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Computational resources associating diseases with genotypes, phenotypes and exposures.

ABSTRACT: The causes of a disease and its therapies are not only related to genotypes, but also associated with other factors, including phenotypes, environmental exposures, drugs and chemical molecules. Distinguishing disease-related factors from many neutral factors is critical as well as difficult. Over the past two decades, bioinformaticians have developed many computational resources to integrate the omics data and discover associations among these factors. However, researchers and clinicians are experiencing difficulties in choosing appropriate resources from hundreds of relevant databases and software tools. Here, in order to assist the researchers and clinicians, we systematically review the public computational resources of human diseases related to genotypes, phenotypes, environment factors, drugs and chemical exposures. We briefly describe the development history of these computational resources, followed by the details of the relevant databases and software tools. We finally conclude with a discussion of current challenges and future opportunities as well as prospects on this topic.

SUBMITTER: Zhang W

PROVIDER: S-EPMC6954426 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Computational resources associating diseases with genotypes, phenotypes and exposures.

Zhang Wenliang W Zhang Haiyue H Yang Huan H Li Miaoxin M Xie Zhi Z Li Weizhong W

Briefings in bioinformatics 20191101 6

The causes of a disease and its therapies are not only related to genotypes, but also associated with other factors, including phenotypes, environmental exposures, drugs and chemical molecules. Distinguishing disease-related factors from many neutral factors is critical as well as difficult. Over the past two decades, bioinformaticians have developed many computational resources to integrate the omics data and discover associations among these factors. However, researchers and clinicians are exp ...[more]

PMID: 30102366

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Project description:Acute Ischemic Stroke (AIS) in the young is increasing in prevalence and the largest subtype within this cohort is cryptogenic. To curb this trend, new ways of defining cryptogenic stroke and associated risk factors are needed. We aimed to gain insights into the presence or absence of cardiovascular risk factors in cases of cryptogenic stroke. We conducted a retrospective cohort study of patients aged 18-49 who presented to an urban tertiary care center with AIS. We manually collected predefined demographic, clinical, laboratory and radiological variables. Clinical risk phenotypes were determined using these variables through multivariate analysis of patients with the small and large vessel disease subtypes (vascular phenotype) and cardioembolic subtype (cardiac phenotype). The resultant phenotype models were applied to cases deemed cryptogenic. Within the 449 patients who met criteria, patients with small and large vessel disease (vascular phenotype) had higher rates of hypertension, intracranial atherosclerosis, and diabetes mellitus, and higher admission glucose, HbA1c, admission blood pressure, and cholesterol compared to the patients with cardioembolic AIS. The cardioembolic subgroup (cardiac phenotype) had significantly higher rates of congestive heart failure (CHF), rheumatic heart disease, atrial fibrillation, clotting disorders, left ventricular hypertrophy, larger left atrial sizes, lower ejection fractions, and higher B-type natriuretic peptide and troponin levels. Adjusted multivariate analysis produced six variables independently associated with the vascular phenotype (age, male sex, hemoglobin A1c, ejection fraction (EF), low-density lipoprotein (LDL) cholesterol, and family history of AIS) and five independently associated with the cardiac phenotype (age, female sex, decreased EF, CHF, and absence of intracranial atherosclerosis). Applying these models to cryptogenic stroke cases yielded that 51.5% fit the vascular phenotype and 3.1% fit the cardiac phenotype. In our cohort, half of young patients with cryptogenic stroke fit the risk factor phenotype of small and large vessel strokes.

Dataset Information

Computational resources associating diseases with genotypes, phenotypes and exposures.

Publications

Computational resources associating diseases with genotypes, phenotypes and exposures.

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