The specific phenotypes studied by the COVID-19 HGI are those that benefit from maximal sample size: primary analysis on disease severity. Two meta-analyses are represented in this track:
Displayed items are colored by GWAS effect: red for positive, blue for negative. -The height of the item reflects the effect size (beta coefficient). -The color saturation indicates statistical significance: p-values smaller than .05 +The height of the item reflects the effect size. The effect size, defined as the +contribution of a SNP to the genetic variance of the trait, was measured as beta coefficient +(beta). The higher the absolute value of the beta coefficient, the stronger the effect. +The color saturation indicates statistical significance: p-values smaller than 1e-5 are brightly colored (bright red , bright blue ), -those with less significance (p >= .05) are paler (light red +those with less significance (p >= 1e-5) are paler (light red , light blue -). For better visualization of the data, only SNPs with p-values smaller than .03 are +). For better visualization of the data, only SNPs with p-values smaller than 1e-3 are displayed by default.
Each track has separate display controls and data can be filtered according to the number of studies, minimum -log10 p-value, and the effect size (beta coefficient), using the track Configure options.
Mouseover on items shows the rs ID (or chrom:pos if none assigned), both the non-effect and effect alleles, the effect size (beta coefficient), the p-value, and the number of studies. Additional information on each variant can be found on the details page by clicking on the item.
-Data were generated from genome-wide SNP array and whole exome and genome sequencing: leveraging -the impact of both common and rare variants. The statistical analysis performed takes into account -differences between sex, ancestry, and date of sample collection. More information about the +COVID-19 Host Genetics Initiative (HGI) GWAS meta-analysis round 3 (July 2020) results were used +in this study. Each participating study partner submitted GWAS summary statistics for up to four +of the COVID-19 phenotype definitions. +
++Data were generated from genome-wide SNP array and whole exome and genome +sequencing, leveraging the impact of both common and rare variants. The statistical analysis +performed takes into account differences between sex, ancestry, and date of sample collection. +Alleles were harmonized across studies and reported allele frequencies are based on gnomAD +version 3.0 reference data. Most study partners used the SAIGE GWAS pipeline in order +to generate summary statistics used for the COVID-19 HGI meta-analysis. The summary statistics +of individual studies were manually examined for inflation, +deflation, and excessive number of false positives. Qualifying summary statistics were filtered for +INFO > 0.6 and MAF > 0.0001 prior to meta-analyzing the entirety of the data. +The meta-analysis was done using inverse variance weighting of effects method, accounting for +strand differences and allele flips in the individual studies. +
++The meta-analysis results of variants appearing in at least three studies (analysis C2) or two +studies (all other analyses) were made publicly available. +The meta-analysis software and workflow are available here. More information about the prospective studies, processing pipeline, results and data sharing can be found here.
The data underlying these tracks and summary statistics results are publicly available in COVID19-hg Release 3 (June 2020). The raw data can be explored interactively with the Table Browser, or the Data Integrator. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information.