About TraitRunner
Input Files
Traits
Info
Select trait data in the database (generated in a previous run) or in tabular (TSV/CSV) format. If you choose tabular format, select the column containing individual IDs (these must match the genotype data and be unique). You can preview values by clicking 'column preview.' Tabular data must be preprocessed and converted to database format. During conversion, a metadata table will be created, recording each column’s type (numeric, single text, multi-text, or mixed). Separators are detected automatically—please verify your TSV formatting before conversion.▶ Column Preview
Sample IDs
Genotypes
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Select genotype datasets. Only complete datasets (BGEN, BGEN.BGI, and sample files) are displayed. If your data is in VCF format, use the VCF Converter first—VCFs will appear there. After selecting datasets (usually one per chromosome), run Validation. You’ll see an overview with basic metrics like chromosome name, SNP, and sample count. Chromosomes 23 and 24 are automatically renamed X and Y to match downstream references. Files with multiple chromosomes are accepted, but SNP counts always refer to the complete file. All analyses use GRCh38; liftover is required if your data uses another reference.VCF Converter
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Select a VCF file for conversion to BGEN. If your VCF has double IDs (e.g., FID_IID), you can split sample names to match your trait data (preview shown below). After conversion, BGEN datasets will be available for selection in the Genotype Panel.Download Session Data
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Select one or more files to download. These contain processed data from your current session and can be used later to resume or extend your analysis. Files stored inside the app are temporary and will not persist after restart. To keep them, download and save them to a directory accessible to the app (e.g. your mounted data directory).Cohorts
Actions
Info
Define a title for your cohort settings and an optional description. Query cohorts after setting selection criteria in the Cohort Settings Panel below.Cohorts
Cohort Settings
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For each selection condition, select a trait column and define values for both cohorts. Columns are automatically formatted for SQL compatibility, and original column numbers are prefixed for reference. Click + to add conditions and the trashcan to remove them. Column types are shown next to each selection. Character values are selected via dropdown, numeric values via slider (fine adjustments with arrow keys). Conditions are combined with AND within each cohort, making selections more specific as criteria increase. After querying, the 'cohorts' panel displays cohort sizes in a Venn diagram and highlights overlaps. Cohorts cannot overlap for burden testing, and the control/case structure must be maintained for accurate analysis.Genomic Region
Gene Selection
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Select a gene to define a genomic region. Only genes on available chromosomes can be selected, illustrated on the Karyoplot. After selection, the region is validated for SNPs in the chosen genotype data and displayed on the Karyoplot. Downstream analyses require at least one valid SNP in the region. Analyses should ideally follow the suggested order. All analyses depend on SNP annotation, which can be updated independently. Burden Analysis and Genotype-First Analysis require up-to-date annotation. Variant Explorer updates annotation automatically at the start.Karyoplot
Variant Explorer
Genetic Variants
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Run Variant Analysis to calculate allele frequencies, genotype distributions, and perform SNP annotation. Most relevant columns are shown by default; additional columns can be selected under 'Display columns.'Log
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Latest log files generated during Variant Analysis are displayed here for debugging.Burden Analysis
Covariates
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Select covariates for burden testing. Recommended covariates are sex and genetic principal components (PC1-PC10).Mask Settings
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Burden testing evaluates the cumulative effect of variants in a region. Frequently used masks (M1–M3) are predefined based on annotated SNPs in the selected region; some annotations may be missing. You can define a custom mask by selecting from available SNP annotations—unavailable terms are disabled.Results
Info
Burden testing evaluates the cumulative effect of variants in a region. Frequently used masks (M1–M3) are predefined based on annotated SNPs in the selected region; some annotations may be missing. You can define a custom mask by selecting from available SNP annotations—unavailable terms are disabled.Export