Genomic and Phenomic Research in the 21st Century

Hebbring, Scott J.

doi:10.1016/j.tig.2018.09.007

Cited by 21 publications

(13 citation statements)

References 103 publications

(106 reference statements)

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“…Future genetic studies should consider performing Phenome-Wide Association Studies (PheWAS) which examine many different phenotypes to see which, if any, are associated with a given genetic variant [78]. The findings of this overview provide potential pathways that could be used in PheWAS to determine the functional relevance of the identified genes, and secondly, to explore associations between genetic polymorphisms and different PCOS phenotypes.…”

Section: Discussionmentioning

confidence: 99%

The Genetics of Polycystic Ovary Syndrome: An Overview of Candidate Gene Systematic Reviews and Genome-Wide Association Studies

Hiam

Moreno-Asso

Teede

et al. 2019

JCM

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Polycystic Ovary Syndrome (PCOS) is a complex condition with mechanisms likely to involve the interaction between genetics and lifestyle. Familial clustering of PCOS symptoms is well documented, providing evidence for a genetic contribution to the condition. This overview aims firstly to systematically summarise the current literature surrounding genetics and PCOS, and secondly, to assess the methodological quality of current systematic reviews and identify limitations. Four databases were searched to identify candidate gene systematic reviews, and quality was assessed with the AMSTAR tool. Genome-wide association studies (GWAS) were identified by a semi structured literature search. Of the candidate gene systematic reviews, 17 were of high to moderate quality and four were of low quality. A total of 19 gene loci have been associated with risk of PCOS in GWAS, and 11 of these have been replicated across two different ancestries. Gene loci were located in the neuroendocrine, metabolic, and reproductive pathways. Overall, the gene loci with the most robust findings were THADA, FSHR, INS-VNTR, and DENND1A, that now require validation. This overview also identified limitations of the current literature and important methodological considerations for future genetic studies. Much work remains to identify causal variants and functional relevance of genes associated with PCOS.

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Section: Discussionmentioning

confidence: 99%

The Genetics of Polycystic Ovary Syndrome: An Overview of Candidate Gene Systematic Reviews and Genome-Wide Association Studies

Hiam

Moreno-Asso

Teede

et al. 2019

JCM

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“…Phecodes are aggregated ICD-9 codes that condense similar disease entities into one code, reducing the number of phenotypes from over 14,000 separate ICD-9 codes to 1,645 phecode groups, and have been utilized in multiple EHR and genetic studies. (38)(39)(40)(41)(42)(43)(44) We also conducted genetic analyses using COPD-associated…”

Section: Applications Of Phenotyping Algorithmmentioning

confidence: 99%

“…We also compared the frequency of phecodes across the two groups. (38)(39)(40)(41)(42)(43)(44) In COPD cases, the most frequent phecodes were chronic airway obstruction (100%), hypertension (77.1%), other symptoms of respiratory system (75.4%), essential hypertension (75.4%), and tobacco use disorder (63.0%).…”

Section: Clinical and Genetic Analyses Using A Phenotyping Algorithmmentioning

confidence: 99%

A Clinical Phenotyping Algorithm to Identify Cases of Chronic Obstructive Pulmonary Disease in Electronic Health Records

Martucci

Liu

Kerchberger

et al. 2019

Preprint

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Rationale. Chronic obstructive pulmonary disease (COPD) is a leading cause of mortality in the United States. Electronic health records provide large-scale healthcare data for clinical research, but have been underutilized in COPD research due to challenges identifying these individuals, especially in the absence of pulmonary function testing data. Objectives.To develop an algorithm to electronically phenotype individuals with COPD at a large tertiary care center. Methods. We identified individuals over 45 years of age at last clinic visit within VanderbiltUniversity Medical Center electronic health records. We tested phenotyping algorithms using combinations of both structured and unstructured text and examined the clinical characteristics of the resulting case sets. Measurement and Main Results.A simple algorithm consisting of 3 International Classification of Disease codes for COPD achieved a sensitivity of 97.6%, a specificity of 76.0%, a positive predictive value of 57.1%, and a negative predictive value of 99.0%. A more complex algorithm consisting of both billing codes and a mention of oxygen on the problem list that achieved a positive predictive value of 86.5%. However, the association of known risk factors with chronic obstructive pulmonary disease was consistent in both algorithm sets, suggesting a simple codeonly algorithm may suffice for many research applications. Conclusions.Simple code-only phenotyping algorithms for chronic obstructive pulmonary disease can identify case populations with epidemiologic and genetic profiles consistent with published literature. Implementation of this phenotyping algorithm will expand opportunities for clinical research and pragmatic trials for COPD.

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“…Understanding the genetic architecture of complex traits and diseases is an active area of research, driven largely by massive next-generation sequencing (NGS) efforts and the availability of public repositories containing genotypic and phenotypic data on hundreds of thousands of individuals [1][2][3] . While the previous obstacle in genetics research was acquiring such datasets, we are now faced with the challenge of figuring out how to effectively and appropriately study these data 4,5 .…”

Section: Introductionmentioning

confidence: 99%

Exautomate: A user-friendly tool for region-based rare variant association analysis (RVAA)

Dron

Robinson

et al. 2019

Preprint

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Region-based rare variant association analysis (RVAA) is a popular method to study rare genetic variation in large datasets, especially in the context of complex traits and diseases.Although this method shows great promise in increasing our understanding of the genetic architecture of complex phenotypes, performing a region-based RVAA can be challenging. The sequence kernel association test (SKAT) can be used to perform this analysis, but its inputs and modifiable parameters can be extremely overwhelming and may lead to results that are difficult to reproduce. We have developed a software package called "Exautomate" that contains the tools necessary to run a region-based RVAA using SKAT and is easy-to-use for any researcher, regardless of their previous bioinformatic experiences. In this report, we discuss the utilities of Exautomate and provide detailed examples of implementing our package. Importantly, we demonstrate a proof-of-principle analysis using a previously studied cohort of 313 familial hypercholesterolemia (FH) patients. Our results show an increased burden of rare variants in genes known to cause FH, thereby demonstrating a successful region-based RVAA using Exautomate. With our easy-to-use package, we hope researchers will be able to perform reproducible region-based RVAA to further our collective understanding behind the genetics of complex traits and diseases.

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Genomic and Phenomic Research in the 21st Century

Cited by 21 publications

References 103 publications

The Genetics of Polycystic Ovary Syndrome: An Overview of Candidate Gene Systematic Reviews and Genome-Wide Association Studies

The Genetics of Polycystic Ovary Syndrome: An Overview of Candidate Gene Systematic Reviews and Genome-Wide Association Studies

A Clinical Phenotyping Algorithm to Identify Cases of Chronic Obstructive Pulmonary Disease in Electronic Health Records

Exautomate: A user-friendly tool for region-based rare variant association analysis (RVAA)

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