Package: colocboost 1.0.9

ColocBoost on the media | Software updates

1. ColocBoost analysis with basic QC steps | 2. Loading Data using colocboost_pipeline function | 2.1. Loading individual-level data from multiple cohorts | 2.2. Loading summary statistics from multiple cohorts or datasets | 3. Perform ColocBoost using colocboost_pipeline function

1. Fine-mapping with individual-level data | 2. Fine-mapping with summary statistics | 3. LD-free fine-mapping with one causal variant assumption

1. The Sumstat_5traits Dataset | Causal variant structure | Important data format for summary data | 2. Multiple summary statistics data with shared LD reference | Results Interpretation | 3. Other summary statistics and LD input combinations | 3.1. Matched LD with multiple sumstat (Trait-specific LD) | 3.2. LD matrix is a superset of variants across different summary statistics | 3.3. Arbitrary LD and sumstat with dictionary provided | 3.4. Using a reference panel genotype matrix (X_ref) instead of LD | 3.5. HyPrColoc compatible format: effect size and standard error matrices

Causal variant structure | 1. Run ColocBoost with partial overlapping variants | 2. Limitations of using only overlapping variables | 3. Disease-prioritized colocalization analysis with variables in the focal trait

1. Individual Level Data | 2. Summary Statistics | 3. Optional: mapping between arbitrary input $X$ and $Y$ | 4. HyPrColoc compatible format: effect size and standard error matrices

1. The Ambiguous_Colocalization Dataset | 2. ColocBoost results | 2. Fine-mapping results from SuSiE and colocalization with COLOC | 3. Get the ambiguous colocalization results and summary | 3.1. Get the ambiguous colocalization results | 3.2. Get the summary of ambiguous colocalization results | 4. Take home message | Acknowledgment

1. The Ind_5traits Dataset | Causal variant structure | 2. Matched individual level input $X$ and $Y$ | Results Interpretation | 3. Other structures of individual level data | 3.1. Single genotype matrix | 3.2. Genotype matrix is a superset of individuals across different phenotypes | 3.3. Arbitrary input matrices with mapping dictionary provided

1. Summarize ColocBoost results | Causal variant structure | 2. Filter colocalization events by relative strength of evidence | 3. More details on ColocBoost output | 3.1. Variant colocalization probability (vcp) | 3.2. Analyzed data information (data_info) | 3.3. Colocalization details (cos_details) | 3.3.1. Colocalized variants for each CoS (cos) | 3.3.2. Colocalized traits for each 95% CoS (cos_outcomes) | 3.4. Model information (model_info) | 3.5. Trait-specific effects information (ucos_details) | 3.5.1. Trait-specific (uncolocalized) confidence sets (ucos) | 3.5.2. Trait-specific (uncolocalized) outcomes (ucos_outcomes) | 3.5.3. Purity across CoS and uCoS (cos_ucos_purity) | 3.5.4. Other components | 3.6. Diagnostic details (diagnostic_details)

1. LD mismatch diagnosis | Why LD Mismatch Matters | Example of including LD mismatch | Running ColocBoost with LD Mismatch | 2. LD-free and LD-mismatch colocalization analysis

1. Loading individual and summary statistics data | Causal variant structure | 2. ColocBoost in disease-agnostic mode | Results Interpretation | 3. ColocBoost in disease-prioritized mode

1. Default plot function | 2. Advanced options | 2.1. Plot with a zoom-in region | 2.2. Plot with marked top variants | 2.3. Plot CoS variants to uncolocalized traits to diagnostic the colocalization. | 2.4. Plot with added highlight points | 2.5. Plot with trait-specific sets if exists | 2.6 Plot with focal trait for disease prioritized colocalization

CRAN (Stable Release) | GitHub | Conda