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Statistical Computing in Medical Genetics: Current Approaches and Applications
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Statistical Computing in Medical Genetics: Current Approaches and Applications

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 (1 March 2022) | Viewed by 5597

Special Issue Editor

Prof. Dr. Rodney A. Lea

E-Mail Website
Guest Editor
Genomics Research Centre, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD 4001, Australia
Interests: computational genomics; statistical genetics; bioinformatics

Special Issue Information

Dear Colleagues,

The field of medical genomics aims to tackle complex questions relating to the etiology and pathology of disease, as well as response to treatment and interaction with environmental exposures. With the reducing cost of high-throughput technologies has come the ability to capture an extensive breadth and depth of “omic” data for clinical cohorts. Along with this has come the need to develop and apply powerful statistical computing approaches to address research questions.

In this Special Issue, we will cover the topic of statistical computing in medical genomics. The issue will highlight research that has applied new or alternate approaches in the field. There will be an emphasis on methods involving a) genomic data integration to identify biological processes and pathways involved in disease pathogenesis; b) variable selection methods for understanding gene–gene and gene–environment interactions; c) machine learning methods to identify genomic signatures predictive of disease outcomes.

Prof. Dr. Rodney A. Lea
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 informatics
  • computational
  • statistical
  • machine learning
  • gene-gene interaction

Published Papers (2 papers)

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Research

28 pages, 2573 KiB  
Article
Capturing SNP Association across the NK Receptor and HLA Gene Regions in Multiple Sclerosis by Targeted Penalised Regression Models
by Sean M. Burnard, Rodney A. Lea, Miles Benton, David Eccles, Daniel W. Kennedy, Jeannette Lechner-Scott and Rodney J. Scott
Genes 2022, 13(1), 87; https://doi.org/10.3390/genes13010087 - 29 Dec 2021
Cited by 1 | Viewed by 2855
Abstract
Conventional genome-wide association studies (GWASs) of complex traits, such as Multiple Sclerosis (MS), are reliant on per-SNP p-values and are therefore heavily burdened by multiple testing correction. Thus, in order to detect more subtle alterations, ever increasing sample sizes are required, while [...] Read more.
Conventional genome-wide association studies (GWASs) of complex traits, such as Multiple Sclerosis (MS), are reliant on per-SNP p-values and are therefore heavily burdened by multiple testing correction. Thus, in order to detect more subtle alterations, ever increasing sample sizes are required, while ignoring potentially valuable information that is readily available in existing datasets. To overcome this, we used penalised regression incorporating elastic net with a stability selection method by iterative subsampling to detect the potential interaction of loci with MS risk. Through re-analysis of the ANZgene dataset (1617 cases and 1988 controls) and an IMSGC dataset as a replication cohort (1313 cases and 1458 controls), we identified new association signals for MS predisposition, including SNPs above and below conventional significance thresholds while targeting two natural killer receptor loci and the well-established HLA loci. For example, rs2844482 (98.1% iterations), otherwise ignored by conventional statistics (p = 0.673) in the same dataset, was independently strongly associated with MS in another GWAS that required more than 40 times the number of cases (~45 K). Further comparison of our hits to those present in a large-scale meta-analysis, confirmed that the majority of SNPs identified by the elastic net model reached conventional statistical GWAS thresholds (p < 5 × 10−8) in this much larger dataset. Moreover, we found that gene variants involved in oxidative stress, in addition to innate immunity, were associated with MS. Overall, this study highlights the benefit of using more advanced statistical methods to (re-)analyse subtle genetic variation among loci that have a biological basis for their contribution to disease risk. Full article
(This article belongs to the Special Issue Statistical Computing in Medical Genetics: Current Approaches and Applications)
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17 pages, 3814 KiB  
Article
Silver: Forging almost Gold Standard Datasets
by Farhad Maleki, Katie Ovens, Ian McQuillan and Anthony J. Kusalik
Genes 2021, 12(10), 1523; https://doi.org/10.3390/genes12101523 - 28 Sep 2021
Cited by 1 | Viewed by 1938
Abstract
Gene set analysis has been widely used to gain insight from high-throughput expression studies. Although various tools and methods have been developed for gene set analysis, there is no consensus among researchers regarding best practice(s). Most often, evaluation studies have reported contradictory recommendations [...] Read more.
Gene set analysis has been widely used to gain insight from high-throughput expression studies. Although various tools and methods have been developed for gene set analysis, there is no consensus among researchers regarding best practice(s). Most often, evaluation studies have reported contradictory recommendations of which methods are superior. Therefore, an unbiased quantitative framework for evaluations of gene set analysis methods will be valuable. Such a framework requires gene expression datasets where enrichment status of gene sets is known a priori. In the absence of such gold standard datasets, artificial datasets are commonly used for evaluations of gene set analysis methods; however, they often rely on oversimplifying assumptions that make them biased in favor of or against a given method. In this paper, we propose a quantitative framework for evaluation of gene set analysis methods by synthesizing expression datasets using real data, without relying on oversimplifying or unrealistic assumptions, while preserving complex gene–gene correlations and retaining the distribution of expression values. The utility of the quantitative approach is shown by evaluating ten widely used gene set analysis methods. An implementation of the proposed method is publicly available. We suggest using Silver to evaluate existing and new gene set analysis methods. Evaluation using Silver provides a better understanding of current methods and can aid in the development of gene set analysis methods to achieve higher specificity without sacrificing sensitivity. Full article
(This article belongs to the Special Issue Statistical Computing in Medical Genetics: Current Approaches and Applications)
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