hello, I have some question about Struct LMM

alldata.xlsx bedbimfam.zip cov_agesex.txt env_bmigroup.txt pheno_glucose.txt

hi @horta, I am a Master degree in Kyung Hee University in Korea. I am majoring in bio informatics. I was impressed with your paper and struct LMM program. I think the idea about Struct LMM is the best. So I tried to assign it to part of our data to your program, and then I want to make sure it worked properly.

Why do I get an error if I do not norm or gausinaize Pheno data?
Is the outcome of the assignment of Pheno, cov, and env equal to your intentions?

outcome : chrom snp cm pos a0 a1 i pv_int pv 1 rs17106184 0.0 50909985 A G 0 0.116081 0.039977

`import os import pandas as pd import scipy as sp from limix_core.util.preprocess import gaussianize from struct_lmm import run_structlmm from struct_lmm.utils.sugar_utils import norm_env_matrix from pandas_plink import read_plink import geno_sugar as gs

if name == "main":

# import genotype file
#bedfile = "data_structlmm/chrom22_subsample20_maf0.10"
bedfile = "rs17106184_N50"
(bim, fam, G) = read_plink(bedfile)

# subsample snps
#Isnp = gs.is_in(bim, ("22", 17500000, 18000000))
#G, bim = gs.snp_query(G, bim, Isnp)

# load phenotype file
phenofile = "pheno_glucose.txt"
dfp = sp.loadtxt(phenofile)
pheno = norm_env_matrix(dfp)
#dfp = pd.read_csv(phenofile, index_col=0)
#pheno = gaussianize(dfp.loc["BMI"].values[:, None])

# load environment file and normalize
envfile = "env_bmigroup.txt"
E = sp.loadtxt(envfile)
E = norm_env_matrix(E)

# mean as fixed effect
#covs = sp.ones((E.shape[0], 1))
covs = "cov_agesex.txt"
covs = sp.loadtxt(covs)

# run analysis with struct lmm
snp_preproc = {"max_miss": 0.01, "min_maf": 0.02}
res = run_structlmm(
    G, bim, pheno, E, covs=covs, batch_size=100, snp_preproc=snp_preproc
)

# export
print("Export")
print(res)
#if not os.path.exists("out"):
#    os.makedirs("out")
#res.to_csv("out/res_structlmm.csv", index=False)

limix / struct-lmm

hello, I have some question about Struct LMM #28