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/dev/ukb-a-import/processed/ukb-a-235/ukb-a-235.vcf.gz \
--ref-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-a-import/processed/ukb-a-235/ldsc.txt \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ 

Beginning analysis at Sun Feb 16 01:54:55 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-a-import/processed/ukb-a-235/ukb-a-235.vcf.gz ...
Read summary statistics for 10877936 SNPs.
Reading reference panel LD Score from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/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 /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/[1-22] ...
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 1281483 SNPs remain.
After merging with regression SNP LD, 1281483 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.1932 (0.009)
Lambda GC: 1.3452
Mean Chi^2: 1.4658
Intercept: 1.0414 (0.0084)
Ratio: 0.0889 (0.018)
Analysis finished at Sun Feb 16 01:56:31 2020
Total time elapsed: 1.0m:36.27s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9533,
    "inflation_factor": 1.2036,
    "mean_EFFECT": -0.0001,
    "n": 110423,
    "n_snps": 10877936,
    "n_clumped_hits": 55,
    "n_p_sig": 9344,
    "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": 356865,
    "n_est": 110099.4779,
    "ratio_se_n": 0.9985,
    "mean_diff": 1.145e-06,
    "ratio_diff": 1.6094,
    "sd_y_est1": 1.0059,
    "sd_y_est2": 1.0045,
    "r2_sum1": 0.0217,
    "r2_sum2": 0.0215,
    "r2_sum3": 0.0215,
    "r2_sum4": 0.0217,
    "ldsc_nsnp_merge_refpanel_ld": 1281483,
    "ldsc_nsnp_merge_regression_ld": 1281483,
    "ldsc_observed_scale_h2_beta": 0.1932,
    "ldsc_observed_scale_h2_se": 0.009,
    "ldsc_intercept_beta": 1.0414,
    "ldsc_intercept_se": 0.0084,
    "ldsc_lambda_gc": 1.3452,
    "ldsc_mean_chisq": 1.4658,
    "ldsc_ratio": 0.0889
}

Flags

name value
af_correlation FALSE
inflation_factor TRUE
n FALSE
is_snpid_non_unique FALSE
mean_EFFECT_nonfinite FALSE
mean_EFFECT_05 FALSE
mean_EFFECT_01 FALSE
mean_chisq TRUE
n_p_sig TRUE
miss_EFFECT FALSE
miss_SE FALSE
miss_PVAL FALSE
ldsc_ratio FALSE
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 numeric.mean numeric.sd numeric.p0 numeric.p25 numeric.p50 numeric.p75 numeric.p100 numeric.hist
character ID 0 1.0000000 3 58 0 10877936 0 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
character ALT 0 1.0000000 1 1 0 4 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 8.598638e+00 5.745666e+00 1.00000e+00 4.000000e+00 8.000000e+00 1.300000e+01 2.200000e+01 ▇▅▃▂▂
numeric POS 0 1.0000000 NA NA NA NA NA 7.911383e+07 5.624931e+07 8.28000e+02 3.287294e+07 6.988889e+07 1.148258e+08 2.492251e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA -9.780000e-05 2.606260e-02 -3.29908e-01 -7.777900e-03 -4.210000e-05 7.658700e-03 3.372270e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 1.844630e-02 1.748630e-02 4.22600e-03 5.224600e-03 1.003360e-02 2.681670e-02 9.646000e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.691123e-01 2.979400e-01 0.00000e+00 2.029191e-01 4.593186e-01 7.277094e-01 1.000000e+00 ▇▆▆▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.691108e-01 2.979408e-01 0.00000e+00 2.029167e-01 4.593176e-01 7.277089e-01 9.999999e-01 ▇▆▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA 1.820865e-01 2.503832e-01 1.00090e-03 6.878700e-03 5.191440e-02 2.739880e-01 9.989990e-01 ▇▂▁▁▁
numeric AF_reference 356865 0.9671937 NA NA NA NA NA 1.877709e-01 2.439191e-01 0.00000e+00 5.191700e-03 7.468050e-02 2.873400e-01 1.000000e+00 ▇▂▁▁▁
numeric N 0 1.0000000 NA NA NA NA NA 1.104230e+05 0.000000e+00 1.10423e+05 1.104230e+05 1.104230e+05 1.104230e+05 1.104230e+05 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 693731 rs12238997 A G -0.0109604 0.0070389 0.1194461 0.1194419 0.1169260 0.1417730 110423
1 717587 rs144155419 G A 0.0110924 0.0190100 0.5595566 0.5595545 0.0143862 0.0045926 110423
1 730087 rs148120343 T C -0.0203628 0.0098222 0.0381610 0.0381590 0.0554733 0.0127796 110423
1 731718 rs142557973 T C -0.0114955 0.0066724 0.0849200 0.0849159 0.1216580 0.1543530 110423
1 734349 rs141242758 T C -0.0116295 0.0066747 0.0814535 0.0814515 0.1215130 0.1525560 110423
1 740284 rs61770167 C T -0.0304558 0.0309496 0.3250948 0.3250934 0.0058556 0.0023962 110423
1 742813 rs112573343 C T -0.0351373 0.0557844 0.5287775 0.5287752 0.0015525 0.1030350 110423
1 753405 rs3115860 C A 0.0112746 0.0063503 0.0758263 0.0758233 0.8706760 0.7517970 110423
1 753541 rs2073813 G A -0.0103733 0.0063658 0.1032029 0.1031991 0.1288330 0.3019170 110423
1 754182 rs3131969 A G 0.0110482 0.0063422 0.0815117 0.0815084 0.8703180 0.6785140 110423
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219387 rs9616832 T C -0.0038284 0.0082188 0.6413558 0.6413545 0.0729806 0.0654952 110423
22 51219704 rs147475742 G A -0.0090659 0.0110299 0.4111128 0.4111119 0.0418225 0.0473243 110423
22 51219766 rs182321900 C T 0.0182121 0.0585019 0.7555670 0.7555666 0.0014546 NA 110423
22 51220088 rs566371895 G A 0.0273639 0.0378130 0.4692745 0.4692722 0.0039843 0.0003994 110423
22 51220146 rs868950473 C T 0.0285488 0.0576059 0.6201861 0.6201850 0.0015139 NA 110423
22 51221731 rs115055839 T C -0.0036236 0.0082266 0.6595914 0.6595911 0.0728101 0.0625000 110423
22 51223637 rs375798137 G A 0.0060090 0.0096725 0.5344376 0.5344358 0.0540289 0.0788738 110423
22 51226692 rs150189434 G A 0.0560465 0.0598348 0.3489209 0.3489194 0.0013983 0.0155751 110423
22 51229805 rs9616985 T C -0.0049085 0.0082599 0.5523368 0.5523358 0.0727671 0.0730831 110423
22 51237063 rs3896457 T C -0.0024771 0.0050016 0.6204089 0.6204092 0.2968410 0.2050720 110423

bcf preview

1   693731  rs12238997  A   G   .   PASS    AF=0.116926 ES:SE:LP:AF:SS:ID   -0.0109604:0.00703889:0.922828:0.116926:110423:rs12238997
1   717587  rs144155419 G   A   .   PASS    AF=0.0143862    ES:SE:LP:AF:SS:ID   0.0110924:0.01901:0.252156:0.0143862:110423:rs144155419
1   730087  rs148120343 T   C   .   PASS    AF=0.0554733    ES:SE:LP:AF:SS:ID   -0.0203628:0.00982219:1.41838:0.0554733:110423:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.121658 ES:SE:LP:AF:SS:ID   -0.0114955:0.00667238:1.07099:0.121658:110423:rs58276399
1   734349  rs141242758 T   C   .   PASS    AF=0.121513 ES:SE:LP:AF:SS:ID   -0.0116295:0.0066747:1.08909:0.121513:110423:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.00585559   ES:SE:LP:AF:SS:ID   -0.0304558:0.0309496:0.48799:0.00585559:110423:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.0015525    ES:SE:LP:AF:SS:ID   -0.0351373:0.0557844:0.276727:0.0015525:110423:rs112573343
1   753405  rs3115860   C   A   .   PASS    AF=0.870676 ES:SE:LP:AF:SS:ID   0.0112746:0.00635027:1.12018:0.870676:110423:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.128833 ES:SE:LP:AF:SS:ID   -0.0103733:0.00636579:0.986308:0.128833:110423:rs2073813
1   754182  rs3131969   A   G   .   PASS    AF=0.870318 ES:SE:LP:AF:SS:ID   0.0110482:0.00634225:1.08878:0.870318:110423:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.870409 ES:SE:LP:AF:SS:ID   0.0109058:0.0063446:1.06735:0.870409:110423:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.870037 ES:SE:LP:AF:SS:ID   0.011177:0.00634193:1.10788:0.870037:110423:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00465304   ES:SE:LP:AF:SS:ID   0.00131147:0.0326547:0.0141408:0.00465304:110423:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00464711   ES:SE:LP:AF:SS:ID   0.000876784:0.0327382:0.0093797:0.00464711:110423:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.00590897   ES:SE:LP:AF:SS:ID   0.00211825:0.0317786:0.0237165:0.00590897:110423:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.870329 ES:SE:LP:AF:SS:ID   0.0108084:0.00633347:1.05597:0.870329:110423:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.87002  ES:SE:LP:AF:SS:ID   0.0117269:0.0063181:1.19761:0.87002:110423:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.869421 ES:SE:LP:AF:SS:ID   0.0108824:0.00631142:1.07228:0.869421:110423:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.870219 ES:SE:LP:AF:SS:ID   0.0109149:0.00632438:1.07377:0.870219:110423:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.870219 ES:SE:LP:AF:SS:ID   0.0109175:0.00632481:1.07404:0.870219:110423:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.870222 ES:SE:LP:AF:SS:ID   0.0109099:0.00632499:1.07288:0.870222:110423:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.870396 ES:SE:LP:AF:SS:ID   0.010953:0.00634032:1.07532:0.870396:110423:rs3131954
1   761732  rs2286139   C   T   .   PASS    AF=0.867743 ES:SE:LP:AF:SS:ID   0.00943492:0.00630705:0.870716:0.867743:110423:rs2286139
1   766007  rs61768174  A   C   .   PASS    AF=0.106958 ES:SE:LP:AF:SS:ID   -0.0152947:0.00701641:1.53357:0.106958:110423:rs61768174
1   768253  rs2977608   A   C   .   PASS    AF=0.763932 ES:SE:LP:AF:SS:ID   0.0119575:0.00501336:1.76763:0.763932:110423:rs2977608
1   768448  rs12562034  G   A   .   PASS    AF=0.105257 ES:SE:LP:AF:SS:ID   -0.00989306:0.00693747:0.812869:0.105257:110423:rs12562034
1   768819  rs12562811  C   T   .   PASS    AF=0.00692766   ES:SE:LP:AF:SS:ID   -0.00986397:0.0263472:0.149893:0.00692766:110423:rs12562811
1   769223  rs60320384  C   G   .   PASS    AF=0.128651 ES:SE:LP:AF:SS:ID   -0.00987721:0.00635715:0.919897:0.128651:110423:rs60320384
1   769224  rs141644775 G   A   .   PASS    AF=0.00143239   ES:SE:LP:AF:SS:ID   -0.0771179:0.0596132:0.708203:0.00143239:110423:rs141644775
1   770181  rs146076599 A   G   .   PASS    AF=0.00907328   ES:SE:LP:AF:SS:ID   0.0153162:0.0245351:0.273713:0.00907328:110423:rs146076599
1   770377  rs112563271 A   T   .   PASS    AF=0.00692321   ES:SE:LP:AF:SS:ID   -0.00861437:0.026444:0.128072:0.00692321:110423:rs112563271
1   771823  rs2977605   T   C   .   PASS    AF=0.869934 ES:SE:LP:AF:SS:ID   0.0107872:0.00632795:1.05426:0.869934:110423:rs2977605
1   771967  rs59066358  G   A   .   PASS    AF=0.128709 ES:SE:LP:AF:SS:ID   -0.00984585:0.00635409:0.91629:0.128709:110423:rs59066358
1   772755  rs2905039   A   C   .   PASS    AF=0.870112 ES:SE:LP:AF:SS:ID   0.0107676:0.00632817:1.05137:0.870112:110423:rs2905039
1   774736  rs28830877  A   C   .   PASS    AF=0.998797 ES:SE:LP:AF:SS:ID   0.0638416:0.0640903:0.495945:0.998797:110423:rs28830877
1   776556  rs151160018 C   T   .   PASS    AF=0.00851871   ES:SE:LP:AF:SS:ID   -0.000774361:0.0233114:0.0116636:0.00851871:110423:rs151160018
1   777122  rs2980319   A   T   .   PASS    AF=0.871122 ES:SE:LP:AF:SS:ID   0.0104451:0.00633567:1.00337:0.871122:110423:rs2980319
1   777232  rs112618790 C   T   .   PASS    AF=0.0961319    ES:SE:LP:AF:SS:ID   -0.00775643:0.007336:0.537045:0.0961319:110423:rs112618790
1   778745  rs1055606   A   G   .   PASS    AF=0.127871 ES:SE:LP:AF:SS:ID   -0.00941483:0.00635723:0.858177:0.127871:110423:rs1055606
1   779322  rs4040617   A   G   .   PASS    AF=0.127984 ES:SE:LP:AF:SS:ID   -0.00960689:0.00634939:0.885149:0.127984:110423:rs4040617
1   780785  rs2977612   T   A   .   PASS    AF=0.870455 ES:SE:LP:AF:SS:ID   0.00960603:0.00632571:0.889835:0.870455:110423:rs2977612
1   781367  rs149821290 A   C   .   PASS    AF=0.00989772   ES:SE:LP:AF:SS:ID   -0.017124:0.0226906:0.346356:0.00989772:110423:rs149821290
1   781845  rs61768199  A   G   .   PASS    AF=0.1044   ES:SE:LP:AF:SS:ID   -0.014449:0.00710222:1.37768:0.1044:110423:rs61768199
1   782721  rs185280546 G   A   .   PASS    AF=0.0143713    ES:SE:LP:AF:SS:ID   -0.0164214:0.0194872:0.39858:0.0143713:110423:rs185280546
1   782981  rs6594026   C   T   .   PASS    AF=0.128338 ES:SE:LP:AF:SS:ID   -0.00895103:0.0063527:0.799054:0.128338:110423:rs6594026
1   783193  rs145767270 G   C   .   PASS    AF=0.0143639    ES:SE:LP:AF:SS:ID   -0.0167926:0.0195745:0.407865:0.0143639:110423:rs145767270
1   783194  rs138555831 G   T   .   PASS    AF=0.0143713    ES:SE:LP:AF:SS:ID   -0.0172433:0.0195715:0.422166:0.0143713:110423:rs138555831
1   783711  rs184266993 G   A   .   PASS    AF=0.00541372   ES:SE:LP:AF:SS:ID   0.0571986:0.0308642:1.19483:0.00541372:110423:rs184266993
1   785050  rs2905062   G   A   .   PASS    AF=0.87014  ES:SE:LP:AF:SS:ID   0.009563:0.00632545:0.884123:0.87014:110423:rs2905062
1   785989  rs2980300   T   C   .   PASS    AF=0.87009  ES:SE:LP:AF:SS:ID   0.00964109:0.00632854:0.893966:0.87009:110423:rs2980300