As a validation experiment, I have run the same GWAS of a quantitative phenotype derived from the UKBiobank, alongside the genomic data from the UKBiobank, once using the program BOLT-LMM and once using SAIGE linear mixed model (with selected quantitative trait tag). I wanted to see if the results would be comparable.
I however encountered consistantly lower p-values of the SAIGE output summary statistics than of the corresponding BOLT ones. In particular, I had many snp loci along the genome that were significant (with 10^(-8) level of significance) according to BOLT but were not significant (relative to the same level) according to SAIGE.
My question is, why might there be such discrepancy (e.g. what have I done wrong) and what is the proper way to set up a liner mixed model GWAS run in SAIGE (or alternatively how to set up its BOLT counterpart) in order for the results of both BOLT and SAIGE to be comparable?
I have included the input tags that I have used in my runs with BOLT and SAIGE (Step1 and Step2), in case this is useful. The (bed, bim, fam), (bgen, bgen-index, sample) as well as (phenotype and covariates table, phenotype and covariate columns) used in both scripts are the same.
/BOLT-LMM_v2.3.4/bolt --bed= --bim= --fam= --remove= --phenoFile= --phenoCol= --covarFile= --qCovarCol= --qCovarCol= --covarCol= --LDscoresMatchBp --maxMissingPerIndiv 1 --lmm --LDscoresFile=LDSCORE.1000G_EUR.tab.gz --geneticMapFile=genetic_map_hg19.txt.gz --numThreads=32 --bgenFile=1.bgen --sampleFile=1.sample --statsFile= --statsFileBgenSnps=
SAIGE Step 1:
/SAIGE/SAIGE-0.35.8.3/extdata/step1_fitNULLGLMM.R --plinkFile= --phenoFile= --sampleIDColinphenoFile= --phenoCol= --traitType=quantitative --invNormalize=TRUE --covarColList= --outputPrefix= --nThreads=32 --LOCO=FALSE --tauInit=1,0
SAIGE Step 2:
/SAIGE/SAIGE-0.35.8.3/extdata/step2_SPAtests.R --minMAF= --minMAC= --bgenFile= --bgenFileIndex= --sampleFile= --GMMATmodelFile= --varianceRatioFile= --SAIGEOutputFile= --numLinesOutput=2 --IsDropMissingDosages=FALSE --LOCO=FALSE
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