Application of Genomic Technology in Disease Outcome Prediction

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

Deadline for manuscript submissions: closed (28 June 2021) | Viewed by 44944

Special Issue Editor


E-Mail Website
Guest Editor
1. Centre for Molecular Medicine and Innovative Therapeutics, Murdoch University, Perth, WA 6150, Australia
2. Perron Institute for Neurological and Translational Science, Perth, WA 6009, Australia
Interests: genomics; polygenic inheritance; medical genomics; genetic pathology; neurodegenerative diseases; complex diseases
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent years have witnessed a fast development in the genomic technologies that has fast spaced the discoveries about the genomic function. This progress is expected to change the medicine and it form a foundation for the precision medicine. One area that would be impacted the most is the prediction of the course of the diseases and the prediction of the drug response. It is well-established that diseases in different individuals have different clinical features with variable clinical courses and variable drug response. Large part of this variability is caused by the genetic differences that interact with the environmental conditions leading to the sophisticated and complex outcomes. While the environmental conditions are not always easy to measure, detection of genomic features is straightforward thanks to the technology advancement. It is understandable that hopes are high to see breakthrough that genomics can potentially offer. Bearing this in mind we pulled together the Special Issue in Genes dedicated to the progress of genomic technologies in predicting disease outcomes.

Prof. Dr. Sulev Koks
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

  • Medical genomics
  • Genomics and transcriptomics
  • Chronic diseases
  • Longitudinal trajectories of diseases
  • Drug response
  • Precision medicine

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (9 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

8 pages, 2264 KiB  
Article
Embryo Screening for Polygenic Disease Risk: Recent Advances and Ethical Considerations
by Laurent C. A. M. Tellier, Jennifer Eccles, Nathan R. Treff, Louis Lello, Simon Fishel and Stephen Hsu
Genes 2021, 12(8), 1105; https://doi.org/10.3390/genes12081105 - 21 Jul 2021
Cited by 16 | Viewed by 8444
Abstract
Machine learning methods applied to large genomic datasets (such as those used in GWAS) have led to the creation of polygenic risk scores (PRSs) that can be used identify individuals who are at highly elevated risk for important disease conditions, such as coronary [...] Read more.
Machine learning methods applied to large genomic datasets (such as those used in GWAS) have led to the creation of polygenic risk scores (PRSs) that can be used identify individuals who are at highly elevated risk for important disease conditions, such as coronary artery disease (CAD), diabetes, hypertension, breast cancer, and many more. PRSs have been validated in large population groups across multiple continents and are under evaluation for widespread clinical use in adult health. It has been shown that PRSs can be used to identify which of two individuals is at a lower disease risk, even when these two individuals are siblings from a shared family environment. The relative risk reduction (RRR) from choosing an embryo with a lower PRS (with respect to one chosen at random) can be quantified by using these sibling results. New technology for precise embryo genotyping allows more sophisticated preimplantation ranking with better results than the current method of selection that is based on morphology. We review the advances described above and discuss related ethical considerations. Full article
(This article belongs to the Special Issue Application of Genomic Technology in Disease Outcome Prediction)
Show Figures

Figure 1

26 pages, 6713 KiB  
Article
Machine Learning Prediction of Biomarkers from SNPs and of Disease Risk from Biomarkers in the UK Biobank
by Erik Widen, Timothy G. Raben, Louis Lello and Stephen D. H. Hsu
Genes 2021, 12(7), 991; https://doi.org/10.3390/genes12070991 - 29 Jun 2021
Cited by 11 | Viewed by 7121
Abstract
We use UK Biobank data to train predictors for 65 blood and urine markers such as HDL, LDL, lipoprotein A, glycated haemoglobin, etc. from SNP genotype. For example, our Polygenic Score (PGS) predictor correlates ∼0.76 with lipoprotein A level, which is highly heritable [...] Read more.
We use UK Biobank data to train predictors for 65 blood and urine markers such as HDL, LDL, lipoprotein A, glycated haemoglobin, etc. from SNP genotype. For example, our Polygenic Score (PGS) predictor correlates ∼0.76 with lipoprotein A level, which is highly heritable and an independent risk factor for heart disease. This may be the most accurate genomic prediction of a quantitative trait that has yet been produced (specifically, for European ancestry groups). We also train predictors of common disease risk using blood and urine biomarkers alone (no DNA information); we call these predictors biomarker risk scores, BMRS. Individuals who are at high risk (e.g., odds ratio of >5× population average) can be identified for conditions such as coronary artery disease (AUC∼0.75), diabetes (AUC∼0.95), hypertension, liver and kidney problems, and cancer using biomarkers alone. Our atherosclerotic cardiovascular disease (ASCVD) predictor uses ∼10 biomarkers and performs in UKB evaluation as well as or better than the American College of Cardiology ASCVD Risk Estimator, which uses quite different inputs (age, diagnostic history, BMI, smoking status, statin usage, etc.). We compare polygenic risk scores (risk conditional on genotype: PRS) for common diseases to the risk predictors which result from the concatenation of learned functions BMRS and PGS, i.e., applying the BMRS predictors to the PGS output. Full article
(This article belongs to the Special Issue Application of Genomic Technology in Disease Outcome Prediction)
Show Figures

Figure 1

16 pages, 2296 KiB  
Article
Synovium-Synovial Fluid Axis in Osteoarthritis Pathology: A Key Regulator of the Cartilage Degradation Process
by Dhanashri Ingale, Priya Kulkarni, Ali Electricwala, Alpana Moghe, Sara Kamyab, Suresh Jagtap, Aare Martson, Sulev Koks and Abhay Harsulkar
Genes 2021, 12(7), 989; https://doi.org/10.3390/genes12070989 - 29 Jun 2021
Cited by 24 | Viewed by 3880
Abstract
Failure of conventional anti-inflammatory therapies in osteoarthritis (OA) underlines the insufficient knowledge about inflammatory mechanisms, patterns and their relationship with cartilage degradation. Considering non-linear nature of cartilage loss in OA, a better understanding of inflammatory milieu and MMP status at different stages of [...] Read more.
Failure of conventional anti-inflammatory therapies in osteoarthritis (OA) underlines the insufficient knowledge about inflammatory mechanisms, patterns and their relationship with cartilage degradation. Considering non-linear nature of cartilage loss in OA, a better understanding of inflammatory milieu and MMP status at different stages of OA is required to design early-stage therapies or personalized disease management. For this, an investigation based on a synovium-synovial fluid (SF) axis was planned to study OA associated changes in synovium and SF along the progressive grades of OA. Gene expressions in synovial-biopsies from different grades OA patients (N = 26) revealed a peak of IL-1β, IL-15, PGE2 and NGF in early OA (Kellgren–Lawrence (KL) grade-I and II); the highest MMP levels were found in advanced stages (KL grade-III and IV). MMPs (MMP-1, 13, 2 and 9) abundance and FALGPA activity estimated in forty SFs of progressive grades showed the maximum protein levels and activity in KL grade-II and III. In an SF challenge test, SW982 and THP1 cells were treated with progressive grade SFs to study the dynamics of MMPs modulation in inflammatory microenvironment; the test yielded a result pattern, which matched with FALGPA and the protein-levels estimation. Inflammatory mediators in SFs served as steering factor for MMP up-regulation. A correlation-matrix of IL-1β and MMPs revealed expressional negative correlation. Full article
(This article belongs to the Special Issue Application of Genomic Technology in Disease Outcome Prediction)
Show Figures

Figure 1

9 pages, 955 KiB  
Article
Hunting for Familial Parkinson’s Disease Mutations in the Post Genome Era
by Steven R. Bentley, Ilaria Guella, Holly E. Sherman, Hannah M. Neuendorf, Alex M. Sykes, Javed Y. Fowdar, Peter A. Silburn, Stephen A. Wood, Matthew J. Farrer and George D. Mellick
Genes 2021, 12(3), 430; https://doi.org/10.3390/genes12030430 - 17 Mar 2021
Cited by 4 | Viewed by 3094
Abstract
Parkinson’s disease (PD) is typically sporadic; however, multi-incident families provide a powerful platform to discover novel genetic forms of disease. Their identification supports deciphering molecular processes leading to disease and may inform of new therapeutic targets. The LRRK2 p.G2019S mutation causes PD in [...] Read more.
Parkinson’s disease (PD) is typically sporadic; however, multi-incident families provide a powerful platform to discover novel genetic forms of disease. Their identification supports deciphering molecular processes leading to disease and may inform of new therapeutic targets. The LRRK2 p.G2019S mutation causes PD in 42.5–68% of carriers by the age of 80 years. We hypothesise similarly intermediately penetrant mutations may present in multi-incident families with a generally strong family history of disease. We have analysed six multiplex families for missense variants using whole exome sequencing to find 32 rare heterozygous mutations shared amongst affected members. Included in these mutations was the KCNJ15 p.R28C variant, identified in five affected members of the same family, two elderly unaffected members of the same family, and two unrelated PD cases. Additionally, the SIPA1L1 p.R236Q variant was identified in three related affected members and an unrelated familial case. While the evidence presented here is not sufficient to assign causality to these rare variants, it does provide novel candidates for hypothesis testing in other modestly sized families with a strong family history. Future analysis will include characterisation of functional consequences and assessment of carriers in other familial cases. Full article
(This article belongs to the Special Issue Application of Genomic Technology in Disease Outcome Prediction)
Show Figures

Figure 1

14 pages, 2633 KiB  
Article
Transcript Variants of Genes Involved in Neurodegeneration Are Differentially Regulated by the APOE and MAPT Haplotypes
by Sulev Koks, Abigail L. Pfaff, Vivien J. Bubb and John P. Quinn
Genes 2021, 12(3), 423; https://doi.org/10.3390/genes12030423 - 15 Mar 2021
Cited by 11 | Viewed by 3438
Abstract
Genetic variations at the Apolipoprotein E (ApoE) and microtubule-associated protein tau (MAPT) loci have been implicated in multiple neurogenerative diseases, but their exact molecular mechanisms are unclear. In this study, we performed transcript level linear modelling using the blood whole transcriptome data and [...] Read more.
Genetic variations at the Apolipoprotein E (ApoE) and microtubule-associated protein tau (MAPT) loci have been implicated in multiple neurogenerative diseases, but their exact molecular mechanisms are unclear. In this study, we performed transcript level linear modelling using the blood whole transcriptome data and genotypes of the 570 subjects in the Parkinson’s Progression Markers Initiative (PPMI) cohort. ApoE, MAPT haplotypes and two SNPs at the SNCA locus (rs356181, rs3910105) were used to detect expression quantitative trait loci eQTLs associated with the transcriptome and differential usage of transcript isoforms. As a result, we identified 151 genes associated with the genotypic variations, 29 cis and 122 trans eQTL positions. Profound effect with genome-wide significance of ApoE e4 haplotype on the expression of TOMM40 transcripts was identified. This finding potentially explains in part the frequently established genetic association with the APOE e4 haplotypes in neurodegenerative diseases. Moreover, MAPT haplotypes had significant differential impact on 23 transcripts from the 17q21.31 and 17q24.1 loci. MAPT haplotypes had also the largest up-regulating (256) and the largest down-regulating (−178) effect sizes measured as β values on two different transcripts from the same gene (LRRC37A2). Intronic SNP in the SNCA gene, rs3910105, differentially induced expression of three SNCA isoforms. In conclusion, this study established clear association between well-known haplotypic variance and transcript specific regulation in the blood. APOE e4 and MAPT H1/H2 haplotypic variants are associated with the expression of several genes related to the neurodegeneration. Full article
(This article belongs to the Special Issue Application of Genomic Technology in Disease Outcome Prediction)
Show Figures

Figure 1

11 pages, 448 KiB  
Article
Angiotensinogen Gene Missense Polymorphisms (rs699 and rs4762): The Association of End-Stage Renal Failure Risk with Type 2 Diabetes and Hypertension in Egyptians
by Islam M. El-Garawani, Eman M. Shaheen, Hesham R. El-Seedi, Shaden A. M. Khalifa, Gaber A. M. Mersal, Mahmoud M. Emara and Zeinab A. Kasemy
Genes 2021, 12(3), 339; https://doi.org/10.3390/genes12030339 - 25 Feb 2021
Cited by 10 | Viewed by 3484
Abstract
Type 2 diabetes mellitus (T2DM) and hypertension are common chronic diseases mainly associated with the development and progression of end-stage renal disease (ESRD) leading to morbidity and mortality. Gene polymorphisms linked to the renin–angiotensin (AGT)–aldosterone system (RAAS) were broadly inspected in [...] Read more.
Type 2 diabetes mellitus (T2DM) and hypertension are common chronic diseases mainly associated with the development and progression of end-stage renal disease (ESRD) leading to morbidity and mortality. Gene polymorphisms linked to the renin–angiotensin (AGT)–aldosterone system (RAAS) were broadly inspected in patients with diabetic nephropathy (DN) and hypertension. This study aimed to investigate the association of AGT gene polymorphisms (rs699 and rs4762) with ESRD in T2DM hypertensive Egyptian patients. Genotyping of rs699 and rs4762 was conducted using the tetra-primers amplification refractory mutation system (ARMS-PCR). The allelic distribution analysis was performed on 103 healthy control subjects, 97 non-ESRD patients, and 104 patients with ESRD. The allelic frequencies of AGT gene polymorphisms (rs4762 and rs699) in all study participants were assessed. For the non-ESRD group, the frequencies of the alleles of AGT-rs4762 (χ2 = 31.88, p < 0.001, OR = 5.17, CI 95%: 2.81–9.51) and AGT-rs699 (χ2 = 4.85, p = 0.027, OR = 1.56, CI 95%: 1.05–2.33) were significantly associated with the non-ESRD group. However, for the ESRD group, the T allele was significantly higher than that in the controls (χ2 = 24.97, p < 0.001, odds ratio (OR) = 4.35, CI 95%: 2.36–8.02). Moreover, AGT (rs699) genotypes showed no significant difference between the ESRD group and controls. In conclusion, AGT gene polymorphisms rs699 and rs4762 were associated with non-ESRD versus controls, without any significant risk observed in all patient groups. However, the AGT (rs4762) variant showed a significant risk in the ESRD group in comparison to controls in Egyptians. Full article
(This article belongs to the Special Issue Application of Genomic Technology in Disease Outcome Prediction)
Show Figures

Figure 1

16 pages, 2253 KiB  
Article
Transcriptional Differences between Canine Cutaneous Epitheliotropic Lymphoma and Immune-Mediated Dermatoses
by Nadja Gerber, Magdalena A. T. Brunner, Vidhya Jagannathan, Tosso Leeb, Nora M. Gerhards, Monika M. Welle and Martina Dettwiler
Genes 2021, 12(2), 160; https://doi.org/10.3390/genes12020160 - 25 Jan 2021
Cited by 2 | Viewed by 3030
Abstract
Canine cutaneous epitheliotropic T-cell lymphoma (CETL) and immune-mediated T-cell predominant dermatoses (IMD) share several clinical and histopathological features, but differ substantially in prognosis. The discrimination of ambiguous cases may be challenging, as diagnostic tests are limited and may prove equivocal. This study aimed [...] Read more.
Canine cutaneous epitheliotropic T-cell lymphoma (CETL) and immune-mediated T-cell predominant dermatoses (IMD) share several clinical and histopathological features, but differ substantially in prognosis. The discrimination of ambiguous cases may be challenging, as diagnostic tests are limited and may prove equivocal. This study aimed to investigate transcriptional differences between CETL and IMD, as a basis for further research on discriminating diagnostic biomarkers. We performed 100bp single-end sequencing on RNA extracted from formalin-fixed and paraffin-embedded skin biopsies from dogs with CETL and IMD, respectively. DESeq2 was used for principal component analysis (PCA) and differential gene expression analysis. Genes with significantly different expression were analyzed for enriched pathways using two different tools. The expression of selected genes and their proteins was validated by RT-qPCR and immunohistochemistry. PCA demonstrated the distinct gene expression profiles of CETL and IMD. In total, 503 genes were upregulated, while 4986 were downregulated in CETL compared to IMD. RT-qPCR confirmed the sequencing results for 5/6 selected genes tested, while the protein expression detected by immunohistochemistry was not entirely consistent. Our study revealed transcriptional differences between canine CETL and IMD, with similarities to human cutaneous lymphoma. Differentially expressed genes are potential discriminatory markers, but require further validation on larger sample collections. Full article
(This article belongs to the Special Issue Application of Genomic Technology in Disease Outcome Prediction)
Show Figures

Figure 1

Review

Jump to: Research

13 pages, 1112 KiB  
Review
A Simple Practical Guide to Genomic Diagnostics in a Pediatric Setting
by Alan Taylor, Zeinab Alloub and Ahmad Abou Tayoun
Genes 2021, 12(6), 818; https://doi.org/10.3390/genes12060818 - 27 May 2021
Cited by 4 | Viewed by 3359
Abstract
With limited access to trained clinical geneticists and/or genetic counselors in the majority of healthcare systems globally, and the expanding use of genetic testing in all specialties of medicine, many healthcare providers do not receive the relevant support to order the most appropriate [...] Read more.
With limited access to trained clinical geneticists and/or genetic counselors in the majority of healthcare systems globally, and the expanding use of genetic testing in all specialties of medicine, many healthcare providers do not receive the relevant support to order the most appropriate genetic test for their patients. Therefore, it is essential to educate all healthcare providers about the basic concepts of genetic testing and how to properly utilize this testing for each patient. Here, we review the various genetic testing strategies and their utilization based on different clinical scenarios, and test characteristics, such as the types of genetic variation identified by each test, turnaround time, and diagnostic yield for different clinical indications. Additional considerations such as test cost, insurance reimbursement, and interpretation of variants of uncertain significance are also discussed. The goal of this review is to aid healthcare providers in utilizing the most appropriate, fastest, and most cost-effective genetic test for their patients, thereby increasing the likelihood of a timely diagnosis and reducing the financial burden on the healthcare system by eliminating unnecessary and redundant testing. Full article
(This article belongs to the Special Issue Application of Genomic Technology in Disease Outcome Prediction)
Show Figures

Figure 1

20 pages, 772 KiB  
Review
Liquid Biopsies: Applications for Cancer Diagnosis and Monitoring
by Ivana Martins, Ilda Patrícia Ribeiro, Joana Jorge, Ana Cristina Gonçalves, Ana Bela Sarmento-Ribeiro, Joana Barbosa Melo and Isabel Marques Carreira
Genes 2021, 12(3), 349; https://doi.org/10.3390/genes12030349 - 27 Feb 2021
Cited by 107 | Viewed by 8050
Abstract
The minimally—or non-invasive detection of circulating tumor-derived components in biofluids, such as blood, liquid biopsy is a revolutionary approach with significant potential for the management of cancer. Genomic and transcriptomic alterations can be accurately detected through liquid biopsies, which provide a more comprehensive [...] Read more.
The minimally—or non-invasive detection of circulating tumor-derived components in biofluids, such as blood, liquid biopsy is a revolutionary approach with significant potential for the management of cancer. Genomic and transcriptomic alterations can be accurately detected through liquid biopsies, which provide a more comprehensive characterization of the heterogeneous tumor profile than tissue biopsies alone. Liquid biopsies could assist diagnosis, prognosis, and treatment selection, and hold great potential to complement current surveilling strategies to monitor disease evolution and treatment response in real-time. In particular, these are able to detect minimal residual disease, to predict progression, and to identify mechanisms of resistance, allowing to re-orient treatment strategies in a timelier manner. In this review we gathered current knowledge regarding the role and potential of liquid biopsies for the diagnosis and follow-up of cancer patients. The presented findings emphasize the strengths of liquid biopsies, revealing their chance of improving the diagnosis and monitoring of several tumor types in the near future. However, despite growing evidence supporting their value as a management tool in oncology, some limitations still need to be overcome for their implementation in the routine clinical setting. Full article
(This article belongs to the Special Issue Application of Genomic Technology in Disease Outcome Prediction)
Show Figures

Figure 1

Back to TopTop