sashagusev

# --- admixture analysis

# sample size
N = 5e3

# mate correlation
AM_cor = 0.75

# variance in ancestry transmission
genetic_sd = 0.025

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# number of samples in each study
N = 2e3
# number of studies to run
seeds = 50

# vectors for storing results
est_a = rep(NA,seeds)
est_c = rep(NA,seeds)
est_e = rep(NA,seeds)
est_y1 = rep(NA,seeds)

sashagusev

set.seed(42)

# sample size
N = 10e3

# generate two groups
group = rbinom( N , 1 , 0.5 )

# generate group-specific environments
shared_env = rnorm(N,group*-15,1)

sashagusev

args = as.numeric(commandArgs(trailingOnly=TRUE))
# N,M,var_direct,var_indirect

# code for implementation of REML:
# https://github.com/sashagusev/SKK-REML-sim/blob/master/func_reml.R

source("reml_func.R")

# Num individuals
N = 1e3

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# Num individuals
N = 10e3
# Num markers
M = 10

# Assortative Mating
AM_cor = 0.75
# Minor allele frequency
maf = 0.5
# Heritability

sashagusev

set.seed(42)

# Num individuals
par( mfrow=c(1,4) )
# Num markers
M = 500
# Minor allele frequency
maf = 0.5

LABEL = TRUE

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# Num individuals
N = 2e3
# Num markers
M = 1e3
# Affected cutoff
aff_thresh = 70
# Minor allele frequency
maf = 0.5

# simulate different parameter settings 

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# reproducibility:
set.seed(42)

# number of causal variants:
M = 5e3
# number of individuals
N = 10e3
# heritability
hsq = 0.25
# sample individuals with admixture

sashagusev

get_correlated_trait = function(input,r) {
  r * (input) + sqrt(1-r^2) * rnorm(length(input),0,1)
}
get_noisy_trait = function(input,noise) {
  sqrt(1-noise) * (input) + sqrt(noise) * rnorm(length(input),0,1)
}

simulate_non_genetic = function(N,AM,ENV,err) {
  set.seed(42)

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N = 50000

get_correlated_trait = function(input,r) {
  r * scale(input) + sqrt(1-r^2) * rnorm(length(input),0,1)
}
get_noisy_trait = function(input,noise) {
  sqrt(1-noise) * scale(input) + sqrt(noise) * rnorm(length(input),0,1)
}

# good parameters from a 2-dof grid search