Candidate lung tumor susceptibility genes identified through whole-genome association analyses in inbred mice

2006 ◽  
Vol 38 (8) ◽  
pp. 888-895 ◽  
Author(s):  
Pengyuan Liu ◽  
Yian Wang ◽  
Haris Vikis ◽  
Anna Maciag ◽  
Daolong Wang ◽  
...  
Keyword(s):  
Lung Tumor ◽  
Inbred Mice ◽  
Susceptibility Genes ◽  
Whole Genome ◽  
Association Analyses ◽  
Tumor Susceptibility ◽  
Genome Association ◽  
Whole Genome Association

Whole-genome association analyses for lifetime reproductive traits in the pig

2011 ◽  
Vol 89 (4) ◽  
pp. 988-995 ◽  
Author(s):  
S. K. Onteru ◽  
B. Fan ◽  
M. T. Nikkilä ◽  
D. J. Garrick ◽  
K. J. Stalder ◽  
...  
Keyword(s):  
Reproductive Traits ◽  
Whole Genome ◽  
Association Analyses ◽  
Genome Association ◽  
Whole Genome Association

scholarly journals Robust Performance of Potentially Functional SNPs in Machine Learning Models for the Prediction of Atorvastatin-Induced Myalgia

2021 ◽  
Vol 12 ◽  
Author(s):  
Brandon N. S. Ooi ◽  
Raechell ◽  
Ariel F. Ying ◽  
Yong Zher Koh ◽  
Yu Jin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Performance ◽  
Whole Genome ◽  
Learning Models ◽  
Association Analyses ◽  
Functional Snps ◽  
Individual Snps ◽  
Genome Association ◽  
Whole Genome Association ◽  
Machine Learning Models

Background:Statins can cause muscle symptoms resulting in poor adherence to therapy and increased cardiovascular risk. We hypothesize that combinations of potentially functional SNPs (pfSNPs), rather than individual SNPs, better predict myalgia in patients on atorvastatin. This study assesses the value of potentially functional single nucleotide polymorphisms (pfSNPs) and employs six machine learning algorithms to identify the combination of SNPs that best predict myalgia.Methods: Whole genome sequencing of 183 Chinese, Malay and Indian patients from Singapore was conducted to identify genetic variants associated with atorvastatin induced myalgia. To adjust for confounding factors, demographic and clinical characteristics were also examined for their association with myalgia. The top factor, sex, was then used as a covariate in the whole genome association analyses. Variants that were highly associated with myalgia from this and previous studies were extracted, assessed for potential functionality (pfSNPs) and incorporated into six machine learning models. Predictive performance of a combination of different models and inputs were compared using the average cross validation area under ROC curve (AUC). The minimum combination of SNPs to achieve maximum sensitivity and specificity as determined by AUC, that predict atorvastatin-induced myalgia in most, if not all the six machine learning models was determined.Results: Through whole genome association analyses using sex as a covariate, a larger proportion of pfSNPs compared to non-pf SNPs were found to be highly associated with myalgia. Although none of the individual SNPs achieved genome wide significance in univariate analyses, machine learning models identified a combination of 15 SNPs that predict myalgia with good predictive performance (AUC >0.9). SNPs within genes identified in this study significantly outperformed SNPs within genes previously reported to be associated with myalgia. pfSNPs were found to be more robust in predicting myalgia, outperforming non-pf SNPs in the majority of machine learning models tested.Conclusion: Combinations of pfSNPs that were consistently identified by different machine learning models to have high predictive performance have good potential to be clinically useful for predicting atorvastatin-induced myalgia once validated against an independent cohort of patients.

P3-256: Whole-genome association approaches identify new susceptibility genes of Alzheimer's disease

2008 ◽  
Vol 4 ◽  
pp. T597-T597
Author(s):  
Matthias Riemenschneider ◽  
Patricia Friedrich ◽  
Simon M. Laws ◽  
Caroline Graff ◽  
Alexander Kurz ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Susceptibility Genes ◽  
Whole Genome ◽  
Genome Association ◽  
Whole Genome Association

scholarly journals Whole-Genome Association Analyses of Sleep-disordered Breathing Phenotypes in the NHLBI TOPMed Program

2019 ◽  
Author(s):  
Brian E. Cade ◽  
Jiwon Lee ◽  
Tamar Sofer ◽  
Heming Wang ◽  
Man Zhang ◽  
...  
Keyword(s):  
Lung Development ◽  
Sleep Disordered Breathing ◽  
Whole Genome Sequence ◽  
Whole Genome ◽  
Association Analyses ◽  
Respiratory Rhythmogenesis ◽  
Genome Association ◽  
Rare Gene ◽  
Whole Genome Association ◽  
Disordered Breathing

AbstractSleep-disordered breathing (SDB) is a common disorder associated with significant morbidity. Through the NHLBI Trans-Omics for Precision Medicine (TOPMed) program we report the first whole-genome sequence analysis of SDB. We identified 4 rare gene-based associations with SDB traits in 7,988 individuals of diverse ancestry and 4 replicated common variant associations with inclusion of additional samples (n=13,257). We identified a multi-ethnic set-based rare-variant association (p = 3.48 × 10−8) on chromosome X with ARMCX3. Transcription factor binding site enrichment identified associations with genes implicated with respiratory and craniofacial traits. Results highlighted associations in genes that modulate lung development, inflammation, respiratory rhythmogenesis and HIF1A-mediated hypoxic response.

Whole Genome Association Identifies Novel Susceptibility Genes for Crohn's Disease and Implicates a Crucial Role for Autophagy

2007 ◽  
Vol 123 ◽  
pp. S172
Author(s):  
John Rioux ◽  
Ramnik Xavier ◽  
Kent Taylor ◽  
Philippe Goyette ◽  
Mark Silverberg ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Crucial Role ◽  
Susceptibility Genes ◽  
Whole Genome ◽  
Genome Association ◽  
Whole Genome Association

Rapid variance components–based method for whole-genome association analysis

2012 ◽  
Vol 44 (10) ◽  
pp. 1166-1170 ◽  
Author(s):  
Gulnara R Svishcheva ◽  
Tatiana I Axenovich ◽  
Nadezhda M Belonogova ◽  
Cornelia M van Duijn ◽  
Yurii S Aulchenko
Keyword(s):  
Association Analysis ◽  
Variance Components ◽  
Whole Genome ◽  
Genome Association ◽  
Whole Genome Association

scholarly journals Whole-genome association analysis of pork meat pH revealed three significant regions and several potential genes in Finnish Yorkshire pigs

BMC Genetics ◽  
2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Lucas L. Verardo ◽  
Marja-Liisa Sevón-Aimonen ◽  
Timo Serenius ◽  
Ville Hietakangas ◽  
Pekka Uimari
Keyword(s):  
Association Analysis ◽  
Whole Genome ◽  
Pork Meat ◽  
Genome Association ◽  
Yorkshire Pigs ◽  
Whole Genome Association ◽  
Meat Ph
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