-derived knowledge.
Genes in the GENOTATOR and GWAS lists were then
subjected to a pathway-oriented analysis in order to have a glance
of the molecular and cellular functions associated to each test set.
The Ingenuity analysis addressed the broader perspective of
‘‘biological function’’ first and then focused on ‘‘signaling
pathways’’ and ‘‘metabolic pathways’’ (the only two categories
contained in IPA canonical pathways) to obtain separate insight
about specific cellular functions.
The ‘‘biological function’’ IPA showed a major overlap between
the pre- (GENOTATOR data set) and post-GWAS knowledge
(GWAS data set) (Fig. 2-B, Table S2 and Figure S1). In particular,
GENOTATOR and GWAS data sets shared 20 out of 25
biological pathways. Of the 5 pathways that were exclusive of
either data set, amino acid metabolism and protein trafficking
emerged from GWAS data, whereas free radical scavenging,
protein synthesis, nucleic acid metabolism emerged from GENO-
TATOR.
Comparison carried out at the signaling pathway level (Fig. 2-C,
Table S3) showed a smaller overlap between the two data sets, as
GENOTATOR and GWAS shared 80 pathways out of 215
(37.2%). Notably, in this case there was a considerable portion of
pathways (135 upon the whole) emerging uniquely from
GENOTATOR data.
The proportion of GENOTATOR pathways that were not
confirmed in GWAS became preponderant in the ‘‘metabolic
pathways’’ IPA, where no pathways were present in both GWAS
and pre-GWAS lists of metabolic pathways (Fig. 2-D and Table
S4).
To extract information that may steer the identification of
‘‘druggable’’ targets, we used the IPA software to find out all the
molecules directly or indirectly interacting with the products of the
genes in the GENOTATOR and GWAS lists. Among these, we
focused our attention on those molecules (being either the original
gene products or the associated proteins linked to them) that were
targeted by registered drugs or by pharmacologically active
(exogenous or endogenous) compounds and found that 9 (CD40,
CD80, CD86, ESR1, HLA-DRB1, IL6, IL7R, IL12B, IL13) were
genes present in both GWAS and GENOTATOR lists. Results of
this analysis and the most significant networks, together with the
related drugs, are described in Fig. 3 (and Table S5).
Contribution of GWAS to Scientific Research
PLOS ONE | www.plosone.org 2 August 2013 | Volume 8 | Issue 8 | e71198
3. Figure 1. Study flow diagram. It summarizes of the methodology we designed and followed to compare the pre- and post-GWAS understanding
of the disease by means of single gene analyses, pathway comparisons, and drug target evaluations.
doi:10.1371/journal.pone.0071198.g001
Figure 2. Comparison of GENOTATOR and GWAS gene lists. (A) results at the single-gene level; (B) results in terms of biological function
derived from IPA analysis. Boxes describe specific biological functions; (C) signaling pathway comparison, resulting from IPA analysis; (D) comparison
performed in terms of metabolic pathways, derived from IPA analysis. Box indicates ‘‘GENOTATOR-only’’ signaling pathways.
doi:10.1371/journal.pone.0071198.g002
Contribution of GWAS to Scientific Research
PLOS ONE | www.plosone.org 3 August 2013 | Volume 8 | Issue 8 | e71198
4. Discussion
In principle, GWAS results are one of the best resources we can
draw on for the development of new therapies in multifactorial
diseases. Unfortunately their interpretation is neither simple nor
granted [1]. Furthermore, the small effect size of the disease-
associated variants discovered so far does not lend them to be
considered as attractive therapeutic targets. However, the true
pathogenetic role of these variants may erroneously appear
limited, in the absence of comprehensive analyses of how this
disease-relevant genetic variation correlates with functional/
phenotypic knowledge. To provide conceptual support to the
new information we confronted GWAS results with pre-GWAS,
functional/phenotypic knowledge.
This comparison confirms, objectively, that GWAS are indeed
broadening and refining our understanding of the genetic
architecture of MS. The majority of the genes identified in
GWAS are new with respect to those in the GENOTATOR list of
pre-GWAS studies. Looking at the pathway-oriented analysis, in
some instances (which were more frequent among ‘‘biological
function’’, less frequent among ‘‘signaling’’ and absent among
‘‘metabolic’’ pathways), the new knowledge strengthens hypothe-
ses that had guided the selection of candidates for single-gene
association analyses prior to the advent of GWAS; in others there
are elements of novelty. Specifically, there are 2 biological
pathways (amino acid metabolism and protein trafficking) that
emerge only from GWAS data (according to IPA’s classification
for bio- and canonical-pathways assessing the trajectory of a given
knowledge dataset). Finally, the lack of overlap between
GENOTATOR and GWAS knowledge at the ‘‘metabolic’’ IPA
level may suggest a substantial denial of previous conjectures about
the involvement of metabolic functions. Although this knowledge
trajectory assessment contains, obviously, a publication bias
(indeed, IPA’s knowledge repository is updated periodically with
data coming from PubMed, KEGG, Gene Expression Omnibus,
and all major scientific data repositories), our analysis can be
repeated, for instance, every year, to update the trajectory where
the GWAS research is overall headed.
The 31 genes that GWAS results have in common with pre-
GWAS knowledge are of particular interest. In fact, in the pre-
GWAS era, they had been selected based on non-genetic,
phenotypic grounds. Therefore, functional information on the
underlying biological processes is, to some extent, already available
and, at least in some of these cases, they may represent bona-fide
functional (endo)phenotypes [21,22] whose pathogenetic relevance
has been supported already. For these reasons genes such as
CD40, CD5, CD80, CD86, CIITA, CXCR5, FCRL3, GALC,
ICAM3, IL12A, IL12B, IL12RB1, IL6, IL7R, MAPK1, NFKB1,
TNFRSF1A, may be considered foreground therapeutic targets
(see Table S6 for functional information).
Among these, some are targeted by registered drugs and can
therefore be placed even higher in an ideal ranking of interest.
Nonetheless, pathogenetic relevance does not necessarily imply
therapeutic efficacy. Additional parameters need to be taken into
account in choosing the most appropriate therapeutic targets. In
MS, the disappointing results of phase II clinical trials with
Ustekinumab (CNTO 1275, StelaraH), a human monoclonal
antibody targeting the interleukin (IL)-12/23 p40 subunit [23],
may suggest that pleiotropic and redundant mediators of the
immune response such as cytokines, while being pathogenetically
relevant through processes that may last several years, are
impractical targets for single therapies that ought to be effective
in a relatively short time interval. Besides IL-12, and apart from
CTLA4 (one published open-label phase 1 clinical trial of infusions
of CTLA4Ig with positive immunologic effects [24] and one
ongoing phase 2 study), there are no other completed or ongoing
proof-of-concept trials on any of the 9 pathogenetically relevant
molecules that may be targeted by registered drugs. The discussion
of the issues that, if properly addressed, may help remove some
roadblocks and facilitate repurposing trials goes beyond the scope
of this study [25].
Figure 3. Results from the analysis of all the molecules directly or indirectly linked to GENOTATOR/GWAS lists of genes. Histogram
chart (center) shows the absolute number of molecules contemporarily targeted by registered drugs or pharmacologically active compounds and
also part of complex molecular networks involving GENOTATOR-only, GWAS-only, or common genes; (left and right): most significant molecular
networks and related drugs.
doi:10.1371/journal.pone.0071198.g003
Contribution of GWAS to Scientific Research
PLOS ONE | www.plosone.org 4 August 2013 | Volume 8 | Issue 8 | e71198
5. Conclusions
Recent, citation metrics comparisons of pre-GWAS and GWAS
publications have shown that GWAS are strong hypothesis
generators [26]. Here, our comparison of pre-GWAS and GWAS
results proposes a rational approach to the interpretation and
exploitation of invaluable information such as that coming from
GWAS, in MS and in other multifactorial diseases. It promises to
become increasingly helpful as new genetic data and new data
warehouses are available, particularly since it may contribute to
prioritize the selection of therapeutic targets.
Supporting Information
Figure S1 IPA line charts for each molecular and cellular
function separately. X-axis indicates the group (GENOTATOR
or GWAS), y-axis indicates the -log10(P value).
(TIFF)
Table S1 GENOTATOR-only and GWAS-only gene datasets.
(XLS)
Table S2 Biological-function comparative analysis for GENO-
TATOR and GWAS gene datasets.
(XLS)
Table S3 Signaling pathway comparative analysis for GENO-
TATOR and GWAS gene datasets.
(XLS)
Table S4 Metabolic pathway comparative analysis for GENO-
TATOR and GWAS gene datasets.
(XLS)
Table S5 Druggability extensive analysis for GENOTATOR-,
GWAS-gene datasets and all the molecules that interact with the
former two.
(XLS)
Table S6 Functional information on foreground therapeutic
targets.
(DOCX)
Author Contributions
Conceived and designed the experiments: MS RU VAGR FG G. Palmieri.
Analyzed the data: RU VAGR. Contributed reagents/materials/analysis
tools: RU. Wrote the paper: MS VAGR RU. Gathered and filtered the
initial raw data: VAGR LG EA MB DM G. Pierelli NDS FC CL.
Conceived the work: MS RU VAGR FG G. Palmieri.
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