Summary

Summary {data-width=650}

Manhattan plot

manhattan_plot

manhattan_plot

QQ plot

qq_plot

qq_plot

AF plot

af_plot

af_plot

P-Z plot

pz_plot

pz_plot

beta_std plot

beta_std_plot

beta_std_plot

Metadata

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}

LDSC

*********************************************************************
* LD Score Regression (LDSC)
* Version 1.0.1
* (C) 2014-2019 Brendan Bulik-Sullivan and Hilary Finucane
* Broad Institute of MIT and Harvard / MIT Department of Mathematics
* GNU General Public License v3
*********************************************************************
Call: 
./ldsc.py \
--h2 /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5855/UKB-b-5855_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5855/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:45:40 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5855/UKB-b-5855_data.vcf.gz ...
Read summary statistics for 8301820 SNPs.
Dropped 6697 SNPs with duplicated rs numbers.
Reading reference panel LD Score from ../reference/eur_w_ld_chr/[1-22] ...
Read reference panel LD Scores for 1290028 SNPs.
Removing partitioned LD Scores with zero variance.
Reading regression weight LD Score from ../reference/eur_w_ld_chr/[1-22] ...
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 1284389 SNPs remain.
After merging with regression SNP LD, 1284389 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0064 (0.0089)
Lambda GC: 1.0777
Mean Chi^2: 1.0805
Intercept: 1.0737 (0.0068)
Ratio: 0.9153 (0.085)
Analysis finished at Thu Oct 17 14:46:56 2019
Total time elapsed: 1.0m:16.0s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9446,
    "inflation_factor": 1.0966,
    "mean_EFFECT": 1.0332e-06,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 1,
    "n_p_sig": 1,
    "n_mono": 0,
    "n_ns": 0,
    "n_mac": 0,
    "is_snpid_unique": true,
    "n_miss_EFFECT": 0,
    "n_miss_SE": 0,
    "n_miss_PVAL": 0,
    "n_miss_AF": 0,
    "n_miss_AF_reference": 78155,
    "n_est": "NA",
    "ratio_se_n": "NA",
    "mean_diff": "NaN",
    "ratio_diff": "NaN",
    "sd_y_est1": "NaN",
    "sd_y_est2": "NA",
    "r2_sum1": 0,
    "r2_sum2": 0,
    "r2_sum3": 0,
    "r2_sum4": 0,
    "ldsc_nsnp_merge_refpanel_ld": 1284389,
    "ldsc_nsnp_merge_regression_ld": 1284389,
    "ldsc_observed_scale_h2_beta": 0.0064,
    "ldsc_observed_scale_h2_se": 0.0089,
    "ldsc_intercept_beta": 1.0737,
    "ldsc_intercept_se": 0.0068,
    "ldsc_lambda_gc": 1.0777,
    "ldsc_mean_chisq": 1.0805,
    "ldsc_ratio": 0.9155
}

Flags

name value
af_correlation FALSE
inflation_factor FALSE
n TRUE
is_snpid_non_unique FALSE
mean_EFFECT_nonfinite FALSE
mean_EFFECT_05 FALSE
mean_EFFECT_01 FALSE
mean_chisq FALSE
n_p_sig FALSE
miss_EFFECT FALSE
miss_SE FALSE
miss_PVAL FALSE
ldsc_ratio TRUE
ldsc_intercept_beta FALSE
n_clumped_hits FALSE
r2_sum1 FALSE
r2_sum2 FALSE
r2_sum3 FALSE
r2_sum4 FALSE

Definitions

General metrics

  • af_correlation: Correlation coefficient between AF and AF_reference.
  • inflation_factor (lambda): Genomic inflation factor.
  • mean_EFFECT: Mean of EFFECT size.
  • n: Maximum value of reported sample size across all SNPs, \(n\).
  • n_clumped_hits: Number of clumped hits.
  • n_snps: Number of SNPs
  • n_p_sig: Number of SNPs with pvalue below 5e-8.
  • n_mono: Number of monomorphic (MAF == 1 or MAF == 0) SNPs.
  • n_ns: Number of SNPs with nonsense values:
    • alleles other than A, C, G or T.
    • P-values < 0 or > 1.
    • negative or infinite standard errors (<= 0 or = Infinity).
    • infinite beta estimates or allele frequencies < 0 or > 1.
  • n_mac: Number of cases where MAC (\(2 \times N \times MAF\)) is less than 6.
  • is_snpid_unique: true if the combination of ID REF ALT is unique and therefore no duplication in snpid.
  • n_miss_<*>: Number of NA observations for <*> column.

se_n metrics

  • n_est: Estimated sample size value, \(\widehat{n}\).
  • ratio_se_n: \(\texttt{ratio_se_n} = \frac{\sqrt{\widehat{n}}}{\sqrt{n}}\). We expect ratio_se_n to be 1. When it is not 1, it implies that the trait did not have a variance of 1, the reported sample size is wrong, or that the SNP-level effective sample sizes differ markedly from the reported sample size.
  • mean_diff: \(\texttt{mean_diff} = \sum_{j} \frac{\widehat{\beta_j^{std}} - \beta_j}{\texttt{n_snps}}\), mean difference between the standardised beta, predicted from P-values, and the observed beta. The difference should be very close to zero if trait has a variance of 1.
    • \(\widehat{\beta_j^{std}} = \sqrt{\frac{{z}_j^2 / ({z}_j^2 + n -2)}{2 \times {MAF}_j \times (1 - {MAF}_j)}} \times sign({z}_j)\),
    • \({z}_j = \frac{\beta_j}{{se}_j}\),
    • and \(\beta_j\) is the reported effect size.
  • ratio_diff: \(\texttt{ratio_diff} = |\frac{\texttt{mean_diff}}{\texttt{mean_diff2}}|\), absolute ratio between the mean of diff and the mean of diff2 (expected difference between the standardised beta predicted from P-values, and the standardised beta derived from the observed beta divided by the predicted SD; NOT reported). The ratio should be close to 1. If different from 1, then implies that the betas are not in a standard deviation scale.
    • \(\texttt{mean_diff2} = \sum_{j} \frac{\widehat{\beta_j^{std}} - \beta^{\prime}_j}{\texttt{n_snps}}\)
    • \(\beta^{\prime}_j = \frac{\beta_j}{\widehat{\texttt{sd2}}_{y}}\)
  • sd_y_est1: The standard deviation for the trait inferred from the reported sample size, median standard errors for the SNP-trait assocations and SNP variances.
    • \(\widehat{\texttt{sd1}}_{y} = \frac{\sqrt{n} \times median({se}_j)}{C}\),
    • \(C = median(\frac{1}{\sqrt{2 \times {MAF}_j \times (1 - {MAF}_j)}})\),
    • and \({se}_j\) is the reported standard error.
  • sd_y_est2: The standard deviation for the trait inferred from the reported sample size, Z statistics for the SNP-trait effects (beta/se) and allele frequency.
    • \(\widehat{\texttt{sd2}}_{y} = median(\widehat{sd_j})\),
    • \(\widehat{sd_j} = \frac{\beta_j}{\widehat{\beta_j^{std}}}\),

r2 metrics

Sum of variance explained, calculated from the clumped top hits sample.

  • r2_sum<*>: r2 statistics under various assumptions
    • 1: \(r^2 = \sum_j{\frac{2 \times \beta_j^2 \times {MAF}_j \times (1 - {MAF}_j)}{\texttt{var1}}}\), \(\texttt{var1} = 1\).
    • 2: \(r^2 = \sum_j{\frac{2 \times \beta_j^2 \times {MAF}_j \times (1 - {MAF}_j)}{\texttt{var2}}}\), \(\texttt{var2} = {\widehat{\texttt{sd1}}_{y}}^2\),
    • 3: \(r^2 = \sum_j{\frac{2 \times \beta_j^2 \times {MAF}_j \times (1 - {MAF}_j)}{\texttt{var3}}}\), \(\texttt{var3} = {\widehat{\texttt{sd2}}_{y}}^2\),
    • 4: \(r^2 = \sum_j{\frac{F_j}{F_j + n - 2}}\), \(F = \frac{\beta_j^2}{{se}_j^2}\).

LDSC metrics

Metrics from LD regression

  • ldsc_nsnp_merge_refpanel_ld: Number of remaining SNPs after merging with reference panel LD.
  • ldsc_nsnp_merge_regression_ld: Number of remaining SNPs after merging with regression SNP LD.
  • ldsc_observed_scale_h2_{beta,se} Coefficient value and SE for total observed scale h2.
  • ldsc_intercept_{beta,se}: Coefficient value and SE for intercept. Intercept is expected to be 1.
  • ldsc_lambda_gc: Lambda GC statistics.
  • ldsc_mean_chisq: Mean \(\chi^2\) statistics.
  • ldsc_ratio: \(\frac{\texttt{ldsc_intercept_beta} - 1}{\texttt{ldsc_mean_chisq} - 1}\), the proportion of the inflation in the mean \(\chi^2\) that the LD Score regression intercepts ascribes to causes other than polygenic heritability. The value of ratio should be close to zero, though in practice values of 0.1-0.2 are not uncommon, probably due to sample/reference LD Score mismatch or model misspecification (e.g., low LD variants have slightly higher \(h^2\) per SNP).

Flags

When a metric needs attention, the flag should return TRUE.

  • af_correlation: abs(af_correlation) < 0.7.
  • inflation_factor: inflation_factor > 1.2.
  • n: n (max reported sample size) < 10000.
  • is_snpid_non_unique: NOT is_snpid_unique.
  • mean_EFFECT_nonfinite: mean(EFFECT) is NA, NaN, or Inf.
  • mean_EFFECT_05: abs(mean(EFFECT)) > 0.5.
  • mean_EFFECT_01: abs(mean(EFFECT)) > 0.1.
  • mean_chisq: ldsc_mean_chisq > 1.3 or ldsc_mean_chisq < 0.7.
  • n_p_sig: n_p_sig > 1000.
  • miss_<*>: n_miss_<*> / n_snps > 0.01.
  • ldsc_ratio: ldsc_ratio > 0.5
  • ldsc_intercept_beta: ldsc_intercept_beta > 1.5
  • n_clumped_hits: n_clumped_hits > 1000
  • r2_sum<*>: r2_sum<*> > 0.5

Plots

  • Manhattan plot
    • Red line: \(-log_{10}^{5 \times 10^{-8}}\)
    • Blue line: \(-log_{10}^{5 \times 10^{-5}}\)
  • QQ plot
  • AF plot
  • P-Z plot
  • beta_std plot: Scatter plot between \(\widehat{\beta_j^{std}}\) and \(\beta_j\)

Diagnostics

Details

Summary stats

skim_type skim_variable n_missing complete_rate character.min character.max character.empty character.n_unique character.whitespace logical.mean logical.count numeric.mean numeric.sd numeric.p0 numeric.p25 numeric.p50 numeric.p75 numeric.p100 numeric.hist
character ID 0 1.0000000 3 58 0 8295153 0 NA NA NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 1 0 4 0 NA NA NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 1 0 4 0 NA NA NA NA NA NA NA NA NA NA
logical N 8301820 0.0000000 NA NA NA NA NA NaN : NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA NA NA 8.655063e+00 5.762498e+00 1.0000000 4.000000e+00 8.000000e+00 1.300000e+01 2.200000e+01 ▇▅▅▂▂
numeric POS 0 1.0000000 NA NA NA NA NA NA NA 7.874249e+07 5.638584e+07 828.0000000 3.233385e+07 6.923249e+07 1.145586e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 1.000000e-06 3.283300e-03 -0.0359437 -1.421700e-03 1.280000e-05 1.453100e-03 2.998080e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 2.664200e-03 1.708400e-03 0.0011365 1.326200e-03 1.897100e-03 3.552200e-03 1.788630e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.876351e-01 2.914016e-01 0.0000000 2.300001e-01 4.799997e-01 7.400005e-01 1.000000e+00 ▇▇▇▆▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.876370e-01 2.913770e-01 0.0000000 2.326881e-01 4.840140e-01 7.390753e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.373497e-01 2.602361e-01 0.0067140 3.012400e-02 1.253250e-01 3.758960e-01 9.932860e-01 ▇▂▂▁▁
numeric AF_reference 78155 0.9905858 NA NA NA NA NA NA NA 2.368363e-01 2.521409e-01 0.0000000 2.995210e-02 1.411740e-01 3.722040e-01 1.000000e+00 ▇▂▂▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 49298 rs200943160 T C -0.0025194 0.0020766 0.2300001 0.2250270 0.622841 0.7821490 NA
1 54676 rs2462492 C T 0.0010430 0.0020654 0.6100002 0.6135764 0.400525 NA NA
1 86028 rs114608975 T C -0.0017507 0.0033132 0.5999997 0.5972175 0.103313 0.0277556 NA
1 91536 rs6702460 G T 0.0016572 0.0020307 0.4100001 0.4144521 0.455575 0.4207270 NA
1 234313 rs8179466 C T -0.0083847 0.0039725 0.0350002 0.0348002 0.075105 NA NA
1 534192 rs6680723 C T -0.0010311 0.0023376 0.6600001 0.6591547 0.238703 NA NA
1 546697 rs12025928 A G 0.0033548 0.0028916 0.2500000 0.2459858 0.913163 NA NA
1 693731 rs12238997 A G 0.0005343 0.0019426 0.7800007 0.7832668 0.116913 0.1417730 NA
1 705882 rs72631875 G A -0.0020438 0.0028623 0.4799997 0.4751980 0.067103 0.0315495 NA
1 706368 rs55727773 A G -0.0015807 0.0014429 0.2700001 0.2732909 0.517124 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0016503 0.0017521 0.3500000 0.3462476 0.137384 0.2052720 NA
22 51219387 rs9616832 T C -0.0021671 0.0022872 0.3400001 0.3433866 0.072834 0.0654952 NA
22 51219704 rs147475742 G A -0.0051795 0.0030265 0.0870001 0.0870086 0.042222 0.0473243 NA
22 51221190 rs369304721 G A -0.0019306 0.0030539 0.5300002 0.5272844 0.049085 NA NA
22 51221731 rs115055839 T C -0.0021955 0.0022907 0.3400001 0.3378430 0.072233 0.0625000 NA
22 51222100 rs114553188 G T 0.0051673 0.0026512 0.0510000 0.0512873 0.054939 0.0880591 NA
22 51223637 rs375798137 G A 0.0050442 0.0026638 0.0580003 0.0582815 0.054553 0.0788738 NA
22 51229805 rs9616985 T C -0.0019053 0.0022981 0.4100001 0.4070773 0.072094 0.0730831 NA
22 51232488 rs376461333 A G 0.0087072 0.0053345 0.1000000 0.1026262 0.020175 NA NA
22 51237063 rs3896457 T C -0.0020181 0.0013832 0.1400000 0.1445774 0.300586 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.622841 ES:SE:LP:AF:ID  -0.00251945:0.00207658:0.638272:0.622841:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400525 ES:SE:LP:AF:ID  0.00104295:0.00206535:0.21467:0.400525:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103313 ES:SE:LP:AF:ID  -0.00175071:0.00331319:0.221849:0.103313:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455575 ES:SE:LP:AF:ID  0.00165723:0.00203071:0.387216:0.455575:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.075105 ES:SE:LP:AF:ID  -0.00838466:0.0039725:1.45593:0.075105:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.238703 ES:SE:LP:AF:ID  -0.00103107:0.0023376:0.180456:0.238703:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913163 ES:SE:LP:AF:ID  0.00335475:0.00289164:0.60206:0.913163:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116913 ES:SE:LP:AF:ID  0.00053434:0.0019426:0.107905:0.116913:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067103 ES:SE:LP:AF:ID  -0.00204381:0.00286229:0.318759:0.067103:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.517124 ES:SE:LP:AF:ID  -0.00158073:0.00144291:0.568636:0.517124:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033883 ES:SE:LP:AF:ID  0.00251132:0.00359765:0.309804:0.033883:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037438 ES:SE:LP:AF:ID  0.00126515:0.00327762:0.154902:0.037438:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037552 ES:SE:LP:AF:ID  0.00138019:0.00326485:0.173925:0.037552:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037223 ES:SE:LP:AF:ID  0.00116939:0.00328945:0.142668:0.037223:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.017195 ES:SE:LP:AF:ID  0.00215151:0.00495702:0.180456:0.017195:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037811 ES:SE:LP:AF:ID  0.00125935:0.00325089:0.154902:0.037811:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037873 ES:SE:LP:AF:ID  0.00104697:0.00324187:0.124939:0.037873:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101546 ES:SE:LP:AF:ID  -0.000486707:0.00238078:0.0757207:0.101546:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958005 ES:SE:LP:AF:ID  -0.000873913:0.00311819:0.107905:0.958005:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031544 ES:SE:LP:AF:ID  0.00469315:0.00573203:0.387216:0.031544:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052132 ES:SE:LP:AF:ID  0.00376616:0.00466115:0.376751:0.052132:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037418 ES:SE:LP:AF:ID  0.000477857:0.00326057:0.0555173:0.037418:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037753 ES:SE:LP:AF:ID  0.00045573:0.00323195:0.05061:0.037753:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841521 ES:SE:LP:AF:ID  -0.000117893:0.00167855:0.0268721:0.841521:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055633 ES:SE:LP:AF:ID  0.00130482:0.00273137:0.200659:0.055633:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.12283  ES:SE:LP:AF:ID  9.07612e-05:0.0018431:0.0177288:0.12283:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.026101 ES:SE:LP:AF:ID  -0.00173708:0.00449766:0.154902:0.026101:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122101 ES:SE:LP:AF:ID  5.65142e-05:0.00184353:0.00877392:0.122101:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132817 ES:SE:LP:AF:ID  0.000256679:0.00181879:0.05061:0.132817:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.01058  ES:SE:LP:AF:ID  0.000693246:0.0067968:0.0362122:0.01058:rs181876450
1   752478  rs146277091 G   A   .   PASS    AF=0.037699 ES:SE:LP:AF:ID  0.00101427:0.00319638:0.124939:0.037699:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837713 ES:SE:LP:AF:ID  -0.000196681:0.00162688:0.0457575:0.837713:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.837359 ES:SE:LP:AF:ID  -0.000245778:0.00162522:0.0555173:0.837359:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869543 ES:SE:LP:AF:ID  3.34268e-05:0.00174822:0.00877392:0.869543:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129992 ES:SE:LP:AF:ID  0.000241977:0.00175209:0.05061:0.129992:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.038196 ES:SE:LP:AF:ID  0.000781473:0.00314378:0.09691:0.038196:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038488 ES:SE:LP:AF:ID  0.000862889:0.00312184:0.107905:0.038488:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868848 ES:SE:LP:AF:ID  2.23778e-05:0.0017445:0.00436481:0.868848:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868948 ES:SE:LP:AF:ID  -1.6894e-05:0.00174505:0.00436481:0.868948:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038368 ES:SE:LP:AF:ID  0.000827982:0.00313772:0.102373:0.038368:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.868854 ES:SE:LP:AF:ID  3.10592e-05:0.00174445:0.00436481:0.868854:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.836845 ES:SE:LP:AF:ID  -0.000214677:0.00162072:0.05061:0.836845:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038362 ES:SE:LP:AF:ID  0.000887698:0.0031434:0.107905:0.038362:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.837491 ES:SE:LP:AF:ID  -0.000218295:0.00162555:0.05061:0.837491:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013317 ES:SE:LP:AF:ID  0.00621523:0.00581427:0.537602:0.013317:rs181660517
1   755775  rs3131965   A   G   .   PASS    AF=0.83869  ES:SE:LP:AF:ID  -0.000605961:0.00164802:0.148742:0.83869:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.869126 ES:SE:LP:AF:ID  -7.43413e-06:0.00174281:-0:0.869126:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.868708 ES:SE:LP:AF:ID  5.50744e-05:0.00173869:0.0132283:0.868708:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867586 ES:SE:LP:AF:ID  1.65854e-05:0.00173546:0.00436481:0.867586:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.868834 ES:SE:LP:AF:ID  -4.16422e-05:0.00173996:0.00877392:0.868834:rs4951929