Function reference • switchgrassGWAS

Datasets Datasets associated with the pvdiv panel
`pvdiv_metadata`	Metadata table for the Panicum virgatum diversity panel
`pvdiv_phenotypes`	Phenotypes available for the Panicum virgatum diversity panel.
`anno_info`	Annotation information from Pvirgatum_516_v5.1.annotation_info
`gff_gene`	GFF3 Gene information
Genome-wide association Functions for genome-wide association on the pvdiv panel
`pvdiv_standard_gwas()`	Juenger lab standard GWAS function.
`pvdiv_gwas()`	Wrapper for bigsnpr for GWAS on Panicum virgatum.
`pvdiv_manhattan()`	Create a Manhattan plot with ggplot2.
`pvdiv_qqplot()`	Create a quantile-quantile plot with ggplot2.
`pvdiv_table_topsnps()`	Create a Table of Annotated Top SNPs for Panicum virgatum
`pvdiv_results_in_folder()`	Identify phenotype names from bigsnpr results in a folder.
`pvdiv_kinship()`	Find a kinship matrix using the van Raden method.
`pvdiv_lambda_GC()`	Return lambda_GC for different numbers of PCs for GWAS on Panicum virgatum.
`pvdiv_fbm_upset_df()`	Plot the -log10p-value distributions for two univariate GWAS against one another using ggplot.
`pvdiv_fbm_qq()`	Plot the -log10p-value distributions for two univariate GWAS against one another using ggplot.
Multivariate adaptive shrinkage Functions for multi-condition SNP effect estimates using mash
`pvdiv_results_in_folder()`	Identify phenotype names from bigsnpr results in a folder.
`pvdiv_bigsnp2mashr()`	Convert bigsnpr output to mashr input dataframes.
`mash_standard_run()`	A standard run of mashr
`mash_plot_Ulist()`	ggplot of specific covariance matrix patterns
`mash_plot_covar()`	ggplot of covariance matrix masses
`mash_plot_effects()`	ggplot of single mash effect
`mash_plot_manhattan_by_condition()`	Manhattan plot in ggplot colored by significant conditions
`mash_plot_pairwise_sharing()`	Create a ggplot of pairwise sharing of mash effects
`mash_plot_sig_by_condition()`	Significant SNPs per number of conditions
`get_colnames()`	Get column names from a mash object
`get_log10bf()`	Return the Bayes Factor for each effect
`get_marker_df()`	Get mash marker_df
`get_n_significant_conditions()`	Count number of conditions each effect is significant in
`get_significant_results()` `get_significant_results()`	From a mash result, get effects that are significant in at least one condition.
`get_estimated_pi()`	Return the estimated mixture proportions. Use get_estimated_pi to extract the estimates of the mixture proportions for different types of covariance matrix. This tells you which covariance matrices have most of the mass.
`get_U_by_mass()`	Get the positions of objects in a mash object Ulist that are above some mass threshold.
`get_mash_metadata()`	Get switchgrass metadata for SNPs in a mash object.
`get_GxE()`	Get data frames of types of GxE from a mash object
`get_pairwise_sharing()`	Compute the proportion of (significant) signals shared by magnitude in each pair of conditions, based on the poterior mean
Edge-case functions Additional utility or expert user functions
`asv_best_PC_df()`	Return best number of PCs in terms of lambda_GC following Cattrell's rule.
`dots()`	Set an advanced argument
`get_best_PC_df()`	Return best number of PCs in terms of lambda_GC for Panicum virgatum. Return best number of PCs in terms of lambda_GC for the CDBN.
`get_date_filename()`	Get current date-time in a filename-appropriate format.
`get_lambdagc()`	Find lambda_GC value for non-NA p-values
`get_ncond()`	Get number of conditions
`gff_metadata`	Metadata lines for the gene GFF3
`load_mash_df()`	Read in the random and the strong datasets
`mt.rawp2adjp()`	Adjusted p-values for simple multiple testing procedures
`pvdiv_autoSVD()`	Wrapper for the snp_autoSVD function for switchgrass.
`pvdiv_bigsnp2tibble()`	Convert A `FBM.code256` SNP subset to a dataframe.
`pvdiv_bigsnp_subset()`	Make a SNP dataframe subset from annotation table or a genomic range.
`pvdiv_fdr()`	Compute Adjusted False Discovery Rate for pvdiv_gwas output.
`pvdiv_subset_popgenome()`	Get GFF subsets
`pvdiv_top_effects_log10p()`	Step One of bigsnp2mashr
`pvdiv_vcf_popgenome()`	Format the VCF subset
`reorder_cormat()`	Reorder correlation matrix
`round2()`	Return a number rounded to some number of digits
`round_xy()`	Return a dataframe binned into 2-d bins by some x and y.
`s_hat_bigsnp()`	Step Two of bigsnp2mashr
`theme_oeco`	Oeco ggplot theme
`thresholdFBM()`	Threshold a FBM

Reference

Datasets

Genome-wide association

Multivariate adaptive shrinkage

Edge-case functions