Statistical Tools for Quantitative Genetic Analyses
LD pruning of summary statistics
Check concordance between marker effect and sparse LD matrix.
Compute prediction accuracy for a quantitative or binary trait
Adjustment of marker summary statistics using clumping and thresholdin...
Adjust B-values
Adjust Linkage Disequilibrium (LD) Using Map Information
Compute AUC
Compute Receiver Operating Curve statistics
Bayesian linear regression models
Compute Genomic BLUP values
Get elements from genotype matrix stored in PLINK bedfiles
Extract elements from genomic relationship matrix (GRM) stored on disk
Retrieve Sparse LD Matrix for a Given Chromosome
Get marker LD sets
Retrieve the map for specified rsids on a given chromosome.
Retrieve marker rsids in a specified genome region.
Retrieve the positions for specified rsids on a given chromosome.
Extract Sparse Linkage Disequilibrium (LD) Information
Filter genetic marker data based on different quality measures
Single marker association analysis using linear models or linear mixed...
Finemapping using Bayesian Linear Regression Models
Prepare genotype data for all statistical analyses
Genomic rescticted maximum likelihood (GREML) analysis
Computing the genomic relationship matrix (GRM)
Genomic scoring based on single marker summary statistics
Gene set enrichment analysis
Genomic simulation
Solve linear mixed model equations
Perform Hardy Weinberg Equilibrium Test
LD score regression
Compute LD (Linkage Disequilibrium) Scores for a Given Chromosome.
Map Sets to RSIDs
Map marker summary statistics to Glist
Merge multiple GRMlist objects
Adjustment of marker effects using correlated trait information
Plot fit from gbayes
Forest plot
Plot LD Matrix
Plot Receiver Operating Curves
Expected AUC for prediction of a binary trait using information on cor...
Expected AUC for prediction of a binary trait using information on a c...
Expected AUC for prediction of a binary trait
Expected R2 for multiple trait prediction of continuous traits
Expected R2 for single trait prediction of a continuous trait
Quality Control of Marker Summary Statistics
Compute Nagelkerke R2
Split Vector with Overlapping Segments
Provides an infrastructure for efficient processing of large-scale genetic and phenotypic data including core functions for: 1) fitting linear mixed models, 2) constructing marker-based genomic relationship matrices, 3) estimating genetic parameters (heritability and correlation), 4) performing genomic prediction and genetic risk profiling, and 5) single or multi-marker association analyses. Rohde et al. (2019) <doi:10.1101/503631>.