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Genome-Wide Association Studies (GWAS) to Understand Disease
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Genome-Wide Association Studies (GWAS) to Understand Disease

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Technologies and Resources for Genetics".

Deadline for manuscript submissions: closed (25 March 2021) | Viewed by 6505

Special Issue Editor

Dr. Tim Beck

E-Mail Website
Guest Editor
Department of Genetics and Genome Biology, University of Leicester, Leicester LE1 7RH, UK
Interests: human genetics; genome-wide association studies (GWAS); bioinformatics; standards; multi-omics data integration; health data harmonisation; research and clinical data integration

Special Issue Information

Dear Colleagues,

Genome-wide association studies (GWAS) - where genetic variants across the genomes of samples from populations are tested for associations to disease traits - have caused a seismic shift in our understanding of disease aetiology.  Over the last decade around 4,000 GWAS have been published, successfully identifying risk loci for many common diseases and traits which have led to insights into the genetic architecture of disease susceptibility and advances in clinical care, including personalised medicine. Over the years, improvements in genotyping technologies available at competitive cost, have allowed more variants to be tested over larger population samples, with this trend set to continue with whole-genome and whole-exome sequencing becoming more accessible. In parallel, bioinformatics advances have kept pace to identify actionable genetic variants on a single-study basis, and also to gather, compare and analyse summary data from across published GWAS.

We would like to invite submissions of original research or review articles on any topic related to “Genome-wide Association Studies (GWAS) to Understand Disease”.  This Special Issue addresses all kinds of research related to our current knowledge on GWAS, from novel health related GWAS findings to methodology and bioinformatics perspectives, as well as critical perspectives on the challenges facing this area.

Dr. Tim Beck
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

  • genome-wide association studies (GWAS)
  • genomics
  • human genetics
  • bioinformatics
  • SNPs
  • disease
  • genotype-phenotype associations

Published Papers (2 papers)

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Research

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18 pages, 4146 KiB  
Article
Hippocampal Subregion and Gene Detection in Alzheimer’s Disease Based on Genetic Clustering Random Forest
by Jin Li, Wenjie Liu, Luolong Cao, Haoran Luo, Siwen Xu, Peihua Bao, Xianglian Meng, Hong Liang and Shiaofen Fang
Genes 2021, 12(5), 683; https://doi.org/10.3390/genes12050683 - 1 May 2021
Cited by 5 | Viewed by 2192
Abstract
The distinguishable subregions that compose the hippocampus are differently involved in functions associated with Alzheimer’s disease (AD). Thus, the identification of hippocampal subregions and genes that classify AD and healthy control (HC) groups with high accuracy is meaningful. In this study, by jointly [...] Read more.
The distinguishable subregions that compose the hippocampus are differently involved in functions associated with Alzheimer’s disease (AD). Thus, the identification of hippocampal subregions and genes that classify AD and healthy control (HC) groups with high accuracy is meaningful. In this study, by jointly analyzing the multimodal data, we propose a novel method to construct fusion features and a classification method based on the random forest for identifying the important features. Specifically, we construct the fusion features using the gene sequence and subregions correlation to reduce the diversity in same group. Moreover, samples and features are selected randomly to construct a random forest, and genetic algorithm and clustering evolutionary are used to amplify the difference in initial decision trees and evolve the trees. The features in resulting decision trees that reach the peak classification are the important “subregion gene pairs”. The findings verify that our method outperforms well in classification performance and generalization. Particularly, we identified some significant subregions and genes, such as hippocampus amygdala transition area (HATA), fimbria, parasubiculum and genes included RYR3 and PRKCE. These discoveries provide some new candidate genes for AD and demonstrate the contribution of hippocampal subregions and genes to AD. Full article
(This article belongs to the Special Issue Genome-Wide Association Studies (GWAS) to Understand Disease)
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Review

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14 pages, 662 KiB  
Review
Statistical Learning Methods Applicable to Genome-Wide Association Studies on Unbalanced Case-Control Disease Data
by Xiaotian Dai, Guifang Fu, Shaofei Zhao and Yifei Zeng
Genes 2021, 12(5), 736; https://doi.org/10.3390/genes12050736 - 13 May 2021
Cited by 4 | Viewed by 3298
Abstract
Despite the fact that imbalance between case and control groups is prevalent in genome-wide association studies (GWAS), it is often overlooked. This imbalance is getting more significant and urgent as the rapid growth of biobanks and electronic health records have enabled the collection [...] Read more.
Despite the fact that imbalance between case and control groups is prevalent in genome-wide association studies (GWAS), it is often overlooked. This imbalance is getting more significant and urgent as the rapid growth of biobanks and electronic health records have enabled the collection of thousands of phenotypes from large cohorts, in particular for diseases with low prevalence. The unbalanced binary traits pose serious challenges to traditional statistical methods in terms of both genomic selection and disease prediction. For example, the well-established linear mixed models (LMM) yield inflated type I error rates in the presence of unbalanced case-control ratios. In this article, we review multiple statistical approaches that have been developed to overcome the inaccuracy caused by the unbalanced case-control ratio, with the advantages and limitations of each approach commented. In addition, we also explore the potential for applying several powerful and popular state-of-the-art machine-learning approaches, which have not been applied to the GWAS field yet. This review paves the way for better analysis and understanding of the unbalanced case-control disease data in GWAS. Full article
(This article belongs to the Special Issue Genome-Wide Association Studies (GWAS) to Understand Disease)
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