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Genetic Epidemiology: The State-of-the-Art and Future Perspectives
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Genetic Epidemiology: The State-of-the-Art and Future Perspectives

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Environmental Health".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 10075

Special Issue Editor

Dr. Farren B. S. Briggs

E-Mail Website
Guest Editor
Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA
Interests: multiple sclerosis; complex diseases; complex disease genetics; gene-environment interactions; metabolomics; multi-omics; modelling complex phenotypes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

The landscape of genetic epidemiology is ever-changing due to constant advances in study designs, the availability of diverse data types, analytical approaches, and biological knowledge, and therefore the field continues to offer the promise of significantly accelerating our understanding of the distribution and determinants of human disease. While there has been great progress identifying genetic factors contributing to many health-related phenotypes, as well as the underlying causal processes, a substantial portion of the heritability component for the vast majority of health conditions remains unidentified. Thus, there is an opportunity to advance our understanding of the biological processes underlying health outcomes by examining diverse populations, considering conditional genetic and non-genetic relationships (i.e., epistasis, gene–environment interactions, host–pathogen relationships), and leveraging multi-omic data types in genetic analyses. There is also a significant need to investigate understudied and less-common conditions that have a significant public health burden, with thoughtful considerations for exogenous exposures. This Special Issue is focused on high-quality applied genetic epidemiology research that aims to address key public health knowledge gaps.

Dr. Farren B. S. Briggs
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Genetic epidemiology
  • Gene–environment interaction
  • Multi-omic analyses
  • Genetic predisposition
  • Genome-wide association analyses

Published Papers (4 papers)

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Research

9 pages, 1006 KiB  
Article
The Impact of Multiple Sclerosis Disease Status and Subtype on Hematological Profile
by Jacob M. Miller, Jeremy T. Beales, Matthew D. Montierth, Farren B. Briggs, Scott F. Frodsham and Mary Feller Davis
Int. J. Environ. Res. Public Health 2021, 18(6), 3318; https://doi.org/10.3390/ijerph18063318 - 23 Mar 2021
Cited by 5 | Viewed by 2308
Abstract
Multiple sclerosis (MS) is an immune-mediated, demyelinating disease of the central nervous system. In this study, an MS cohort and healthy controls were stratified into Caucasian and African American groups. Patient hematological profiles—composed of complete blood count (CBC) and complete metabolic panel (CMP) [...] Read more.
Multiple sclerosis (MS) is an immune-mediated, demyelinating disease of the central nervous system. In this study, an MS cohort and healthy controls were stratified into Caucasian and African American groups. Patient hematological profiles—composed of complete blood count (CBC) and complete metabolic panel (CMP) test values—were analyzed to identify differences between MS cases and controls and between patients with different MS subtypes. Additionally, random forest models were used to determine the aggregate utility of common hematological tests in determining MS disease status and subtype. The most significant and relevant results were increased bilirubin and creatinine in MS cases. The random forest models achieved some success in differentiating between MS cases and controls (AUC values: 0.725 and 0.710, respectively) but were not successful in differentiating between subtypes. However, larger samples that adjust for possible confounding variables, such as treatment status, may reveal the value of these tests in differentiating between MS subtypes. Full article
(This article belongs to the Special Issue Genetic Epidemiology: The State-of-the-Art and Future Perspectives)
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11 pages, 975 KiB  
Article
Mining Complex Genetic Patterns Conferring Multiple Sclerosis Risk
by Farren B. S. Briggs and Corriene Sept
Int. J. Environ. Res. Public Health 2021, 18(5), 2518; https://doi.org/10.3390/ijerph18052518 - 03 Mar 2021
Cited by 1 | Viewed by 2167
Abstract
(1) Background: Complex genetic relationships, including gene-gene (G × G; epistasis), gene(n), and gene-environment (G × E) interactions, explain a substantial portion of the heritability in multiple sclerosis (MS). Machine learning and data mining methods are promising approaches for uncovering higher [...] Read more.
(1) Background: Complex genetic relationships, including gene-gene (G × G; epistasis), gene(n), and gene-environment (G × E) interactions, explain a substantial portion of the heritability in multiple sclerosis (MS). Machine learning and data mining methods are promising approaches for uncovering higher order genetic relationships, but their use in MS have been limited. (2) Methods: Association rule mining (ARM), a combinatorial rule-based machine learning algorithm, was applied to genetic data for non-Latinx MS cases (n = 207) and controls (n = 179). The objective was to identify patterns (rules) amongst the known MS risk variants, including HLA-DRB1*15:01 presence, HLA-A*02:01 absence, and 194 of the 200 common autosomal variants. Probabilistic measures (confidence and support) were used to mine rules. (3) Results: 114 rules met minimum requirements of 80% confidence and 5% support. The top ranking rule by confidence consisted of HLA-DRB1*15:01, SLC30A7-rs56678847 and AC093277.1-rs6880809; carriers of these variants had a significantly greater risk for MS (odds ratio = 20.2, 95% CI: 8.5, 37.5; p = 4 × 10−9). Several variants were shared across rules, the most common was INTS8-rs78727559, which was in 32.5% of rules. (4) Conclusions: In summary, we demonstrate evidence that specific combinations of MS risk variants disproportionately confer elevated risk by applying a robust analytical framework to a modestly sized study population. Full article
(This article belongs to the Special Issue Genetic Epidemiology: The State-of-the-Art and Future Perspectives)
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13 pages, 1715 KiB  
Article
Variation of DNA Methylation in Newborns Associated with Exhaled Carbon Monoxide during Pregnancy
by Ediane De Queiroz Andrade, Gabriela Martins Costa Gomes, Adam Collison, Jane Grehan, Vanessa E. Murphy, Peter Gibson, Joerg Mattes and Wilfried Karmaus
Int. J. Environ. Res. Public Health 2021, 18(4), 1597; https://doi.org/10.3390/ijerph18041597 - 08 Feb 2021
Cited by 3 | Viewed by 2462
Abstract
Fetal exposure to tobacco smoke is an adverse risk factor for newborns. A plausible mechanism of how this exposure may negatively impact long term health is differential methylation of deoxyribonucleic acid (DNAm) and its relation to birth weight. We examined whether self-reported gestational [...] Read more.
Fetal exposure to tobacco smoke is an adverse risk factor for newborns. A plausible mechanism of how this exposure may negatively impact long term health is differential methylation of deoxyribonucleic acid (DNAm) and its relation to birth weight. We examined whether self-reported gestational smoking status and maternal exhaled carbon monoxide (eCO) during early pregnancy were associated with methylation of cytosine by guanines (CpG) sites that themselves predicted birth weight. We focused first on CpGs associated with maternal smoking, and secondly, among these, on CpGs related to birth weight found in another cohort. Then in 94 newborns from the Breathing for Life Trial (BLT) DNAm levels in cord blood were determined using Infinium Methylation EPIC BeadChip measuring >850K CpGs. We regressed CpGs on eCO and tested via mediation analysis whether CpGs link eCO to birth weight. Nine smoking related CpG sites were significantly associated with birth weight. Among these nine CpGs the methylation of cg02264407 on the LMO7 gene was statistically significant and linked with eCO measurements. eCO greater than six ppm showed a 2.3% decrease in infant DNAm (p = 0.035) on the LMO7 gene. A 1% decrease in methylation at this site resulted in decreased birth weight by 44.8 g (p = 0.003). None of the nine CpGs tested was associated with self-reported smoking. This is the first study to report potential mediation of DNA methylation, linking eCO measurements during early pregnancy with birth weight. Full article
(This article belongs to the Special Issue Genetic Epidemiology: The State-of-the-Art and Future Perspectives)
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13 pages, 2340 KiB  
Article
The GGLEAM Study: Understanding Glaucoma in the Ohio Amish
by Andrea R. Waksmunski, Yeunjoo E. Song, Tyler G. Kinzy, Reneé A. Laux, Jane Sewell, Denise Fuzzell, Sarada Fuzzell, Sherri Miller, Janey L. Wiggs, Louis R. Pasquale, Jonathan M. Skarie, Jonathan L. Haines and Jessica N. Cooke Bailey
Int. J. Environ. Res. Public Health 2021, 18(4), 1551; https://doi.org/10.3390/ijerph18041551 - 06 Feb 2021
Viewed by 2549
Abstract
Glaucoma leads to millions of cases of visual impairment and blindness around the world. Its susceptibility is shaped by both environmental and genetic risk factors. Although over 120 risk loci have been identified for glaucoma, a large portion of its heritability is still [...] Read more.
Glaucoma leads to millions of cases of visual impairment and blindness around the world. Its susceptibility is shaped by both environmental and genetic risk factors. Although over 120 risk loci have been identified for glaucoma, a large portion of its heritability is still unexplained. Here we describe the foundation of the Genetics of GLaucoma Evaluation in the AMish (GGLEAM) study to investigate the genetic architecture of glaucoma in the Ohio Amish, which exhibits lower genetic and environmental heterogeneity compared to the general population. To date, we have enrolled 81 Amish individuals in our study from Holmes County, Ohio. As a part of our enrollment process, 62 GGLEAM study participants (42 glaucoma-affected and 20 unaffected individuals) received comprehensive eye examinations and glaucoma evaluations. Using the data from the Anabaptist Genealogy Database, we found that 80 of the GGLEAM study participants were related to one another through a large, multigenerational pedigree containing 1586 people. We plan to integrate the health and kinship data obtained for the GGLEAM study to interrogate glaucoma genetics and pathophysiology in this unique population. Full article
(This article belongs to the Special Issue Genetic Epidemiology: The State-of-the-Art and Future Perspectives)
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