Genome-Wide-Association Studies (GWAS)
Bhagirathi Dash
Machine
AWS m4.xlarge (16 GiB RAM)
References
** “A tutorial on conducting Genome-Wide-Association Studies: Quality control and statistical analysis” (https://www.ncbi.nlm.nih.gov/pubmed/29484742)**
Softwares
$ plink2
PLINK v1.90p 64-bit (25 Mar 2016) https://www.cog-genomics.org/plink2 (C) 2005-2016 Shaun Purcell, Christopher Chang GNU General Public License v3
plink [input flag(s)…] {command flag(s)…} {other flag(s)…} plink –help {flag name(s)…}
Commands include –make-bed, –recode, –flip-scan, –merge-list, –write-snplist, –list-duplicate-vars, –freqx, –missing, –test-mishap, –hardy, –mendel, –ibc, –impute-sex, –indep-pairphase, –r2, –show-tags, –blocks, –distance, –genome, –homozyg, –make-rel, –make-grm-gz, –rel-cutoff, –cluster, –pca, –neighbour, –ibs-test, –regress-distance, –model, –bd, –gxe, –logistic, –dosage, –lasso, –test-missing, –make-perm-pheno, –unrelated-heritability, –tdt, –dfam, –qfam, –annotate, –clump, –gene-report, –meta-analysis, –epistasis, –fast-epistasis, and –score.
‘plink –help | more’ describes all functions (warning: long).
library(pdftools)Obtain data
~/GWAS_IJMPR$ git clone https://github.com/MareesAT/GWA_tutorial.git
Cloning into ‘GWA_tutorial’…
~/GWAS_IJMPR$ ls GWA_tutorial/
1_QC_GWAS.zip
2_Population_stratification.zip
3_Association_GWAS.zip
4_PRS.doc
README.md
~/GWAS_IJMPR/GWA_tutorial$ unzip 1_QC_GWAS.zip
Archive: 1_QC_GWAS.zip inflating: 1_QC_GWAS/1_Main_script_QC_GWAS.txt
inflating: 1_QC_GWAS/check_heterozygosity_rate.R
inflating: 1_QC_GWAS/gender_check.R
inflating: 1_QC_GWAS/HapMap_3_r3_1.bed
inflating: 1_QC_GWAS/HapMap_3_r3_1.bim
inflating: 1_QC_GWAS/HapMap_3_r3_1.fam
inflating: 1_QC_GWAS/heterozygosity_outliers_list.R
inflating: 1_QC_GWAS/hist_miss.R
inflating: 1_QC_GWAS/hwe.R
inflating: 1_QC_GWAS/inversion.txt
inflating: 1_QC_GWAS/MAF_check.R
inflating: 1_QC_GWAS/pops_HapMap_3_r3
inflating: 1_QC_GWAS/Relatedness.R
~/GWAS_IJMPR/GWA_tutorial$ unzip 2_Population_stratification.zip
Archive: 2_Population_stratification.zip inflating: 2_Population_stratification/1_Main_script_QC_GWAS.txt
inflating: 2_Population_stratification/2_Main_script_MDS.txt
inflating: 2_Population_stratification/MDS_merged.R
~/GWAS_IJMPR/GWA_tutorial$ unzip 3_Association_GWAS.zip
Archive: 3_Association_GWAS.zip inflating: 3_Association_GWAS/3_Main_script_association_GWAS.txt
inflating: 3_Association_GWAS/Manhattan_plot.R
inflating: 3_Association_GWAS/QQ_plot.R
Check directory contents
All files were copied to the ‘~/GWAS_IJMPR’ folder.
~/GWAS_IJMPR$ ls -l
-rw-rw-r-- 1 ubuntu ubuntu 12609 Sep 21 13:26 1_Main_script_QC_GWAS.txt
-rw-rw-r-- 1 ubuntu ubuntu 61231677 Jan 25 2016 HapMap_3_r3_1.bed**
-rw-rw-r-- 1 ubuntu ubuntu 40635824 Jan 25 2016 HapMap_3_r3_1.bim
-rw-rw-r-- 1 ubuntu ubuntu 4136 Jan 25 2016 HapMap_3_r3_1.fam
-rw-rw-r-- 1 ubuntu ubuntu 162 Sep 1 2015 MAF_check.R
-rw-rw-r-- 1 ubuntu ubuntu 923 Nov 26 2015 Relatedness.R
-rw-rw-r-- 1 ubuntu ubuntu 235 Jan 22 2016 check_heterozygosity_rate.R
-rw-rw-r-- 1 ubuntu ubuntu 352 Aug 28 2015 gender_check.R
-rw-rw-r-- 1 ubuntu ubuntu 371 Aug 20 2015 heterozygosity_outliers_list.R
-rw-rw-r-- 1 ubuntu ubuntu 410 Sep 10 2015 hist_miss.R
-rw-rw-r-- 1 ubuntu ubuntu 287 Sep 2 2015 hwe.R
-rw-rw-r-- 1 ubuntu ubuntu 88 Jun 25 2015 inversion.txt
-rw-rw-r-- 1 ubuntu ubuntu 39242 Sep 7 2015 pops_HapMap_3_r3
-rw-rw-r-- 1 ubuntu ubuntu 11905 Jun 22 2017 1_Main_script_QC_GWAS.txt
-rw-rw-r-- 1 ubuntu ubuntu 10886 Sep 21 17:27 2_Main_script_MDS.txt
**-rw-rw-r-- 1 ubuntu ubuntu 30050331 Nov 29 03:26 HapMap_3_r3_12.bed**
**-rw-rw-r-- 1 ubuntu ubuntu 29846054 Nov 29 03:26 HapMap_3_r3_12.bim**
**-rw-rw-r-- 1 ubuntu ubuntu 2296 Nov 29 03:26 HapMap_3_r3_12.fam**
**-rw-rw-r-- 1 ubuntu ubuntu 997 Nov 29 03:26 HapMap_3_r3_12.log**
-rw-rw-r-- 1 ubuntu ubuntu 1343 Jan 22 2016 MDS_merged.R
**-rw-rw-r-- 1 ubuntu ubuntu 1058938 Nov 29 03:26 indepSNP.prune.in**
-rw-rw-r-- 1 ubuntu ubuntu 5949 Sep 21 14:04 3_Main_script_association_GWAS.txt
-rw-rw-r-- 1 ubuntu ubuntu 596 Sep 21 14:06 Manhattan_plot.R
-rw-rw-r-- 1 ubuntu ubuntu 525 Sep 21 14:06 QQ_plot.R
-Double asterisk (**) quoted files will be generated and should be deleted before beginning.
-HapMap_3_r3_1 is sourced from the link given below:
http://hapmap.ncbi.nlm.nih.gov/downloads/genotypes/2010-05_phaseIII/plink_format/
1_Quality control and statistical analysis for Genome-Wide-Association Studies (GWAS)
Preview file contents
bed/genotype file
bim/map file
fam/ped file
bim or map file (chromosome no., snp.name, cM, position, allele.1, allele.2)
$ head HapMap_3_r3_1.bim
1 rs2185539 0 556738 T C
1 rs11510103 0 557616 G A
1 rs11240767 0 718814 T C
1 rs3131972 0 742584 A G
1 rs3131969 0 744045 A G
1 rs1048488 0 750775 C T
1 rs12562034 0 758311 A G
1 rs12124819 0 766409 G A
1 rs4040617 0 769185 G A
1 rs2905036 0 782343 C T
fam file (pedigree no, member, father, mother, sex, affected)
$ head HapMap_3_r3_1.fam
1328 NA06989 0 0 2 2
1377 NA11891 0 0 1 2
1349 NA11843 0 0 1 1
1330 NA12341 0 0 2 2
1444 NA12739 NA12748 NA12749 1 -9
1344 NA10850 0 NA12058 2 -9
1328 NA06984 0 0 1 2
1463 NA12877 NA12889 NA12890 1 -9
1418 NA12275 0 0 2 1
13291 NA06986 0 0 1 1
Preview file contents and/or data using snpStat
bed/genotype file
bim/map file
fam/ped file
library(snpStats)gwas <- read.plink("1_QC_GWAS/HapMap_3_r3_1.bed", "1_QC_GWAS/HapMap_3_r3_1.bim", "1_QC_GWAS/HapMap_3_r3_1.fam")str(gwas)List of 3
$ genotypes:Formal class 'SnpMatrix' [package "snpStats"] with 1 slot
.. ..@ .Data: raw [1:165, 1:1457897] 03 03 03 03 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:165] "NA06989" "NA11891" "NA11843" "NA12341" ...
.. .. .. ..$ : chr [1:1457897] "rs2185539" "rs11510103" "rs11240767" "rs3131972" ...
$ fam :'data.frame': 165 obs. of 6 variables:
..$ pedigree: int [1:165] 1328 1377 1349 1330 1444 1344 1328 1463 1418 13291 ...
..$ member : chr [1:165] "NA06989" "NA11891" "NA11843" "NA12341" ...
..$ father : chr [1:165] NA NA NA NA ...
..$ mother : chr [1:165] NA NA NA NA ...
..$ sex : int [1:165] 2 1 1 2 1 2 1 1 2 1 ...
..$ affected: int [1:165] 2 2 1 2 NA NA 2 NA 1 1 ...
$ map :'data.frame': 1457897 obs. of 6 variables:
..$ chromosome: int [1:1457897] 1 1 1 1 1 1 1 1 1 1 ...
..$ snp.name : chr [1:1457897] "rs2185539" "rs11510103" "rs11240767" "rs3131972" ...
..$ cM : logi [1:1457897] NA NA NA NA NA NA ...
..$ position : int [1:1457897] 556738 557616 718814 742584 744045 750775 758311 766409 769185 782343 ...
..$ allele.1 : chr [1:1457897] "T" "G" "T" "A" ...
..$ allele.2 : chr [1:1457897] "C" "A" "C" "G" ...gwas$genotypesA SnpMatrix with 165 rows and 1457897 columns
Row names: NA06989 ... NA12865
Col names: rs2185539 ... rs1973881 head(col.summary(gwas$genotypes)$MAF)[1] 0.00000000 0.00621118 0.00000000 0.15757576 0.13030303 0.15853659head(gwas$fam)head(gwas$map)summary(gwas) Length Class Mode
genotypes 240553005 SnpMatrix raw
fam 6 data.frame list
map 6 data.frame listInvestigate missingness per individual and per SNP
$ plink –bfile HapMap_3_r3_1 –missing
**1457897 variants loaded from .bim file.**
**165 people (80 males, 85 females) loaded from .fam.**
**112 phenotype values loaded from .fam.**
........
**Before main variant filters, 112 founders and 53 nonfounders present.**
Calculating allele frequencies...
.......................
Warning: 225 het. haploid genotypes present (see plink.hh );
many commands treat these as missing.
Total genotyping rate is 0.997378.
--missing:
Sample missing data report written to **plink.imiss**,
and
variant-based missing data report written to **plink.lmiss.**
$ Rscript –no-save hist_miss.R
pdf_convert("1_QC_GWAS/histimiss.pdf", format = "jpeg", dpi = 300)
proportion of missing SNPs per individual
pdf_convert("1_QC_GWAS/histlmiss.pdf", format = "jpeg", dpi = 300)
proportion of missing individuals per SNP
Delete SNPs and individuals with high levels of missingness
Delete SNPs with missingness >0.2.
$ plink –bfile HapMap_3_r3_1 –geno 0.2 –make-bed –out HapMap_3_r3_2
1457897 variants loaded from .bim file.
165 people (80 males, 85 females) loaded from .fam.
112 phenotype values loaded from .fam.
Before main variant filters, 112 founders and 53 nonfounders present.
Calculating allele frequencies...
...................
Warning: 225 het. haploid genotypes present (see HapMap_3_r3_2.hh );
many commands treat these as missing.
Total genotyping rate is 0.997378.
0 variants removed due to missing genotype data (--geno).
1457897 variants and 165 people pass filters and QC.
Among remaining phenotypes, 56 are cases and 56 are controls. (53 phenotypes are missing.)
--make-bed to HapMap_3_r3_2.bed + HapMap_3_r3_2.bim + HapMap_3_r3_2.fam ...
Delete individuals with missingness >0.2.
$ plink –bfile HapMap_3_r3_2 –mind 0.2 –make-bed –out HapMap_3_r3_3
1457897 variants loaded from .bim file.
165 people (80 males, 85 females) loaded from .fam.
112 phenotype values loaded from .fam.
0 people removed due to missing genotype data (--mind).
Before main variant filters, 112 founders and 53 nonfounders present.
Calculating allele frequencies...
...................
Warning: 225 het. haploid genotypes present (see HapMap_3_r3_3.hh );
many commands treat these as missing.
Total genotyping rate is 0.997378.
1457897 variants and 165 people pass filters and QC.
Among remaining phenotypes, 56 are cases and 56 are controls. (53 phenotypes are missing.)
--make-bed to HapMap_3_r3_3.bed + HapMap_3_r3_3.bim + HapMap_3_r3_3.fam ...
Delete SNPs with missingness >0.02.
$ plink –bfile HapMap_3_r3_3 –geno 0.02 –make-bed –out HapMap_3_r3_4
1457897 variants loaded from .bim file.
165 people (80 males, 85 females) loaded from .fam.
112 phenotype values loaded from .fam.
Before main variant filters, 112 founders and 53 nonfounders present.
...................
Warning: 225 het. haploid genotypes present (see HapMap_3_r3_4.hh ); many
commands treat these as missing.
Total genotyping rate is 0.997378.
**27473 variants removed due to missing genotype data (--geno).**
**1430424 variants and 165 people pass filters and QC.**
Among remaining phenotypes, 56 are cases and 56 are controls. (53 phenotypes are missing.)
--make-bed to HapMap_3_r3_4.bed + HapMap_3_r3_4.bim + HapMap_3_r3_4.fam ...
Delete individuals with missingness >0.02.
$ plink –bfile HapMap_3_r3_4 –mind 0.02 –make-bed –out HapMap_3_r3_5
1430424 variants loaded from .bim file.
165 people (80 males, 85 females) loaded from .fam.
112 phenotype values loaded from .fam.
0 people removed due to missing genotype data (--mind).
Before main variant filters, 112 founders and 53 nonfounders present.
Calculating allele frequencies...
......................
Warning: 157 het. haploid genotypes present (see HapMap_3_r3_5.hh ); many
commands treat these as missing.
Total genotyping rate is 0.997899.
1430424 variants and 165 people pass filters and QC.
Among remaining phenotypes, 56 are cases and 56 are controls. (53 phenotypes
are missing.)
--make-bed to HapMap_3_r3_5.bed + HapMap_3_r3_5.bim + HapMap_3_r3_5.fam ...
Check for sex discrepancy and impute sex if needed
Females must have a F value of <0.2, and males must have a F value >0.8.
This F value is based on the X chromosome inbreeding (homozygosity) estimate.
$ plink –bfile HapMap_3_r3_5 –check-sex
1430424 variants loaded from .bim file.
165 people (80 males, 85 females) loaded from .fam.
112 phenotype values loaded from .fam.
Before main variant filters, 112 founders and 53 nonfounders present.
Calculating allele frequencies...
...................
Warning: 157 het. haploid genotypes present (see plink.hh ); many commands treat these as missing.
Total genotyping rate is 0.997899.
1430424 variants and 165 people pass filters and QC.
Among remaining phenotypes, 56 are cases and 56 are controls. (53 phenotypes are missing.)
**--check-sex: 23410 Xchr and 0 Ychr variant(s) scanned, 1 problem detected.**
**Report written to plink.sexcheck .**
$ head plink.sexcheck
FID IID PEDSEX SNPSEX STATUS F
1328 NA06989 2 2 OK -0.01176
1377 NA11891 1 1 OK 1
1349 NA11843 1 1 OK 1
1330 NA12341 2 2 OK -0.01271
1444 NA12739 1 1 OK 1
1344 NA10850 2 2 OK 0.01491
1328 NA06984 1 1 OK 1
1463 NA12877 1 1 OK 1
1418 NA12275 2 2 OK -0.1024
$ grep “PROBLEM” plink.sexcheck
1349 NA10854 2 1 PROBLEM 0.99
$ Rscript –no-save gender_check.R
pdf_convert("1_QC_GWAS/Gender_check.pdf", format = "jpeg", dpi = 300)
gender_check
pdf_convert("1_QC_GWAS/Women_check.pdf", format = "jpeg", dpi = 300)
women_check
pdf_convert("1_QC_GWAS/Men_check.pdf", format = "jpeg", dpi = 300)
men_check
Impute sex
$ plink –bfile HapMap_3_r3_5 –impute-sex –make-bed –out HapMap_3_r3_6
1430424 variants loaded from .bim file.
**165 people (80 males, 85 females) loaded from .fam.**
112 phenotype values loaded from .fam.
Before main variant filters, 112 founders and 53 nonfounders present.
Calculating allele frequencies...
..................
Warning: 157 het. haploid genotypes present (see HapMap_3_r3_6.hh ); many
commands treat these as missing.
Total genotyping rate is 0.997899.
1430424 variants and 165 people pass filters and QC.
Among remaining phenotypes, 56 are cases and 56 are controls. (53 phenotypes are missing.)
**--impute-sex: 23410 Xchr and 0 Ychr variant(s) scanned, all sexes imputed.**
Report written to HapMap_3_r3_6.sexcheck .
--make-bed to HapMap_3_r3_6.bed + HapMap_3_r3_6.bim + HapMap_3_r3_6.fam ...
Generate a bfile with autosomal SNPs only and delete SNPs with a low minor allele frequency (MAF).
Select autosomal SNPs only (i.e., from chromosomes 1 to 22) into a text file.
$ awk ‘{ if ($1 >= 1 && $1 <= 22) print $2 }’ HapMap_3_r3_6.bim > snp_1_22.txt
$ head snp_1_22.txt
rs2185539
rs11240767
rs3131972
rs3131969
rs1048488
rs12562034
rs12124819
rs4040617
rs2905036
rs4245756
Extract autosomal SNPs only (i.e., from chromosomes 1 to 22) and make bed, bim and fam files
$ plink –bfile HapMap_3_r3_6 –extract snp_1_22.txt –make-bed –out HapMap_3_r3_7
1430424 variants loaded from .bim file.
**165 people (81 males, 84 females) loaded from .fam.**
112 phenotype values loaded from .fam.
**--extract: 1398544 variants remaining.**
Before main variant filters, 112 founders and 53 nonfounders present.
Calculating allele frequencies...
...................
Total genotyping rate is 0.99806.
**1398544 variants and 165 people pass filters and QC.**
Among remaining phenotypes, 56 are cases and 56 are controls. (53 phenotypes are missing.)
--make-bed to HapMap_3_r3_7.bed + HapMap_3_r3_7.bim + HapMap_3_r3_7.fam ...
Plot minor allele frequency (MAF) distribution.
$ plink –bfile HapMap_3_r3_7 –freq –out MAF_check
1398544 variants loaded from .bim file.
165 people (81 males, 84 females) loaded from .fam.
112 phenotype values loaded from .fam.
Before main variant filters, 112 founders and 53 nonfounders present.
Calculating allele frequencies...
..................
Total genotyping rate is 0.99806.
**--freq: Allele frequencies (founders only) written to MAF_check.frq .**
$ head MAF_check.frq
CHR SNP A1 A2 MAF NCHROBS
1 rs2185539 T C 0 224
1 rs11240767 T C 0 224
1 rs3131972 A G 0.1652 224
1 rs3131969 A G 0.1339 224
1 rs1048488 C T 0.1667 222
1 rs12562034 A G 0.1027 224
1 rs12124819 G A 0.2902 224
1 rs4040617 G A 0.1295 224
1 rs2905036 C T 0 224$ Rscript –no-save MAF_check.R
pdf_convert("1_QC_GWAS/MAF_distribution.pdf", format = "jpeg", dpi = 300)
MAF_distribution
Remove SNPs with a low MAF frequency.
A conventional MAF threshold for a regular GWAS is between 0.01 or 0.05, depending on sample size.$ plink –bfile HapMap_3_r3_7 –maf 0.05 –make-bed –out HapMap_3_r3_8
**1398544 variants loaded from .bim file.**
165 people (81 males, 84 females) loaded from .fam.
112 phenotype values loaded from .fam.
Before main variant filters, 112 founders and 53 nonfounders present.
Calculating allele frequencies...
........................
Total genotyping rate is 0.99806.
**325318 variants removed due to minor allele threshold(s)**
(--maf/--max-maf/--mac/--max-mac).
**1073226 variants and 165 people pass filters and QC.**
Among remaining phenotypes, 56 are cases and 56 are controls. (53 phenotypes are missing.)
--make-bed to HapMap_3_r3_8.bed + HapMap_3_r3_8.bim + HapMap_3_r3_8.fam ...
Delete SNPs which are not in Hardy-Weinberg equilibrium (HWE)
Check the distribution of HWE p-values of all SNPs.
$ plink –bfile HapMap_3_r3_8 –hardy
1073226 variants loaded from .bim file.
165 people (81 males, 84 females) loaded from .fam.
112 phenotype values loaded from .fam.
Before main variant filters, 112 founders and 53 nonfounders present.
Calculating allele frequencies...
................
Total genotyping rate is 0.998047.
**--hardy: Writing Hardy-Weinberg report (founders only) to plink.hwe ...**
$ head plink.hwe
CHR SNP TEST A1 A2 GENO O(HET) E(HET) P
1 rs3131972 ALL A G 2/33/77 0.2946 0.2758 0.7324
1 rs3131972 AFF A G 1/19/36 0.3393 0.3047 0.667
1 rs3131972 UNAFF A G 1/14/41 0.25 0.2449 1
1 rs3131969 ALL A G 2/26/84 0.2321 0.232 1
1 rs3131969 AFF A G 1/17/38 0.3036 0.2817 1
1 rs3131969 UNAFF A G 1/9/46 0.1607 0.1771 0.4189
1 rs1048488 ALL C T 2/33/76 0.2973 0.2778 0.7324
1 rs1048488 AFF C T 1/19/35 0.3455 0.3089 0.6661
1 rs1048488 UNAFF C T 1/14/41 0.25 0.2449 1$ awk ‘{ if ($9 <0.00001) print $0 }’ plink.hwe > plinkzoomhwe.hwe
wc -l plinkzoomhwe.hwe
13 plinkzoomhwe.hwe
$ head plinkzoomhwe.hwe
3 rs7623291 ALL T C 22/28/62 0.25 0.4362 8.938e-06
7 rs34238522 ALL C T 0/64/48 0.5714 0.4082 3.515e-06
8 rs3102841 ALL C A 8/78/23 0.7156 0.4905 1.899e-06
9 rs354831 ALL C T 12/18/82 0.1607 0.3047 6.339e-06
9 rs10990625 ALL C T 23/28/61 0.25 0.4424 9.391e-06
9 rs10990625 AFF C T 15/8/33 0.1429 0.4483 3.574e-07
10 rs2918624 ALL C T 0/62/50 0.5536 0.4004 8.54e-06
10 rs4934139 ALL C A 0/65/47 0.5804 0.4119 1.722e-06
12 rs2303632 UNAFF T G 15/10/31 0.1786 0.4592 4.934e-06
12 rs7963063 UNAFF C T 15/10/31 0.1786 0.4592 4.934e-06
$ Rscript –no-save hwe.R
pdf_convert("1_QC_GWAS/histhwe.pdf", format = "jpeg", dpi = 300)
hist_hwe
pdf_convert("1_QC_GWAS/histhwe_below_theshold.pdf", format = "jpeg", dpi = 300)
hist_below_hwe
$ plink –bfile HapMap_3_r3_8 –hwe 1e-6 –make-bed –out HapMap_hwe_filter_step1
1073226 variants loaded from .bim file.
165 people (81 males, 84 females) loaded from .fam.
112 phenotype values loaded from .fam.
Before main variant filters, 112 founders and 53 nonfounders present.
Calculating allele frequencies...
....................
Total genotyping rate is 0.998047.
**--hwe: 0 variants removed due to Hardy-Weinberg exact test.**
1073226 variants and 165 people pass filters and QC.
Among remaining phenotypes, 56 are cases and 56 are controls. (53 phenotypes are missing.)
--make-bed to HapMap_hwe_filter_step1.bed + HapMap_hwe_filter_step1.bim + HapMap_hwe_filter_step1.fam ...
$ plink –bfile HapMap_hwe_filter_step1 –hwe 1e-10 –hwe-all –make-bed –out HapMap_3_r3_9
**Note: --hwe-all flag deprecated. Use '--hwe include-nonctrl'.**
1073226 variants loaded from .bim file.
165 people (81 males, 84 females) loaded from .fam.
112 phenotype values loaded from .fam.
Before main variant filters, 112 founders and 53 nonfounders present.
Calculating allele frequencies...
......................
Total genotyping rate is 0.998047.
**--hwe: 0 variants removed due to Hardy-Weinberg exact test.**
1073226 variants and 165 people pass filters and QC.
Among remaining phenotypes, 56 are cases and 56 are controls. (53 phenotypes
are missing.)
--make-bed to HapMap_3_r3_9.bed + HapMap_3_r3_9.bim + HapMap_3_r3_9.fam ...
Exclude high inversion regions (inversion.txt [High LD regions]), prune the SNPs and remove individuals with a heterozygosity rate deviating more than 3 sd from the mean
wc -l inversion.txt
2 inversion.txt
$ cat inversion.txt
6 25500000 33500000 8 HLA
8 8135000 12000000 Inversion8
$ plink –bfile HapMap_3_r3_9 –exclude inversion.txt –indep-pairwise 50 5 0.2 –out indepSNP –range
**Note: --range flag deprecated. Use e.g. '--extract range [filename]'.**
1073226 variants loaded from .bim file.
165 people (81 males, 84 females) loaded from .fam.
112 phenotype values loaded from .fam.
**--exclude range: 9893 variants excluded.**
**--exclude range: 1063333 variants remaining.**
Before main variant filters, 112 founders and 53 nonfounders present.
Calculating allele frequencies...
.......................
Total genotyping rate is 0.998042.
**1063333 variants and 165 people pass filters and QC.**
Among remaining phenotypes, 56 are cases and 56 are controls. (53 phenotypes are missing.)
Pruned 79323 variants from chromosome 1, leaving 8397.
Pruned 81833 variants from chromosome 2, leaving 7965.
.........................
Pruned 13514 variants from chromosome 22, leaving 1954.
Pruning complete. **959189 of 1063333 variants removed.**
Writing...
Marker lists written to **indepSNP.prune.in** and **indepSNP.prune.out** .
$ wc -l indepSNP.prune.out
959189 indepSNP.prune.out
$ wc -l indepSNP.prune.in
104144 indepSNP.prune.in
$ plink –bfile HapMap_3_r3_9 –extract indepSNP.prune.in –het –out R_check
1073226 variants loaded from .bim file.
165 people (81 males, 84 females) loaded from .fam.
112 phenotype values loaded from .fam.
**--extract: 104144 variants remaining.**
Before main variant filters, 112 founders and 53 nonfounders present.
Calculating allele frequencies...
.................
Total genotyping rate is 0.998036.
**104144 variants and 165 people pass filters and QC.**
Among remaining phenotypes, 56 are cases and 56 are controls. (53 phenotypes are missing.)
**--het: 104144 variants scanned, report written to R_check.het .**
$ head R_check.het
FID IID O(HOM) E(HOM) N(NM) F
1328 NA06989 67039 6.747e+04 103911 -0.01172
1377 NA11891 66847 6.684e+04 102970 0.0001494
1349 NA11843 67262 6.756e+04 104071 -0.00829
1330 NA12341 66654 6.74e+04 103826 -0.02051
1444 NA12739 66687 6.656e+04 102519 0.003602
1344 NA10850 67421 6.752e+04 104001 -0.002778
1328 NA06984 66942 6.725e+04 103600 -0.008481
1463 NA12877 67384 6.75e+04 103970 -0.003193
1418 NA12275 66826 6.743e+04 103871 -0.01662$ Rscript –no-save check_heterozygosity_rate.R
pdf_convert("1_QC_GWAS/heterozygosity.pdf", format = "jpeg", dpi = 300)
heterozygosity rate distribution
Rscript –no-save heterozygosity_outliers_list.R
Output of the command above: fail-het-qc.txt
$ wc -l fail-het-qc.txt
3 fail-het-qc.txt
$ cat fail-het-qc.txt
"FID" "IID" "O.HOM." "E.HOM." "N.NM." "F" "HET_RATE" "HET_DST"
1330 "NA12342" 68049 67240 103571 0.02229 0.342972453679119 -3.66942601978601
1459 "NA12874" 68802 67560 104068 0.0339 0.338874582004074 -5.05308902088892
$ sed ‘s/"// g’ fail-het-qc.txt | awk ‘{print$1, $2}’> het_fail_ind.txt
$ cat het_fail_ind.txt
FID IID
1330 NA12342
1459 NA12874
$ plink –bfile HapMap_3_r3_9 –remove het_fail_ind.txt –make-bed –out HapMap_3_r3_10
**1073226 variants loaded from .bim file.**
165 people (81 males, 84 females) loaded from .fam.
112 phenotype values loaded from .fam.
**--remove: 163 people remaining.**
**Before main variant filters, 110 founders and 53 nonfounders present.**
Calculating allele frequencies...
......................
Total genotyping rate in remaining samples is 0.998071.
**1073226 variants and 163 people pass filters and QC.**
Among remaining phenotypes, 55 are cases and 55 are controls. (53 phenotypes are missing.)
--make-bed to HapMap_3_r3_10.bed + HapMap_3_r3_10.bim + HapMap_3_r3_10.fam ...
Analysis for cryptic relatedness: Exclude all individuals above the pihat threshold of 0.2
Two approaches: Cryptic relatedness approach and Founders approach
Cryptic relatedness approach
$ plink –bfile HapMap_3_r3_10 –extract indepSNP.prune.in –genome –min 0.2 –out pihat_min0.2
**1073226 variants loaded from .bim file.**
163 people (79 males, 84 females) loaded from .fam.
110 phenotype values loaded from .fam.
**--extract: 104144 variants remaining.**
**Before main variant filters, 110 founders and 53 nonfounders present.**
Calculating allele frequencies...
.....................
Total genotyping rate is 0.998051.
**104144 variants and 163 people pass filters and QC.**
Among remaining phenotypes, 55 are cases and 55 are controls. (53 phenotypes are missing.)
1152 markers complete.
2304 markers complete.
3456 markers complete.
..................
103680 markers complete.
**104144 markers complete.**
IBD calculations complete.
Writing... 99%
**Finished writing pihat_min0.2.genome .**
$ wc -l pihat_min0.2.genome
98 pihat_min0.2.genome
$ head pihat_min0.2.genome
FID1 IID1 FID2 IID2 RT EZ Z0 Z1 Z2 PI_HAT PHE DST PPC RATIO
1377 NA11891 1377 NA10865 PO 0.5 0.0013 0.9977 0.0010 0.4998 0 0.823854 1.0000 3611.0000
1349 NA11843 1349 NA10853 PO 0.5 0.0021 0.9905 0.0073 0.5026 -1 0.824896 1.0000 926.5000
1330 NA12341 1330 NA12335 PO 0.5 0.0000 1.0000 0.0000 0.5000 0 0.822874 1.0000 924.2500
1444 NA12739 1444 NA12749 PO 0.5 0.0018 0.9946 0.0037 0.5010 -1 0.824290 1.0000 NA
1444 NA12739 1444 NA12748 PO 0.5 0.0015 0.9949 0.0037 0.5011 0 0.824316 1.0000 1227.3333
1463 NA12877 1463 NA12890 PO 0.5 0.0000 1.0000 0.0000 0.5000 0 0.823256 1.0000 1231.6667
1463 NA12877 1463 NA12889 PO 0.5 0.0140 0.9789 0.0071 0.4965 0 0.823620 1.0000 159.0435
1418 NA12275 1418 NA10836 PO 0.5 0.0023 0.9970 0.0007 0.4992 -1 0.823716 1.0000 NA
13291 NA06986 13291 NA06997 PO 0.5 0.0016 0.9951 0.0033 0.5008 -1 0.824240 1.0000 1835.5000
$ awk ‘{ if ($8 >0.9) print $0 }’ pihat_min0.2.genome > zoom_pihat.genome
$ wc -l zoom_pihat.genome
95 zoom_pihat.genome
$ head zoom_pihat.genome
FID1 IID1 FID2 IID2 RT EZ Z0 Z1 Z2 PI_HAT PHE DST PPC RATIO
1377 NA11891 1377 NA10865 PO 0.5 0.0013 0.9977 0.0010 0.4998 0 0.823854 1.0000 3611.0000
1349 NA11843 1349 NA10853 PO 0.5 0.0021 0.9905 0.0073 0.5026 -1 0.824896 1.0000 926.5000
1330 NA12341 1330 NA12335 PO 0.5 0.0000 1.0000 0.0000 0.5000 0 0.822874 1.0000 924.2500
1444 NA12739 1444 NA12749 PO 0.5 0.0018 0.9946 0.0037 0.5010 -1 0.824290 1.0000 NA
1444 NA12739 1444 NA12748 PO 0.5 0.0015 0.9949 0.0037 0.5011 0 0.824316 1.0000 1227.3333
1463 NA12877 1463 NA12890 PO 0.5 0.0000 1.0000 0.0000 0.5000 0 0.823256 1.0000 1231.6667
1463 NA12877 1463 NA12889 PO 0.5 0.0140 0.9789 0.0071 0.4965 0 0.823620 1.0000 159.0435
1418 NA12275 1418 NA10836 PO 0.5 0.0023 0.9970 0.0007 0.4992 -1 0.823716 1.0000 NA
13291 NA06986 13291 NA06997 PO 0.5 0.0016 0.9951 0.0033 0.5008 -1 0.824240 1.0000 1835.5000
$ Rscript –no-save Relatedness.R
pdf_convert("1_QC_GWAS/hist_relatedness.pdf", format = "jpeg", dpi = 300)
hist_relatedness
Founders approach
$ plink –bfile HapMap_3_r3_10 –filter-founders –make-bed –out HapMap_3_r3_11
1073226 variants loaded from .bim file.
**163 people (79 males, 84 females) loaded from .fam.**
110 phenotype values loaded from .fam.
**53 people removed due to founder status (--filter-founders).**
**Before main variant filters, 110 founders and 0 nonfounders present.**
Calculating allele frequencies...
.................
Total genotyping rate in remaining samples is 0.998025.
**1073226 variants and 110 people pass filters and QC.**
**Among remaining phenotypes, 55 are cases and 55 are controls.**
--make-bed to HapMap_3_r3_11.bed + HapMap_3_r3_11.bim + HapMap_3_r3_11.fam ...
$ plink –bfile HapMap_3_r3_11 –extract indepSNP.prune.in –genome –min 0.2 –out pihat_min0.2_in_founders
Extract individuals with a pihat >0.2
1073226 variants loaded from .bim file.
110 people (55 males, 55 females) loaded from .fam.
110 phenotype values loaded from .fam.
--extract: 104144 variants remaining.
Before main variant filters, 110 founders and 0 nonfounders present.
Calculating allele frequencies...
............
Total genotyping rate is 0.997978.
104144 variants and 110 people pass filters and QC.
Among remaining phenotypes, 55 are cases and 55 are controls.
1152 markers complete.
2304 markers complete.
3456 markers complete.
...........................
103680 markers complete.
104144 markers complete.
IBD calculations complete.
Writing... 99%
**Finished writing pihat_min0.2_in_founders.genome .**
$ cat pihat_min0.2_in_founders.genome
FID1 IID1 FID2 IID2 RT EZ Z0 Z1 Z2 PI_HAT PHE DST PPC RATIO
13291 NA07045 1454 NA12813 UN NA 0.2572 0.5007 0.2421 0.4924 0 0.839777 1.0000 9.7022$ plink –bfile HapMap_3_r3_11 –missing
Removes the individual with the lowest call rate
1073226 variants loaded from .bim file.
110 people (55 males, 55 females) loaded from .fam.
110 phenotype values loaded from .fam.
Before main variant filters, 110 founders and 0 nonfounders present.
Calculating allele frequencies...
........
Total genotyping rate is 0.998025.
--missing: Sample missing data report written to plink.imiss, and variant-based
**missing data report written to plink.lmiss.**
$ wc -l plink.imiss
111 plink.imiss
$ head -n 5 plink.imiss
FID IID MISS_PHENO N_MISS N_GENO F_MISS
1328 NA06989 N 2181 1073226 0.002032
1377 NA11891 N 13586 1073226 0.01266
1349 NA11843 N 813 1073226 0.0007575
1330 NA12341 N 3697 1073226 0.003445
$ wc -l 0.2_low_call_rate_pihat.txt
1 0.2_low_call_rate_pihat.txt
$ cat 0.2_low_call_rate_pihat.txt
13291 NA07045
$ plink2 –bfile HapMap_3_r3_11 –make-bed –out HapMap_3_r3_12 –remove 0.2_low_call_rate_pihat.txt
Deletes the individuals with the lowest call rate in ‘related’ pairs with a pihat > 0.2
1073226 variants loaded from .bim file.
110 people (55 males, 55 females) loaded from .fam.
110 phenotype values loaded from .fam.
**--remove: 109 people remaining.**
**Before main variant filters, 109 founders and 0 nonfounders present.**
Calculating allele frequencies...
.............
Total genotyping rate in remaining samples is 0.998028.
**1073226 variants and 109 people pass filters and QC.**
**Among remaining phenotypes, 54 are cases and 55 are controls.**
--make-bed to HapMap_3_r3_12.bed + HapMap_3_r3_12.bim + HapMap_3_r3_12.fam ...
Final directory contents at the end of QC
~/GWAS_IJMPR/GWA_tutorial/1_QC_GWAS$ ls -l
-rw-rw-r-- 1 ubuntu ubuntu 14 Nov 29 03:19 0.2_low_call_rate_pihat.txt
-rw-rw-r-- 1 ubuntu ubuntu 12620 Nov 29 03:21 1_Main_script_QC_GWAS.txt
-rw-rw-r-- 1 ubuntu ubuntu 4572 Nov 29 02:32 Gender_check.pdf
-rw-rw-r-- 1 ubuntu ubuntu 61231677 Jan 25 2016 HapMap_3_r3_1.bed
-rw-rw-r-- 1 ubuntu ubuntu 40635824 Jan 25 2016 HapMap_3_r3_1.bim
-rw-rw-r-- 1 ubuntu ubuntu 4136 Jan 25 2016 HapMap_3_r3_1.fam
-rw-rw-r-- 1 ubuntu ubuntu 44002269 Nov 29 02:57 HapMap_3_r3_10.bed
-rw-rw-r-- 1 ubuntu ubuntu 29846054 Nov 29 02:57 HapMap_3_r3_10.bim
-rw-rw-r-- 1 ubuntu ubuntu 4094 Nov 29 02:57 HapMap_3_r3_10.fam
-rw-rw-r-- 1 ubuntu ubuntu 1016 Nov 29 02:57 HapMap_3_r3_10.log
-rw-rw-r-- 1 ubuntu ubuntu 30050331 Nov 29 03:01 HapMap_3_r3_11.bed
-rw-rw-r-- 1 ubuntu ubuntu 29846054 Nov 29 03:01 HapMap_3_r3_11.bim
-rw-rw-r-- 1 ubuntu ubuntu 2318 Nov 29 03:01 HapMap_3_r3_11.fam
-rw-rw-r-- 1 ubuntu ubuntu 1007 Nov 29 03:01 HapMap_3_r3_11.log
**-rw-rw-r-- 1 ubuntu ubuntu 30050331 Nov 29 03:20 HapMap_3_r3_12.bed**
**-rw-rw-r-- 1 ubuntu ubuntu 29846054 Nov 29 03:20 HapMap_3_r3_12.bim**
**-rw-rw-r-- 1 ubuntu ubuntu 2296 Nov 29 03:20 HapMap_3_r3_12.fam**
**-rw-rw-r-- 1 ubuntu ubuntu 997 Nov 29 03:20 HapMap_3_r3_12.log**
-rw-rw-r-- 1 ubuntu ubuntu 61231677 Nov 29 02:27 HapMap_3_r3_2.bed
-rw-rw-r-- 1 ubuntu ubuntu 40635824 Nov 29 02:27 HapMap_3_r3_2.bim
-rw-rw-r-- 1 ubuntu ubuntu 4136 Nov 29 02:27 HapMap_3_r3_2.fam
-rw-rw-r-- 1 ubuntu ubuntu 5226 Nov 29 02:27 HapMap_3_r3_2.hh
-rw-rw-r-- 1 ubuntu ubuntu 1109 Nov 29 02:27 HapMap_3_r3_2.log
-rw-rw-r-- 1 ubuntu ubuntu 61231677 Nov 29 02:28 HapMap_3_r3_3.bed
-rw-rw-r-- 1 ubuntu ubuntu 40635824 Nov 29 02:28 HapMap_3_r3_3.bim
-rw-rw-r-- 1 ubuntu ubuntu 4136 Nov 29 02:28 HapMap_3_r3_3.fam
-rw-rw-r-- 1 ubuntu ubuntu 5226 Nov 29 02:28 HapMap_3_r3_3.hh
-rw-rw-r-- 1 ubuntu ubuntu 1107 Nov 29 02:28 HapMap_3_r3_3.log
-rw-rw-r-- 1 ubuntu ubuntu 60077811 Nov 29 02:29 HapMap_3_r3_4.bed
-rw-rw-r-- 1 ubuntu ubuntu 39869059 Nov 29 02:29 HapMap_3_r3_4.bim
-rw-rw-r-- 1 ubuntu ubuntu 4136 Nov 29 02:29 HapMap_3_r3_4.fam
-rw-rw-r-- 1 ubuntu ubuntu 5226 Nov 29 02:29 HapMap_3_r3_4.hh
-rw-rw-r-- 1 ubuntu ubuntu 1114 Nov 29 02:29 HapMap_3_r3_4.log
-rw-rw-r-- 1 ubuntu ubuntu 60077811 Nov 29 02:29 HapMap_3_r3_5.bed
-rw-rw-r-- 1 ubuntu ubuntu 39869059 Nov 29 02:29 HapMap_3_r3_5.bim
-rw-rw-r-- 1 ubuntu ubuntu 4136 Nov 29 02:29 HapMap_3_r3_5.fam
-rw-rw-r-- 1 ubuntu ubuntu 3642 Nov 29 02:29 HapMap_3_r3_5.hh
-rw-rw-r-- 1 ubuntu ubuntu 1108 Nov 29 02:29 HapMap_3_r3_5.log
-rw-rw-r-- 1 ubuntu ubuntu 60077811 Nov 29 02:36 HapMap_3_r3_6.bed
-rw-rw-r-- 1 ubuntu ubuntu 39869059 Nov 29 02:36 HapMap_3_r3_6.bim
-rw-rw-r-- 1 ubuntu ubuntu 4136 Nov 29 02:36 HapMap_3_r3_6.fam
-rw-rw-r-- 1 ubuntu ubuntu 3642 Nov 29 02:36 HapMap_3_r3_6.hh
-rw-rw-r-- 1 ubuntu ubuntu 1171 Nov 29 02:36 HapMap_3_r3_6.log
-rw-rw-r-- 1 ubuntu ubuntu 11620 Nov 29 02:36 HapMap_3_r3_6.sexcheck
-rw-rw-r-- 1 ubuntu ubuntu 58738851 Nov 29 02:38 HapMap_3_r3_7.bed
-rw-rw-r-- 1 ubuntu ubuntu 38962079 Nov 29 02:38 HapMap_3_r3_7.bim
-rw-rw-r-- 1 ubuntu ubuntu 4136 Nov 29 02:38 HapMap_3_r3_7.fam
-rw-rw-r-- 1 ubuntu ubuntu 994 Nov 29 02:38 HapMap_3_r3_7.log
-rw-rw-r-- 1 ubuntu ubuntu 45075495 Nov 29 02:42 HapMap_3_r3_8.bed
-rw-rw-r-- 1 ubuntu ubuntu 29846054 Nov 29 02:42 HapMap_3_r3_8.bim
-rw-rw-r-- 1 ubuntu ubuntu 4136 Nov 29 02:42 HapMap_3_r3_8.fam
-rw-rw-r-- 1 ubuntu ubuntu 1035 Nov 29 02:42 HapMap_3_r3_8.log
-rw-rw-r-- 1 ubuntu ubuntu 45075495 Nov 29 02:50 HapMap_3_r3_9.bed
-rw-rw-r-- 1 ubuntu ubuntu 29846054 Nov 29 02:50 HapMap_3_r3_9.bim
-rw-rw-r-- 1 ubuntu ubuntu 4136 Nov 29 02:50 HapMap_3_r3_9.fam
-rw-rw-r-- 1 ubuntu ubuntu 1090 Nov 29 02:50 HapMap_3_r3_9.log
-rw-rw-r-- 1 ubuntu ubuntu 45075495 Nov 29 02:48 HapMap_hwe_filter_step1.bed
-rw-rw-r-- 1 ubuntu ubuntu 29846054 Nov 29 02:48 HapMap_hwe_filter_step1.bim
-rw-rw-r-- 1 ubuntu ubuntu 4136 Nov 29 02:48 HapMap_hwe_filter_step1.fam
-rw-rw-r-- 1 ubuntu ubuntu 1044 Nov 29 02:48 HapMap_hwe_filter_step1.log
-rw-rw-r-- 1 ubuntu ubuntu 162 Sep 1 2015 MAF_check.R
-rw-r--r-- 1 ubuntu ubuntu 68528705 Nov 29 02:39 MAF_check.frq
-rw-rw-r-- 1 ubuntu ubuntu 765 Nov 29 02:39 MAF_check.log
-rw-rw-r-- 1 ubuntu ubuntu 4755 Nov 29 02:39 MAF_distribution.pdf
-rw-rw-r-- 1 ubuntu ubuntu 4526 Nov 29 02:32 Men_check.pdf
-rw-r--r-- 1 ubuntu ubuntu 11620 Nov 29 02:53 R_check.het
-rw-rw-r-- 1 ubuntu ubuntu 969 Nov 29 02:53 R_check.log
-rw-rw-r-- 1 ubuntu ubuntu 923 Nov 26 2015 Relatedness.R
-rw-rw-r-- 1 ubuntu ubuntu 4599 Nov 29 02:32 Women_check.pdf
-rw-rw-r-- 1 ubuntu ubuntu 235 Jan 22 2016 check_heterozygosity_rate.R
-rw-rw-r-- 1 ubuntu ubuntu 218 Nov 29 02:54 fail-het-qc.txt
-rw-rw-r-- 1 ubuntu ubuntu 352 Aug 28 2015 gender_check.R
-rw-rw-r-- 1 ubuntu ubuntu 34 Nov 29 02:56 het_fail_ind.txt
-rw-rw-r-- 1 ubuntu ubuntu 4657 Nov 29 02:53 heterozygosity.pdf
-rw-rw-r-- 1 ubuntu ubuntu 371 Aug 20 2015 heterozygosity_outliers_list.R
-rw-rw-r-- 1 ubuntu ubuntu 410 Sep 10 2015 hist_miss.R
-rw-rw-r-- 1 ubuntu ubuntu 4574 Nov 29 02:59 hist_relatedness.pdf
-rw-rw-r-- 1 ubuntu ubuntu 4705 Nov 29 02:47 histhwe.pdf
-rw-rw-r-- 1 ubuntu ubuntu 4602 Nov 29 02:47 histhwe_below_theshold.pdf
-rw-rw-r-- 1 ubuntu ubuntu 4599 Nov 29 02:23 histimiss.pdf
-rw-rw-r-- 1 ubuntu ubuntu 4764 Nov 29 02:23 histlmiss.pdf
-rw-rw-r-- 1 ubuntu ubuntu 287 Sep 2 2015 hwe.R
-rw-rw-r-- 1 ubuntu ubuntu 2397 Nov 29 02:52 indepSNP.log
**-rw-rw-r-- 1 ubuntu ubuntu 1058938 Nov 29 02:52 indepSNP.prune.in**
-rw-rw-r-- 1 ubuntu ubuntu 9747179 Nov 29 02:52 indepSNP.prune.out
-rw-rw-r-- 1 ubuntu ubuntu 88 Jun 25 2015 inversion.txt
-rw-rw-r-- 1 ubuntu ubuntu 10342 Nov 29 02:58 pihat_min0.2.genome
-rw-rw-r-- 1 ubuntu ubuntu 988 Nov 29 02:58 pihat_min0.2.log
-rw-rw-r-- 1 ubuntu ubuntu 208 Nov 29 03:02 pihat_min0.2_in_founders.genome
-rw-rw-r-- 1 ubuntu ubuntu 981 Nov 29 03:02 pihat_min0.2_in_founders.log
-rw-rw-r-- 1 ubuntu ubuntu 3642 Nov 29 02:31 plink.hh
-rw-r--r-- 1 ubuntu ubuntu 283331753 Nov 29 02:44 plink.hwe
-rw-r--r-- 1 ubuntu ubuntu 6260 Nov 29 03:03 plink.imiss
-rw-r--r-- 1 ubuntu ubuntu 47221988 Nov 29 03:03 plink.lmiss
-rw-rw-r-- 1 ubuntu ubuntu 805 Nov 29 03:03 plink.log
-rw-rw-r-- 1 ubuntu ubuntu 11620 Nov 29 02:31 plink.sexcheck
-rw-rw-r-- 1 ubuntu ubuntu 1144 Nov 29 02:46 plinkzoomhwe.hwe
-rw-rw-r-- 1 ubuntu ubuntu 39242 Sep 7 2015 pops_HapMap_3_r3
-rw-rw-r-- 1 ubuntu ubuntu 5006 Nov 29 02:59 relatedness.pdf
-rw-rw-r-- 1 ubuntu ubuntu 14275908 Nov 29 02:37 snp_1_22.txt
-rw-rw-r-- 1 ubuntu ubuntu 10030 Nov 29 02:59 zoom_pihat.genome
-rw-rw-r-- 1 ubuntu ubuntu 4998 Nov 29 02:59 zoom_relatedness.pdf
2_Population_stratification
Obtain genetic data of 629 individuals from different ethnic backgrounds presented in a 1000 Genomes dataset
~/GWAS_IJMPR$ wget ftp://ftp-trace.ncbi.nih.gov/1000genomes/ftp/release/20100804/ALL.2of4intersection.20100804.genotypes.vcf.gz
Additional directory contents
~/GWAS_IJMPR$ ls -l
-rw-rw-r– 1 ubuntu ubuntu 65672092096 Nov 29 03:44 ALL.2of4intersection.20100804.genotypes.vcf.gz
$ zmore ALL.2of4intersection.20100804.genotypes.vcf.gz
##fileformat=VCFv4.0
##filedat=20101112
##datarelease=20100804
**##samples=629**
##description="Where **BI calls** are present, genotypes and alleles are from BI.
In there absence, **UM genotypes** are used.
If neither are available, no genotype in formation is present and the alleles are from the **NCBI calls**."
##FORMAT=<ID=AD,Number=.,Type=Integer,Description="**Allelic depths** for the ref and alt alleles in the order listed">
##FORMAT=<ID=DP,Number=1,Type=Integer,Description="**Read Depth** (only filtered reads used for calling)">
##FORMAT=<ID=GL,Number=3,Type=Float,Description="**Log-scaled likelihoods for AA,AB,BB genotypes**
where A=ref and B=alt; not applicable if site is not biallelic">
##FORMAT=<ID=GQ,Number=1,Type=Float,Description="**Genotype Quality**">
##FORMAT=<ID=GT,Number=1,Type=String,Description="**Genotype**">
##FORMAT=<ID=GD,Number=1,Type=Float,Description="**Genotype dosage**. Expected count of non-ref alleles [0,2]">
##FORMAT=<ID=OG,Number=1,Type=String,Description="**Original Genotype** input to Beagle">
##INFO=<ID=AF,Number=.,Type=Float,Description="**Allele Frequency**, for each ALT allele, in the same order as listed">
##INFO=<ID=DP,Number=1,Type=Integer,Description="**Total Depth**">
##INFO=<ID=CB,Number=.,Type=String,Description="List of centres that called,
**UM (University of Michigan)**, **BI (Broad Institute)**, **BC (Boston College)**, **NCBI**">
##INFO=<ID=EUR_R2,Number=1,Type=Float,Description="**R2 From Beagle based on European Samples**">
##INFO=<ID=AFR_R2,Number=1,Type=Float,Description="**R2 From Beagle based on AFRICAN Samples**">
##INFO=<ID=ASN_R2,Number=1,Type=Float,Description="**R2 From Beagle based on Asian Samples**">
#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT
HG00098 HG00100 HG00106 HG00112 HG00114 HG00116 HG00117 HG00118 HG00119 HG00120 HG00122
HG00123 HG00124 HG00126 HG00131 HG00141 HG00142 HG00143 HG00144 HG00145 HG00146 HG00147
HG00148 HG00149 HG00150 HG00151 HG00152 HG00153 HG00156 HG00158 HG00159
Quality control the 1000 genome data (steps similar to those described in part 1 will be undertaken )
Check for population stratification using data from the 1000 Genomes Project.
Individuals with a non-European ethnic background will be removed.
Generate a covariate file which helps to adust for remaining population stratification within the European subjects.
Convert vcf to Plink format
$ plink –vcf ALL.2of4intersection.20100804.genotypes.vcf.gz –make-bed –out ALL.2of4intersection.20100804.genotypes
....................
--vcf: 25,488k variants complete.
--vcf: ALL.2of4intersection.20100804.genotypes-temporary.bed +
ALL.2of4intersection.20100804.genotypes-temporary.bim +
ALL.2of4intersection.20100804.genotypes-temporary.fam written.
25,488,488 variants loaded from .bim file.
629 people (0 males, 0 females, 629 ambiguous) loaded from .fam.
Ambiguous sex IDs written to ALL.2of4intersection.20100804.genotypes.nosex .
..............
Before main variant filters, 629 founders and 0 nonfounders present.
Calculating allele frequencies...
...................
Total genotyping rate is 0.615305.
25,488,488 variants and 629 people pass filters and QC.
Note: No phenotypes present.
--make-bed to ALL.2of4intersection.20100804.genotypes.bed +
ALL.2of4intersection.20100804.genotypes.bim +
ALL.2of4intersection.20100804.genotypes.fam ...
................
Additional directory contents
~/GWAS_IJMPR$ ls -l
-rw-rw-r-- 1 ubuntu ubuntu 4027181107 Nov 29 04:25 ALL.2of4intersection.20100804.genotypes.bed
-rw-rw-r-- 1 ubuntu ubuntu 635485636 Nov 29 04:25 ALL.2of4intersection.20100804.genotypes.bim
-rw-rw-r-- 1 ubuntu ubuntu 15725 Nov 29 04:25 ALL.2of4intersection.20100804.genotypes.fam
-rw-rw-r-- 1 ubuntu ubuntu 1255 Nov 29 04:25 ALL.2of4intersection.20100804.genotypes.log
-rw-rw-r-- 1 ubuntu ubuntu 10064 Nov 29 04:24 ALL.2of4intersection.20100804.genotypes.nosex
Assign unique indentifiers to the SNPs with a missing rs-identifier (i.e., the SNPs with “.”).
$ plink –bfile ALL.2of4intersection.20100804.genotypes –set-missing-var-ids @:#[b37]$1,$2 –make-bed –out ALL.2of4intersection.20100804.genotypes_no_missing_IDs
25,488,488 variants loaded from .bim file.
**10,375,501 missing IDs set.**
629 people (0 males, 0 females, 629 ambiguous) loaded from .fam.
**Ambiguous sex IDs written to ALL.2of4intersection.20100804.genotypes_no_missing_IDs.nosex .**
...........
Before main variant filters, 629 founders and 0 nonfounders present.
Calculating allele frequencies...
.............................
Total genotyping rate is 0.615305.
**25488488 variants and 629 people pass filters and QC.**
Note: No phenotypes present.
--make-bed to ALL.2of4intersection.20100804.genotypes_no_missing_IDs.bed +
ALL.2of4intersection.20100804.genotypes_no_missing_IDs.bim +
ALL.2of4intersection.20100804.genotypes_no_missing_IDs.fam ...
Remove variants based on missing genotype data
$ plink –bfile ALL.2of4intersection.20100804.genotypes_no_missing_IDs –geno 0.2 –allow-no-sex –make-bed –out 1kG_MDS
25488488 variants loaded from .bim file.
629 people (0 males, 0 females, 629 ambiguous) loaded from .fam.
**Ambiguous sex IDs written to 1kG_MDS.nosex .**
................
Before main variant filters, 629 founders and 0 nonfounders present.
......................
Total genotyping rate is 0.615305.
**16481066 variants removed due to missing genotype data (--geno).**
**9,007,422 variants and 629 people pass filters and QC.**
Note: No phenotypes present.
--make-bed to 1kG_MDS.bed + 1kG_MDS.bim + 1kG_MDS.fam ...
Remove individuals based on missing genotype data.
$ plink –bfile 1kG_MDS –mind 0.2 –allow-no-sex –make-bed –out 1kG_MDS2
**9,007,422 variants loaded from .bim file.**
629 people (0 males, 0 females, 629 ambiguous) loaded from .fam.
Ambiguous sex IDs written to 1kG_MDS2.nosex .
**0 people removed due to missing genotype data (--mind).**
........
Before main variant filters, 629 founders and 0 nonfounders present.
Calculating allele frequencies...
.......................
Total genotyping rate is 0.995911.
**9007422 variants and 629 people pass filters and QC.**
Note: No phenotypes present.
--make-bed to 1kG_MDS2.bed + 1kG_MDS2.bim + 1kG_MDS2.fam ...
Remove variants based on missing genotype data.
$ plink –bfile 1kG_MDS2 –geno 0.02 –allow-no-sex –make-bed –out 1kG_MDS3
9007422 variants loaded from .bim file.
629 people (0 males, 0 females, 629 ambiguous) loaded from .fam.
Ambiguous sex IDs written to 1kG_MDS3.nosex .
........
Before main variant filters, 629 founders and 0 nonfounders present.
.........................
Total genotyping rate is 0.995911.
**766,677 variants removed due to missing genotype data (--geno).**
**8,240,745 variants and 629 people pass filters and QC.**
Note: No phenotypes present.
--make-bed to 1kG_MDS3.bed + 1kG_MDS3.bim + 1kG_MDS3.fam ...
# Remove individuals based on missing genotype data
$ plink –bfile 1kG_MDS3 –mind 0.02 –allow-no-sex –make-bed –out 1kG_MDS4
8240745 variants loaded from .bim file.
629 people (0 males, 0 females, 629 ambiguous) loaded from .fam.
Ambiguous sex IDs written to 1kG_MDS4.nosex .
0 people removed due to missing genotype data (--mind).
Using 1 thread (no multithreaded calculations invoked).
Before main variant filters, 629 founders and 0 nonfounders present.
Calculating allele frequencies...
....................
Total genotyping rate is 0.999477.
8240745 variants and 629 people pass filters and QC.
Note: No phenotypes present.
--make-bed to 1kG_MDS4.bed + 1kG_MDS4.bim + 1kG_MDS4.fam ...
Remove variants due to minor allele threshold(s)
$ plink –bfile 1kG_MDS4 –maf 0.05 –allow-no-sex –make-bed –out 1kG_MDS5
**8240745 variants loaded from .bim file.**
629 people (0 males, 0 females, 629 ambiguous) loaded from .fam.
Ambiguous sex IDs written to 1kG_MDS5.nosex .
..........
Before main variant filters, 629 founders and 0 nonfounders present.
Calculating allele frequencies...
...............
Total genotyping rate is 0.999477.
**2,432,435 variants removed due to minor allele threshold(s)**
(--maf/--max-maf/--mac/--max-mac).
**5,808,310 variants and 629 people pass filters and QC.**
Note: No phenotypes present.
--make-bed to 1kG_MDS5.bed + 1kG_MDS5.bim + 1kG_MDS5.fam ...
$ wc -l 1kG_MDS5.bim
5808310 1kG_MDS5.bim
$ head -n 5 1kG_MDS5.bim
1 rs58108140 0 10583 A G
1 1:11508[b37]A,G 0 11508 A G
1 1:15820[b37]G,T 0 15820 T G
1 1:16257[b37]C,G 0 16257 C G
1 1:16378[b37]C,T 0 16378 T C
Extract the variants present in HapMap dataset from the 1000 genomes dataset.
$ awk ‘{print$2}’ HapMap_3_r3_12.bim > HapMap_SNPs.txt
$ wc -l HapMap_SNPs.txt
1073226 HapMap_SNPs.txt$ plink –bfile 1kG_MDS5 –extract HapMap_SNPs.txt –make-bed –out 1kG_MDS6
**58,08,310 variants loaded from .bim file.**
629 people (0 males, 0 females, 629 ambiguous) loaded from .fam.
Ambiguous sex IDs written to 1kG_MDS6.nosex .
**--extract: 1000993 variants remaining.**
...
Before main variant filters, 629 founders and 0 nonfounders present.
Calculating allele frequencies...
....................
Total genotyping rate is 0.999411.
**1000993 variants and 629 people pass filters and QC.**
Note: No phenotypes present.
--make-bed to 1kG_MDS6.bed + 1kG_MDS6.bim + 1kG_MDS6.fam ...
Extract the variants present in 1000 Genomes dataset from the HapMap dataset.
$ awk ‘{print$2}’ 1kG_MDS6.bim > 1kG_MDS6_SNPs.txt
$ wc -l 1kG_MDS6.bim
1000993 1kG_MDS6.bim
$ plink –bfile HapMap_3_r3_12 –extract 1kG_MDS6_SNPs.txt –recode –make-bed –out HapMap_MDS
**1073226 variants loaded from .bim file.**
109 people (55 males, 54 females) loaded from .fam.
109 phenotype values loaded from .fam.
**--extract: 1000993 variants remaining.**
..........
Before main variant filters, 109 founders and 0 nonfounders present.
Calculating allele frequencies...
.....................
Total genotyping rate is 0.998036.
**1000993 variants and 109 people pass filters and QC.**
Among remaining phenotypes, 54 are cases and 55 are controls.
--make-bed to **HapMap_MDS.bed + HapMap_MDS.bim + HapMap_MDS.fam** ...
................
--recode to **HapMap_MDS.ped + HapMap_MDS.map** ...
$ wc -l HapMap_MDS.ped
109 HapMap_MDS.ped
$ wc -l HapMap_MDS.map
1000993 HapMap_MDS.map
Datasets must have the same build: Change the build 1000 Genomes data build
$ awk ‘{print$2,$4}’ HapMap_MDS.map > buildhapmap.txt
$ wc -l buildhapmap.txt
1000993 buildhapmap.txt
$ head -n 5 buildhapmap.txt
rs3131969 744045
rs1048488 750775
rs12562034 758311
rs12124819 766409
rs4970383 828418
$ plink –bfile 1kG_MDS6 –update-map buildhapmap.txt –make-bed –out 1kG_MDS7
**1000993 variants loaded from .bim file.**
629 people (0 males, 0 females, 629 ambiguous) loaded from .fam.
Ambiguous sex IDs written to 1kG_MDS7.nosex .
--update-map: 1000993 values updated.
**Warning: Base-pair positions are now unsorted!**
..........
Before main variant filters, 629 founders and 0 nonfounders present.
Calculating allele frequencies...
....................
Total genotyping rate is 0.999411.
1000993 variants and 629 people pass filters and QC.
Note: No phenotypes present.
--make-bed to 1kG_MDS7.bed + 1kG_MDS7.bim + 1kG_MDS7.fam ...
Merge the HapMap and 1000 Genomes data sets
Set reference genome
$ awk ‘{print$2,$5}’ 1kG_MDS7.bim > 1kg_ref-list.txt
$ wc -l 1kg_ref-list.txt
1000993 1kg_ref-list.txt
$ plink –bfile HapMap_MDS –reference-allele 1kg_ref-list.txt –make-bed –out HapMap-adj
**1000993 variants loaded from .bim file.**
109 people (55 males, 54 females) loaded from .fam.
109 phenotype values loaded from .fam.
.........
Before main variant filters, 109 founders and 0 nonfounders present.
Calculating allele frequencies...
.............................
Total genotyping rate is 0.998036.
Warning: Impossible A1 allele assignment for variant rs11488462.
Warning: Impossible A1 allele assignment for variant rs4648786.
Warning: Impossible A1 allele assignment for variant rs28635343.
............................
Warning: Impossible A1 allele assignment for variant rs9928892.
Warning: Impossible A1 allele assignment for variant rs1048149.
Warning: Impossible A1 allele assignment for variant rs2273454.
.....
**--a1-allele: 1000993 assignments made.**
**1000993 variants and 109 people pass filters and QC.**
**Among remaining phenotypes, 54 are cases and 55 are controls.**
--make-bed to HapMap-adj.bed + HapMap-adj.bim + HapMap-adj.fam ...
Resolve strand issues
$ awk ‘{print$2,$5,$6}’ 1kG_MDS7.bim > 1kGMDS7_tmp
$ awk ‘{print$2,$5,$6}’ HapMap-adj.bim > HapMap-adj_tmp
$ sort 1kGMDS7_tmp HapMap-adj_tmp |uniq -u > all_differences.txt
wc -l all_differences.txt
1624 all_differences.txt
$ head -n 5 all_differences.txt
rs10006274 C T
rs10006274 G A
rs10060593 A T
rs10060593 C T
rs10083559 A GFlip SNPs for resolving strand issues
$ awk ‘{print$1}’ all_differences.txt | sort -u > flip_list.txt
wc -l flip_list.txt
812 flip_list.txt$ plink –bfile HapMap-adj –flip flip_list.txt –reference-allele 1kg_ref-list.txt –make-bed –out corrected_hapmap
**1000993 variants loaded from .bim file.**
109 people (55 males, 54 females) loaded from .fam.
109 phenotype values loaded from .fam.
**--flip: 812 SNPs flipped.**
.......
Before main variant filters, 109 founders and 0 nonfounders present.
Calculating allele frequencies...
..............................
Total genotyping rate is 0.998036.
Warning: Impossible A1 allele assignment for variant rs34030886.
Warning: Impossible A1 allele assignment for variant rs11687477.
Warning: Impossible A1 allele assignment for variant rs2581195.
Warning: Impossible A1 allele assignment for variant rs1498840.
Warning: Impossible A1 allele assignment for variant rs9614750.
**--a1-allele: 1000993 assignments made.**
1000993 variants and 109 people pass filters and QC.
Among remaining phenotypes, 54 are cases and 55 are controls.
--make-bed to corrected_hapmap.bed + corrected_hapmap.bim + corrected_hapmap.fam ...
Check for SNPs which are still problematic after they have been flipped
$ awk ‘{print$2,$5,$6}’ corrected_hapmap.bim > corrected_hapmap_tmp
$ sort 1kGMDS7_tmp corrected_hapmap_tmp |uniq -u > uncorresponding_SNPs.txt
$ wc -l uncorresponding_SNPs.txt
84 uncorresponding_SNPs.txt$ head -n 5 uncorresponding_SNPs.txt
rs10060593 A G
rs10060593 A T
rs10083559 T C
rs10083559 T G
rs10116901 C A
$ awk ‘{print$1}’ uncorresponding_SNPs.txt | sort -u > SNPs_for_exlusion.txt
$ wc -l SNPs_for_exlusion.txt
42 SNPs_for_exlusion.txt
$ head -n 5 SNPs_for_exlusion.txt
rs10060593
rs10083559
rs10116901
rs11524965
rs11687477
Remove the 42 problematic SNPs from hapmap datasets
$ plink –bfile corrected_hapmap –exclude SNPs_for_exlusion.txt –make-bed –out HapMap_MDS2
1000993 variants loaded from .bim file.
109 people (55 males, 54 females) loaded from .fam.
109 phenotype values loaded from .fam.
**--exclude: 1000951 variants remaining.**
....
Before main variant filters, 109 founders and 0 nonfounders present.
Calculating allele frequencies...
...................
Total genotyping rate is 0.998036.
**1000951 variants and 109 people pass filters and QC.**
Among remaining phenotypes, 54 are cases and 55 are controls.
--make-bed to HapMap_MDS2.bed + HapMap_MDS2.bim + HapMap_MDS2.fam ...
Remove the 42 problematic SNPs from 1000 genome datasets.
$ plink –bfile 1kG_MDS7 –exclude SNPs_for_exlusion.txt –make-bed –out 1kG_MDS8
1000993 variants loaded from .bim file.
**629 people (0 males, 0 females, 629 ambiguous) loaded from .fam.**
Ambiguous sex IDs written to 1kG_MDS8.nosex .
**--exclude: 1000951 variants remaining.**
............
Before main variant filters, 629 founders and 0 nonfounders present.
Calculating allele frequencies...
.....................
Total genotyping rate is 0.999411.
1000951 variants and 629 people pass filters and QC.
Note: No phenotypes present.
--make-bed to 1kG_MDS8.bed + 1kG_MDS8.bim + 1kG_MDS8.fam ...
Merge HapMap with 1000 Genomes Data
$ plink –bfile HapMap_MDS2 –bmerge 1kG_MDS8.bed 1kG_MDS8.bim 1kG_MD DS8.fam –allow-no-sex –make-bed –out MDS_merge2
109 people loaded from HapMap_MDS2.fam.
629 people to be merged from 1kG_MDS8.fam.
Of these, 629 are new, while 0 are present in the base dataset.
Warning: Multiple chromosomes seen for variant 'rs1640558'.
**1000951 markers loaded from HapMap_MDS2.bim.**
**1000951 markers to be merged from 1kG_MDS8.bim.**
Of these, 0 are new, while 1000951 are present in the base dataset.
**Performing single-pass merge (738 people, 1000951 variants).**
Pass 1: fileset #1 complete.
Merged fileset written to MDS_merge2-merge.bed + MDS_merge2-merge.bim + MDS_merge2-merge.fam .
1000951 variants loaded from .bim file.
**738 people (55 males, 54 females, 629 ambiguous) loaded from .fam.**
Ambiguous sex IDs written to MDS_merge2.nosex .
109 phenotype values loaded from .fam.
...............
Before main variant filters, 738 founders and 0 nonfounders present.
......................
Total genotyping rate is 0.999208.
**1000951 variants and 738 people pass filters and QC.**
Among remaining phenotypes, 54 are cases and 55 are controls. (629 phenotypes are missing.)
--make-bed to MDS_merge2.bed + MDS_merge2.bim + MDS_merge2.fam ...
Perform multi-dimensional scaling (MDS) on HapMap-CEU data anchored by 1000 Genomes data: Using a set of pruned SNPs
$ plink –bfile MDS_merge2 –extract indepSNP.prune.in –genome –out MDS_merge2
1000951 variants loaded from .bim file.
738 people (55 males, 54 females, 629 ambiguous) loaded from .fam.
Ambiguous sex IDs written to MDS_merge2.nosex .
109 phenotype values loaded from .fam.
**--extract: 93331 variants remaining.**
Using up to 4 threads (change this with --threads).
Before main variant filters, 738 founders and 0 nonfounders present.
Calculating allele frequencies...
..................
Total genotyping rate is 0.99925.
93331 variants and 738 people pass filters and QC.
Among remaining phenotypes, 54 are cases and 55 are controls. (629 phenotypes are missing.)
1152 markers complete.
...............
93331 markers complete.
IBD calculations complete.
Writing... 1%
..................
Writing... 99%
Finished writing MDS_merge2.genome .
$ plink –bfile MDS_merge2 –read-genome MDS_merge2.genome –cluster –mds-plot 10 –out MDS_merge2
1000951 variants loaded from .bim file.
738 people (55 males, 54 females, 629 ambiguous) loaded from .fam.
Ambiguous sex IDs written to MDS_merge2.nosex .
109 phenotype values loaded from .fam.
................
Before main variant filters, 738 founders and 0 nonfounders present.
Calculating allele frequencies...
................
Total genotyping rate is 0.999208.
1000951 variants and 738 people pass filters and QC.
Among remaining phenotypes, 54 are cases and 55 are controls. (629 phenotypes are missing.)
Clustering... [sorting IBS values]
.....................
Clustering... [700 merges performed]
Clustering... done.
Writing cluster solution...
........................
**Cluster solution written to MDS_merge2.cluster1 , MDS_merge2.cluster2 , and MDS_merge2.cluster3 .**
**Performing multidimensional scaling analysis (SVD algorithm, 10 dimensions)... done.**
**MDS solution written to MDS_merge2.mds .**
$ wc -l MDS_merge2.cluster1
1 MDS_merge2.cluster1
$ wc -l MDS_merge2.cluster2
738 MDS_merge2.cluster2
$ wc -l MDS_merge2.cluster3
739 MDS_merge2.cluster3
$ head -n 5 MDS_merge2.mds
FID IID SOL C1 C2 C3 C4 C5 C6 C7 C8 C9 C10
1328 NA06984 0 -0.056673 0.0484244 0.00450315 -0.0046481 0.0211372 -0.0220683 0.00178486 0.0127743 -0.0380989 0.0244196
1328 NA06989 0 -0.0569928 0.0479001 0.00909747 -0.0109876 0.00823615 -0.0389311 -0.023994 -0.0811805 -0.0392789 0.021354
1330 NA12340 0 -0.0570225 0.0487898 0.00527852 -0.00913983 -0.0055688 -0.00731278 0.0192658 0.0222587 -0.0318775 -0.00545362
1330 NA12341 0 -0.0548805 0.0480437 0.00946487 -0.00810759 0.00934789 -0.00204984 0.0069026 -0.0150844 0.00396743 0.0142435 MDS-plot
Download 20100804.ALL.panel which contains population codes of the individuals of 1000 genomes
~/GWAS_IJMPR$ wget ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20100804/20100804.ALL.panel
–2018-11-29 05:08:11– ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20100804/20100804.ALL.panel => ‘20100804.ALL.panel’
$ head -n 5 20100804.ALL.panel
HG00098 GBR ILLUMINA
HG00100 GBR ILLUMINA
HG00106 GBR ILLUMINA
HG00112 GBR ILLUMINA
HG00114 GBR ILLUMINA
$ awk ‘{print$2}’ 20100804.ALL.panel | sort | uniq
ASW
CEU
CHB
CHS
FIN
GBR
JPT
LWK
MXL
PUR
TSI
YRI$ grep “ASW” 20100804.ALL.panel | wc -l
24
$ grep “CEU” 20100804.ALL.panel | wc -l
90
$ grep “CHB” 20100804.ALL.panel | wc -l
68
$ grep “CHS” 20100804.ALL.panel | wc -l
25
$ grep “FIN” 20100804.ALL.panel | wc -l
36
$ grep “GBR” 20100804.ALL.panel | wc -l
43
$ grep “JPT” 20100804.ALL.panel | wc -l
84
$ grep “LWK” 20100804.ALL.panel | wc -l
67
$ grep “MXL” 20100804.ALL.panel | wc -l
17
$ grep “PUR” 20100804.ALL.panel | wc -l
5
$ grep “TSI” 20100804.ALL.panel | wc -l
92
$ grep “YRI” 20100804.ALL.panel | wc -l
78
Convert population codes into superpopulation codes (i.e., AFR,AMR,ASN, and EUR)
$ awk ‘{print$1,$1,$2}’ 20100804.ALL.panel > race_1kG.txt
$ head -n 5 race_1kG.txt
HG00098 HG00098 GBR
HG00100 HG00100 GBR
HG00106 HG00106 GBR
HG00112 HG00112 GBR
HG00114 HG00114 GBR$ sed ‘s/JPT/ASN/g’ race_1kG.txt>race_1kG2.txt
$ sed ‘s/ASW/AFR/g’ race_1kG2.txt>race_1kG3.txt
$ sed ‘s/CEU/EUR/g’ race_1kG3.txt>race_1kG4.txt
$ sed ‘s/CHB/ASN/g’ race_1kG4.txt>race_1kG5.txt
$ sed ‘s/CHD/ASN/g’ race_1kG5.txt>race_1kG6.txt
$ sed ‘s/YRI/AFR/g’ race_1kG6.txt>race_1kG7.txt
$ sed ‘s/LWK/AFR/g’ race_1kG7.txt>race_1kG8.txt
$ sed ‘s/TSI/EUR/g’ race_1kG8.txt>race_1kG9.txt
$ sed ‘s/MXL/AMR/g’ race_1kG9.txt>race_1kG10.txt
$ sed ‘s/GBR/EUR/g’ race_1kG10.txt>race_1kG11.txt
$ sed ‘s/FIN/EUR/g’ race_1kG11.txt>race_1kG12.txt
$ sed ‘s/CHS/ASN/g’ race_1kG12.txt>race_1kG13.txt
$ sed ‘s/PUR/AMR/g’ race_1kG13.txt>race_1kG14.txt
create a race file,
$ awk ‘{print$1,$2,“OWN”}’ HapMap_MDS.fam > racefile_own.txt
$ head -n 5 HapMap_MDS.fam
1328 NA06989 0 0 2 2
1377 NA11891 0 0 1 2
1349 NA11843 0 0 1 1
1330 NA12341 0 0 2 2
1328 NA06984 0 0 1 2$ head -n 5 racefile_own.txt
1328 NA06989 OWN
1377 NA11891 OWN
1349 NA11843 OWN
1330 NA12341 OWN
1328 NA06984 OWN
$ cat race_1kG14.txt racefile_own.txt | sed -e ‘1iIID race’ > racefile.txt
$ head -n 5 racefile.txt
FID IID race
HG00098 HG00098 EUR
HG00100 HG00100 EUR
HG00106 HG00106 EUR
HG00112 HG00112 EUR
$ tail -n 5 racefile.txt
1362 NA11994 OWN
1459 NA12873 OWN
1454 NA12815 OWN
1346 NA12043 OWN
1375 NA12264 OWN
$ Rscript MDS_merged.R
library(pdftools)pdf_convert("2_Population_stratification/MDS.pdf", format = "jpeg", dpi = 300)
MDS
Exclude ethnic outliers
$ awk ‘{ if ($4 <-0.04 && $5 >0.03) print $1,$2 }’ MDS_merge2.mds > EUR_MDS_merge2
$ head -n 5 MDS_merge2.mds
FID IID SOL C1 C2 C3 C4 C5 C6 C7 C8 C9 C10
1328 NA06984 0 -0.056673 0.0484244 0.00450315 -0.0046481 0.0211372 -0.0220683 0.00178486 0.0127743 -0.0380989 0.0244196
1328 NA06989 0 -0.0569928 0.0479001 0.00909747 -0.0109876 0.00823615 -0.0389311 -0.023994 -0.0811805 -0.0392789 0.021354
1330 NA12340 0 -0.0570225 0.0487898 0.00527852 -0.00913983 -0.0055688 -0.00731278 0.0192658 0.0222587 -0.0318775 -0.00545362
1330 NA12341 0 -0.0548805 0.0480437 0.00946487 -0.00810759 0.00934789 -0.00204984 0.0069026 -0.0150844 0.00396743 0.0142435
$ head -n 5 EUR_MDS_merge2
1328 NA06984
1328 NA06989
1330 NA12340
1330 NA12341
1330 NA12343
Extract these individuals in HapMap data
$ plink –bfile HapMap_3_r3_12 –keep EUR_MDS_merge2 –make-bed –out HapMap_3_r3_13
1073226 variants loaded from .bim file.
109 people (55 males, 54 females) loaded from .fam.
109 phenotype values loaded from .fam.
--keep: 109 people remaining.
........
Before main variant filters, 109 founders and 0 nonfounders present.
Calculating allele frequencies...
......................
Total genotyping rate is 0.998028.
1073226 variants and 109 people pass filters and QC.
Among remaining phenotypes, 54 are cases and 55 are controls.
--make-bed to HapMap_3_r3_13.bed + HapMap_3_r3_13.bim + HapMap_3_r3_13.fam ...
$ plink –bfile HapMap_3_r3_13 –extract indepSNP.prune.in –genome –out HapMap_3_r3_13
1073226 variants loaded from .bim file.
109 people (55 males, 54 females) loaded from .fam.
109 phenotype values loaded from .fam.
**--extract: 104144 variants remaining.**
..............
Before main variant filters, 109 founders and 0 nonfounders present.
Calculating allele frequencies...
........................
Total genotyping rate is 0.99798.
**104144 variants and 109 people pass filters and QC.**
Among remaining phenotypes, 54 are cases and 55 are controls.
1152 markers complete.
2304 markers complete.
......................
104144 markers complete.
IBD calculations complete.
..................
Finished writing **HapMap_3_r3_13.genome .**$ plink –bfile HapMap_3_r3_13 –read-genome HapMap_3_r3_13.genome –cluster –mds-plot 10 –out HapMap_3_r3_13_mds
1073226 variants loaded from .bim file.
109 people (55 males, 54 females) loaded from .fam.
109 phenotype values loaded from .fam.
Before main variant filters, 109 founders and 0 nonfounders present.
Calculating allele frequencies...
............................
Total genotyping rate is 0.998028.
1073226 variants and 109 people pass filters and QC.
Among remaining phenotypes, 54 are cases and 55 are controls.
Clustering... [sorting IBS values]
Clustering... [100 merges performed]
Clustering... done.
Writing cluster solution...
`
**Cluster solution written to HapMap_3_r3_13_mds.cluster1 , HapMap_3_r3_13_mds.cluster2 , and HapMap_3_r3_13_mds.cluster3 .**
Performing multidimensional scaling analysis (SVD algorithm, 10 dimensions)... done.
MDS solution written to **HapMap_3_r3_13_mds.mds .**
$ head -n 5 HapMap_3_r3_13_mds.mds
FID IID SOL C1 C2 C3 C4 C5 C6 C7 C8 C9 C10
1328 NA06989 0 0.0160249 -0.0527081 0.0532834 -0.00151572 0.00862577 -0.00979541 0.019497 -0.0257653 0.00946611 -0.0147235
1377 NA11891 0 0.00880326 -0.0302948 -0.0051995 0.0268125 -0.0139691 -0.0156051 -0.000492102 0.00316405 0.0260371 0.0140428
1349 NA11843 0 -0.000948607 0.0140868 -0.00435938 -0.0145398 -0.0156304 0.0211412 -0.00908916 0.0145217 -0.00769973 0.0126226
1330 NA12341 0 -0.0133513 -0.0112818 0.00311679 0.00684165 0.0266296 0.0144824 -0.0295584 -0.00327526 -0.00396523 0.0245783 Change the format of the .mds file into a plink covariate file.
$ awk ‘{print$1, $2, $4, $5, $6, $7, $8, $9, $10, $11, $12, $13}’ HapMap_3_r3_13_mds.mds > covar_mds.txt
$ head -n 5 covar_mds.txt
FID IID C1 C2 C3 C4 C5 C6 C7 C8 C9 C10
1328 NA06989 0.0160249 -0.0527081 0.0532834 -0.00151572 0.00862577 -0.00979541 0.019497 -0.0257653 0.00946611 -0.0147235
1377 NA11891 0.00880326 -0.0302948 -0.0051995 0.0268125 -0.0139691 -0.0156051 -0.000492102 0.00316405 0.0260371 0.0140428
1349 NA11843 -0.000948607 0.0140868 -0.00435938 -0.0145398 -0.0156304 0.0211412 -0.00908916 0.0145217 -0.00769973 0.0126226
1330 NA12341 -0.0133513 -0.0112818 0.00311679 0.00684165 0.0266296 0.0144824 -0.0295584 -0.00327526 -0.00396523 0.0245783
All additional directory contents
~/GWAS_IJMPR$ ls -l
-rw-rw-r-- 1 ubuntu ubuntu 11905 Jun 22 2017 1_Main_script_QC_GWAS.txt
-rw-rw-r-- 1 ubuntu ubuntu 14169506 Nov 29 05:01 1kGMDS7_tmp
-rw-rw-r-- 1 ubuntu ubuntu 1423172679 Nov 29 04:32 1kG_MDS.bed
-rw-rw-r-- 1 ubuntu ubuntu 262619729 Nov 29 04:33 1kG_MDS.bim
-rw-rw-r-- 1 ubuntu ubuntu 15725 Nov 29 04:32 1kG_MDS.fam
-rw-rw-r-- 1 ubuntu ubuntu 1017 Nov 29 04:33 1kG_MDS.log
-rw-rw-r-- 1 ubuntu ubuntu 10064 Nov 29 04:32 1kG_MDS.nosex
-rw-rw-r-- 1 ubuntu ubuntu 1423172679 Nov 29 04:33 1kG_MDS2.bed
-rw-rw-r-- 1 ubuntu ubuntu 262619729 Nov 29 04:33 1kG_MDS2.bim
-rw-rw-r-- 1 ubuntu ubuntu 15725 Nov 29 04:33 1kG_MDS2.fam
-rw-rw-r-- 1 ubuntu ubuntu 965 Nov 29 04:33 1kG_MDS2.log
-rw-rw-r-- 1 ubuntu ubuntu 10064 Nov 29 04:33 1kG_MDS2.nosex
-rw-rw-r-- 1 ubuntu ubuntu 1302037713 Nov 29 04:34 1kG_MDS3.bed
-rw-rw-r-- 1 ubuntu ubuntu 239747957 Nov 29 04:34 1kG_MDS3.bim
-rw-rw-r-- 1 ubuntu ubuntu 15725 Nov 29 04:34 1kG_MDS3.fam
-rw-rw-r-- 1 ubuntu ubuntu 974 Nov 29 04:34 1kG_MDS3.log
-rw-rw-r-- 1 ubuntu ubuntu 10064 Nov 29 04:34 1kG_MDS3.nosex
-rw-rw-r-- 1 ubuntu ubuntu 1302037713 Nov 29 04:34 1kG_MDS4.bed
-rw-rw-r-- 1 ubuntu ubuntu 239747957 Nov 29 04:34 1kG_MDS4.bim
-rw-rw-r-- 1 ubuntu ubuntu 15725 Nov 29 04:34 1kG_MDS4.fam
-rw-rw-r-- 1 ubuntu ubuntu 967 Nov 29 04:34 1kG_MDS4.log
-rw-rw-r-- 1 ubuntu ubuntu 10064 Nov 29 04:34 1kG_MDS4.nosex
-rw-rw-r-- 1 ubuntu ubuntu 917712983 Nov 29 04:35 1kG_MDS5.bed
-rw-rw-r-- 1 ubuntu ubuntu 164283306 Nov 29 04:35 1kG_MDS5.bim
-rw-rw-r-- 1 ubuntu ubuntu 15725 Nov 29 04:35 1kG_MDS5.fam
-rw-rw-r-- 1 ubuntu ubuntu 1003 Nov 29 04:35 1kG_MDS5.log
-rw-rw-r-- 1 ubuntu ubuntu 10064 Nov 29 04:35 1kG_MDS5.nosex
-rw-rw-r-- 1 ubuntu ubuntu 158156897 Nov 29 04:36 1kG_MDS6.bed
-rw-rw-r-- 1 ubuntu ubuntu 27841723 Nov 29 04:36 1kG_MDS6.bim
-rw-rw-r-- 1 ubuntu ubuntu 15725 Nov 29 04:36 1kG_MDS6.fam
-rw-rw-r-- 1 ubuntu ubuntu 947 Nov 29 04:36 1kG_MDS6.log
-rw-rw-r-- 1 ubuntu ubuntu 10064 Nov 29 04:36 1kG_MDS6.nosex
-rw-rw-r-- 1 ubuntu ubuntu 10165534 Nov 29 04:37 1kG_MDS6_SNPs.txt
-rw-rw-r-- 1 ubuntu ubuntu 158156897 Nov 29 04:38 1kG_MDS7.bed
-rw-rw-r-- 1 ubuntu ubuntu 27838095 Nov 29 04:38 1kG_MDS7.bim
-rw-rw-r-- 1 ubuntu ubuntu 15725 Nov 29 04:38 1kG_MDS7.fam
-rw-rw-r-- 1 ubuntu ubuntu 996 Nov 29 04:38 1kG_MDS7.log
-rw-rw-r-- 1 ubuntu ubuntu 10064 Nov 29 04:38 1kG_MDS7.nosex
-rw-rw-r-- 1 ubuntu ubuntu 158150261 Nov 29 05:05 1kG_MDS8.bed
-rw-rw-r-- 1 ubuntu ubuntu 27836923 Nov 29 05:05 1kG_MDS8.bim
-rw-rw-r-- 1 ubuntu ubuntu 15725 Nov 29 05:05 1kG_MDS8.fam
-rw-rw-r-- 1 ubuntu ubuntu 953 Nov 29 05:05 1kG_MDS8.log
-rw-rw-r-- 1 ubuntu ubuntu 10064 Nov 29 05:05 1kG_MDS8.nosex
-rw-rw-r-- 1 ubuntu ubuntu 12167520 Nov 29 04:39 1kg_ref-list.txt
-rw-rw-r-- 1 ubuntu ubuntu 13499 Nov 29 05:08 20100804.ALL.panel
-rw-rw-r-- 1 ubuntu ubuntu 3216 Nov 29 03:52 2_Main_script.txt
-rw-rw-r-- 1 ubuntu ubuntu 10886 Sep 21 17:27 2_Main_script_MDS.txt
-rw-rw-r-- 1 ubuntu ubuntu 4027181107 Nov 29 04:25 ALL.2of4intersection.20100804.genotypes.bed
-rw-rw-r-- 1 ubuntu ubuntu 635485636 Nov 29 04:25 ALL.2of4intersection.20100804.genotypes.bim
-rw-rw-r-- 1 ubuntu ubuntu 15725 Nov 29 04:25 ALL.2of4intersection.20100804.genotypes.fam
-rw-rw-r-- 1 ubuntu ubuntu 1255 Nov 29 04:25 ALL.2of4intersection.20100804.genotypes.log
-rw-rw-r-- 1 ubuntu ubuntu 10064 Nov 29 04:24 ALL.2of4intersection.20100804.genotypes.nosex
-rw-rw-r-- 1 ubuntu ubuntu 65672092096 Nov 29 03:44 ALL.2of4intersection.20100804.genotypes.vcf.gz
-rw-rw-r-- 1 ubuntu ubuntu 4027181107 Nov 29 04:31 ALL.2of4intersection.20100804.genotypes_no_missing_IDs.bed
-rw-rw-r-- 1 ubuntu ubuntu 818551938 Nov 29 04:31 ALL.2of4intersection.20100804.genotypes_no_missing_IDs.bim
-rw-rw-r-- 1 ubuntu ubuntu 15725 Nov 29 04:31 ALL.2of4intersection.20100804.genotypes_no_missing_IDs.fam
-rw-rw-r-- 1 ubuntu ubuntu 1207 Nov 29 04:31 ALL.2of4intersection.20100804.genotypes_no_missing_IDs.log
-rw-rw-r-- 1 ubuntu ubuntu 10064 Nov 29 04:31 ALL.2of4intersection.20100804.genotypes_no_missing_IDs.nosex
-rw-rw-r-- 1 ubuntu ubuntu 5616 Nov 29 05:15 EUR_MDS_merge2
-rw-rw-r-- 1 ubuntu ubuntu 28027807 Nov 29 04:39 HapMap-adj.bed
-rw-rw-r-- 1 ubuntu ubuntu 27838096 Nov 29 04:39 HapMap-adj.bim
-rw-rw-r-- 1 ubuntu ubuntu 2296 Nov 29 04:39 HapMap-adj.fam
-rw-rw-r-- 1 ubuntu ubuntu 52842 Nov 29 04:39 HapMap-adj.log
-rw-rw-r-- 1 ubuntu ubuntu 14169506 Nov 29 05:02 HapMap-adj_tmp
-rw-rw-r-- 1 ubuntu ubuntu 30050331 Nov 29 03:26 HapMap_3_r3_12.bed
-rw-rw-r-- 1 ubuntu ubuntu 29846054 Nov 29 03:26 HapMap_3_r3_12.bim
-rw-rw-r-- 1 ubuntu ubuntu 2296 Nov 29 03:26 HapMap_3_r3_12.fam
-rw-rw-r-- 1 ubuntu ubuntu 997 Nov 29 03:26 HapMap_3_r3_12.log
**-rw-rw-r-- 1 ubuntu ubuntu 30050331 Nov 29 05:16 HapMap_3_r3_13.bed**
**-rw-rw-r-- 1 ubuntu ubuntu 29846054 Nov 29 05:16 HapMap_3_r3_13.bim**
**-rw-rw-r-- 1 ubuntu ubuntu 2296 Nov 29 05:16 HapMap_3_r3_13.fam**
-rw-rw-r-- 1 ubuntu ubuntu 612248 Nov 29 05:16 HapMap_3_r3_13.genome
-rw-rw-r-- 1 ubuntu ubuntu 966 Nov 29 05:16 HapMap_3_r3_13.log
-rw-rw-r-- 1 ubuntu ubuntu 1431 Nov 29 05:18 HapMap_3_r3_13_mds.cluster1
-rw-rw-r-- 1 ubuntu ubuntu 1642 Nov 29 05:18 HapMap_3_r3_13_mds.cluster2
-rw-rw-r-- 1 ubuntu ubuntu 33063 Nov 29 05:18 HapMap_3_r3_13_mds.cluster3
-rw-rw-r-- 1 ubuntu ubuntu 1171 Nov 29 05:18 HapMap_3_r3_13_mds.log
-rw-rw-r-- 1 ubuntu ubuntu 17160 Nov 29 05:18 HapMap_3_r3_13_mds.mds
-rw-rw-r-- 1 ubuntu ubuntu 28027807 Nov 29 04:37 HapMap_MDS.bed
-rw-rw-r-- 1 ubuntu ubuntu 27838096 Nov 29 04:37 HapMap_MDS.bim
-rw-rw-r-- 1 ubuntu ubuntu 2296 Nov 29 04:37 HapMap_MDS.fam
-rw-rw-r-- 1 ubuntu ubuntu 1041 Nov 29 04:37 HapMap_MDS.log
-rw-rw-r-- 1 ubuntu ubuntu 23834124 Nov 29 04:37 HapMap_MDS.map
-rw-rw-r-- 1 ubuntu ubuntu 436435244 Nov 29 04:37 HapMap_MDS.ped
-rw-rw-r-- 1 ubuntu ubuntu 28026631 Nov 29 05:05 HapMap_MDS2.bed
-rw-rw-r-- 1 ubuntu ubuntu 27836924 Nov 29 05:05 HapMap_MDS2.bim
-rw-rw-r-- 1 ubuntu ubuntu 2296 Nov 29 05:05 HapMap_MDS2.fam
-rw-rw-r-- 1 ubuntu ubuntu 986 Nov 29 05:05 HapMap_MDS2.log
-rw-rw-r-- 1 ubuntu ubuntu 10905522 Nov 29 04:36 HapMap_SNPs.txt
-rw-rw-r-- 1 ubuntu ubuntu 20587 Nov 29 05:13 MDS.pdf
-rw-rw-r-- 1 ubuntu ubuntu 185175938 Nov 29 05:06 MDS_merge2.bed
-rw-rw-r-- 1 ubuntu ubuntu 27836924 Nov 29 05:06 MDS_merge2.bim
-rw-rw-r-- 1 ubuntu ubuntu 11495 Nov 29 05:07 MDS_merge2.cluster1
-rw-rw-r-- 1 ubuntu ubuntu 12964 Nov 29 05:07 MDS_merge2.cluster2
-rw-rw-r-- 1 ubuntu ubuntu 1906614 Nov 29 05:07 MDS_merge2.cluster3
-rw-rw-r-- 1 ubuntu ubuntu 18021 Nov 29 05:06 MDS_merge2.fam
-rw-rw-r-- 1 ubuntu ubuntu 28746914 Nov 29 05:07 MDS_merge2.genome
-rw-rw-r-- 1 ubuntu ubuntu 1217 Nov 29 05:07 MDS_merge2.log
-rw-rw-r-- 1 ubuntu ubuntu 115284 Nov 29 05:07 MDS_merge2.mds
-rw-rw-r-- 1 ubuntu ubuntu 10064 Nov 29 05:07 MDS_merge2.nosex
-rw-rw-r-- 1 ubuntu ubuntu 1343 Jan 22 2016 MDS_merged.R
-rw-rw-r-- 1 ubuntu ubuntu 435 Nov 29 05:04 SNPs_for_exlusion.txt
-rw-rw-r-- 1 ubuntu ubuntu 22926 Nov 29 05:02 all_differences.txt
-rw-rw-r-- 1 ubuntu ubuntu 19405847 Nov 29 04:38 buildhapmap.txt
-rw-rw-r-- 1 ubuntu ubuntu 28027807 Nov 29 05:03 corrected_hapmap.bed
-rw-rw-r-- 1 ubuntu ubuntu 27838096 Nov 29 05:03 corrected_hapmap.bim
-rw-rw-r-- 1 ubuntu ubuntu 2296 Nov 29 05:03 corrected_hapmap.fam
-rw-rw-r-- 1 ubuntu ubuntu 1368 Nov 29 05:03 corrected_hapmap.log
-rw-rw-r-- 1 ubuntu ubuntu 14169506 Nov 29 05:03 corrected_hapmap_tmp
**-rw-rw-r-- 1 ubuntu ubuntu 13339 Nov 29 05:18 covar_mds.txt**
-rw-rw-r-- 1 ubuntu ubuntu 8215 Nov 29 05:02 flip_list.txt
-rw-rw-r-- 1 ubuntu ubuntu 1058938 Nov 29 03:26 indepSNP.prune.in
-rw-rw-r-- 1 ubuntu ubuntu 12580 Nov 29 05:08 race_1kG.txt
-rw-rw-r-- 1 ubuntu ubuntu 12580 Nov 29 05:11 race_1kG10.txt
-rw-rw-r-- 1 ubuntu ubuntu 12580 Nov 29 05:11 race_1kG11.txt
-rw-rw-r-- 1 ubuntu ubuntu 12580 Nov 29 05:12 race_1kG12.txt
-rw-rw-r-- 1 ubuntu ubuntu 12580 Nov 29 05:12 race_1kG13.txt
-rw-rw-r-- 1 ubuntu ubuntu 12580 Nov 29 05:12 race_1kG14.txt
-rw-rw-r-- 1 ubuntu ubuntu 12580 Nov 29 05:09 race_1kG2.txt
-rw-rw-r-- 1 ubuntu ubuntu 12580 Nov 29 05:10 race_1kG3.txt
-rw-rw-r-- 1 ubuntu ubuntu 12580 Nov 29 05:10 race_1kG4.txt
-rw-rw-r-- 1 ubuntu ubuntu 12580 Nov 29 05:10 race_1kG5.txt
-rw-rw-r-- 1 ubuntu ubuntu 12580 Nov 29 05:10 race_1kG6.txt
-rw-rw-r-- 1 ubuntu ubuntu 12580 Nov 29 05:11 race_1kG7.txt
-rw-rw-r-- 1 ubuntu ubuntu 12580 Nov 29 05:11 race_1kG8.txt
-rw-rw-r-- 1 ubuntu ubuntu 12580 Nov 29 05:11 race_1kG9.txt
-rw-rw-r-- 1 ubuntu ubuntu 14453 Nov 29 05:13 racefile.txt
-rw-rw-r-- 1 ubuntu ubuntu 1860 Nov 29 05:13 racefile_own.txt
-rw-rw-r-- 1 ubuntu ubuntu 1206 Nov 29 05:04 uncorresponding_SNPs.txt
3-1. Association analysis (for binary traits) without using covariates
$ plink –bfile HapMap_3_r3_13 –assoc –out assoc_results
1,073,226 variants loaded from .bim file.
109 people (55 males, 54 females) loaded from .fam.
109 phenotype values loaded from .fam.
Using 1 thread (no multithreaded calculations invoked).
Before main variant filters, 109 founders and 0 nonfounders present.
Calculating allele frequencies...
.....................
Total genotyping rate is 0.998028.
1073226 variants and 109 people pass filters and QC.
Among remaining phenotypes, 54 are cases and 55 are controls.
**Writing C/C --assoc report to assoc_results.assoc ...**
$ wc -l assoc_results.assoc
1073227 assoc_results.assoc
$ head -n 5 assoc_results.assoc
CHR SNP BP A1 F_A F_U A2 CHISQ P OR
1 rs3131972 742584 A 0.1944 0.1455 G 0.9281 0.3354 1.418
1 rs3131969 744045 A 0.1759 0.1 G 2.647 0.1037 1.921
1 rs1048488 750775 C 0.1981 0.1455 T 1.054 0.3045 1.451
1 rs12562034 758311 A 0.06481 0.1182 G 1.863 0.1723 0.5171
$ awk ‘{ if ($9 <= 5.0E-6) print $2 }’ assoc_results.assoc > subset_assoc.results.assoc.txt
$ wc -l subset_assoc.results.assoc.txt
12 subset_assoc.results.assoc.txt
$ cat subset_assoc.results.assoc.txt
rs2085079
rs834858
rs834864
rs1598120
rs1097157
rs1840290
rs834843
rs7673394
rs279466
rs279460
rs279453
rs2623297
Rscript –no-save QQ_plot.R
QQ PLOT_assoc
Rscript –no-save Manhattan_plot.R
Manhattan plot_assoc
3-2. Association analsysis (for binary traits) using covariates: logistic approach
We will be using 10 principal components as covariates and the MDS components calculated previously: covar_mds.txt.
$ plink –bfile HapMap_3_r3_13 –covar covar_mds.txt –logistic –hide-covar –out logistic_results
**Note: --hide-covar flag deprecated. Use e.g. '--linear hide-covar'.**
**--hide-covar only shows the additive results of the SNPs in the output file.**
1073226 variants loaded from .bim file.
109 people (55 males, 54 females) loaded from .fam.
109 phenotype values loaded from .fam.
Using 1 thread (no multithreaded calculations invoked).
**--covar: 10 covariates loaded.**
Before main variant filters, 109 founders and 0 nonfounders present.
Calculating allele frequencies...
...........................
Total genotyping rate is 0.998028.
1073226 variants and 109 people pass filters and QC.
Among remaining phenotypes, 54 are cases and 55 are controls.
**Writing logistic model association results to logistic_results.assoc.logistic**
$ wc -l logistic_results.assoc.logistic
1073227 logistic_results.assoc.logistic
$ head -n 5 logistic_results.assoc.logistic
CHR SNP BP A1 TEST NMISS OR STAT P
1 rs3131972 742584 A ADD 109 1.957 1.552 0.1207
1 rs3131969 744045 A ADD 109 2.322 1.8 0.07179
1 rs1048488 750775 C ADD 108 1.986 1.591 0.1117
1 rs12562034 758311 A ADD 109 0.3292 -1.912 0.05586
Remove NA values, those might give problems generating plots in later steps.
$ awk ‘!/’NA’/’ logistic_results.assoc.logistic > logistic_results.assoc_2.logistic
wc -l logistic_results.assoc_2.logistic
1073214 logistic_results.assoc_2.logistic
$ head -n 5 logistic_results.assoc_2.logistic
CHR SNP BP A1 TEST NMISS OR STAT P
1 rs3131972 742584 A ADD 109 1.957 1.552 0.1207
1 rs3131969 744045 A ADD 109 2.322 1.8 0.07179
1 rs1048488 750775 C ADD 108 1.986 1.591 0.1117
1 rs12562034 758311 A ADD 109 0.3292 -1.912 0.05586
Rscript –no-save QQ_plot.R
QQ PLOT_logistic
Rscript –no-save Manhattan_plot.R
Manhattan PLOT_logistic
Multiple testing
There are various way to deal with multiple testing outside of the conventional genome-wide significance threshold of 5.0E-8.
Multiple testing via Bonferroni test
Command below will give a Bonferroni corrected p-value, along with FDR and others.
$ plink –bfile HapMap_3_r3_13 -assoc –adjust –out adjusted_assoc_results
1073226 variants loaded from .bim file.
109 people (55 males, 54 females) loaded from .fam.
109 phenotype values loaded from .fam.
Using 1 thread (no multithreaded calculations invoked).
Before main variant filters, 109 founders and 0 nonfounders present.
Calculating allele frequencies...
.....................
Total genotyping rate is 0.998028.
1073226 variants and 109 people pass filters and QC.
Among remaining phenotypes, 54 are cases and 55 are controls.
**Writing C/C --assoc report to adjusted_assoc_results.assoc ...**
.................
--adjust: Genomic inflation est. lambda (based on median chisq) = 1.00965.
...................
--adjust values (1073226 variants) written to **adjusted_assoc_results.assoc.adjusted**.
$ wc -l adjusted_assoc_results.assoc
1073227 adjusted_assoc_results.assoc
$ head -n 5 adjusted_assoc_results.assoc
CHR SNP BP A1 F_A F_U A2 CHISQ P OR
1 rs3131972 742584 A 0.1944 0.1455 G 0.9281 0.3354 1.418
1 rs3131969 744045 A 0.1759 0.1 G 2.647 0.1037 1.921
1 rs1048488 750775 C 0.1981 0.1455 T 1.054 0.3045 1.451
1 rs12562034 758311 A 0.06481 0.1182 G 1.863 0.1723 0.5171
$ wc -l adjusted_assoc_results.assoc.adjusted
1073227 adjusted_assoc_results.assoc.adjusted
$ head -n 5 adjusted_assoc_results.assoc.adjusted
CHR SNP UNADJ GC BONF HOLM SIDAK_SS SIDAK_SD FDR_BH FDR_BY
3 rs1097157 1.66e-06 1.861e-06 1 1 0.8316 0.8316 0.2871 1
3 rs1840290 1.66e-06 1.861e-06 1 1 0.8316 0.8316 0.2871 1
8 rs279466 2.441e-06 2.727e-06 1 1 0.9272 0.9272 0.2871 1
8 rs279460 2.441e-06 2.727e-06 1 1 0.9272 0.9272 0.2871 1
Multiple testing via permutation
We permute a subset of the SNPs from chromosome 22 to reduce computational time.
The EMP2 collumn provides multiple testing corrected p-value.$ awk ‘{ if ($4 >= 21595000 && $4 <= 21605000) print $2 }’ HapMap_3_r3_13.bim > subset_snp_chr_22.txt
$ wc -l subset_snp_chr_22.txt
84 subset_snp_chr_22.txt
$ head -n 5 subset_snp_chr_22.txt
rs12563141
rs12124123
rs2796343
rs7555660
rs12494223
$ plink –bfile HapMap_3_r3_13 –extract subset_snp_chr_22.txt –make-bed –out HapMap_subset_for_perm
1073226 variants loaded from .bim file.
109 people (55 males, 54 females) loaded from .fam.
109 phenotype values loaded from .fam.
**--extract: 84 variants remaining.**
Using 1 thread (no multithreaded calculations invoked).
Before main variant filters, 109 founders and 0 nonfounders present.
Calculating allele frequencies...
...................
Total genotyping rate is 0.99727.
84 variants and 109 people pass filters and QC.
Among remaining phenotypes, 54 are cases and 55 are controls.
**--make-bed to HapMap_subset_for_perm.bed + HapMap_subset_for_perm.bim + HapMap_subset_for_perm.fam ... **
$ plink –bfile HapMap_subset_for_perm –assoc –mperm 1000000 –out subset_1M_perm_result
**Note: --mperm flag deprecated. Use e.g. '--model mperm=[value]'.**
84 variants loaded from .bim file.
109 people (55 males, 54 females) loaded from .fam.
109 phenotype values loaded from .fam.
Using up to 4 threads (change this with --threads).
Before main variant filters, 109 founders and 0 nonfounders present.
Calculating allele frequencies...
....................
Total genotyping rate is 0.99727.
84 variants and 109 people pass filters and QC.
Among remaining phenotypes, 54 are cases and 55 are controls.
**Writing C/C --assoc report to subset_1M_perm_result.assoc ...**
[generating permutations]
............................
**677248 permutations complete.**
**1000000 max(T) permutations complete.**
**Permutation test report written to subset_1M_perm_result.assoc.mperm . **
$ wc -l subset_1M_perm_result.assoc.mperm
85 subset_1M_perm_result.assoc.mperm
$ head -n 5 subset_1M_perm_result.assoc.mperm
CHR SNP EMP1 EMP2
1 rs12563141 0.659 1
1 rs12124123 0.4988 1
1 rs2796343 0.4765 1
1 rs7555660 0.6932 1
$ sort -gk 4 subset_1M_perm_result.assoc.mperm > sorted_subset.txt
$ head -n 5 sorted_subset.txt
CHR SNP EMP1 EMP2
22 rs8135996 0.06854 0.9071
3 rs1457588 0.03196 0.914
5 rs16874721 0.1006 0.9733
16 rs7194128 0.07167 0.9929
All additional directory contents
~/GWAS_IJMPR$ ls -l
-rw-rw-r-- 1 ubuntu ubuntu 5949 Sep 21 14:04 3_Main_script_association_GWAS.txt
-rw-rw-r-- 1 ubuntu ubuntu 30050331 Nov 29 05:26 HapMap_3_r3_13.bed
-rw-rw-r-- 1 ubuntu ubuntu 29846054 Nov 29 05:26 HapMap_3_r3_13.bim
-rw-rw-r-- 1 ubuntu ubuntu 2296 Nov 29 05:26 HapMap_3_r3_13.fam
-rw-rw-r-- 1 ubuntu ubuntu 2355 Nov 29 05:33 HapMap_subset_for_perm.bed
-rw-rw-r-- 1 ubuntu ubuntu 2332 Nov 29 05:33 HapMap_subset_for_perm.bim
-rw-rw-r-- 1 ubuntu ubuntu 2296 Nov 29 05:33 HapMap_subset_for_perm.fam
-rw-rw-r-- 1 ubuntu ubuntu 1008 Nov 29 05:33 HapMap_subset_for_perm.log
**-rw-rw-r-- 1 ubuntu ubuntu 32307 Nov 29 05:36 Logistic_manhattan.jpeg**
-rw-rw-r-- 1 ubuntu ubuntu 596 Sep 21 14:06 Manhattan_plot.R
**-rw-rw-r-- 1 ubuntu ubuntu 16355 Nov 29 05:41 QQ-Plot_assoc.jpeg**
**-rw-rw-r-- 1 ubuntu ubuntu 16757 Nov 29 05:41 QQ-Plot_logistic.jpeg**
-rw-rw-r-- 1 ubuntu ubuntu 525 Sep 21 14:06 QQ_plot.R
-rw-rw-r-- 1 ubuntu ubuntu 103029792 Nov 29 05:31 adjusted_assoc_results.assoc
-rw-rw-r-- 1 ubuntu ubuntu 113762061 Nov 29 05:31 adjusted_assoc_results.assoc.adjusted
-rw-rw-r-- 1 ubuntu ubuntu 1075 Nov 29 05:31 adjusted_assoc_results.log
**-rw-rw-r-- 1 ubuntu ubuntu 32726 Nov 29 05:37 assoc_manhattan.jpeg**
-rw-rw-r-- 1 ubuntu ubuntu 103029792 Nov 29 05:28 assoc_results.assoc
-rw-rw-r-- 1 ubuntu ubuntu 885 Nov 29 05:28 assoc_results.log
-rw-rw-r-- 1 ubuntu ubuntu 13339 Nov 29 05:27 covar_mds.txt
-rw-rw-r-- 1 ubuntu ubuntu 96590431 Nov 29 05:29 logistic_results.assoc.logistic
-rw-rw-r-- 1 ubuntu ubuntu 96589261 Nov 29 05:30 logistic_results.assoc_2.logistic
-rw-rw-r-- 1 ubuntu ubuntu 1058 Nov 29 05:29 logistic_results.log
-rw-rw-r-- 1 ubuntu ubuntu 3825 Nov 29 05:34 sorted_subset.txt
-rw-rw-r-- 1 ubuntu ubuntu 8245 Nov 29 05:33 subset_1M_perm_result.assoc
-rw-rw-r-- 1 ubuntu ubuntu 3825 Nov 29 05:33 subset_1M_perm_result.assoc.mperm
-rw-rw-r-- 1 ubuntu ubuntu 1087 Nov 29 05:33 subset_1M_perm_result.log
-rw-rw-r-- 1 ubuntu ubuntu 851 Nov 29 05:32 subset_snp_chr_22.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