Employing a systematic approach to biobanking and analyzing clinical and genetic data for advancing COVID-19 research

Daga, Sergio; Fallerini, Chiara; Baldassarri, Margherita; Fava, Francesca; Valentino, Floriana; Doddato, Gabriella; Benetti, Elisa; Furini, Simone; Giliberti, Annarita; Tita, Rossella; Amitrano, Sara; Bruttini, Mirella; Meloni, Ilaria; Pinto, Anna Maria; Raimondi, Francesco; Stella, Alessandra; Biscarini, Filippo; Picchiotti, Nicola; Gori, Marco; Pinoli, Pietro; Ceri, Stefano; Sanarico, Maurizio; Crawley, Francis P.; Birolo, Giovanni; Renieri, Alessandra; Mari, Francesca; Frullanti, Elisa

doi:10.1038/s41431-020-00793-7

Cited by 38 publications

(49 citation statements)

References 32 publications

(34 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We applied LASSO logistic regression analysis, after correcting for Principal Components, to a synthetic boolean representation of the entire set of genes of the X chromosome on the extreme phenotypic ends of the male subset of the Italian GEN-COVID cohort (https://sites.google.com/dbm.unisi.it/gen-covid) 11 . The GEN-COVID study was consistent with Toll-like receptor (TLR7) was picked up as one of the most important susceptibility genes by LASSO Logistic Regression analysis (Figure 1).…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Association of Toll-like receptor 7 variants with life-threatening COVID-19 disease in males: findings from a nested case-control study

Fallerini

Daga

Mantovani

et al. 2021

eLife

Self Cite

161

155

View full text Add to dashboard Cite

Background: Recently, loss-of-function variants in TLR7 were identified in two families in which COVID-19 segregates like an X-linked recessive disorder environmentally conditioned by SARS-CoV-2. We investigated whether the two families represent the tip of the iceberg of a subset of COVID-19 male patients. Methods: This is a nested case-control study in which we compared male participants with extreme phenotype selected from the Italian GEN-COVID cohort of SARS-CoV-2-infected participants (<60y, 79 severe cases versus 77 control cases). We applied the LASSO Logistic Regression analysis, considering only rare variants on young male subsets with extreme phenotype, picking up TLR7 as the most important susceptibility gene. Results: Overall, we found TLR7 deleterious variants in 2.1% of severely affected males and in none of the asymptomatic participants. The functional gene expression profile analysis demonstrated a reduction in TLR7-related gene expression in patients compared with controls demonstrating an impairment in type I and II IFN responses. Conclusion: Young males with TLR7 loss-of-function variants and severe COVID-19 represent a subset of male patients contributing to disease susceptibility in up to 2% of severe COVID-19.

show abstract

Section: Resultsmentioning

confidence: 99%

“…A subset of 156 young (<60 years) male COVID-19 patients was selected from the Italian GEN-COVID cohort of 1,178 SARS-CoV-2-infected participants (https://sites.google.com/dbm.unisi.it/gen-covid) 11 . The study (GEN-COVID) was consistent with…”

Section: Patients and Samplesmentioning

confidence: 99%

Association of Toll-like receptor 7 variants with life-threatening COVID-19 disease in males: findings from a nested case-control study

Fallerini

Daga

Mantovani

et al. 2021

eLife

Self Cite

161

155

View full text Add to dashboard Cite

show abstract

“…Whole Exome Sequencing (WES) data stored in the Genetic Data Repository of the GEN-COVID Multicenter Study (GCGDR) and coming from biospecimens of 1,300 SARS-CoV-2 PCR positive subjects were used for the analysis [7]. Since age and gender are strong determinants of the clinical outcome, we stratified the cohort by gender and then applied ordered logistic regression to re-classify the patients ( Figure 1) as: i) severe, subjects falling above the expected treatment according to age; ii) intermediate, subjects matching the expected treatment according to age; iii) mild = subjects falling below the expected treatment according to age.…”

Section: Resultsmentioning

confidence: 99%

“…Demographic and clinical characteristics of the data set were reported in [7]. The number of cases used in this study is 1,318.…”

Section: Methodsmentioning

confidence: 99%

“…In a previous study, we explored host genetics through Whole Exome Sequencing analysis (WES) in a cohort of 35 hospitalized COVID-19 patients, which led to the definition of a preliminarily combined model of rare and common variants impacting the clinical outcome [6]. Then, within the Italian GEN-COVID Multicenter Study, biospecimens from more than 1,000 SARS-CoV-2 positive individuals have been collected in the GEN-COVID Biobank (GCB), with clinical data stored in the related Patient Registry (GCPR), and genetic data have in the connected Genetic Data Repository (GCGDR) [7]. SNP genotyping of this cohort contributed to the identification of some loci associated with COVID-19 [3], while WES analysis pinpointed additional common non bi-allelic polymorphisms [8] and rare variants [9].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Post-Mendelian genetic model in COVID-19

Picchiotti

Benetti

Fallerini

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Host genetics is an emerging theme in COVID-19 and few common polymorphisms and some rare variants have been identified, either by GWAS or candidate gene approach, respectively. However, an organic model is still missing. Here, we propose a new model that takes into account common and rare germline variants applied in a cohort of 1,300 Italian SARS-CoV-2 positive individuals. Ordered logistic regression of clinical WHO grading on sex and age was used to obtain a binary phenotypic classification. Genetic variability from WES was synthesized in several boolean representations differentiated according to allele frequencies and genotype effect. LASSO logistic regression was used for extracting relevant genes. We defined about 100 common driver polymorphisms corresponding to classical “threshold model”. Extracted genes were demonstrated to be gender specific. Stochastic rare more penetrant events on about additional 100 extracted genes, when occurred in a medium or severe background (common within the family), simulate Mendelian inheritance in 14% of subjects (having only 1 mutation) or oligogenic inheritance (in 10% having 2 mutations, in 11% having 3 mutations, etc).The combined effect of common and rare results can be described as an integrated polygenic score computed as: (nseverity − nmildness) + F (mseverity − mmildness)where n is the number of common driver genes, m is the number of driver rare variants and F is a factor for appropriately weighing the more powerful rare variants. We called the model “post-Mendelian”. The model well describes the cohort, and patients are clustered in severe or mild by the integrated polygenic scores, the F factor being calibrated around 2, with a prediction capacity of 65% in males and 70% in females. In conclusion, this is the first comprehensive model interpreting host genetics in a holistic post-Mendelian manner. Further validations are needed in order to consolidate and refine the model which however holds true in thousands of SARS-CoV-2 Italian subjects.

show abstract

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

et al. 2021

Self Cite

View full text Add to dashboard Cite

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.

show abstract

Employing a systematic approach to biobanking and analyzing clinical and genetic data for advancing COVID-19 research

Cited by 38 publications

References 32 publications

Association of Toll-like receptor 7 variants with life-threatening COVID-19 disease in males: findings from a nested case-control study

Association of Toll-like receptor 7 variants with life-threatening COVID-19 disease in males: findings from a nested case-control study

Post-Mendelian genetic model in COVID-19

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

Contact Info

Product

Resources

About