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Table 1 Pearson correlation between Nei’s G ST and imputation quality scores of the 20 POPRES populations in dependence on reference panel, imputation software and measure of imputation quality, CIG = correctly imputed genotypes

From: Impact of genetic similarity on imputation accuracy

Reference

Software

HELLI > =0.45 (%)

SEN > =0.45 (%)

CIG (%)

Rsq/Info mean

Rsq/Info > =0.8 (%)

CEU

MaCH

−0.907

−0.911

−0.902

−0.959

−0.930

MaCH_minimac

−0.870

−0.871

−0.868

−0.914

−0.865

IMPUTE2

−0.877

−0.888

−0.876

−0.943

−0.898

YRI

MaCH

−0.962

−0.969

−0.966

−0.975

−0.849

MaCH_minimac

−0.950

−0.961

−0.953

−0.971

−0.620

IMPUTE2

−0.953

−0.966

−0.956

−0.969

−0.507

CHB. JPT

MaCH

−0.907

−0.905

−0.910

−0.767

−0.739

MaCH_minimac

−0.892

−0.893

−0.896

−0.767

−0.785

IMPUTE2

−0.853

−0.852

−0.856

−0.784

−0.806

MEX

MaCH

−0.917

−0.915

−0.914

−0.946

−0.923

MaCH_minimac

−0.898

−0.892

−0.898

−0.908

−0.882

IMPUTE2

−0.898

−0.902

−0.901

−0.932

−0.904

  1. By estimating the association between G ST and imputation accuracy score and by computing a test of the correlation being zero, we got p-value < 9.52e-13 for all three scenarios