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Advances in Human Genetics and Multi-omics
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Advances in Human Genetics and Multi-omics

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Human Genomics and Genetic Diseases".

Deadline for manuscript submissions: closed (10 June 2024) | Viewed by 10791

Special Issue Editor

Dr. Ayse Demirkan

E-Mail Website
Guest Editor
1. Section of Statistical Multi-Omics, Department of Clinical and Experimental Medicine, University of Surrey, Guildford GU2 7XH, UK
2. Institute for People-Centred AI, University of Surrey, Guildford GU2 7X, UK
Interests: genetics; multi-omics; gut microbiome; metabolomics; neurodegenerative diseases; metabolic diseases; machine learning

Special Issue Information

Dear Colleagues,

As new analytical options revolutionize human genetic studies, we found it necessary to put together this Special Issue. After the publication of the first genome-wide association study (GWAS) in 2005, we witnessed the field of genetic epidemiology grow into a major resource of knowledge influencing risk prediction and precision medicine.

GWAS has pointed out thousands of genetic loci for a range of traits and diseases, and the number of them is only increasing. A growing amount of statistical methods and softwares which can integrate other types of data, e.g., individual-level genetic data, metabolic pathways, tissue-specific gene expression and non-coding marks on the genome, into GWAS has made it possible to answer a range of multi-omics questions, going beyond the initial purpose of GWAS. These include but are not limited to pointing out enriched pathways, estimating (partitioned) heritability, calculating genetic correlations, making genetic risk predictions and inferring potential causal relationships between traits. Recently there is also interest in using machine learning for genetic prediction and even further, to perform explainable GWAS experiments.

With this Special Issue, our goal is to update our knowledge on this fast-moving field, particularly by welcoming research on new methodologies and their real data applications related to the use and improving performance of GWAS and its related applications. We also welcome research on using integrated multi-omics data to understand complex genetic traits, as well as novel translational applications of genetics in medicine.

We look forward to hearing from you about your potential contribution to our Special Issue.

Dr. Ayse Demirkan
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. Genes 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 2600 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

  • GWAS
  • multi-omics
  • biome
  • machine learning
  • complex diseases

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Published Papers (5 papers)

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Research

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14 pages, 1036 KiB  
Article
Role of Selected Genetic Polymorphisms in the Development of Rheumatoid Arthritis in a British White Population
by Sarabjit Mastana, Ella Knight, Abigail Hampson, Liz Akam, David John Hunter, Anant Ghelani, Ash Samanta and Puneetpal Singh
Genes 2024, 15(8), 1009; https://doi.org/10.3390/genes15081009 - 1 Aug 2024
Cited by 1 | Viewed by 913
Abstract
Background: Rheumatoid arthritis (RA) is a complex autoimmune disease that negatively affects synovial joints, leading to the deterioration of movement and mobility of patients. This chronic disease is considered to have a strong genetic inheritance, with genome-wide association studies (GWAS) highlighting many genetic [...] Read more.
Background: Rheumatoid arthritis (RA) is a complex autoimmune disease that negatively affects synovial joints, leading to the deterioration of movement and mobility of patients. This chronic disease is considered to have a strong genetic inheritance, with genome-wide association studies (GWAS) highlighting many genetic loci associated with the disease. Moreover, numerous confounding and non-genetic factors also contribute to the risk of the disease. Aims: This study investigates the association of selected genetic polymorphisms with rheumatoid arthritis risk and develops a polygenic risk score (PRS) based on selected genes. Methods: A case-control study recruited fully consenting participants from the East Midlands region of the UK. DNA samples were genotyped for a range of polymorphisms and genetic associations were calculated under several inheritance models. PRS was calculated at crude (unweighted) and weighted levels, and its associations with clinical parameters were determined. Results: There were significant associations with the risk of RA at six genetic markers and their associated risk alleles (TNRF2*G, TRAF1*A, PTPN22*T, HLA-DRB1*G, TNFα*A, and IL4-590*T). The TTG haplotype at the VDR locus increased the risk of RA with an OR of 3.05 (CI 1.33–6.98, p = 0.009). The GA haplotype of HLADRB1-TNFα-308 was a significant contributor to the risk of RA in this population (OR = 2.77, CI 1.23–6.28, p = 0.01), although linkage disequilibrium was low. The polygenic risk score was significantly higher in cases over controls in both unweighted (mean difference = 1.48, t285 = 5.387, p < 0.001) and weighted (mean difference = 2.75, t285 = 6.437, p < 0.001) results. Conclusion: Several genetic loci contribute to the increased risk of RA in the British White sample. The PRS is significantly higher in those with RA and can be used for clinical applications and personalised prevention of disease. Full article
(This article belongs to the Special Issue Advances in Human Genetics and Multi-omics)
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16 pages, 1157 KiB  
Article
Association Study of a Comprehensive Panel of Neuropeptide-Related Polymorphisms Suggest Potential Roles in Verbal Learning and Memory
by Nesli Avgan, Heidi G. Sutherland, Rod A. Lea, Larisa M. Haupt, David H. K. Shum and Lyn R. Griffiths
Genes 2024, 15(1), 30; https://doi.org/10.3390/genes15010030 - 24 Dec 2023
Viewed by 1354
Abstract
Neuropeptides are mostly expressed in regions of the brain responsible for learning and memory and are centrally involved in cognitive pathways. The majority of neuropeptide research has been performed in animal models; with acknowledged differences between species, more research into the role of [...] Read more.
Neuropeptides are mostly expressed in regions of the brain responsible for learning and memory and are centrally involved in cognitive pathways. The majority of neuropeptide research has been performed in animal models; with acknowledged differences between species, more research into the role of neuropeptides in humans is necessary to understand their contribution to higher cognitive function. In this study, we investigated the influence of genetic polymorphisms in neuropeptide genes on verbal learning and memory. Variants in genes encoding neuropeptides and neuropeptide receptors were tested for association with learning and memory measures using the Hopkins Verbal Learning Test—Revised (HVLT-R) in a healthy cohort of individuals (n = 597). The HVLT-R is a widely used task for verbal learning and memory assessment and provides five sub-scores: recall, delay, learning, retention, and discrimination. To determine the effect of candidate variants on learning and memory performance, genetic association analyses were performed for each HVLT-R sub-score with over 1300 genetic variants from 124 neuropeptide and neuropeptide receptor genes, genotyped on Illumina OmniExpress BeadChip arrays. This targeted analysis revealed numerous suggestive associations between HVLT-R test scores and neuropeptide and neuropeptide receptor gene variants; candidates include the SCG5, IGFR1, GALR1, OXTR, CCK, and VIPR1 genes. Further characterization of these genes and their variants will improve our understanding of the genetic contribution to learning and memory and provide insight into the importance of the neuropeptide network in humans. Full article
(This article belongs to the Special Issue Advances in Human Genetics and Multi-omics)
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14 pages, 3435 KiB  
Article
An Artificial Intelligence Prediction Model of Insulin Sensitivity, Insulin Resistance, and Diabetes Using Genes Obtained through Differential Expression
by Jesús María González-Martín, Laura B. Torres-Mata, Sara Cazorla-Rivero, Cristina Fernández-Santana, Estrella Gómez-Bentolila, Bernardino Clavo and Francisco Rodríguez-Esparragón
Genes 2023, 14(12), 2119; https://doi.org/10.3390/genes14122119 - 23 Nov 2023
Cited by 1 | Viewed by 1243
Abstract
Insulin is a powerful pleiotropic hormone that affects processes such as cell growth, energy expenditure, and carbohydrate, lipid, and protein metabolism. The molecular mechanisms by which insulin regulates muscle metabolism and the underlying defects that cause insulin resistance have not been fully elucidated. [...] Read more.
Insulin is a powerful pleiotropic hormone that affects processes such as cell growth, energy expenditure, and carbohydrate, lipid, and protein metabolism. The molecular mechanisms by which insulin regulates muscle metabolism and the underlying defects that cause insulin resistance have not been fully elucidated. This study aimed to perform a microarray data analysis to find differentially expressed genes. The analysis has been based on the data of a study deposited in Gene Expression Omnibus (GEO) with the identifier “GSE22309”. The selected data contain samples from three types of patients after taking insulin treatment: patients with diabetes (DB), patients with insulin sensitivity (IS), and patients with insulin resistance (IR). Through an analysis of omics data, 20 genes were found to be differentially expressed (DEG) between the three possible comparisons obtained (DB vs. IS, DB vs. IR, and IS vs. IR); these data sets have been used to develop predictive models through machine learning (ML) techniques to classify patients with respect to the three categories mentioned previously. All the ML techniques present an accuracy superior to 80%, reaching almost 90% when unifying IR and DB categories. Full article
(This article belongs to the Special Issue Advances in Human Genetics and Multi-omics)
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Review

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18 pages, 333 KiB  
Review
Machine Learning to Advance Human Genome-Wide Association Studies
by Rafaella E. Sigala, Vasiliki Lagou, Aleksey Shmeliov, Sara Atito, Samaneh Kouchaki, Muhammad Awais, Inga Prokopenko, Adam Mahdi and Ayse Demirkan
Genes 2024, 15(1), 34; https://doi.org/10.3390/genes15010034 - 25 Dec 2023
Cited by 2 | Viewed by 3867
Abstract
Machine learning, including deep learning, reinforcement learning, and generative artificial intelligence are revolutionising every area of our lives when data are made available. With the help of these methods, we can decipher information from larger datasets while addressing the complex nature of biological [...] Read more.
Machine learning, including deep learning, reinforcement learning, and generative artificial intelligence are revolutionising every area of our lives when data are made available. With the help of these methods, we can decipher information from larger datasets while addressing the complex nature of biological systems in a more efficient way. Although machine learning methods have been introduced to human genetic epidemiological research as early as 2004, those were never used to their full capacity. In this review, we outline some of the main applications of machine learning to assigning human genetic loci to health outcomes. We summarise widely used methods and discuss their advantages and challenges. We also identify several tools, such as Combi, GenNet, and GMSTool, specifically designed to integrate these methods for hypothesis-free analysis of genetic variation data. We elaborate on the additional value and limitations of these tools from a geneticist’s perspective. Finally, we discuss the fast-moving field of foundation models and large multi-modal omics biobank initiatives. Full article
(This article belongs to the Special Issue Advances in Human Genetics and Multi-omics)
28 pages, 2930 KiB  
Review
Alternative Transcripts Diversify Genome Function for Phenome Relevance to Health and Diseases
by Shane A. Carrion, Jennifer J. Michal and Zhihua Jiang
Genes 2023, 14(11), 2051; https://doi.org/10.3390/genes14112051 - 8 Nov 2023
Viewed by 2608
Abstract
Manipulation using alternative exon splicing (AES), alternative transcription start (ATS), and alternative polyadenylation (APA) sites are key to transcript diversity underlying health and disease. All three are pervasive in organisms, present in at least 50% of human protein-coding genes. In fact, ATS and [...] Read more.
Manipulation using alternative exon splicing (AES), alternative transcription start (ATS), and alternative polyadenylation (APA) sites are key to transcript diversity underlying health and disease. All three are pervasive in organisms, present in at least 50% of human protein-coding genes. In fact, ATS and APA site use has the highest impact on protein identity, with their ability to alter which first and last exons are utilized as well as impacting stability and translation efficiency. These RNA variants have been shown to be highly specific, both in tissue type and stage, with demonstrated importance to cell proliferation, differentiation and the transition from fetal to adult cells. While alternative exon splicing has a limited effect on protein identity, its ubiquity highlights the importance of these minor alterations, which can alter other features such as localization. The three processes are also highly interwoven, with overlapping, complementary, and competing factors, RNA polymerase II and its CTD (C-terminal domain) chief among them. Their role in development means dysregulation leads to a wide variety of disorders and cancers, with some forms of disease disproportionately affected by specific mechanisms (AES, ATS, or APA). Challenges associated with the genome-wide profiling of RNA variants and their potential solutions are also discussed in this review. Full article
(This article belongs to the Special Issue Advances in Human Genetics and Multi-omics)
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