GWAS imputation是什么？
- Genotype imputation 是運用連鎖不平衡的原理依據一個高密度的參考基因組填補要研究數據的一種方法。
- 常用的參考基因組數據庫包括The HapMap Consortium database (International HapMap 3 Consortium, 2010)、the 1000 Genomes Project (1KG; 1000 Genomes Project Consortium,2012)。千人基因組計劃涵蓋了世界各地的1,092個人的全基因組信息 (其中181 samples from Admixed American, 246 from African, 286 from East Asian, and 379 from European ancestry groups)。
原理如下圖：
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為什么要做GWAS imputation？
常用的GWAS芯片大約60萬個位點，經過質控后大約只剩下30多萬個位點，對于全基因組30億個堿基來說，只覆蓋了全基因組萬分之一的區域，因此大片的區域為空白。經過基因型填補后，SNP密度大大增加，如果與表型相關聯的位置沒有SNP，填補前是沒有顯著性的，填補后則有可能出現顯著性。因此，基因型填補可以大大增加GWAS的統計效能。
GWAS imputation 常用軟件
- Minimac, IMPUTE2，PLINK, BEAGLE, MaCH+minimac, fastPHASE
GWAS imputation 步驟
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1、在線資源
- 很多在線的GWAS imputation服務網站，上傳數據即可，如https://imputationserver.sph.umich.edu/index.html#!pages/help
https://imputation.sanger.ac.uk/
2、本地運行
imputation一般步驟：
- Liftover PED/MAP files from build 36 to build 37：由于基因組版本的問題，數據中SNP位點的位置在不同的版本中可能有差異，因此要轉換為統一的版本以便后續比對。一般被稱為pre-processing過程。
具體包括：
- Create a ?bed file based on MAP file.
- Map the bed file to build 37 using ?UCSVC liftover tool.
- Create list of unmapped SNPs.
- Create plink mappings file.
- Create new plink data without unmapped SNPs using ?Plink.
- Quality control of study data：對原始數據進行質控（Quality control）提高填補的準確性
具體包括：
- 將要填補的數據與參考基因組比對，看SNP在正鏈還是負鏈上，過濾掉翻轉的SNP
- 要求SNP符合哈迪溫伯格平衡>10-4, MAF>0.01, call rate>0.95。不符合條件的SNP被過濾掉。
- 檢查SNP是否在參考基因組中，有的參考基因組中沒有這個SNP，也會被過濾掉
- 檢查位點在要填補的數據和參考基因組中的頻率是否差異過大，如果差異超過25%該SNP將會被過濾掉
- 比較數據和參考基因組之間的haplotype結構差異（r-squared > 0.1的SNP），差異過大的SNP被過濾掉。
- Phasing the study data
具體包括：
- Gather the map files as provided in the ALL_1000G_phase1integrated_v3_impute.tgz from the impute website.
- Phase the study data using the map files.
- Imputing study data
- 將數據按染色體分開，用 Impute2軟件填補經過phased的數據, 一般填補最小分辨率為5 million base。
- 將數據合并到一起
流程：
由于以上步驟很多，為了方便imputation過程，很多人開發了自動化的pipeline，如
- https://github.com/CNSGenomics/impute-pipe
- https://github.com/pgxcentre/genipe
- http://www.arrayserver.com/wiki/index.php?title=Genetics_GwasImputePipeline.pdf
- https://github.com/molgenis/molgenis-pipelines
step1: QC
##QC1: Remove SNPs with missing genotype rate >5%
${plink} --bfile ${outpath4step}/${name} --missing --out ${outpath4step}/${name}.plink
awk 'NR < 2 { next } $5 > 0.05' ${outpath4step}/${name}.plink.lmiss > ${outpath4step}/${name}.plink.lmiss.exclude
${plink} --bfile ${outpath4step}/${name} \
--exclude ${outpath4step}/${name}.plink.lmiss.exclude \
--make-bed --out ${outpath4step}/${name}.plink_QC1
##QC2 :Remove SNPs deviated from HWE (10e-4)
${plink} --bfile ${outpath4step}/${name}.plink_QC1 \
--hardy --out ${outpath4step}/${name}.plink_QC1
awk '$3=="UNAFF" && $9 <0.0001 {print $2}' ${outpath4step}/${name}.plink_QC1.hwe \
> ${outpath4step}/${name}.plink_QC1.hwe.exclude
${plink} --bfile ${outpath4step}/${name}.plink_QC1 \
--exclude ${outpath4step}/${name}.plink_QC1.hwe.exclude \
--make-bed --out ${outpath4step}/${name}.plink_QC2
##QC3 :Remove MAF>0.01
${plink} --bfile ${outpath4step}/${name}.plink_QC2 \
--maf 0.01 \
--make-bed \
--out ${outpath4step}/${name}.plink_QC3
Step2: Alignment of the SNPs
# make sure that the GWAS dataset is well aligned with the reference panel of haplotypes
# if not, use the UCSC liftOver tool to perform the conversion to build37 coordinates
# download 1000 genome refernce file
for i in {1..22}
do
nohup wget -c -q http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/ALL.chr$i.phase3_shapeit2_mvncall_integrated_v5a.20130502.genotypes.vcf.gz &
nohup wget -c -q http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/ALL.chr$i.phase3_shapeit2_mvncall_integrated_v5a.20130502.genotypes.vcf.gz.tbi &
done
# Combine all chromosomes in a single file
bcftools concat ALL.chr{1..22}.phase3_shapeit2_mvncall_integrated_v5a.20130502.genotypes.vcf.gz \
-Oz -o ALL.autosomes.phase3_shapeit2_mvncall_integrated_v5a.20130502.genotypes.vcf.gz --threads 48
# Preparing a VCF file
bgzip -c example.vcf > example.vcf.gz
tabix -p vcf example.vcf.gz
# convert VCF to PLINK
# To build PLINK compatible files from the VCF files, duplicate positions and SNP id need to be merged or removed.
# remove all duplicate entries.
# Catalogue duplicate SNP id:
grep -v '^#' <(zcat ALL.autosomes.phase3_shapeit2_mvncall_integrated_v5a.20130502.genotypes.vcf.gz) | cut -f 3 | sort | uniq -d > ALL.autosomes.dups
# Using BCFTools, split multi-allelic SNPs, and using plink remove duplicate SNPs id found in previous step:
bcftools norm -d both -m +any -Ob ALL.autosomes.phase3_shapeit2_mvncall_integrated_v5a.20130502.genotypes.vcf.gz --threads 64 \
| plink --bcf /dev/stdin --make-bed --out ALL.autosomes --allow-extra-chr 0 --memory 60000 --exclude ALL.autosomes.dups
java -jar GenotypeHarmonizer.jar \
--inputType PLINK_BED \
--input /hapmap3CeuChr20B37Mb6RandomStrand \
--update-id \
--outputType PLINK_BED \
--output /all_chrs \
--refType VCF \
--ref /ALL.autosomes.phase3_shapeit2_mvncall_integrated_v5a.20130502.genotypes.vcf.gz
step3.pre-phasing using SHAPEIT2
shapeit -B all_chrs \
-M /home/zhouwei/zhou_data/GWAS/imputation/impute-pipe/data/reference/ALL_1000G_phase1integrated_v3_impute/genetic_map_chr20_combined_b37.txt \
-O phased_all_chrs -T 64
Step 4: Imputation into pre-phased haplotypes
impute2 -use_prephased_g -Ne 20000 -iter 30 -align_by_maf_g -os 0 1 2 3 -seed 1000000 \
-o_gz -int 1 5e6 \
-h /ALL_1000G_phase1integrated_v3_chr20_impute.hap.gz \
-l /ALL_1000G_phase1integrated_v3_chr20_impute.legend.gz \
-m /genetic_map_chr20_combined_b37.txt \
-known_haps_g phased_all_chrs.haps \
-o /chr20.chunk1
impute2 \
-use_prephased_g -Ne 20000 -iter 30 -align_by_maf_g -os 0 1 2 3 -seed 1000000 -o_gz -int 5e6 10e6 \
-h /ALL_1000G_phase1integrated_v3_chr20_impute.hap.gz \
-l /ALL_1000G_phase1integrated_v3_chr20_impute.legend.gz \
-m /genetic_map_chr20_combined_b37.txt \
-known_haps_g phased_all_chrs.haps \
-o chr20.chunk2
Step 5: Combine all the chunks
cat chr22.chunk1.gz chr22.chunk2.gz > chr22_chunkAll.gen.gz
待完善
參考：
http://www.bbmriwiki.nl/wiki/Impute2Pipeline
http://databeauty.com/blog/tutorial/2017/02/20/GWAS-prephasing-and-imputation.html
