Publications

BibTex format

@article{Fallerini:2022:10.1007/s00439-021-02397-7,
author = {Fallerini, C and Picchiotti, N and Baldassarri, M and Zguro, K and Daga, S and Fava, F and Benetti, E and Amitrano, S and Bruttini, M and Palmieri, M and Croci, S and Lista, M and Beligni, G and Valentino, F and Meloni, I and Tanfoni, M and Minnai, F and Colombo, F and Cabri, E and Fratelli, M and Gabbi, C and Mantovani, S and Frullanti, E and Gori, M and Crawley, FP and Butler-Laporte, G and Richards, B and Zeberg, H and Lipcsey, M and Hultström, M and Ludwig, KU and Schulte, EC and Pairo-Castineira, E and Baillie, JK and Schmidt, A and Frithiof, R and WESWGS, Working Group Within the HGI and GenOMICC, Consortium and GEN-COVID, Multicenter Study and Mari, F and Renieri, A and Furini, S},
doi = {10.1007/s00439-021-02397-7},
journal = {Human Genetics},
pages = {147--173},
title = {Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity},
url = {http://dx.doi.org/10.1007/s00439-021-02397-7},
volume = {141},
year = {2022}
}

Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Fallerini,C
AU  - Picchiotti,N
AU  - Baldassarri,M
AU  - Zguro,K
AU  - Daga,S
AU  - Fava,F
AU  - Benetti,E
AU  - Amitrano,S
AU  - Bruttini,M
AU  - Palmieri,M
AU  - Croci,S
AU  - Lista,M
AU  - Beligni,G
AU  - Valentino,F
AU  - Meloni,I
AU  - Tanfoni,M
AU  - Minnai,F
AU  - Colombo,F
AU  - Cabri,E
AU  - Fratelli,M
AU  - Gabbi,C
AU  - Mantovani,S
AU  - Frullanti,E
AU  - Gori,M
AU  - Crawley,FP
AU  - Butler-Laporte,G
AU  - Richards,B
AU  - Zeberg,H
AU  - Lipcsey,M
AU  - Hultström,M
AU  - Ludwig,KU
AU  - Schulte,EC
AU  - Pairo-Castineira,E
AU  - Baillie,JK
AU  - Schmidt,A
AU  - Frithiof,R
AU  - WESWGS,Working Group Within the HGI
AU  - GenOMICC,Consortium
AU  - GEN-COVID,Multicenter Study
AU  - Mari,F
AU  - Renieri,A
AU  - Furini,S
DO  - 10.1007/s00439-021-02397-7
EP  - 173
PY  - 2022///
SN  - 0340-6717
SP  - 147
TI  - Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity
T2  - Human Genetics
UR  - http://dx.doi.org/10.1007/s00439-021-02397-7
UR  - https://www.ncbi.nlm.nih.gov/pubmed/34889978
UR  - http://hdl.handle.net/10044/1/96008
VL  - 141
ER  -

Download

DrBrijeshPatel

Contact

Location

Summary

Citation

BibTex format

RIS format (EndNote, RefMan)