Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity.
Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity.
复制标题
DOI：
10.1007/s00439-021-02397-7
复制
发表时间：
2022-01
期刊：
影响因子：
5.3
通讯作者：
Furini S
中科院分区：
文献类型：
--
作者：
Fallerini C;Picchiotti N;Baldassarri M;Zguro K;Daga S;Fava F;Benetti E;Amitrano S;Bruttini M;Palmieri M;Croci S;Lista M;Beligni G;Valentino F;Meloni I;Tanfoni M;Minnai F;Colombo F;Cabri E;Fratelli M;Gabbi C;Mantovani S;Frullanti E;Gori M;Crawley FP;Butler-Laporte G;Richards B;Zeberg H;Lipcsey M;Hultström M;Ludwig KU;Schulte EC;Pairo-Castineira E;Baillie JK;Schmidt A;Frithiof R;WES/WGS Working Group Within the HGI;GenOMICC Consortium;GEN-COVID Multicenter Study;Mari F;Renieri A;Furini S
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. The online version contains supplementary material available at 10.1007/s00439-021-02397-7.
登录
查看更多内容
在586,157个人中，泛管理外显域的联想分析。
影响因子：
9.8
作者：
Kosmicki JA;Horowitz JE;Banerjee N;Lanche R;Marcketta A;Maxwell E;Bai X;Sun D;Backman JD;Sharma D;Kury FSP;Kang HM;O'Dushlaine C;Yadav A;Mansfield AJ;Li AH;Watanabe K;Gurski L;McCarthy SE;Locke AE;Khalid S;O'Keeffe S;Mbatchou J;Chazara O;Huang Y;Kvikstad E;O'Neill A;Nioi P;Parker MM;Petrovski S;Runz H;Szustakowski JD;Wang Q;Wong E;Cordova-Palomera A;Smith EN;Szalma S;Zheng X;Esmaeeli S;Davis JW;Lai YP;Chen X;Justice AE;Leader JB;Mirshahi T;Carey DJ;Verma A;Sirugo G;Ritchie MD;Rader DJ;Povysil G;Goldstein DB;Kiryluk K;Pairo-Castineira E;Rawlik K;Pasko D;Walker S;Meynert A;Kousathanas A;Moutsianas L;Tenesa A;Caulfield M;Scott R;Wilson JF;Baillie JK;Butler-Laporte G;Nakanishi T;Lathrop M;Richards JB;Regeneron Genetics Center;UKB Exome Sequencing Consortium;Jones M;Balasubramanian S;Salerno W;Shuldiner AR;Marchini J;Overton JD;Habegger L;Cantor MN;Reid JG;Baras A;Abecasis GR;Ferreira MAR
通讯作者：
Ferreira MAR
影响因子：
64.8
作者：
COVID-19 Host Genetics Initiative
通讯作者：
COVID-19 Host Genetics Initiative
影响因子：
3.7
作者：
Benetti E;Giliberti A;Emiliozzi A;Valentino F;Bergantini L;Fallerini C;Anedda F;Amitrano S;Conticini E;Tita R;d'Alessandro M;Fava F;Marcantonio S;Baldassarri M;Bruttini M;Mazzei MA;Montagnani F;Mandalà M;Bargagli E;Furini S;GEN-COVID Multicenter Study;Renieri A;Mari F
通讯作者：
Mari F
影响因子：
168.9
作者：
Huang, Chaolin;Wang, Yeming;Cao, Bin
通讯作者：
Cao, Bin
影响因子：
5.2
作者：
Benetti, Elisa;Tita, Rossella;Pinto, Anna Maria
通讯作者：
Pinto, Anna Maria