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-9253/UKB-b-9253_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-9253/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:42:21 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-9253/UKB-b-9253_data.vcf.gz ...
Read summary statistics for 3526214 SNPs.
Dropped 547 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, 860421 SNPs remain.
After merging with regression SNP LD, 860421 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.001 (0.0013)
Lambda GC: 1.1805
Mean Chi^2: 1.1794
Intercept: 1.1696 (0.0098)
Ratio: 0.9453 (0.0545)
Analysis finished at Thu Oct 17 14:43:03 2019
Total time elapsed: 42.47s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.8528,
    "inflation_factor": 1.1999,
    "mean_EFFECT": 1.4218e-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": 28385,
    "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": 860421,
    "ldsc_nsnp_merge_regression_ld": 860421,
    "ldsc_observed_scale_h2_beta": 0.001,
    "ldsc_observed_scale_h2_se": 0.0013,
    "ldsc_intercept_beta": 1.1696,
    "ldsc_intercept_se": 0.0098,
    "ldsc_lambda_gc": 1.1805,
    "ldsc_mean_chisq": 1.1794,
    "ldsc_ratio": 0.9454
}

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 3525670 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 3526214 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 8.664028e+00 5.773490e+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 7.853240e+07 5.672911e+07 828.0000000 3.158010e+07 6.890340e+07 1.147040e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 1.400000e-06 1.821000e-04 -0.0010240 -1.202000e-04 1.300000e-06 1.227000e-04 1.044800e-03 ▁▂▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.663000e-04 1.750000e-05 0.0001422 1.510000e-04 1.607000e-04 1.786000e-04 5.199000e-04 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.724800e-01 2.951464e-01 0.0000000 2.099999e-01 4.600002e-01 7.300002e-01 1.000000e+00 ▇▆▆▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.724829e-01 2.951222e-01 0.0000000 2.105058e-01 4.618222e-01 7.273711e-01 9.999994e-01 ▇▇▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 4.140154e-01 1.964868e-01 0.1496370 2.419110e-01 3.744845e-01 5.634310e-01 8.503630e-01 ▇▅▃▃▂
numeric AF_reference 28385 0.9919503 NA NA NA NA NA NA NA 4.005452e-01 2.057208e-01 0.0000000 2.324280e-01 3.684110e-01 5.507190e-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 -0.0000316 0.0002615 0.9000000 0.9039194 0.623761 0.782149 NA
1 54676 rs2462492 C T 0.0001931 0.0002591 0.4600002 0.4561285 0.400375 NA NA
1 91536 rs6702460 G T 0.0000433 0.0002551 0.8700001 0.8652532 0.456836 0.420727 NA
1 534192 rs6680723 C T 0.0002327 0.0002914 0.4199997 0.4245264 0.240953 NA NA
1 706368 rs55727773 A G 0.0003498 0.0001809 0.0530005 0.0531086 0.515612 0.275160 NA
1 729679 rs4951859 C G 0.0003387 0.0002116 0.1100001 0.1094699 0.843181 0.639976 NA
1 752566 rs3094315 G A 0.0004194 0.0002049 0.0409996 0.0406978 0.838917 0.718251 NA
1 752721 rs3131972 A G 0.0004079 0.0002047 0.0460002 0.0463241 0.838545 0.653355 NA
1 754503 rs3115859 G A 0.0004033 0.0002041 0.0479999 0.0481895 0.837997 0.663938 NA
1 754964 rs3131966 C T 0.0004176 0.0002047 0.0409996 0.0413424 0.838629 0.663339 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51182485 rs6009961 A G 0.0001875 0.0001709 0.2700001 0.2724910 0.715458 0.6383790 NA
22 51186143 rs2879914 T C 0.0001593 0.0001585 0.3100002 0.3147093 0.381788 0.2733630 NA
22 51186228 rs3865766 C T 0.0000731 0.0001544 0.6400000 0.6360849 0.451013 0.4532750 NA
22 51192586 rs5771006 G A -0.0001536 0.0002081 0.4600002 0.4603840 0.167631 0.0848642 NA
22 51193227 rs34608236 T G 0.0003399 0.0002127 0.1100001 0.1099873 0.168459 0.0692891 NA
22 51197266 rs61290853 A G -0.0001820 0.0001594 0.2500000 0.2537063 0.386301 0.4229230 NA
22 51198027 rs34939255 A G 0.0000769 0.0001805 0.6700003 0.6699309 0.254546 0.0984425 NA
22 51211106 rs9628250 T C 0.0000211 0.0001789 0.9100000 0.9059587 0.271542 0.1671330 NA
22 51212875 rs2238837 A C -0.0000337 0.0001700 0.8400000 0.8427199 0.331443 0.3724040 NA
22 51237063 rs3896457 T C -0.0001186 0.0001741 0.5000000 0.4956834 0.297972 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623761 ES:SE:LP:AF:ID  -3.15713e-05:0.000261543:0.0457575:0.623761:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400375 ES:SE:LP:AF:ID  0.000193114:0.000259131:0.337242:0.400375:rs2462492
1   91536   rs6702460   G   T   .   PASS    AF=0.456836 ES:SE:LP:AF:ID  4.32873e-05:0.000255095:0.0604807:0.456836:rs6702460
1   534192  rs6680723   C   T   .   PASS    AF=0.240953 ES:SE:LP:AF:ID  0.000232722:0.000291415:0.376751:0.240953:rs6680723
1   706368  rs12029736  A   G   .   PASS    AF=0.515612 ES:SE:LP:AF:ID  0.000349844:0.000180888:1.27572:0.515612:rs12029736
1   729679  rs4951859   C   G   .   PASS    AF=0.843181 ES:SE:LP:AF:ID  0.000338702:0.000211612:0.958607:0.843181:rs4951859
1   752566  rs3094315   G   A   .   PASS    AF=0.838917 ES:SE:LP:AF:ID  0.000419419:0.000204935:1.38722:0.838917:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838545 ES:SE:LP:AF:ID  0.000407876:0.000204713:1.33724:0.838545:rs3131972
1   754503  rs3115859   G   A   .   PASS    AF=0.837997 ES:SE:LP:AF:ID  0.000403328:0.000204145:1.31876:0.837997:rs3115859
1   754964  rs3131966   C   T   .   PASS    AF=0.838629 ES:SE:LP:AF:ID  0.000417643:0.000204719:1.38722:0.838629:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.839744 ES:SE:LP:AF:ID  0.000416642:0.000207489:1.34679:0.839744:rs3131965
1   760912  rs1048488   C   T   .   PASS    AF=0.838279 ES:SE:LP:AF:ID  0.000415256:0.000203758:1.37675:0.838279:rs1048488
1   761147  rs3115850   T   C   .   PASS    AF=0.8384   ES:SE:LP:AF:ID  0.000413908:0.000203902:1.37675:0.8384:rs3115850
1   763394  rs3115847   G   A   .   PASS    AF=0.706746 ES:SE:LP:AF:ID  0.000539237:0.000212088:1.95861:0.706746:rs3115847
1   768253  rs2977608   A   C   .   PASS    AF=0.761299 ES:SE:LP:AF:ID  0.000318527:0.000173094:1.18046:0.761299:rs2977608
1   776546  rs12124819  A   G   .   PASS    AF=0.265388 ES:SE:LP:AF:ID  -6.31756e-05:0.00019335:0.130768:0.265388:rs12124819
1   798400  rs10900604  A   G   .   PASS    AF=0.206588 ES:SE:LP:AF:ID  -0.000337024:0.000184662:1.16749:0.206588:rs10900604
1   798959  rs11240777  G   A   .   PASS    AF=0.206418 ES:SE:LP:AF:ID  -0.000333712:0.000184741:1.14874:0.206418:rs11240777
1   808631  rs11240779  G   A   .   PASS    AF=0.772608 ES:SE:LP:AF:ID  0.000307374:0.000175734:1.09691:0.772608:rs11240779
1   808928  rs11240780  C   T   .   PASS    AF=0.772836 ES:SE:LP:AF:ID  0.000321407:0.00017603:1.16749:0.772836:rs11240780
1   814495  rs74461805  C   A   .   PASS    AF=0.340373 ES:SE:LP:AF:ID  0.000264805:0.000248067:0.537602:0.340373:rs74461805
1   830181  rs28444699  A   G   .   PASS    AF=0.697212 ES:SE:LP:AF:ID  0.000341382:0.000165934:1.39794:0.697212:rs28444699
1   831489  rs4970385   C   T   .   PASS    AF=0.705384 ES:SE:LP:AF:ID  0.00020769:0.000162939:0.69897:0.705384:rs4970385
1   831909  rs9697642   C   T   .   PASS    AF=0.705429 ES:SE:LP:AF:ID  0.000205839:0.000162933:0.677781:0.705429:rs9697642
1   832066  rs9697380   G   C   .   PASS    AF=0.705615 ES:SE:LP:AF:ID  0.000211242:0.000162941:0.721246:0.705615:rs9697380
1   832318  rs4500250   C   A   .   PASS    AF=0.705643 ES:SE:LP:AF:ID  0.000211088:0.000162958:0.69897:0.705643:rs4500250
1   832398  rs4553118   T   C   .   PASS    AF=0.730107 ES:SE:LP:AF:ID  0.000226215:0.000167397:0.744727:0.730107:rs4553118
1   832918  rs28765502  T   C   .   PASS    AF=0.29439  ES:SE:LP:AF:ID  -0.000206272:0.00016295:0.677781:0.29439:rs28765502
1   833223  rs13303211  C   T   .   PASS    AF=0.236755 ES:SE:LP:AF:ID  -0.000129885:0.000173469:0.346787:0.236755:rs13303211
1   833302  rs28752186  C   T   .   PASS    AF=0.236744 ES:SE:LP:AF:ID  -0.000128595:0.000173471:0.337242:0.236744:rs28752186
1   833641  rs28594623  T   C   .   PASS    AF=0.239808 ES:SE:LP:AF:ID  -0.000153248:0.000172914:0.420216:0.239808:rs28594623
1   833824  rs28484835  T   C   .   PASS    AF=0.236744 ES:SE:LP:AF:ID  -0.000128314:0.00017347:0.337242:0.236744:rs28484835
1   833927  rs28593608  T   C   .   PASS    AF=0.212482 ES:SE:LP:AF:ID  -0.000143793:0.000180293:0.366532:0.212482:rs28593608
1   834198  rs28385272  T   C   .   PASS    AF=0.212377 ES:SE:LP:AF:ID  -0.000141907:0.000180325:0.366532:0.212377:rs28385272
1   834832  rs4411087   G   C   .   PASS    AF=0.23723  ES:SE:LP:AF:ID  -0.000145808:0.000173337:0.39794:0.23723:rs4411087
1   834928  rs4422949   A   G   .   PASS    AF=0.213024 ES:SE:LP:AF:ID  -0.000152292:0.000180068:0.39794:0.213024:rs4422949
1   834999  rs28570054  G   A   .   PASS    AF=0.212986 ES:SE:LP:AF:ID  -0.000146278:0.000180105:0.376751:0.212986:rs28570054
1   835499  rs4422948   A   G   .   PASS    AF=0.241215 ES:SE:LP:AF:ID  -0.000182274:0.000172125:0.537602:0.241215:rs4422948
1   836529  rs28731045  C   G   .   PASS    AF=0.213606 ES:SE:LP:AF:ID  -0.000143417:0.000179838:0.366532:0.213606:rs28731045
1   836896  rs28705752  T   C   .   PASS    AF=0.269554 ES:SE:LP:AF:ID  -0.00017331:0.000166096:0.522879:0.269554:rs28705752
1   836924  rs72890788  G   A   .   PASS    AF=0.213576 ES:SE:LP:AF:ID  -0.000146265:0.00017986:0.376751:0.213576:rs72890788
1   838387  rs4970384   T   C   .   PASS    AF=0.214648 ES:SE:LP:AF:ID  -0.000148464:0.000179513:0.387216:0.214648:rs4970384
1   838555  rs4970383   C   A   .   PASS    AF=0.246257 ES:SE:LP:AF:ID  -0.000175165:0.000170948:0.508638:0.246257:rs4970383
1   839103  rs28562941  A   G   .   PASS    AF=0.270067 ES:SE:LP:AF:ID  -0.000162283:0.000166214:0.481486:0.270067:rs28562941
1   840753  rs4970382   T   C   .   PASS    AF=0.400159 ES:SE:LP:AF:ID  -0.000256375:0.000150301:1.05552:0.400159:rs4970382
1   841085  rs1574243   C   G   .   PASS    AF=0.237176 ES:SE:LP:AF:ID  -0.000183867:0.000174554:0.537602:0.237176:rs1574243
1   842013  rs7419119   T   G   .   PASS    AF=0.215457 ES:SE:LP:AF:ID  -0.000145017:0.000179634:0.376751:0.215457:rs7419119
1   842362  rs28540380  C   T   .   PASS    AF=0.235364 ES:SE:LP:AF:ID  -0.00021376:0.000177179:0.638272:0.235364:rs28540380
1   843405  rs11516185  A   G   .   PASS    AF=0.362556 ES:SE:LP:AF:ID  2.00204e-05:0.000186618:0.0409586:0.362556:rs11516185
1   844300  rs61769713  C   G   .   PASS    AF=0.818888 ES:SE:LP:AF:ID  2.50471e-05:0.000191793:0.0457575:0.818888:rs61769713