In charlotte-ngs/asmss2022: Applied Statistical Methods in Animal Science - Spring Semester 2022

Simulaiton

Based on the pedigree simulation, we start with

library(pedSimulate)
ped <- simulatePed(
  F0size = 4,
  Va0 = 15,
  Ve = 225,
  littersize = 2,
  ngen = 2,
  overlap.s = 1,
  m.rate = 0.5,
  seed = 5298
)

The simulated pedigree

ped

Genotypes

Adding genotypes can be done by

set.seed(9157)
n_nr_SNP <- 3
AF <- runif(n_nr_SNP, 0.1, 0.9)
gen <- simulateGen(ped  = ped,
                   AF   = AF,
                   seed = 6984)

The genotypes

gen

Marker Effects

Marker effects are assumed

vec_mrk_eff <- c(13.7, 2.1, 9.9)

Contributions to add

vec_p_to_add <- gen %*% vec_mrk_eff
vec_p_to_add

ped$P <- as.vector(ped$P + vec_p_to_add)
ped

Preparing data for Output

tbl_gen <- tibble::as_tibble(gen)
tbl_gen

A vector ov column names

vec_col_names <- sapply(1:n_nr_SNP, function(x) paste0("SNP", x, collapse = ""), USE.NAMES = FALSE)
vec_col_names

colnames(tbl_gen) <- vec_col_names
tbl_gen

Append genotypes to ped

library(dplyr)
tbl_ped <- tibble::as_tibble(ped)
# select relevant columns
tbl_ped_data_out <- ped %>%
  select(ID, SIRE, DAM, SEX, P)
# set unknown paretns to NA
tbl_ped_data_out$SIRE[tbl_ped_data_out$SIRE == 0] <- NA
tbl_ped_data_out$DAM[tbl_ped_data_out$DAM == 0] <- NA
tbl_gen_data_out <- tbl_gen[(!is.na(tbl_ped_data_out$SIRE) & !is.na(tbl_ped_data_out$DAM)), ]

tbl_ped_data_out <- tbl_ped_data_out[(!is.na(tbl_ped_data_out$SIRE) & !is.na(tbl_ped_data_out$DAM)), ]

# remove founder genotypes
tbl_gen_data_out <- dplyr::bind_cols(tbl_ped_data_out, tbl_gen_data_out)
tbl_gen_data_out

Further preparation

# rouding
tbl_gen_data_out$P <- round(tbl_gen_data_out$P, digits = 1)
tbl_gen_data_out

Write data to a file

s_data_out_path <- file.path(here::here(), "docs", "data", "asm_exam_p05.csv")
if (!file.exists(s_data_out_path))
  readr::write_csv(tbl_gen_data_out, s_data_out_path)

charlotte-ngs/asmss2022 documentation built on June 7, 2022, 1:33 p.m.