marker_effect_per_env_FarmCPU: Compute marker P-values for each environment with GWAS.

In cjubin/learnMET: Predictions with Machine Learning methods in Multi Environment Trials (MET)

Compute marker P-values for each environment with GWAS.

Description

GWAS method implemented: FarmCPU from GAPIT3. Multiple testing correction: Benjamini–Hochberg procedure with alpha=0.05.

Usage

marker_effect_per_env_FarmCPU(
  geno,
  pheno,
  map,
  environment,
  pheno_trait,
  nb_pcs = 5,
  ...
)

Arguments

geno	data.framewith markers in columns and inviduals in rows. Typical input is METData$geno from the create_METData() function.
pheno	data.frame object with at least 4 columns. geno_ID character contains the genotype identifiers. year numeric contains the year of the observation. location character contains the name of the location. From the fourth column on: each column numeric contains phenotypic values for a phenotypic trait observed in a combination Year x Location. Names of the traits can be provided as column names. Last column: IDenv (combination LocationxYear) Typical input is METData$pheno from the create_METData() function.
map	data.frame with 3 columns. marker character with marker names chr numeric with chromosome number pos numeric with marker position. Typical input is METData$map from the create_METData() function.
environment	character indicating the name of the environment for which marker effects should be computed
pheno_trait	character Name of the trait under study for which marker effects should be estimated
nb_pcs	numeric Number of principal components to use in FarmCPU analyses.

Details

For more information about the GWAS method, please consult the original publication from the authors: Liu, Xiaolei, et al. "Iterative usage of fixed and random effect models for powerful and efficient genome-wide association studies." PLoS genetics 12.2 (2016): e1005767. https://doi.org/10.1371/journal.pgen.1005767

Value

a list which contains the following elements:

GWAS_results

data.frame FarmCPU results for all SNPs

threshold

numeric Cutoff derived from Benjamini-Hochberg procedure

selected_markers

character Vector containing the names of the SNPs passing the threshold

Author(s)

Cathy C. Westhues [e-mail]

cjubin/learnMET documentation built on Nov. 4, 2024, 6:23 p.m.