1. Biopop, Seoul National University
Han Kyung University
GWAS
( Practice )
Hyeon soo Jeong
December 2014
Biopop, Seoul National University

2. Contents Basic statistics Linux system PLINK Quality control
Linear regression
Manhattan plot
Kinship analysis
IBS matrix using EMMAX
Mixed linear model for dealing inbred
QQ-plot Comparison

3. Contents
Basic Statistics

4. Basic Statistics 𝑦~𝑋β+ε Regression analysis
Conall M. O'Seaghdha & Caroline S. Fox, 2012, Nat Rev Neprolo

5. Basic Statistics Hypothesis Null Hypothesis : β=0
Alternative Hypothesis : β≠0 (Two tails)

6. Basic Statistics
Type I and Type II error

7. Contents
Linux system

8. Linux system
Ubuntu LTS
Password :

9. Linux system 리눅스 명령어 (Ubuntu ) 명령어 기능 cd <dir>ls
파일 또는 폴더 리스트를 출력 ll
ls 보다 더욱 상세한 파일 또는 폴더 정보 출력
mkdir
디렉토리 생성 cp
파일 또는 폴더 복사 mv
파일 이동 rm
파일 삭제
mv -r
디렉토리 이동
rm -r
디렉토리 삭제

10. Data Structure Pedigree file (PED file) 1 2 3 4 5 6 7 ~~
IID FID father Mother sex phenotype genotype~ ~~
Fam1 Ind G T
Fam2 Ind G G
Fam2 Ind G T
Fam2 Ind T T
Fam3 Ind T T
Fam4 Ind G T

11. Data management MAP file 1 2 3 4 chr SNP_ID recom position
1 SNP
1 SNP
1 SNP
2 SNP4 0 13
2 SNP
2 SNP

12. Data management PED & MAP 1 SNP1 0 13435 1 SNP2 0 25256 1 SNP3 0 28242
Fam1 Ind G T A A G G
Fam2 Ind G G A A G T
Fam2 Ind G T T A G G
Fam2 Ind T T A A T T
Fam3 Ind T T T T T T
Fam4 Ind G T A A T T

13. Contents
PLINK : Whole Genome Data Analysis Toolkit

14. PLINK ( Download & install )
Program download

15. PLINK ( Download & install )
Program download
$ wget

16. PLINK ( Download & install )
installation
$ unzip plink-1.07-x86_64.zip

17. PLINK ( Download & install )
$ wget
$ unzip plink-1.07-x86_64.zip
$ cd plink-1.07-x86_64
$ sudo cp plink /usr/local/bin/

18. Data management How to convert ped file to bed file?
$ plink --cow --file Testfile --make-bed --out TestData --noweb
.bed : genotype information (열 수 없음.)
.fam : family information (ped file 1~6 columns)
.bim : SNP information (map file + allele types)

19. Data management 각종 여러 가지 데이터 변형 관련 명령어

20. Contents
Quality Control

21. Quality control Hardy-Weinberg Equilibrium
$ plink --noweb --cow --bfile TestData --hardy2 --out TestData_hardy
$ vi TestData_hardy.hwe

22. Quality control Hardy-Weinberg Equilibrium filteration
$ plink --noweb --cow --bfile TestData --hwe make-bed --out TestData_hwe

23. Quality control Minor allele frequency
$ plink --cow --bfile TestData --freq --out TestData_Freq --noweb

24. Quality control Minor allele frequency filteration
$ plink --cow --bfile TestData --maf make-bed --out TestData_maf --noweb

25. Quality control Missing data filteration
$ plink --cow --bfile TestData --missing --out TestData_missing --noweb
imiss
lmiss

26. Quality control --geno : genotype filter (missing > 0.9)
--mind : individual filter (missing > 0.9)
$ plink --cow --bfile TestData --geno make-bed --out TestData_geno --noweb
$ plink --cow --bfile TestData --mind make-bed --out TestData_mind --noweb
$ plink --cow --bfile TestData --mind geno maf hwe out 1_QC_TestData --noweb

27. Quality control

28. Contents
Linear Regression Analysis

29. Linear Regression Analysis
##Linear model
plink --bfile 1_QC_TestData --pheno Data/nBFzH.pheno --linear --ci out 2_LM_QC_TestData --noweb --cow
plink --bfile 1_QC_TestData --pheno Data/nBFzH.pheno --linear --ci dominant --out 2_LM_QC_dom_TestData --noweb --cow
plink --bfile 1_QC_TestData --pheno Data/nBFzH.pheno --linear --ci recessive --out 2_LM_QC_rec_TestData --noweb --cow

30. Linear Regression Analysis

31. Linear Regression Analysis
Convert “Space” to “Tab” determination
#1.LM_Convert.py
#!/usr/bin/python
inputs = raw_input("input file : ")
data = open(inputs, 'r')
outf = open('QQinput.txt', 'w')
for i in data:
ip = i.strip().split(' ')
co = ip.count('')
for i2 in range(co):
ip.remove('')
for i3 in ip:
outf.write(i3+'\t')
outf.write('\n')
data.close()
outf.close()

32. Linear Regression Analysis
Convert “Space” to “Tab” determination
#1.LM_Convert.py
python 1.LM_Conver.py

33. Manhattan plot #2.Manhattan.R
Data <- read.table("QQinput.txt", sep= '\t', header = T) # file import
library(gap) # import gap library
png("Manhattan.png", height = 6000, width=7000, res=900) # Save as png format
mhtdata <- with(Data,cbind(CHR,BP,P)) # Select columns
color <- rep(c("blue","red"),15) # Manhattan plot color
par(cex=0.5)
ops <- mht.control(colors=color,yline=1.5,xline=1)
mhtplot(mhtdata,ops,pch=25)
axis(2,pos=1,at=1:25)
bon <- -log10(1.0E-05)
abline(h=bon, col="black")
abline(h=0)
title("Main title",cex.main=2.5)
dev.off()

34. Manhattan plot
#2.Manhattan.R
Rscript 2.Manhattan.R

35. Manhattan plot

36. Contents
Mixed Linear Model

37. Mixed Linear Model
QQ-plot
#3.QQplot.R
Input: QQinput.txt

38. Mixed Linear Model
QQ-plot
Significant loci
Real??

39. Mixed Linear Model Kinship Analysis
$ wget

40. Mixed Linear Model Kinship Analysis and its problem Identical twins
Family
king -b 1_QC_TestData.bed --kinship

41. Mixed Linear Model
EMMAX

42. Mixed Linear Model IBS Matrix using EMMAX
$ emmax-kin-intel64 -v -s -M 1 -d 10 3_Trans_QC_TestData

43. Mixed Linear Model Mixed Linear Model using EMMAX
$ emmax-intel64 -v -d 10 -t 3_Trans_QC_TestData -p Data/nBFzH.pheno -k 3_Trans_QC_TestData.aIBS.kinf -o 4_MLL_QC_TestData

44. Contents
QQ-plot Comparison (PLINK vs EMMAX)

45. QQ-plot Comparison
PLINK vs. EMMAX

46. Contents
Functional Analysis

47. Functional Analysis
Coremine

48. Functional Analysis
DAVID

49. THANK YOU