LPS evaluation

Evaluation methods

To assess imputation performance, two key metrics are used: Imputation Accuracy and Imputation Coverage. These metrics quantify the quality and completeness of imputed genetic variants, and are calculated per chromosome across all autosomes.

Metric	Description	Purpose
Imputation Accuracy	Mean \(r^2\) of sites within a bin	Measures how well imputed values match true genotypes
Imputation Coverage	Proportion of variants with \(r^2 \geq 0.8\) in a bin	Assesses the proportion of high-confidence imputations

Evaluation process

Input data

• restructed lpWGS VCFs
• restructed SNP-array VCFs
• True VCFs

Imputation AccuracyImputation coverage

Code

    compute_MAF.sh chr${i}_${pop}_true.vcf.gz maf.txt

    run_evaluate.py --true_vcf chr${i}_${pop}_true.vcf.gz \
                    --imputed_vcf ${imputed_vcf} \
                    --af maf.txt \
                    --out_snp_wise chr${i}_${lps_cov}_${pop}_snp_wise.acc

• compute_MAF.sh: Retrieve MAF values from true VCF files
• run_evaluate.py: Evaluation by using SNP-wise matrix

Code

  get_coverage.py --input ${res_snp_wise} \
                  --cov perbin_${res_snp_wise}_cov.txt \
                  --acc perbin_${res_snp_wise}_mean_r2.txt 

• get_coverage.py: Evaluation using Imputation coverage matrix

Output

Evaluation process output:

	LPS	Pseudo array
SNP-wise accuracy	lps_all_acc.txt	array_all_acc.txt
Imputation accuracy	lps_all_cov.txt	array_all_cov.txt