pvdiv_standard_gwas: Juenger lab standard GWAS function.
In Alice-MacQueen/switchgrassGWAS: Genome Wide Association on Panicum virgatum

Description Usage Arguments Value Examples

This function is a wrapper around the standard GWAS procedures in the Juenger lab. Singular value decomposition of the SNPs is done to get principal components for population structure correction; the 'best' number of PCs is chosen as the one that makes lambda_GC, the Genomic Control coefficient, closest to 1. (See the lambdagc parameter to set this yourself.) Next, genome-wide association is conducted, and the GWAS output can be saved, as well as Manhattan plots, QQ-plots, and annotation information for the top SNPs for each phenotype.

pvdiv_standard_gwas(
  snp,
  df = switchgrassGWAS::pvdiv_phenotypes,
  type = c("linear", "logistic"),
  ncores = nb_cores(),
  outputdir = ".",
  covar = NULL,
  lambdagc = TRUE,
  savegwas = FALSE,
  savetype = c("rds", "fbm", "both"),
  suffix = "",
  saveplots = TRUE,
  saveannos = FALSE,
  txdb = NULL,
  minphe = 200,
  ...
)

`snp`	A "bigSNP" object; load with `bigsnpr::snp_attach()`. Here, genomic information for Panicum virgatum. SNP data is available at doi:10.18738/T8/ET9UAU
`df`	Dataframe of phenotypes where the first column is PLANT_ID.
`type`	Character string. Type of univarate regression to run for GWAS. Options are "linear" or "logistic".
`ncores`	Number of cores to use. Default is one.
`outputdir`	String or file.path() to the output directory. Default is the working directory.
`covar`	Optional covariance matrix to include in the regression. You can generate these using `pvdiv_autoSVD()`.
`lambdagc`	Default is TRUE - should lambda_GC be used to find the best population structure correction? Alternatively, you can provide a data frame containing "NumPCs" and the phenotype names containing lambda_GC values. This is saved to the output directory by pvdiv_standard_gwas and saved or generated by pvdiv_lambda_GC.
`savegwas`	Logical. Should the gwas output be saved to the working directory? These files are typically quite large. Default is FALSE.
`savetype`	Character string. Type of GWAS save file. Options are 'rds', which saves individual rds files for each GWAS; 'fbm', which saves one filebacked big matrix (using the bigsnpr package), or 'both', which saves both file types. These files are typically quite large.
`suffix`	Optional character vector to give saved files a unique search string/name.
`saveplots`	Logical. Should Manhattan and QQ-plots be generated and saved to the working directory? Default is TRUE.
`saveannos`	Logical. Should annotation tables for top SNPs be generated and saved to the working directory? Default is FALSE. Can take additional arguments; requires a txdb.sqlite object used in AnnotationDbi.
`txdb`	A txdb object such as 'Pvirgatum_516_v5.1.gene.txdb.sqlite'. Load this into your environment with AnnotationDbi::loadDb.
`minphe`	Integer. What's the minimum number of phenotyped individuals to conduct a GWAS on? Default is 200. Use lower values with caution.
`...`	Other arguments to `pvdiv_lambda_GC` or `pvdiv_table_topsnps`.

A big_SVD object.

## Not run: 
# Here we specify that we do want to generate and save the gwas dataframes,
# the Manhattan and QQ-plots, and the annotation tables.
pvdiv_standard_gwas(snp, df = pvdiv_phenotypes, type = "linear", covar = svd,
    ncores = nb_cores(), lambdagc = TRUE, savegwas = TRUE, saveplots = TRUE,
    saveannos = TRUE, txdb = txdb)

## End(Not run)
# In this example, we run GWAS on all the phenotypes in pvdiv_phenotypes
# using an example SNP set of ~1800 SNPs.
snpfile <- system.file("extdata", "example_bigsnp.rds", package = "switchgrassGWAS")
library(bigsnpr)
snp <- snp_attach(snpfile)
pvdiv_standard_gwas(snp, df = pvdiv_phenotypes, type = "linear", savegwas = FALSE,
    saveplots = FALSE, ncores = 1)