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homework_6_ancestry [2015/11/13 21:00] – Created page with " <syntaxhighlight lang="bash"> # PSYC 7102 -- Statistical Genetics # Homework #6: Ancestry # Due: November 25th # PLEASE CONTACT DAVID TO GET YOUR NEW VCF. # Overview..." scotthomework_6_ancestry [2015/11/16 17:18] (current) – scott
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-  
- # PSYC 7102 -- Statistical Genetics 
- # Homework #6: Ancestry 
- # Due: November 25th 
-  
- # PLEASE CONTACT DAVID TO GET YOUR NEW VCF. 
-  
- # Overview: Using your variant calls contained in your VCF files, 
- # combined with the existing 1000 Genomes variant calls from the 1000 
- # Genomes project, you will conduct a principal components analysis 
- # of the 1000 Genomes samples using PLINK and estimate your position in 
- # the 1000G PCA space 
-  
- # This homework is a little bit different because the commands are 
- # slightly complicated, so I've written them all for you. In addition 
- # to specific conceptual questions, I ask you to run the commands 
- # below (changing directories appropriately as needed) and then 
- # explain to me what the command did and why we did it. I don't need 
- # super technical details but simply a solid conceptual overview of 
- # the purpose of the command. 
-  
- ### ONLY EXPLAIN COMMANDS WHERE I SPECIFICALLY REQUEST IT! YOU DO NOT 
- ### HAVE TO EXPLAIN EVERY COMMAND! 
-  
- # For many questions you'll want to run analyses by chromosome. To do 
- #  this, please create an interactive PBS session like this: 
-  
- qsub -I -l walltime=23:00:00 -q short -l mem=10gb -l nodes=1:ppn=22 
-  
-  
-  
- ############### 
- ### STEP 1  ### 
- ############### 
- # Here I take a minute to describe the various files you'll use to get started 
-  
- #    The 1000 Genomes vcfs from the 1000 Genomes Consortium 
- /Users/scvr9332/1000Gp3/variant_calls/ALL.chr[1-22].phase3_shapeit2_mvncall_integrated_v5a.20130502.genotypes.vcf.gz 
-  
-  
- #    A vcf file with, depending on your level of comfort, your own 
- #    genotypes or 1kg sample HG000096. All examples that follow use 
- #    HG00096 for convenience, which can be obtained as follows. To use 
- #    your own sample just replace the $10 with $NF, assuming your 
- #    sample is in the last column of the vcf. 
-  
-    (zcat chr1.filtered.PASS.beagled.vcf.gz          | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
-    zgrep -v '#' chr2.filtered.PASS.beagled.vcf.gz  | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
-    zgrep -v '#' chr3.filtered.PASS.beagled.vcf.gz  | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
-    zgrep -v '#' chr4.filtered.PASS.beagled.vcf.gz  | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
-    zgrep -v '#' chr5.filtered.PASS.beagled.vcf.gz  | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
-    zgrep -v '#' chr6.filtered.PASS.beagled.vcf.gz  | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
-    zgrep -v '#' chr7.filtered.PASS.beagled.vcf.gz  | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
-    zgrep -v '#' chr8.filtered.PASS.beagled.vcf.gz  | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
-    zgrep -v '#' chr9.filtered.PASS.beagled.vcf.gz  | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
-    zgrep -v '#' chr10.filtered.PASS.beagled.vcf.gz | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
-    zgrep -v '#' chr11.filtered.PASS.beagled.vcf.gz | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
-    zgrep -v '#' chr12.filtered.PASS.beagled.vcf.gz | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
-    zgrep -v '#' chr13.filtered.PASS.beagled.vcf.gz | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
-    zgrep -v '#' chr14.filtered.PASS.beagled.vcf.gz | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
-    zgrep -v '#' chr15.filtered.PASS.beagled.vcf.gz | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
-    zgrep -v '#' chr16.filtered.PASS.beagled.vcf.gz | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
-    zgrep -v '#' chr17.filtered.PASS.beagled.vcf.gz | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
-    zgrep -v '#' chr18.filtered.PASS.beagled.vcf.gz | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
-    zgrep -v '#' chr19.filtered.PASS.beagled.vcf.gz | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
-    zgrep -v '#' chr20.filtered.PASS.beagled.vcf.gz | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
-    zgrep -v '#' chr21.filtered.PASS.beagled.vcf.gz | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
-    zgrep -v '#' chr22.filtered.PASS.beagled.vcf.gz | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}') | bgzip -c > chrALL.filtered.PASS.beagled.HG00096.vcf.gz 
-  
- ### Add in rsIDs from dbSNP. PLINK needs these to reconcile 
- ### positions/alleles 
- vcf-add-rsid -vcf chrALL.filtered.PASS.beagled.HG00096.vcf.gz -db /Users/scvr9332/reference_data/dbsnp.144.b37.vcf.gz | bgzip -c > chrALL.filtered.PASS.beagled.HG00096.rsID.vcf.gz 
- ### The previous command keeps only variants with rsIDs, otherwise 
- ### plink throws an error that there are >1 variants with ID = "." 
- ### 
- ###------ QUESTION 1: WHAT DOES THIS COMMAND DO? 
- zgrep -E '#|\srs[0-9]' chrALL.filtered.PASS.beagled.HG00096.rsID.vcf.gz | bgzip -c > chrALL.filtered.PASS.beagled.HG00096.rsIDsOnly.vcf.gz 
-  
- ############## 
- ### STEP 2 ### 
- ############## 
- ### Whole bunch of data cleaning. Retain in the 1000 Genomes 
- ###   Consortium VCFs only biallelic SNPs that also exist in your vcf 
- ###   files. PLINK 1.9 can't handle all variant types gracefully. 
-  
- #  Get list of your SNPs 
- zcat chrALL.filtered.PASS.beagled.HG00096.rsIDsOnly.vcf.gz | cut -f3 | grep '^rs' > MyrsIDs.txt 
- # 
- #  Reduce the 1000 Genomes VCFs to bi-allelic SNPs (that's all PLINK 
- #  can handle anyway). BE SURE TO LOG INTO A COMPUTE NODE BEFORE 
- #  RUNNING ANY OF THESE LOOPS!!!!! 
- ###------ QUESTION 2: WHAT DOES THIS COMMAND (THE ENTIRE FOR LOOP) DO? 
- for i in {1..22}; do 
-     zgrep -E '^#|\s[ACTG]\s[ACTG]\s' /Users/scvr9332/1000Gp3/variant_calls /ALL.chr$i.phase3_shapeit2_mvncall_integrated_v5a.20130502.genotypes.vcf.gz | bgzip -c > chr$i.1000G.biallelic.vcf.gz & 
- done 
-  
- ### Retain in the 1000 Genomes VCF only your SNPs that are also fairly common 
- ### 
- ###------ QUESTION 3: WHY WOULD WE REMOVE COMMON SNPS, OTHER THAN IT 
- ###------             MAKES EVERY COMMAND LATER FASTER? 
- for i in {1..22}; do 
-     plink --vcf chr$i.1000G.biallelic.vcf.gz \ 
-         --extract MyrsIDs.txt \ 
-         --maf .05 \ 
-         --make-bed \ 
-         --out chr$i.1000G.biallelic.common.plink & 
- done 
-  
- ### Remove any remaining duplicates from the 1000G reference files 
- ### A) Identify duplicate sites 
- ### 
- ###------ QUESTION 4: WALK ME THROUGH WHAT THIS COMMAND (THE ENTIRE 
- ###------             FOR LOOP) IS DOING. 
- for i in {1..22}; do 
-     zcat chr$i.1000G.biallelic.vcf.gz | cut -f3 | sort | uniq -c | grep -E '\s2\s' | grep '^rs' | gawk '{print $2}' >  dups$i.txt &  
- done 
-  
- ### B) Remove the duplicate sites 
- for i in {1..22}; do 
-     plink --bfile chr$i.1000G.biallelic.common.plink \ 
-         --exclude dups$i.txt \ 
-         --make-bed \ 
-         --out chr$i.1000G.biallelic.common.plink.nodups & 
- done 
-  
- rm chr*.1000G.biallelic.vcf.gz 
- rm chr*.1000G.biallelic.common.plink.bed 
- rm chr*.1000G.biallelic.common.plink.bim 
- rm chr*.1000G.biallelic.common.plink.fam 
-  
-  
- ## Identify variants in LD 
- for i in {1..22}; do 
-     plink --bfile chr$i.1000G.biallelic.common.plink.nodups \ 
-         --indep-pairwise 100k 5 .01 \  #------ QUESTION 5: EXPLAIN WHAT THIS ARGUMENT IS DOING 
-         --maf .05 \ 
-         --out chr$i.snpsToExtract & 
- done 
-  
-  
- ## Extract LD-independent variants 
- for i in {1..22}; do 
-     plink --bfile chr$i.1000G.biallelic.common.plink.nodups \ 
-         --extract chr$i.snpsToExtract.prune.in \ 
-         --make-bed \ 
-         --out chr$i.1000G.biallelic.common.plink.nodups.LDpruned & 
- done 
-  
- rm chr*.1000G.biallelic.common.plink.nodups.bed 
- rm chr*.1000G.biallelic.common.plink.nodups.bim 
- rm chr*.1000G.biallelic.common.plink.nodups.fam 
- rm dups* 
-  
-  
- ############## 
- ### STEP 3 ### 
- ############## 
- ### Merge the new and improved 1000 Genomes plink files into a single 
-###  file with all chromosomes. This is easier to do when they're vcf 
-###  files 
  
-#  Example Command:+==== Notes ==== 
 + 
 + 
 + 
 +==== Code for Homework ==== 
 + 
 + 
 +# PSYC 7102 -- Statistical Genetics 
 +# Homework #6: Ancestry 
 +# Due: November 25th 
 + 
 +# PLEASE CONTACT DAVID TO GET YOUR NEW VCF. 
 + 
 +# Overview: Using your variant calls contained in your VCF files, 
 +# combined with the existing 1000 Genomes variant calls from the 1000 
 +# Genomes project, you will conduct a principal components analysis 
 +# of the 1000 Genomes samples using PLINK and estimate your position in 
 +# the 1000G PCA space 
 + 
 +# This homework is a little bit different because the commands are 
 +# slightly complicated, so I've written them all for you. In addition 
 +# to specific conceptual questions, I ask you to run the commands 
 +# below (changing directories appropriately as needed) and then 
 +# explain to me what the command did and why we did it. I don't need 
 +# super technical details but simply a solid conceptual overview of 
 +# the purpose of the command. 
 + 
 +### ONLY EXPLAIN COMMANDS WHERE I SPECIFICALLY REQUEST IT! YOU DO NOT 
 +### HAVE TO EXPLAIN EVERY COMMAND! But please run all commands to produce  
 +### in the end a PCA plot containing yourself compared to all 1000 Genomes  
 +### samples. 
 + 
 +# For many questions you'll want to run analyses by chromosome. To do 
 +#  this, please create an interactive PBS session like this: 
 + 
 +qsub -I -l walltime=23:00:00 -q short -l mem=10gb -l nodes=1:ppn=22 
 + 
 +### Load apigenome, plink 
 +module load apigenome_0.0.2 
 +module load plink_latest 
 +module load tabix_0.2.6 
 + 
 + 
 +############### 
 +### STEP 1  ### 
 +############### 
 +# Here I take a minute to describe the various files you'll use to get started 
 + 
 +#    The 1000 Genomes vcfs from the 1000 Genomes Consortium 
 +/Users/scvr9332/1000Gp3/variant_calls/ALL.chr[1-22].phase3_shapeit2_mvncall_integrated_v5a.20130502.genotypes.vcf.gz 
 + 
 + 
 +#    A vcf file with, depending on your level of comfort, your own 
 +#    genotypes or 1kg sample HG000096. All examples that follow use 
 +#    HG00096 for convenience, which can be obtained as follows. To use 
 +#    your own sample just replace the $10 with $NF, assuming your 
 +#    sample is in the last column of the vcf. 
 + 
 +   (zcat chr1.filtered.PASS.beagled.vcf.gz          | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
 +   zgrep -v '#' chr2.filtered.PASS.beagled.vcf.gz  | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
 +   zgrep -v '#' chr3.filtered.PASS.beagled.vcf.gz  | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
 +   zgrep -v '#' chr4.filtered.PASS.beagled.vcf.gz  | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
 +   zgrep -v '#' chr5.filtered.PASS.beagled.vcf.gz  | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
 +   zgrep -v '#' chr6.filtered.PASS.beagled.vcf.gz  | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
 +   zgrep -v '#' chr7.filtered.PASS.beagled.vcf.gz  | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
 +   zgrep -v '#' chr8.filtered.PASS.beagled.vcf.gz  | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
 +   zgrep -v '#' chr9.filtered.PASS.beagled.vcf.gz  | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
 +   zgrep -v '#' chr10.filtered.PASS.beagled.vcf.gz | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
 +   zgrep -v '#' chr11.filtered.PASS.beagled.vcf.gz | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
 +   zgrep -v '#' chr12.filtered.PASS.beagled.vcf.gz | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
 +   zgrep -v '#' chr13.filtered.PASS.beagled.vcf.gz | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
 +   zgrep -v '#' chr14.filtered.PASS.beagled.vcf.gz | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
 +   zgrep -v '#' chr15.filtered.PASS.beagled.vcf.gz | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
 +   zgrep -v '#' chr16.filtered.PASS.beagled.vcf.gz | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
 +   zgrep -v '#' chr17.filtered.PASS.beagled.vcf.gz | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
 +   zgrep -v '#' chr18.filtered.PASS.beagled.vcf.gz | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
 +   zgrep -v '#' chr19.filtered.PASS.beagled.vcf.gz | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
 +   zgrep -v '#' chr20.filtered.PASS.beagled.vcf.gz | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
 +   zgrep -v '#' chr21.filtered.PASS.beagled.vcf.gz | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}'; \ 
 +   zgrep -v '#' chr22.filtered.PASS.beagled.vcf.gz | gawk '{print $1"\t"$2"\t"$3"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10}') | bgzip -c > chrALL.filtered.PASS.beagled.HG00096.vcf.gz 
 + 
 +### Add in rsIDs from dbSNP. PLINK needs these to reconcile 
 +### positions/alleles 
 +vcf-add-rsid -vcf chrALL.filtered.PASS.beagled.HG00096.vcf.gz -db /Users/scvr9332/reference_data/dbsnp_144/dbsnp.144.b37.vcf.gz | bgzip -c > chrALL.filtered.PASS.beagled.HG00096.rsID.vcf.gz 
 +### The previous command keeps only variants with rsIDs, otherwise 
 +### plink throws an error that there are >1 variants with ID = "." 
 +### 
 +###------ QUESTION 1: WHAT DOES THIS COMMAND DO? (1 point) 
 +zgrep -E '#|\srs[0-9]' chrALL.filtered.PASS.beagled.HG00096.rsID.vcf.gz | bgzip -c > chrALL.filtered.PASS.beagled.HG00096.rsIDsOnly.vcf.gz 
 + 
 +############## 
 +### STEP 2 ### 
 +############## 
 +### Whole bunch of data cleaning. Retain in the 1000 Genomes 
 +###   Consortium VCFs only biallelic SNPs that also exist in your vcf 
 +###   files. PLINK 1.9 can't handle all variant types gracefully. 
 + 
 +#  Get list of your SNPs 
 +zcat chrALL.filtered.PASS.beagled.HG00096.rsIDsOnly.vcf.gz | cut -f3 | grep '^rs' > MyrsIDs.txt 
 +# 
 +#  Reduce the 1000 Genomes VCFs to bi-allelic SNPs (that's all PLINK 
 +#  can handle anyway). BE SURE TO LOG INTO A COMPUTE NODE BEFORE 
 +#  RUNNING ANY OF THESE LOOPS!!!!! 
 +### 
 +###------ QUESTION 2: WHAT DOES THIS COMMAND (THE ENTIRE FOR LOOP) DO? (2 points) 
 +for i in {1..22}; do 
 +    zgrep -E '^#|\s[ACTG]\s[ACTG]\s' /Users/scvr9332/1000Gp3/variant_calls/ALL.chr$i.phase3_shapeit2_mvncall_integrated_v5a.20130502.genotypes.vcf.gz | bgzip -c > chr$i.1000G.biallelic.vcf.gz & 
 +done 
 + 
 +### Retain in the 1000 Genomes VCF only your SNPs that are also fairly common 
 +### because we're going to conduct PCA on these SNPs and only want common ones. 
 +###  
 +###------ QUESTION 3: WHY WOULD WE RETAIN ONLY COMMON SNPS, OTHER THAN IT 
 +###------             MAKES EVERY COMMAND LATER FASTER? (2 points) 
 +for i in {1..22}; do 
 +    plink --vcf chr$i.1000G.biallelic.vcf.gz \ 
 +        --extract MyrsIDs.txt \ 
 +        --maf .05 \ 
 +        --make-bed \ 
 +        --out chr$i.1000G.biallelic.common.plink & 
 +done 
 + 
 +### Remove any remaining duplicates from the 1000G reference files 
 +### A) Identify duplicate sites 
 +### 
 +###------ QUESTION 4: WALK ME THROUGH WHAT THIS COMMAND (THE ENTIRE 
 +###------             FOR LOOP) IS DOING. (3 points) 
 +for i in {1..22}; do 
 +    zcat chr$i.1000G.biallelic.vcf.gz | cut -f3 | sort | uniq -c | grep -E '\s2\s' | grep '^rs' | gawk '{print $2}' >  dups$i.txt &  
 +done 
 + 
 +### B) Remove the duplicate sites 
 +for i in {1..22}; do 
 +    plink --bfile chr$i.1000G.biallelic.common.plink \ 
 +        --exclude dups$i.txt \ 
 +        --make-bed \ 
 +        --out chr$i.1000G.biallelic.common.plink.nodups & 
 +done 
 + 
 +rm chr*.1000G.biallelic.vcf.gz 
 +rm chr*.1000G.biallelic.common.plink.bed 
 +rm chr*.1000G.biallelic.common.plink.bim 
 +rm chr*.1000G.biallelic.common.plink.fam 
 + 
 + 
 +## Identify variants in LD 
 +for i in {1..22}; do 
 +    plink --bfile chr$i.1000G.biallelic.common.plink.nodups \ 
 +        --indep-pairwise 100k 5 .01 \  #------ QUESTION 5: EXPLAIN WHAT THIS ARGUMENT IS DOING (1 point) 
 +        --maf .05 \ 
 +        --out chr$i.snpsToExtract & 
 +done 
 + 
 + 
 +## Extract LD-independent variants 
 +for i in {1..22}; do 
 +    plink --bfile chr$i.1000G.biallelic.common.plink.nodups \ 
 +        --extract chr$i.snpsToExtract.prune.in \ 
 +        --make-bed \ 
 +        --out chr$i.1000G.biallelic.common.plink.nodups.LDpruned & 
 +done 
 + 
 +rm chr*.1000G.biallelic.common.plink.nodups.bed 
 +rm chr*.1000G.biallelic.common.plink.nodups.bim 
 +rm chr*.1000G.biallelic.common.plink.nodups.fam 
 +rm dups* 
 + 
 + 
 +############## 
 +### STEP 3 ### 
 +############## 
 +### Merge the new and improved 1000 Genomes plink files into a single 
 +### file with all chromosomes. This is easier to do when they're vcf 
 +### files 
 +### Example Command:
 for i in {1..22}; do  for i in {1..22}; do 
     plink --bfile chr$i.1000G.biallelic.common.plink.nodups.LDpruned --recode-vcf --out tmp$i &      plink --bfile chr$i.1000G.biallelic.common.plink.nodups.LDpruned --recode-vcf --out tmp$i & 
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 ###----- QUESTION 6: HOW MANY VARIANTS ARE IN THE OUTPUT FILE FROM THE ###----- QUESTION 6: HOW MANY VARIANTS ARE IN THE OUTPUT FILE FROM THE
-###-----             COMMAND ABOVE?+###-----             COMMAND ABOVE? (1 point)
  
  
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 ### average for each site in the score file. ### average for each site in the score file.
 ### ###
-###------ QUESTION 7: EXPLAIN WHAT THE FOLLOWING COMMANDS ARE DOING +###------ QUESTION 7: EXPLAIN WHAT THE FOLLOWING COMMANDS ARE DOING (2 points)
 plink --vcf chrALL.filtered.PASS.beagled.HG00096.rsIDsOnly.vcf.gz  --score PCA-score-file.txt 2 3 4 header --out PC1 plink --vcf chrALL.filtered.PASS.beagled.HG00096.rsIDsOnly.vcf.gz  --score PCA-score-file.txt 2 3 4 header --out PC1
 plink --vcf chrALL.filtered.PASS.beagled.HG00096.rsIDsOnly.vcf.gz  --score PCA-score-file.txt 2 3 5 header --out PC2 plink --vcf chrALL.filtered.PASS.beagled.HG00096.rsIDsOnly.vcf.gz  --score PCA-score-file.txt 2 3 5 header --out PC2
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 R R
 ### Then in this R session run: 'install.packages('car', repos='http://cran.r-project.org')' ### Then in this R session run: 'install.packages('car', repos='http://cran.r-project.org')'
-### If you log out and back in, you'll have to again run this command:+### If you log out and back in, you'll have to again run this command again to get the car library loaded:
 export R_LIBS=/Users/scvr9332/R export R_LIBS=/Users/scvr9332/R
  
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 ###------ QUESTION 8: PLEASE INTERPRET THE RESULTING GRAPH. WHAT IS ###------ QUESTION 8: PLEASE INTERPRET THE RESULTING GRAPH. WHAT IS
 ###------             BEING PLOTTED? WHAT DOES IT SAY ABOUT THE ###------             BEING PLOTTED? WHAT DOES IT SAY ABOUT THE
-###------             ANCESTRY OF YOUR SAMPLE (OR WHATEVER+###------             ANCESTRY OF YOUR SAMPLE (OR WHATEVER SAMPLE YOU 
 +###------             USED) (2 points)
 echo " echo "
  
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 ### 'Self-reported' ancestry of 1000g participants ### 'Self-reported' ancestry of 1000g participants
-kg_sf <- read.table('20130502.sequence.index', header=T, sep='\t', fill=T, stringsAsFactors=F)+kg_sf <- read.table('/Users/scvr9332/PCA/20130502.sequence.index', header=T, sep='\t', fill=T, stringsAsFactors=F)
  
 sample_ids <- unique(data.frame(IID=kg_sf$SAMPLE_NAME, POPULATION=kg_sf$POPULATION, stringsAsFactors=F)) sample_ids <- unique(data.frame(IID=kg_sf$SAMPLE_NAME, POPULATION=kg_sf$POPULATION, stringsAsFactors=F))
homework_6_ancestry.1447448424.txt.gz · Last modified: by scott