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TagSNP_evaluation

Evaluation tag SNP selection strategies

These scipts tested in Ubuntu v18 environment.

Software requirements

Leave one out imputation cross validation requires R, vcftools, bcftools, minimac3, minimac4, and plink.

Clone this project and config pipeline:

git clone https://github.com/datngu/TagSNP_evaluation

I. RUNNING PIPELINES

Assumming that you have a processed (Bialellic SNPs with MAF >= 1%) vcf.gz file named: chr10_EAS.vcf.gz and want to compare impuatation accuracy with 30000 tag SNP selected.

1. Running TagIt

cd TagSNP_evaluation/TagIt
bash configure.sh

bash TagIt_pipeline.sh ../chr10_EAS.vcf.gz chr10_EAS

# picking top 30000 tag SNP selected from 
head -n 30000 chr10_EAS/chr10_EAS_tags_cleaned.txt > chr10_EAS_tags_30000_cleaned.txt

You output should be: TagSNP_evaluation/TagIt/chr10_EAS_tags_30000_cleaned.txt

2. Running FastTagger

cd TagSNP_evaluation/FastTagger 

bash bash configure.sh

bash FastTagger_pipeline.sh  ../chr10_EAS.vcf.gz chr10_EAS 30000 chr10_EAS

You output should be: TagSNP_evaluation/FastTagger/cleaned_EAS_chr10_EAS_30000.tagSNP.txt

3. Running EQ_uniform

cd TagSNP_evaluation/EQ_uniform

Rscript EQ_uniform.R vcf=../chr10_EAS.vcf.gz size=30000 out=EQ_uniform_array_30000.txt

You output should be: TagSNP_evaluation/EQ_uniform/EQ_uniform_array_30000.txt

4. Running EQ_MAF

cd TagSNP_evaluation/EQ_MAF

Rscript EQ_MAF.R vcf=../chr10_EAS.vcf.gz size=30000 out=EQ_MAF_array_30000.txt

You output should be: TagSNP_evaluation/EQ_MAF/EQ_MAF_array_30000.txt

II. EVALUATION WITH LEAVE ONE OUT CROSS VALIDATION

1. Create imputation reference pannel

This step take very long time, depends on your size of chromosome and population.

cd TagSNP_evaluation
create_imputation_ref.sh -v chr10_EAS.vcf.gz -o chr10_EAS_imputation_ref -p 16

2. Leave one out imputation with your pre-buit reference panel and computing Pearson's correlation

cd TagSNP_evaluation
# 1. TagIt
## imputation
imputation_with_prebuilt_ref.sh -t TagIt/chr10_EAS_tags_30000_cleaned.txt -r chr10_EAS_imputation_ref -o TagIt_EAS -p 16
## computing Pearson's correlation
compute_imputation_accuracy.R imputation=TagIt_EAS out=TagIt_EAS.Rdata

# 2. FastTagger
## imputation
imputation_with_prebuilt_ref.sh -t FastTagger/cleaned_EAS_chr10_EAS_30000.tagSNP.txt -r chr10_EAS_imputation_ref -o FastTagger_EAS -p 16
## computing Pearson's correlation
compute_imputation_accuracy.R imputation=FastTagger_EAS out=FastTagger_EAS.Rdata


# 3. EQ_uniform
## imputation
imputation_with_prebuilt_ref.sh -t EQ_uniform/EQ_uniform_array_30000.txt -r chr10_EAS_imputation_ref -o EQ_uniform_EAS -p 16
## computing Pearson's correlation
compute_imputation_accuracy.R imputation=EQ_uniform_EAS out=EQ_uniform_EAS.Rdata

# 4. EQ_MAF
## imputation
imputation_with_prebuilt_ref.sh -t EQ_MAF/EQ_MAF_array_30000.txt -r chr10_EAS_imputation_ref -o EQ_MAF_EAS -p 16
## computing Pearson's correlation
compute_imputation_accuracy.R imputation=EQ_MAF_EAS out=EQ_MAF_EAS.Rdata

You output should be: TagIt_EAS.Rdata, FastTagger_EAS.Rdata, EQ_uniform_EAS.Rdata, EQ_MAF_EAS.Rdata

III. REFERENCE

Nguyen, D. T., Dinh, H. Q., Vu, G. M., Nguyen, D. T., & Vo, N. S. (2021, November). A comprehensive imputation-based evaluation of tag SNP selection strategies. In 2021 13th International Conference on Knowledge and Systems Engineering (KSE) (pp. 1-6). IEEE. https://doi.org/10.1109/KSE53942.2021.9648614