scholarly journals An explainable model of host genetic interactions linked to COVID-19 severity

2022 ◽  
Author(s):  
Anthony Onoja ◽  
Nicola Picchiotti ◽  
Chiara Fallerini ◽  
Margherita Baldassarri ◽  
Francesca Fava ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Genetic Factors ◽  
Association Studies ◽  
Principal Component ◽  
Phenotypic Traits ◽  
Genome Wide Association Studies ◽  
Lectin Receptor ◽  
Increased Risk ◽  
Importance Analysis ◽  
Host Genetic

Abstract We employed a multifaceted computational strategy to identify the genetic factors contributing to increased risk of severe COVID-19 infection from a Whole Exome Sequencing (WES) dataset of a cohort of 2000 Italian patients. We coupled a stratified k-fold screening, to rank variants more associated with severity, with training of multiple supervised classifiers, to predict severity on the basis of screened features. Feature importance analysis from tree-based models allowed to identify a handful of 16 variants with highest support which, together with age and gender covariates, were found to be most predictive of COVID-19 severity. When tested on a follow-up cohort, our ensemble of models predicted severity with good accuracy (ACC=81.88%; ROC_AUC=96%; MCC=61.55%). Principal Component Analysis (PCA) and clustering of patients on important variants orthogonally identified two groups of individuals with a higher fraction of severe cases. Our model recapitulated a vast literature of emerging molecular mechanisms and genetic factors linked to COVID-19 response and extends previous landmark Genome Wide Association Studies (GWAS). It revealed a network of interplaying genetic signatures converging on established immune system and inflammatory processes linked to viral infection response, such as JAK-STAT, Cytokine, Interleukin, and C-type lectin receptor signaling. It also identified additional processes cross-talking with immune pathways, such as GPCR signalling, which might offer additional opportunities for therapeutic intervention and patient stratification. Publicly available PheWAS datasets revealed that several variants were significantly associated with phenotypic traits such as “Respiratory or thoracic disease”, confirming their link with COVID-19 severity outcome. Taken together, our analysis suggests that curated genetic information can be effectively integrated along with other patient clinical covariates to forecast COVID-19 disease severity and dissect the underlying host genetic mechanisms for personalized medicine treatments.

scholarly journals An explainable model of host genetic interactions linked to COVID-19 severity

2021 ◽  
Author(s):  
Anthony Onoja ◽  
Nicola Picchiotti ◽  
Chiara Fallerini ◽  
Margherita Baldassarri ◽  
Francesca Fava ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Genetic Factors ◽  
Association Studies ◽  
Principal Component ◽  
Phenotypic Traits ◽  
Genome Wide Association Studies ◽  
Lectin Receptor ◽  
Increased Risk ◽  
Importance Analysis ◽  
Host Genetic

Abstract We employed a multifaceted computational strategy to identify the genetic factors contributing to increased risk of severe COVID-19 infection from a Whole Exome Sequencing (WES) dataset of a cohort of 2000 Italian patients. We coupled a stratified k-fold screening, to rank variants more associated with severity, with training of multiple supervised classifiers, to predict severity on the basis of screened features. Feature importance analysis from decision-tree models allowed to identify a handful of 16 variants with highest support which, together with age and gender covariates, were found to be most predictive of COVID-19 severity. When tested on a follow-up cohort, our ensemble of models predicted severity with good accuracy (ACC=81.88%; ROC_AUC=96%; MCC=61.55%). Principal Component Analysis (PCA) and clustering of patients on important variants orthogonally identified two groups of individuals with a higher fraction of severe cases. Our model recapitulated a vast literature of emerging molecular mechanisms and genetic factors linked to COVID-19 response and extends previous landmark Genome Wide Association Studies (GWAS). It revealed a network of interplaying genetic signatures converging on established immune system and inflammatory processes linked to viral infection response, such as JAK-STAT, Cytokine, Interleukin, and C-type lectin receptor signaling. It also identified additional processes cross-talking with immune pathways, such as GPCR signalling, which might offer additional opportunities for therapeutic intervention and patient stratification. Publicly available PheWAS datasets revealed that several variants were significantly associated with phenotypic traits (e.g. “Respiratory or thoracic disease”), confirming their link with COVID-19 severity outcome. Taken together, our analysis suggests that curated genetic information can be effectively integrated along with other patient clinical covariates to forecast COVID-19 disease severity and dissect the underlying host genetic mechanisms for personalized medicine treatments.

scholarly journals Genomic Analysis, Progress and Future Perspectives in Dairy Cattle Selection: A Review

Animals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 599
Author(s):  
Miguel A. Gutierrez-Reinoso ◽  
Pedro M. Aponte ◽  
Manuel Garcia-Herreros
Keyword(s):  
Dairy Cattle ◽  
Production Efficiency ◽  
Association Studies ◽  
Phenotypic Traits ◽  
Progeny Testing ◽  
Genome Wide Association Studies ◽  
Genetic Progress ◽  
Breeding Programs ◽  
Increased Risk ◽  
Selection Of

Genomics comprises a set of current and valuable technologies implemented as selection tools in dairy cattle commercial breeding programs. The intensive progeny testing for production and reproductive traits based on genomic breeding values (GEBVs) has been crucial to increasing dairy cattle productivity. The knowledge of key genes and haplotypes, including their regulation mechanisms, as markers for productivity traits, may improve the strategies on the present and future for dairy cattle selection. Genome-wide association studies (GWAS) such as quantitative trait loci (QTL), single nucleotide polymorphisms (SNPs), or single-step genomic best linear unbiased prediction (ssGBLUP) methods have already been included in global dairy programs for the estimation of marker-assisted selection-derived effects. The increase in genetic progress based on genomic predicting accuracy has also contributed to the understanding of genetic effects in dairy cattle offspring. However, the crossing within inbred-lines critically increased homozygosis with accumulated negative effects of inbreeding like a decline in reproductive performance. Thus, inaccurate-biased estimations based on empirical-conventional models of dairy production systems face an increased risk of providing suboptimal results derived from errors in the selection of candidates of high genetic merit-based just on low-heritability phenotypic traits. This extends the generation intervals and increases costs due to the significant reduction of genetic gains. The remarkable progress of genomic prediction increases the accurate selection of superior candidates. The scope of the present review is to summarize and discuss the advances and challenges of genomic tools for dairy cattle selection for optimizing breeding programs and controlling negative inbreeding depression effects on productivity and consequently, achieving economic-effective advances in food production efficiency. Particular attention is given to the potential genomic selection-derived results to facilitate precision management on modern dairy farms, including an overview of novel genome editing methodologies as perspectives toward the future.

scholarly journals A systematic review and meta-analysis of host genetic factors associated with influenza severity

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Nina Van Goethem ◽  
Célestin Danwang ◽  
Nathalie Bossuyt ◽  
Herman Van Oyen ◽  
Nancy H. C. Roosens ◽  
...  
Keyword(s):  
Systematic Review ◽  
Genetic Factors ◽  
Association Studies ◽  
Influenza Infection ◽  
Meta Analysis ◽  
Genetic Association Studies ◽  
Genome Wide Association Studies ◽  
Severe Influenza ◽  
Host Genetic ◽  
Host Genetic Factors

Abstract Background The severity of influenza disease can range from mild symptoms to severe respiratory failure and can partly be explained by host genetic factors that predisposes the host to severe influenza. Here, we aimed to summarize the current state of evidence that host genetic variants play a role in the susceptibility to severe influenza infection by conducting a systematic review and performing a meta-analysis for all markers with at least three or more data entries. Results A total of 34 primary human genetic association studies were identified that investigated a total of 20 different genes. The only significant pooled ORs were retrieved for the rs12252 polymorphism: an overall OR of 1.52 (95% CI [1.06–2.17]) for the rs12252-C allele compared to the rs12252-T allele. A stratified analysis by ethnicity revealed opposite effects in different populations. Conclusion With exception for the rs12252 polymorphism, we could not identify specific genetic polymorphisms to be associated with severe influenza infection in a pooled meta-analysis. This advocates for the use of large, hypothesis-free, genome-wide association studies that account for the polygenic nature and the interactions with other host, pathogen and environmental factors.

scholarly journals Exploration of Lung Cancer-Related Genetic Factors via Mendelian Randomization Method Based on Genomic and Transcriptomic Summarized Data

2021 ◽  
Vol 9 ◽  
Author(s):  
Nitao Cheng ◽  
Xinran Cui ◽  
Chen Chen ◽  
Changsheng Li ◽  
Jingyu Huang
Keyword(s):  
Lung Cancer ◽  
Lung Carcinoma ◽  
Large Scale ◽  
Genetic Factors ◽  
Mendelian Randomization ◽  
Familial Aggregation ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Increased Risk ◽  
The Impact

Lung carcinoma is one of the most deadly malignant tumors in mankind. With the rising incidence of lung cancer, searching for the high effective cures become more and more imperative. There has been sufficient research evidence that living habits and situations such as smoking and air pollution are associated with an increased risk of lung cancer. Simultaneously, the influence of individual genetic susceptibility on lung carcinoma morbidity has been confirmed, and a growing body of evidence has been accumulated on the relationship between various risk factors and the risk of different pathological types of lung cancer. Additionally, the analyses from many large-scale cancer registries have shown a degree of familial aggregation of lung cancer. To explore lung cancer-related genetic factors, Genome-Wide Association Studies (GWAS) have been used to identify several lung cancer susceptibility sites and have been widely validated. However, the biological mechanism behind the impact of these site mutations on lung cancer remains unclear. Therefore, this study applied the Summary data-based Mendelian Randomization (SMR) model through the integration of two GWAS datasets and four expression Quantitative Trait Loci (eQTL) datasets to identify susceptibility genes. Using this strategy, we found ten of Single Nucleotide Polymorphisms (SNPs) sites that affect the occurrence and development of lung tumors by regulating the expression of seven genes. Further analysis of the signaling pathway about these genes not only provides important clues to explain the pathogenesis of lung cancer but also has critical significance for the diagnosis and treatment of lung cancer.

scholarly journals Maternal Immune Activation and Schizophrenia–Evidence for an Immune Priming Disorder

2021 ◽  
Vol 12 ◽  
Author(s):  
Zahra Choudhury ◽  
Belinda Lennox
Keyword(s):  
Animal Models ◽  
Immune Activation ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Neurodevelopmental Disorder ◽  
Genome Wide Association Studies ◽  
Maternal Infection ◽  
Immune Priming ◽  
Maternal Immune Activation ◽  
Increased Risk

Schizophrenia is a complex neurodevelopmental disorder affecting around 19. 8 million people worldwide. The etiology of the disorder is due to many interacting genetic and environmental factors, with no one element causing the full spectrum of disease symptoms. Amongst these factors, maternal immune activation (MIA) acting during specific gestational timings has been implicated in increasing schizophrenia risk in offspring. Epidemiological studies have provided the rationale for this link with prevalence of maternal infection correlating to increased risk, but these studies have been unable to prove causality due to lack of control of confounding factors like genetic susceptibility and inability to identify specific cellular and molecular mechanisms. Animal models have proved significantly more useful in establishing the extent to which MIA can predispose an individual to schizophrenia, displaying how maternal infection alone can directly result in behavioral abnormalities in rodent offspring. Alongside information from genome wide association studies (GWAS), animal models have been able to identify the role of complement proteins, particularly C4, and display how alterations in this system can cause development of schizophrenia-associated neuropathology and behavior. This article will review the current literature in order to assess whether schizophrenia can, therefore, be viewed as an immune priming disorder.

scholarly journals Metabolic Host–Microbiota Interactions in Autophagy and the Pathogenesis of Inflammatory Bowel Disease (IBD)

2021 ◽  
Vol 14 (8) ◽  
pp. 708
Author(s):  
Alexander S. Dowdell ◽  
Sean P. Colgan
Keyword(s):  
Inflammatory Bowel Disease ◽  
Bowel Disease ◽  
Cellular Response ◽  
Molecular Mechanisms ◽  
Current Knowledge ◽  
Association Studies ◽  
The United States ◽  
Genome Wide Association Studies ◽  
Increased Risk ◽  
Inflammatory Bowel

Inflammatory bowel disease (IBD) is a family of conditions characterized by chronic, relapsing inflammation of the gastrointestinal tract. IBD afflicts over 3 million adults in the United States and shows increasing prevalence in the Westernized world. Current IBD treatments center on modulation of the damaging inflammatory response and carry risks such as immunosuppression, while the development of more effective treatments is hampered by our poor understanding of the molecular mechanisms of IBD pathogenesis. Previous genome-wide association studies (GWAS) have demonstrated that gene variants linked to the cellular response to microorganisms are most strongly associated with an increased risk of IBD. These studies are supported by mechanistic work demonstrating that IBD-associated polymorphisms compromise the intestine’s anti-microbial defense. In this review, we summarize the current knowledge regarding IBD as a disease of defects in host–microbe interactions and discuss potential avenues for targeting this mechanism for future therapeutic development.

scholarly journals Deep resequencing identifies candidate functional genes in leprosy GWAS loci

2021 ◽  
Vol 15 (12) ◽  
pp. e0010029
Author(s):  
Vinicius M. Fava ◽  
Monica Dallmann-Sauer ◽  
Marianna Orlova ◽  
Wilian Correa-Macedo ◽  
Nguyen Van Thuc ◽  
...  
Keyword(s):  
Genetic Epidemiology ◽  
Genetic Factors ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Mycobacterial Disease ◽  
Protein Coding ◽  
The Past ◽  
Genome Wide ◽  
Host Genetic ◽  
Coding Variants

Leprosy is the second most prevalent mycobacterial disease globally. Despite the existence of an effective therapy, leprosy incidence has consistently remained above 200,000 cases per year since 2010. Numerous host genetic factors have been identified for leprosy that contribute to the persistently high case numbers. In the past decade, genetic epidemiology approaches, including genome-wide association studies (GWAS), identified more than 30 loci contributing to leprosy susceptibility. However, GWAS loci commonly encompass multiple genes, which poses a challenge to define causal candidates for each locus. To address this problem, we hypothesized that genes contributing to leprosy susceptibility differ in their frequencies of rare protein-altering variants between cases and controls. Using deep resequencing we assessed protein-coding variants for 34 genes located in GWAS or linkage loci in 555 Vietnamese leprosy cases and 500 healthy controls. We observed 234 nonsynonymous mutations in the targeted genes. A significant depletion of protein-altering variants was detected for the IL18R1 and BCL10 genes in leprosy cases. The IL18R1 gene is clustered with IL18RAP and IL1RL1 in the leprosy GWAS locus on chromosome 2q12.1. Moreover, in a recent GWAS we identified an HLA-independent signal of association with leprosy on chromosome 6p21. Here, we report amino acid changes in the CDSN and PSORS1C2 genes depleted in leprosy cases, indicating them as candidate genes in the chromosome 6p21 locus. Our results show that deep resequencing can identify leprosy candidate susceptibility genes that had been missed by classic linkage and association approaches.

scholarly journals Implementing Privacy-Preserving Genotype Analysis with Consideration for Population Stratification

Cryptography ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 21
Author(s):  
Andre Ostrak ◽  
Jaak Randmets ◽  
Ville Sokk ◽  
Sven Laur ◽  
Liina Kamm
Keyword(s):  
Population Stratification ◽  
Data Exchange ◽  
Association Studies ◽  
Principal Component ◽  
Heterogeneous Data ◽  
Privacy Preserving ◽  
Phenotypic Traits ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Secure Computing

In bioinformatics, genome-wide association studies (GWAS) are used to detect associations between single-nucleotide polymorphisms (SNPs) and phenotypic traits such as diseases. Significant differences in SNP counts between case and control groups can signal association between variants and phenotypic traits. Most traits are affected by multiple genetic locations. To detect these subtle associations, bioinformaticians need access to more heterogeneous data. Regulatory restrictions in cross-border health data exchange have created a surge in research on privacy-preserving solutions, including secure computing techniques. However, in studies of such scale, one must account for population stratification, as under- and over-representation of sub-populations can lead to spurious associations. We improve on the state of the art of privacy-preserving GWAS methods by showing how to adapt principal component analysis (PCA) with stratification control (EIGENSTRAT), FastPCA, EMMAX and the genomic control algorithm for secure computing. We implement these methods using secure computing techniques—secure multi-party computation (MPC) and trusted execution environments (TEE). Our algorithms are the most complex ones at this scale implemented with MPC. We present performance benchmarks and a security and feasibility trade-off discussion for both techniques.

scholarly journals Genome-Wide Polygenic Risk Score for Predicting High Risk Glaucoma Individuals of Han Chinese Ancestry

2021 ◽  
Vol 11 (11) ◽  
pp. 1169
Author(s):  
Yu-Jer Hsiao ◽  
Hao-Kai Chuang ◽  
Sheng-Chu Chi ◽  
Yung-Yu Wang ◽  
Pin-Hsuan Chiang ◽  
...  
Keyword(s):  
Risk Score ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Han Chinese ◽  
Polygenic Risk Score ◽  
Genome Wide Association Studies ◽  
Genetic Characteristics ◽  
Polygenic Risk ◽  
Genome Wide ◽  
Increased Risk

Glaucoma is a progressive and irreversible blindness-causing disease. However, the underlying genetic factors and molecular mechanisms remain poorly understood. Previous genome-wide association studies (GWAS) have made tremendous progress on the SNP-based disease association and characterization. However, most of them were conducted for Europeans. Since differential genetic characteristics among ethnic groups were evident in glaucoma, it is worthwhile to complete its genetic landscape from the larger cohorts of Asian individuals. Here, we present a GWAS based on the Taiwan Biobank. Among 1013 glaucoma patients and 36,562 controls, we identified a total of 138 independent glaucoma-associated SNPs at the significance level of p < 1 × 10−5. After clumping genetically linked SNPs (LD clumping), 134 independent SNPs with p < 10−4 were recruited to construct a Polygenic Risk Score (PRS). The model achieved an area under the receiver operating characteristic curve (AUC) of 0.8387 (95% CI = [0.8269–0.8506]), and those within the top PRS quantile had a 45.48-fold increased risk of glaucoma compared with those within the lowest quantile. The PRS model was validated with an independent cohort that achieved an AUC of 0.7283, thereby showing the effectiveness of our polygenic risk score in predicting individuals in the Han Chinese population with higher glaucoma risks.

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