I want to run an association analysis on my dataset using linear regression to identify SNPs associated with bipolar disorder. I then created a phenotype file to specify samples that are either bipolar (1/2) or control (0/1).
./plink --ped filtered.ped --map filtered.map --genome --keep keep_bipolar.txt –recode --out outfile
./plink --noweb --file outfile --read-genome outfile.genome --cluster --mds-plot 2
./plink --noweb --file outfile --linear --covar cov_bipolar.txt --pheno pheno_bipolar.txt --pheno-name Indication --all-pheno
Traceback:
94 people had missing value(s). Before main variant filters, 94 founders and 0 nonfounders present. Calculating allele frequencies... done. Warning: 152476 het. haploid genotypes present (see plink.hh ); many commands treat these as missing. Total genotyping rate is 0.993823. 282157 variants and 94 people pass filters and QC. Note: No phenotypes present. 46 phenotype values present after --pheno. Indication has 0 cases, 46 controls, and 48 missing phenotypes. Warning: Skipping --logistic since # variables >= # samples. (Check your covariates--all samples with at least one missing covariate are excluded from this analysis.)
Data
dput(dat) structure(list(FID = c("AC13", "AC14", "AC18", "AC1", "AC20", "AC21", "AC23", "AC2", "AC32", "AC34", "AC36", "AC42", "AC46", "AC48", "AC50", "AC57", "AC58", "AC5", "AC61", "AC62", "AC63", "AC64", "AC69", "AC6", "AC72", "AC74", "AC76", "AC89", "AC8", "AC102", "AC104", "AC16", "DE13", "DE14", "DE27", "DE36", "DE3", "DE40", "DE45", "DE51", "DE52", "DE55", "DE57", "DE7", "DE32", "DE43", "AC15", "AC19", "AC24", "AC27", "AC29", "AC30", "AC33", "AC35", "AC38", "AC43", "AC49", "AC51", "AC52", "AC53", "AC54", "AC56", "AC60", "AC65", "AC67", "AC70", "AC71", "AC77", "AC79", "AC80", "AC83", "AC84", "AC86", "AC90", "AC91", "AC103", "AC105", "AC95", "AC96", "AC99", "DE10", "DE12", "DE16", "DE17", "DE22", "DE23", "DE37", "DE38", "DE39", "DE46", "DE47", "DE4", "DE50", "DE59"), IID = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), SOL = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), C1 = c(-0.0285172, -0.0422061, 0.0335788, -0.0196805, 0.00871406, -0.0359998, 0.00776039, -0.0292564, -0.0104566, -0.011623, 0.00327281, -0.00564814, 0.00209183, -0.0245178, -0.0012923, 0.0403667, 0.0420627, -0.0234294, 0.00216892, -0.0174861, 0.000902948, 0.0250136, -0.0314508, 0.0014534, 0.030759, 0.029896, 0.0257204, -0.0103523, -0.00141674, 0.0254432, -0.0220576, 0.00114805, -0.0200095, 0.00230828, 0.0155191, 0.00425367, -0.0154211, 0.0154046, -0.00126067, 0.00853699, -0.0373163, 0.0147421, -0.0425991, -0.0197213, -0.0115728, -0.0127898, -0.0135105, 0.00453814, -0.0152431, -0.0157209, 0.0235421, 0.000164687, -0.0078923, 0.0173727, -0.0101589, 0.0232102, 0.0295213, 0.00775297, 0.019803, 0.00521905, 0.0143496, 0.0243968, -0.00923894, 0.131506, 0.036772, 0.0123288, 0.0152248, -0.0132957, -0.00942661, 0.0286468, 0.0297961, -0.0225471, 0.0185018, -0.00553813, 0.0141762, -0.0124213, 0.0278842, -0.013759, 0.00493565, -0.0146888, -0.00463272, -0.0332577, -0.0232427, 0.00433733, -0.0244282, -0.0245909, 0.00868522, -0.0347233, 0.00191488, 0.00826277, -0.0124104, -0.0136281, -0.023651, -0.0198727), C2 = c(0.00450319, 0.00394058, 0.00310475, 0.0108606, 0.0195024, -0.0109932, 0.02644, -0.0353571, 0.0278649, 0.0198699, 0.00636933, 0.0247255, 0.00418658, -0.0106317, -0.0490193, -0.0157994, 0.0225328, 0.0420733, 0.0117474, -0.00108231, -0.053874, -0.0351118, -0.0214653, 0.0150199, -0.0151206, 0.0163763, -0.015727, 0.0200251, -0.0246757, -0.00324928, -0.0143993, -0.00089652, -0.0188119, 0.0403222, 0.021987, 0.0268481, 0.00559716, 0.00779719, -0.0181187, 0.0457278, 0.0098323, -0.00865206, 0.0248558, 0.00451314, 0.0337946, 0.0267819, -0.0316527, 0.016075, 0.00177053, -0.0180791, -0.0128455, 0.00868615, -0.0143205, 0.00600335, -0.0245385, -0.0019047, 0.0329249, -0.0322256, -0.00525161, 0.0127281, -0.0210357, 0.048556, -0.0722942, 0.000781904, -0.0188162, 0.0339645, -0.0335628, -0.0232298, 0.0172519, 0.0348876, -0.026288, 0.0383726, -0.018123, 0.0184051, 0.0249351, 0.0129067, -0.00150337, -0.0477761, 0.00873251, -0.0184572, 0.00115896, 0.0252723, -0.00957213, -0.0280059, 0.0183744, -0.025548, -0.0267149, 0.00712551, -0.0115199, -0.0182654, -0.0263084, 0.00126466, 0.0397802, -0.0163334), Indication = c("1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "1/2", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1", "0/1"), sex = c("F", "F", "M", "M", "F", "M", "F", "M", "F", "F", "M", "M", "M", "F", "F", "M", "F", "M", "F", "F", "F", "M", "F", "F", "F", "F", "F", "F", "M", "M", "M", "M", "F", "M", "F", "M", "M", "F", "F", "M", "M", "F", "M", "M", "M", "M", "F", "M", "M", "M", "M", "M", "F", "F", "M", "M", "M", "M", "M", "F", "M", "F", "M", "M", "M", "M", "F", "F", "M", "M", "M", "M", "M", "M", "M", "M", "F", "M", "M", "F", "M", "M", "F", "F", "M", "M", "F", "M", "M", "M", "F", "M", "F", "F"), Left_Brain = c("Fixed", "Frozen", "Frozen", "Frozen", "Fixed", "Frozen", "Fixed", "Frozen", "Frozen", "Frozen", "Frozen", "Fixed", "Fixed", "Fixed", "Frozen", "Fixed", "Frozen", "Frozen", "Fixed", "Frozen", "Frozen", "Fixed", "Frozen", "Frozen", "Fixed", "Frozen", "Frozen", "Frozen", "Fixed", "Frozen", "Fixed", "Fixed", "Fixed", "Frozen", "Frozen", "Fixed", "Frozen", "Frozen", "Fixed", "Fixed", "Fixed", "Frozen", "Fixed", "Fixed", "Fixed", "Frozen", "Fixed", "Fixed", "Frozen", "Frozen", "Fixed", "Frozen", "Fixed", "Fixed", "Fixed", "Fixed", "Fixed", "Frozen", "Fixed", "Fixed", "Frozen", "Fixed", "Frozen", "Frozen", "Frozen", "Frozen", "Frozen", "Frozen", "Fixed", "Fixed", "Fixed", "Frozen", "Frozen", "Frozen", "Frozen", "Frozen", "Fixed", "Fixed", "Fixed", "Fixed", "Fixed", "Frozen", "Fixed", "Frozen", "Frozen", "Fixed", "Fixed", "Frozen", "Fixed", "Frozen", "Frozen", "Frozen", "Fixed", "Frozen")), row.names = c(NA, -94L), class = "data.frame")
