scholarly journals Linking rare and common disease: mapping clinical disease-phenotypes to ontologies in therapeutic target validation

2016 ◽  
Vol 7 (1) ◽  
Author(s):  
Sirarat Sarntivijai ◽  
Drashtti Vasant ◽  
Simon Jupp ◽  
Gary Saunders ◽  
A. Patrícia Bento ◽  
...  
Keyword(s):  
Therapeutic Target ◽  
Disease Mapping ◽  
Clinical Disease ◽  
Common Disease ◽  
Target Validation ◽  
Disease Phenotypes

PSVII-27 Genetic analysis of disease resilience of nursery-to-finish pigs under a natural disease challenge model using reaction norms

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 235-235
Author(s):  
Jian Cheng ◽  
KyuSang Lim ◽  
Austin Putz ◽  
Anna Wolc ◽  
John Harding ◽  
...  
Keyword(s):  
Genetic Model ◽  
Average Daily Gain ◽  
Genetic Correlations ◽  
Reaction Norm ◽  
Cubic Spline ◽  
Clinical Disease ◽  
Random Regression ◽  
Disease Phenotypes ◽  
Linear Reaction ◽  
Better Than

Abstract Disease resilience is the ability of an animal to maintain performance across environments with different disease challenge loads (CL) and can be quantified using random regression reaction norm models that describe phenotype as a function of CL. Objectives of this study were to: 1) develop measures of CL using growth rate and clinical disease phenotypes under a natural disease challenge; 2) evaluate genetic variation in disease resilience. Data used were late nursery and finisher growth rates and clinical disease phenotypes, including medical treatment and mortality rates, and subjective health scores, collected on 50 batches of 60/75 crossbred (LRxY) barrows under a polymicrobial natural disease challenge. All pigs were genotyped using a 650K SNP panel. Different CL were derived from estimates of contemporary group effects and used as environmental covariates in reaction norm analyses of average daily gain (ADG) and treatment rate (TRT). The CL were compared based on model loglikelihoods and estimates of genetic variance, using both linear and cubic spline reaction norm models. Linear reaction norm models fitted the data significantly better than the standard genetic model and the cubic spline models fitted the data significantly better than the linear reaction norm model for most traits. CL based on early finisher ADG provided the best fit for nursery ADG, while CL based on clinical disease phenotypes was best for finisher ADG and TRT. With increasing CL, estimates of heritability for ADG initially decreased and then increased, while estimates of heritability for TRT generally increased with CL. Genetic correlations were low between ADG or TRT at high versus low CL but high for close CLs. Results can be used to select more resilient pigs across different CL levels, or high-performance animals at a given CL level, or a combination of these. Funded by Genome Canada, Genome Alberta, USDA-NIFA, and PigGenCanada.

scholarly journals Mathematical modeling of atopic dermatitis reveals “double-switch” mechanisms underlying 4 common disease phenotypes

2017 ◽  
Vol 139 (6) ◽  
pp. 1861-1872.e7 ◽  
Author(s):  
Elisa Domínguez-Hüttinger ◽  
Panayiotis Christodoulides ◽  
Kosuke Miyauchi ◽  
Alan D. Irvine ◽  
Mariko Okada-Hatakeyama ◽  
...  
Keyword(s):  
Mathematical Modeling ◽  
Atopic Dermatitis ◽  
Common Disease ◽  
Disease Phenotypes ◽  
Double Switch

Apolipoprotein E Polymorphism and Clinical Disease Phenotypes in Arab Patients with Schizophrenia

2009 ◽  
Vol 60 (2) ◽  
pp. 67-72 ◽  
Author(s):  
A.O. Akanji ◽  
J.U. Ohaeri ◽  
S.N. Al-Shammri ◽  
H.R. Fatania
Keyword(s):  
Apolipoprotein E ◽  
Clinical Disease ◽  
Disease Phenotypes ◽  
Apolipoprotein E Polymorphism

scholarly journals A combined pre-clinical meta-analysis and randomized confirmatory trial approach to improve data validity for therapeutic target validation

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Pamela WM. Kleikers ◽  
Carlijn Hooijmans ◽  
Eva Göb ◽  
Friederike Langhauser ◽  
Sarah SJ. Rewell ◽  
...  
Keyword(s):  
Therapeutic Target ◽  
Meta Analysis ◽  
Target Validation ◽  
Data Validity ◽  
Confirmatory Trial

scholarly journals Best Paper Selection

2017 ◽  
Vol 26 (01) ◽  
pp. e13-e14
Keyword(s):  
Rare Diseases ◽  
Adverse Drug Event ◽  
Medical Data ◽  
Drug Event ◽  
Data Repository ◽  
Common Disease ◽  
Target Validation ◽  
Genetic Determinants ◽  
Safety Research ◽  
Phenotype Similarity

Banda JM, Evans L, Vanguri RS, Tatonetti NP, Ryan PB, Shah NH. A curated and standardized adverse drug event resource to accelerate drug safety research. Sci Data 2016;3:160026 https://www.nature.com/articles/sdata201626 Bauer CR, Ganslandt T, Baum B, Christoph J, Engel I, Lobe M, Mate S, Staubert S, Drepper J, Prokosch HU, Winter A, Sax U. Integrated Data Repository Toolkit (IDRT). A Suite of Programs to Facilitate Health Analytics on Heterogeneous Medical Data. Methods Inf Med 2016;55(2):125-35 https://methods.schattauer.de/en/contents/archivestandard/issue/2324/manuscript/25160.html Greene D, NIHR BioResource, Richardson S, Turro E. Phenotype Similarity Regression for Identifying the Genetic Determinants of Rare Diseases. Am J Hum Genet 2016;98(3):490-9 https://linkinghub.elsevier.com/retrieve/pii/S0002-9297(16)00014-8 Sarntivijai S, Vasant D, Jupp S, Saunders G, Bento AP, Gonzalez D, Betts J, Hasan S, Koscielny G, Dunham I, Parkinson H, Malone J. Linking rare and common disease: mapping clinical diseasephenotypes to ontologies in therapeutic target validation. J Biomed Semantics 2016;7-8 https://jbiomedsem.biomedcentral.com/articles/10.1186/s13326-016-0051-7

Pharmacogenomics and Therapeutic Target Validation in Cerebral Vasospasm

2000 ◽  
Vol 36 ◽  
pp. S1-S4 ◽  
Author(s):  
Toshio Tanaka ◽  
Yuhei Nishimura ◽  
Hiroshi Tsunoda ◽  
Michiko Naka
Keyword(s):  
Cerebral Vasospasm ◽  
Therapeutic Target ◽  
Target Validation

scholarly journals Best Paper Selection

2017 ◽  
Vol 26 (01) ◽  
pp. 151-151
Keyword(s):  
Rare Diseases ◽  
Adverse Drug Event ◽  
Medical Data ◽  
Drug Event ◽  
Data Repository ◽  
Common Disease ◽  
Target Validation ◽  
Genetic Determinants ◽  
Safety Research ◽  
Phenotype Similarity

Banda JM, Evans L, Vanguri RS, Tatonetti NP, Ryan PB, Shah NH. A curated and standardized adverse drug event resource to accelerate drug safety research. Sci Data 2016;3:160026 https://www.nature.com/articles/sdata201626 Bauer CR, Ganslandt T, Baum B, Christoph J, Engel I, Lobe M, Mate S, Staubert S, Drepper J, Prokosch HU, Winter A, Sax U. Integrated Data Repository Toolkit (IDRT). A Suite of Programs to Facilitate Health Analytics on Heterogeneous Medical Data. Methods Inf Med 2016;55(2):125-35 https://methods.schattauer.de/en/contents/archivestandard/issue/2324/manuscript/25160.html Greene D, NIHR BioResource, Richardson S, Turro E. Phenotype Similarity Regression for Identifying the Genetic Determinants of Rare Diseases. Am J Hum Genet 2016;98(3):490-9 https://linkinghub.elsevier.com/retrieve/pii/S0002-9297(16)00014-8 Sarntivijai S, Vasant D, Jupp S, Saunders G, Bento AP, Gonzalez D, Betts J, Hasan S, Koscielny G, Dunham I, Parkinson H, Malone J. Linking rare and common disease: mapping clinical diseasephenotypes to ontologies in therapeutic target validation. J Biomed Semantics 2016;7-8 https://jbiomedsem.biomedcentral.com/articles/10.1186/s13326-016-0051-7

scholarly journals Ribozymes as Tools for Therapeutic Target Validation in Arthritis

2000 ◽  
Vol 165 (1) ◽  
pp. 493-498 ◽  
Author(s):  
Thale C. Jarvis ◽  
Karyn S. Bouhana ◽  
Mark E. Lesch ◽  
Suzy A. Brown ◽  
Tom J. Parry ◽  
...  
Keyword(s):  
Therapeutic Target ◽  
Target Validation

scholarly journals 203 Preliminary therapeutic target validation of the IL-36 receptor in psoriasis

2017 ◽  
Vol 137 (10) ◽  
pp. S227
Author(s):  
S.K. Mahil ◽  
M. Peakman ◽  
R. Trembath ◽  
J. Wright ◽  
J. Barker ◽  
...  
Keyword(s):  
Therapeutic Target ◽  
Target Validation
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