Genetic region characterization (Gene RECQuest) - software to assist in identification and selection of candidate genes from genomic regions

BACKGROUND: The availability of research platforms like the web tools of the National Center for Biotechnology Information (NCBI) has transformed the time-consuming task of identifying candidate genes from genetic studies to an interactive process where data from a variety of sources are obtained to select likely genes for follow-up. This process presents its own set of challenges, as the genetic researcher has to interact with several tools in a time-intensive, manual, and cumbersome manner. We developed a method and implemented an effective software system to address these challenges by multidisciplinary efforts of professional software developers with domain experts. The method presented in this paper, Gene RECQuest, simplifies the interaction with existing research platforms through the use of advanced integration technologies. FINDINGS: Gene RECQuest is a web-based application that assists in the identification of candidate genes from linkage and association studies using information from Online Mendelian Inheritance in Man (OMIM) and PubMed. To illustrate the utility of Gene RECQuest we used it to identify genes physically located within a linkage region as potential candidate genes for a quantitative trait locus (QTL) for very low density lipoprotein (VLDL) response on chromosome 18. CONCLUSION: Gene RECQuest provides a tool which enables researchers to easily identify and organize literature supporting their own expertise and make informed decisions. It is important to note that Gene RECQuest is a data acquisition and organization software, and not a data analysis method

Short-term effect of fenofibrate on C-reactive protein: A meta-analysis of randomized controlled trials

<p>Abstract</p> <p>Background</p> <p>C-reactive protein (CRP) is positively associated with risk for cardiovascular disease and all-cause mortality. Some but not all randomized and non-randomized clinical trials found significant associations between fenofibrate therapy and CRP but the direction and magnitude of the association varied across studies. The duration of treatment, patient populations and sample sizes varied greatly, and most short-term studies (i.e., ≤ 12 weeks) had fewer than 50 patients. In this study we meta-analyzed randomized clinical trials to determine the short-term effect of fenofibrate on CRP.</p> <p>Methods</p> <p>Two reviewers independently searched PubMed and other online databases for short-term randomized clinical trials that reported CRP concentrations before and after fenofibrate treatment. Of the 81 studies examined, 14 studies with 540 patients were found eligible. Data for the change in CRP and corresponding measures of dispersion were extracted for use in the meta-analysis.</p> <p>Results</p> <p>The weighted mean CRP concentrations before and after fenofibrate therapy were 2.15 mg/L and 1.53 mg/L (-28.8% change), respectively. Inverse-variance weighted random effects meta-analysis revealed that short-term fenofibrate treatment significantly lowers CRP by 0.58 mg/L (95% CI: 0.36-0.80). There was significant heterogeneity between studies (Q statistic = 64.5, <it>P</it>< 0.0001, I²= 79.8%). There was no evidence of publication bias and sensitivity analysis revealed that omitting any of the 14 studies did not lead to a different conclusion from the overall meta-analysis result.</p> <p>Conclusion</p> <p>Short-term treatment with fenofibrate significantly lowers CRP concentration. Randomized trials that will recruit patients based with high baseline CRP concentrations and with change in CRP as a primary outcome are needed.</p

Data for GAW20: Genome-Wide DNA Sequence Variation and Epigenome-Wide DNA Methylation Before and After Fenofibrate Treatment in a Family Study of Metabolic Phenotypes

GAW20 provided participants with an opportunity to comprehensively examine genetic and epigenetic variation among related individuals in the context of drug treatment response. GAW20 used data from 188 families (N = 1105) participating in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study (clinicaltrials.gov identifier NCT00083369), which included CD4+ T-cell DNA methylation at 463,995 cytosine-phosphate-guanine (CpG) sites measured before and after a 3-week treatment with fenofibrate, single-nucleotide variation at 906,600 loci, metabolic syndrome components ascertained before and after the drug intervention, and relevant covariates. All GOLDN participants were of European descent, with an average age of 48 years. In addition, approximately half were women and approximately 40% met the diagnostic criteria for metabolic syndrome. Unique advantages of the GAW20data set included longitudinal (3 weeks apart) measurements of DNA methylation, the opportunity to explore the contributions of both genotype and DNA methylation to the interindividual variability in drug treatment response, and the familial relationships between study participants. The principal disadvantage of GAW20/GOLDN data was the spurious correlation between batch effects and fenofibrate effects on methylation, which arose because the pre- and posttreatment methylation data were generated and normalized separately, and any attempts to remove time-dependent technical artifacts would also remove biologically meaningful changes brought on by fenofibrate. Despite this limitation, the GAW20 data set offered informative, multilayered omics data collected in a large population-based study of common disease traits, which resulted in creative approaches to integration and analysis of inherited human variation

Epigenetics of Lipid Phenotypes

Dyslipidemia is a well-established risk factor for cardiovascular disease, the main cause of death worldwide. Blood lipid profiles are patterned by both genetic and environmental factors. In recent years, epigenetics has emerged as a paradigm that unifies these influences. In this review, we have summarized the latest evidence implicating epigenetic mechanisms—DNA methylation, histone modification, and regulation by RNAs—in lipid homeostasis. Key findings have emerged in a number of novel epigenetic loci located in biologically plausible genes (eg, CPT1A, ABCG1, SREBF1, and others), as well as microRNA-33a/b. Evidence from animal and cell culture models suggests a complex interplay between different classes of epigenetic processes in the lipid-related genomic regions. Although epigenetic findings hold the potential to explain the interindividual variability in lipid profiles as well as the underlying mechanisms, they have yet to be translated into effective therapies for dyslipidemia

Genetic-Based Hypertension Subtype Identification Using Informative SNPs

In this work, we proposed a process to select informative genetic variants for identifying clinically meaningful subtypes of hypertensive patients. We studied 575 African American (AA) and 612 Caucasian hypertensive participants enrolled in the Hypertension Genetic Epidemiology Network (HyperGEN) study and analyzed each race-based group separately. All study participants underwent GWAS (Genome-Wide Association Studies) and echocardiography. We applied a variety of statistical methods and filtering criteria, including generalized linear models, F statistics, burden tests, deleterious variant filtering, and others to select the most informative hypertension-related genetic variants. We performed an unsupervised learning algorithm non-negative matrix factorization (NMF) to identify hypertension subtypes with similar genetic characteristics. Kruskal–Wallis tests were used to demonstrate the clinical meaningfulness of genetic-based hypertension subtypes. Two subgroups were identified for both African American and Caucasian HyperGEN participants. In both AAs and Caucasians, indices of cardiac mechanics differed significantly by hypertension subtypes. African Americans tend to have more genetic variants compared to Caucasians; therefore, using genetic information to distinguish the disease subtypes for this group of people is relatively challenging, but we were able to identify two subtypes whose cardiac mechanics have statistically different distributions using the proposed process. The research gives a promising direction in using statistical methods to select genetic information and identify subgroups of diseases, which may inform the development and trial of novel targeted therapies

Effect of sunlight exposure on cognitive function among depressed and non-depressed participants: a REGARDS cross-sectional study

<p>Abstract</p> <p>Background</p> <p>Possible physiological causes for the effect of sunlight on mood are through the suprachiasmatic nuclei and evidenced by serotonin and melatonin regulation and its associations with depression. Cognitive function involved in these same pathways may potentially be affected by sunlight exposure. We evaluated whether the amount of sunlight exposure (i.e. insolation) affects cognitive function and examined the effect of season on this relationship.</p> <p>Methods</p> <p>We obtained insolation data for residential regions of 16,800 participants from a national cohort study of blacks and whites, aged 45+. Cognitive impairment was assessed using a validated six-item screener questionnaire and depression status was assessed using the Center for Epidemiologic Studies Depression Scale. Logistic regression was used to find whether same-day or two-week average sunlight exposure was related to cognitive function and whether this relationship differed by depression status.</p> <p>Results</p> <p>Among depressed participants, a dose-response relationship was found between sunlight exposure and cognitive function, with lower levels of sunlight associated with impaired cognitive status (odds ratio = 2.58; 95% CI 1.43–6.69). While both season and sunlight were correlated with cognitive function, a significant relation remained between each of them and cognitive impairment after controlling for their joint effects.</p> <p>Conclusion</p> <p>The study found an association between decreased exposure to sunlight and increased probability of cognitive impairment using a novel data source. We are the first to examine the effects of two-week exposure to sunlight on cognition, as well as the first to look at sunlight's effects on cognition in a large cohort study.</p

A 6-CpG Validated Methylation Risk Score Model for Metabolic Syndrome: The HyperGEN and GOLDN Studies

There has been great interest in genetic risk prediction using risk scores in recent years, however, the utility of scores developed in European populations and later applied to non-European populations has not been successful. The goal of this study was to create a methylation risk score (MRS) for metabolic syndrome (MetS), demonstrating the utility of MRS across race groups using cross-sectional data from the Hypertension Genetic Epidemiology Network (HyperGEN, N = 614 African Americans (AA)) and the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN, N = 995 European Americans (EA)). To demonstrate this, we first selected cytosine-guanine dinucleotides (CpG) sites measured on Illumina Methyl450 arrays previously reported to be significantly associated with MetS and/or component conditions in more than one race/ethnic group (CPT1A cg00574958, PHOSPHO1 cg02650017, ABCG1 cg06500161, SREBF1 cg11024682, SOCS3 cg18181703, TXNIP cg19693031). Second, we calculated the parameter estimates for the 6 CpGs in the HyperGEN data (AA) and used the beta estimates as weights to construct a MRS in HyperGEN (AA), which was validated in GOLDN (EA). We performed association analyses using logistic mixed models to test the association between the MRS and MetS, adjusting for covariates. Results showed the MRS was significantly associated with MetS in both populations. In summary, a MRS for MetS was a strong predictor for the condition across two race groups, suggesting MRS may be useful to examine metabolic disease risk or related complications across race/ethnic groups

Advancing Stroke Genomic Research in the Age of Trans-Omics Big Data Science: Emerging Priorities and Opportunities

Background—We systematically reviewed the genetic variants associated with stroke in genome-wide association studies (GWAS) and examined the emerging priorities and opportunities for rapidly advancing stroke research in the era of Trans-Omics science. Methods—Using the PRISMA guideline, we searched PubMed and NHGRI- EBI GWAS catalog for stroke studies from 2007 till May 2017. Results—We included 31 studies. The major challenge is that the few validated variants could not account for the full genetic risk of stroke and have not been translated for clinical use. None of the studies included continental Africans. Genomic study of stroke among Africans presents a unique opportunity for the discovery, validation, functional annotation, trans-omics study and translation of genomic determinants of stroke with implications for global populations. This is because all humans originated from Africa, a continent with a unique genomic architecture and a distinctive epidemiology of stroke; as well as substantially higher heritability and resolution of fine mapping of stroke genes. Conclusion—Understanding the genomic determinants of stroke and the corresponding molecular mechanisms will revolutionize the development of a new set of precise biomarkers for stroke prediction, diagnosis and prognostic estimates as well as personalized interventions for reducing the global burden of stroke

Genetics, Ancestry, and Hypertension: Implications for Targeted Antihypertensive Therapies

Hypertension is the most common chronic condition seen by physicians in ambulatory care and a condition for which life-long medications are commonly prescribed. There is evidence for genetic factors influencing blood pressure variation in populations and response to medications. This review summarizes recent genetic discoveries that surround blood pressure, hypertension, and antihypertensive drug response from genome-wide association studies, while highlighting ancestry-specific findings and any potential implication for drug therapy targets. Genome-wide association studies have identified several novel loci for inter-individual variation of blood pressure and hypertension risk in the general population. Evidence from pharmacogenetic studies suggests that genes influence the blood pressure response to antihypertensive drugs, although results are somewhat inconsistent across studies. There is still much work that remains to be done to identify genes both for efficacy and adverse events of antihypertensive medications

The role of SNP-loop diuretic interactions in hypertension across ethnic groups in HyperGEN

Blood pressure (BP) is significantly influenced by genetic factors; however, less than 3% of the BP variance has been accounted for by variants identified from genome-wide association studies (GWAS) of primarily European-descent cohorts. Other genetic influences, including gene-environment (GxE) interactions, may explain more of the unexplained variance in BP. African Americans (AA) have a higher prevalence and earlier age of onset of hypertension (HTN) as compared with European Americans (EA); responses to anti-hypertensive drugs vary across race groups. To examine potential interactions between the use of loop diuretics and HTN traits, we analyzed systolic (SBP) and diastolic (DBP) blood BP from 1,222 AA and 1,231 EA participants in the Hypertension Genetic Epidemiology Network (HyperGEN). Population-specific score tests were used to test associations of SBP and DBP, using a panel of genotyped and imputed single nucleotide polymorphisms (SNPs) for African Americans (2.9 million SNPs) and European Americans (2.3 million SNPs). Several promising loci were identified through gene-loop diuretic interactions, although no SNP reached genome-wide significance after adjustment for genomic inflation. In AA, SNPs in or near the genes NUDT12, CHL1, GRIA1, CACNB2, and PYHIN1 were identified for SBP, and SNPs near ID3 were identified for DBP. For EA, promising SNPs for SBP were identified in ESR1and for DBP in SPATS2L and EYA2. Among these SNPs, none were common across phenotypes or population groups. Biologic plausibility exists for many of the identified genes, suggesting that these are candidate genes for regulation of BP and/or anti-hypertensive drug response. The lack of genome-wide significance is understandable in this small study employing gene-drug interactions. These findings provide a set of prioritized SNPs/candidate genes for future studies in HTN. Studies in more diversified population samples may help identify previously missed variants