scholarly journals Genome-wide association studies of Shigella spp. and Enteroinvasive Escherichia coli isolates demonstrate an absence of genetic markers for prediction of disease severity

BMC Genomics ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Amber C. A. Hendriks ◽  
◽  
Frans A. G. Reubsaet ◽  
A. M. D. ( Mirjam) Kooistra-Smid ◽  
John W. A. Rossen ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Disease Severity ◽  
Genetic Markers ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Shigella Spp

scholarly journals Genome-wide association studies of Shigella spp. and Enteroinvasive Escherichia coli isolates demonstrate an absence of genetic markers for prediction of disease severity

2019 ◽  
Author(s):  
Amber C. A. Hendriks ◽  
Frans A.G. Reubsaet ◽  
A.M.D. (Mirjam) Kooistra ◽  
John W. A. Rossen ◽  
Bas E. Dutilh ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Disease Severity ◽  
Genetic Variants ◽  
Association Studies ◽  
Control Measures ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Severity Scores ◽  
Shigella Spp

Abstract Background We investigated the association of symptoms and disease severity of shigellosis patients with genetic determinants of infecting Shigella and entero-invasive Escherichia coli (EIEC), because determinants that predict disease outcome per individual patient could be used to prioritize control measures. For this purpose, genome wide association studies (GWAS) were performed using presence or absence of single genes, combinations of genes, and k-mers. All genetic variants were derived from draft genome sequences of isolates from a multicenter cross-sectional study conducted in the Netherlands during 2016 and 2017. Clinical data of patients consisting of binary/dichotomous representation of symptoms and their calculated severity scores were also available from this study. To verify the suitability of the used methods, the genetic differences between the genera Shigella and Escherichia were used as control. Results The obtained isolates were representative for a population structure as encountered in a Western European country. No association was found between single genes or combinations of genes and separate symptoms or disease severity scores. One potentially associated intergenic region was found using a k-mer approach, however, this turned out to be a false positive. Our benchmark characteristic, genus, resulted in eight associated genes and >3,000,000 k-mers, indicating adequate performance of the used algorithms. Conclusions To conclude, using several microbial GWAS methods, genetic variants in Shigella spp. and EIEC that can predict specific symptoms or a more severe course of disease were not identified, suggesting that disease severity of shigellosis is dependent on other factors than the genetic variation of the infecting bacteria. Specific genes or gene fragments of isolates from patients are unsuitable to predict outcomes and cannot be used for development, prioritization and optimization of guidelines for control measures of shigellosis or infections with EIEC.

scholarly journals Genome-wide association studies of Shigella spp. and Enteroinvasive Escherichia coli isolates demonstrate an absence of genetic markers for prediction of disease severity

2019 ◽  
Author(s):  
Amber C. A. Hendriks ◽  
Frans A.G. Reubsaet ◽  
A.M.D. (Mirjam) Kooistra ◽  
John W. A. Rossen ◽  
Bas E. Dutilh ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Disease Severity ◽  
Genetic Variants ◽  
Association Studies ◽  
Control Measures ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Severity Scores ◽  
Shigella Spp

Abstract Background We investigated the association of symptoms and disease severity of shigellosis patients with genetic determinants of infecting Shigella and entero-invasive Escherichia coli (EIEC), because determinants that predict disease outcome per individual patient could be used to prioritize control measures. For this purpose, genome wide association studies (GWAS) were performed using presence or absence of single genes, combinations of genes, and k-mers. All genetic variants were derived from draft genome sequences of isolates from a multicenter cross-sectional study conducted in the Netherlands during 2016 and 2017. Clinical data of patients consisting of binary/dichotomous representation of symptoms and their calculated severity scores were also available from this study. To verify the suitability of the used methods, the genetic differences between the genera Shigella and Escherichia were used as control. Results The obtained isolates were representative for a population structure as encountered in a Western European country. No association was found between single genes or combinations of genes and separate symptoms or disease severity scores. One potentially associated intergenic region was found using a k-mer approach, however, this turned out to be a false positive. Our benchmark characteristic, genus, resulted in eight associated genes and >3,000,000 k-mers, indicating adequate performance of the used algorithms. Conclusions To conclude, using several microbial GWAS methods, genetic variants in Shigella spp. and EIEC that can predict specific symptoms or a more severe course of disease were not identified, suggesting that disease severity of shigellosis is dependent on other factors than the genetic variation of the infecting bacteria. Specific genes or gene fragments of isolates from patients are unsuitable to predict outcomes and cannot be used for development, prioritization and optimization of guidelines for control measures of shigellosis or infections with EIEC.

scholarly journals Genome-wide association studies of Shigella spp. and Enteroinvasive Escherichia coli isolates demonstrate an absence of genetic markers for prediction of disease severity

2020 ◽  
Author(s):  
Amber C. A. Hendriks ◽  
Frans A.G. Reubsaet ◽  
A.M.D. (Mirjam) Kooistra ◽  
John W. A. Rossen ◽  
Bas E. Dutilh ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Disease Severity ◽  
Genetic Variants ◽  
Association Studies ◽  
Control Measures ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Severity Scores ◽  
Shigella Spp

Abstract Background: We investigated the association of symptoms and disease severity of shigellosis patients with genetic determinants of infecting Shigella and entero-invasive Escherichia coli (EIEC), because determinants that predict disease outcome per individual patient could be used to prioritize control measures. For this purpose, genome wide association studies (GWAS) were performed using presence or absence of single genes, combinations of genes, and k-mers. All genetic variants were derived from draft genome sequences of isolates from a multicenter cross-sectional study conducted in the Netherlands during 2016 and 2017. Clinical data of patients consisting of binary/dichotomous representation of symptoms and their calculated severity scores were also available from this study. To verify the suitability of the methods used, the genetic differences between the genera Shigella and Escherichia were used as control. Results: The isolates obtained were representative of the population structure encountered in other Western European countries. No association was found between single genes or combinations of genes and separate symptoms or disease severity scores. Our benchmark characteristic, genus, resulted in eight associated genes and >3,000,000 k-mers, indicating adequate performance of the algorithms used. Conclusions: To conclude, using several microbial GWAS methods, genetic variants in Shigella spp. and EIEC that can predict specific symptoms or a more severe course of disease were not identified, suggesting that disease severity of shigellosis is dependent on other factors than the genetic variation of the infecting bacteria. Specific genes or gene fragments of isolates from patients are unsuitable to predict outcomes and cannot be used for development, prioritization and optimization of guidelines for control measures of shigellosis or infections with EIEC.

scholarly journals Meta-analysis of Genome Wide Association Studies Identifies Genetic Markers of Late Toxicity Following Radiotherapy for Prostate Cancer

EBioMedicine ◽  
2016 ◽  
Vol 10 ◽  
pp. 150-163 ◽  
Author(s):  
Sarah L. Kerns ◽  
Leila Dorling ◽  
Laura Fachal ◽  
Søren Bentzen ◽  
Paul D.P. Pharoah ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Genetic Markers ◽  
Association Studies ◽  
Meta Analysis ◽  
Late Toxicity ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

scholarly journals Understanding Polycystic Ovary Syndrome: A Multifactorial Endocrine Disorder

2021 ◽  
Author(s):  
Surankita Sukul ◽  
Pushkal Sinduvadi Ramesh ◽  
Narahari Agasti
Keyword(s):  
Polycystic Ovary Syndrome ◽  
Genetic Markers ◽  
Polycystic Ovary ◽  
Association Studies ◽  
Reproductive Age ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Ovary Syndrome ◽  
Endocrine Disorder ◽  
Genome Wide

Polycystic Ovary Syndrome (PCOS) is a common endocrine disorder affecting a significant population of women of reproductive age group worldwide. Due to the complex pathophysiology and overlapping symptoms, this disorder is often difficult to diagnose. Genome-wide association studies have identified several new risk loci and candidate genes for PCOS. However, currently, there are no standard genetic markers for PCOS susceptibility testing owing to the inconsistent findings. Despite the advent of the genomic era, the challenge to identify and pinpoint the heritable genetic basis of PCOS still exists. This mini-review explores the basic definition and phenotypes of PCOS, the different criteria for the diagnosis, the incidence, gestational complications associated with it, the basis of genetic heritability, and the influence of various gene polymorphisms. Also, this review briefly summarises the reports of genome-wide association studies conducted to identify candidate genetic markers to aid in understanding the complex pathophysiology of PCOS.

Venous thromboembolism and prostate cancer: what about genetic markers?

2021 ◽  
Vol 22 (6) ◽  
pp. 365-373
Author(s):  
Sofia Coelho Abreu ◽  
Valéria Tavares ◽  
Filipa Carneiro ◽  
Rui Medeiros
Keyword(s):  
Prostate Cancer ◽  
Venous Thromboembolism ◽  
Genetic Markers ◽  
Association Studies ◽  
Genome Wide Association ◽  
Clinical Implications ◽  
Genome Wide Association Studies ◽  
Genetic Determinants ◽  
Genome Wide ◽  
The Relationship

Aim & methods: To review the existing literature concerning the relationship between venous thromboembolism (VTE) and prostate cancer (PC) and explore the putative biological and clinical implications of VTE genetic markers on PC patients by screening the PubMed database. Results: Considering the roles of VTE genome-wide association studies-identified genetic determinants in disease development in the general population, these variants might also underlie the susceptibility for PC-related VTE. Therefore, they could help to identify those with a positive benefit-to-harm ratio for thromboprophylaxis approaches during cancer therapy management, thereby improving patient’s prognosis. Conclusion: Future studies are mandatory to explore the relationship between VTE and PC and dissect the predictive value of VTE genome-wide association studies-identified genetic determinants in PC patients, given their clinical implications.

Genes, Psychology and Population History

2021 ◽  
Author(s):  
Ken Richardson
Keyword(s):  
Social Policy ◽  
Genetic Markers ◽  
Complex Traits ◽  
Population Stratification ◽  
Association Studies ◽  
Population History ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Historical Dynamics

Genome wide association studies (GWAS) are being increasingly used to identify genetic markers of variation in complex traits such as intelligence and education. However, GWAS are compromised by population stratification (PS) leading to spurious associations, and attempts to correct for them statistically are also proving to be inadequate. This suggests the need for a deeper understanding of the sources of such PS and how its roots in complex social and historical dynamics can seriously mislead interpretations from GWAS/PGS to social policy.

scholarly journals networkGWAS: A network-based approach for genome-wide association studies in structured populations

2021 ◽  
Author(s):  
Giulia Muzio ◽  
Leslie O'Bray ◽  
Laetitia Meng-Papaxanthos ◽  
Juliane Klatt ◽  
Karsten Borgwardt
Keyword(s):  
Genetic Markers ◽  
Complex Traits ◽  
Multiple Testing ◽  
Association Studies ◽  
Search Space ◽  
Structured Populations ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Multiple Testing Correction ◽  
Genome Wide

While the search for associations between genetic markers and complex traits has discovered tens of thousands of trait-related genetic variants, the vast majority of these only explain a tiny fraction of observed phenotypic variation. One possible strategy to detect stronger associations is to aggregate the effects of several genetic markers and to test entire genes, pathways or (sub)networks of genes for association to a phenotype. The latter, network-based genome-wide association studies, in particular suffers from a huge search space and an inherent multiple testing problem. As a consequence, current approaches are either based on greedy feature selection, thereby risking that they miss relevant associations, and/or neglect doing a multiple testing correction, which can lead to an abundance of false positive findings. To address the shortcomings of current approaches of network-based genome-wide association studies, we propose <tt>networkGWAS</tt>, a computationally efficient and statistically sound approach to gene-based genome-wide association studies based on mixed models and neighborhood aggregation. It allows for population structure correction and for well-calibrated p-values, which we obtain through a block permutation scheme. <tt>networkGWAS</tt> successfully detects known or plausible associations on simulated rare variants from H. sapiens data as well as semi-simulated and real data with common variants from A. thaliana and enables the systematic combination of gene-based genome-wide association studies with biological network information.

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