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-19779/UKB-b-19779_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19779/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:41:36 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19779/UKB-b-19779_data.vcf.gz ...
Read summary statistics for 3384800 SNPs.
Dropped 499 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, 830717 SNPs remain.
After merging with regression SNP LD, 830717 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0012 (0.0016)
Lambda GC: 1.2024
Mean Chi^2: 1.1994
Intercept: 1.1863 (0.0111)
Ratio: 0.9342 (0.0557)
Analysis finished at Thu Oct 17 14:42:22 2019
Total time elapsed: 46.11s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.8443,
    "inflation_factor": 1.1999,
    "mean_EFFECT": -1.2777e-06,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 1,
    "n_p_sig": 2,
    "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": 27153,
    "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": 830717,
    "ldsc_nsnp_merge_regression_ld": 830717,
    "ldsc_observed_scale_h2_beta": 0.0012,
    "ldsc_observed_scale_h2_se": 0.0016,
    "ldsc_intercept_beta": 1.1863,
    "ldsc_intercept_se": 0.0111,
    "ldsc_lambda_gc": 1.2024,
    "ldsc_mean_chisq": 1.1994,
    "ldsc_ratio": 0.9343
}

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 1.000000 3 58 0 3384304 0 NA NA NA NA NA NA NA NA NA NA
character REF 0 1.000000 1 1 0 4 0 NA NA NA NA NA NA NA NA NA NA
character ALT 0 1.000000 1 1 0 4 0 NA NA NA NA NA NA NA NA NA NA
logical N 3384800 0.000000 NA NA NA NA NA NaN : NA NA NA NA NA NA NA NA
numeric CHROM 0 1.000000 NA NA NA NA NA NA NA 8.663714e+00 5.773207e+00 1.0000000 4.000000e+00 8.000000e+00 1.300000e+01 2.200000e+01 ▇▅▃▂▂
numeric POS 0 1.000000 NA NA NA NA NA NA NA 7.855062e+07 5.672600e+07 828.0000000 3.158950e+07 6.892547e+07 1.147619e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.000000 NA NA NA NA NA NA NA -1.300000e-06 1.741000e-04 -0.0010180 -1.178000e-04 -1.100000e-06 1.149000e-04 1.125000e-03 ▁▂▇▁▁
numeric SE 0 1.000000 NA NA NA NA NA NA NA 1.586000e-04 1.540000e-05 0.0001369 1.452000e-04 1.538000e-04 1.695000e-04 3.591000e-04 ▇▁▁▁▁
numeric PVAL 0 1.000000 NA NA NA NA NA NA NA 4.714003e-01 2.958776e-01 0.0000000 2.099999e-01 4.600002e-01 7.300002e-01 1.000000e+00 ▇▇▇▆▆
numeric PVAL_ztest 0 1.000000 NA NA NA NA NA NA NA 4.714041e-01 2.958525e-01 0.0000000 2.082813e-01 4.603140e-01 7.281447e-01 9.999996e-01 ▇▆▆▆▆
numeric AF 0 1.000000 NA NA NA NA NA NA NA 4.198348e-01 1.911031e-01 0.1601100 2.522430e-01 3.829740e-01 5.662923e-01 8.398900e-01 ▇▆▅▃▂
numeric AF_reference 27153 0.991978 NA NA NA NA NA NA NA 4.056675e-01 2.020642e-01 0.0000000 2.414140e-01 3.759980e-01 5.535140e-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 -0.0000848 0.0002518 0.7400005 0.7363716 0.623765 0.782149 NA
1 54676 rs2462492 C T 0.0007905 0.0002495 0.0015000 0.0015341 0.400401 NA NA
1 91536 rs6702460 G T 0.0002632 0.0002457 0.2800000 0.2839999 0.456846 0.420727 NA
1 534192 rs6680723 C T -0.0002706 0.0002806 0.3300000 0.3349169 0.240959 NA NA
1 706368 rs55727773 A G -0.0002492 0.0001742 0.1499999 0.1525168 0.515645 0.275160 NA
1 752566 rs3094315 G A -0.0004502 0.0001974 0.0230001 0.0225504 0.838945 0.718251 NA
1 752721 rs3131972 A G -0.0004494 0.0001972 0.0230001 0.0226491 0.838573 0.653355 NA
1 754503 rs3115859 G A -0.0004410 0.0001966 0.0250000 0.0249049 0.838026 0.663938 NA
1 754964 rs3131966 C T -0.0004473 0.0001972 0.0230001 0.0232882 0.838657 0.663339 NA
1 755775 rs3131965 A G -0.0004741 0.0001998 0.0179999 0.0176709 0.839770 NA NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51182485 rs6009961 A G -0.0003250 0.0001646 0.0479999 0.0482953 0.715502 0.6383790 NA
22 51186143 rs2879914 T C 0.0000014 0.0001526 0.9900000 0.9925053 0.381825 0.2733630 NA
22 51186228 rs3865766 C T -0.0000021 0.0001487 0.9900000 0.9887291 0.451061 0.4532750 NA
22 51192586 rs5771006 G A 0.0000869 0.0002004 0.6600001 0.6645892 0.167627 0.0848642 NA
22 51193227 rs34608236 T G -0.0002082 0.0002049 0.3100002 0.3095455 0.168490 0.0692891 NA
22 51197266 rs61290853 A G 0.0000775 0.0001536 0.6100002 0.6139448 0.386333 0.4229230 NA
22 51198027 rs34939255 A G -0.0002699 0.0001738 0.1199999 0.1204760 0.254562 0.0984425 NA
22 51211106 rs9628250 T C -0.0002080 0.0001724 0.2300001 0.2274204 0.271547 0.1671330 NA
22 51212875 rs2238837 A C -0.0000302 0.0001638 0.8499999 0.8538126 0.331457 0.3724040 NA
22 51237063 rs3896457 T C 0.0000199 0.0001677 0.9100000 0.9056580 0.297974 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623765 ES:SE:LP:AF:ID  -8.47877e-05:0.000251848:0.130768:0.623765:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400401 ES:SE:LP:AF:ID  0.000790465:0.000249504:2.82391:0.400401:rs2462492
1   91536   rs6702460   G   T   .   PASS    AF=0.456846 ES:SE:LP:AF:ID  0.000263202:0.000245667:0.552842:0.456846:rs6702460
1   534192  rs6680723   C   T   .   PASS    AF=0.240959 ES:SE:LP:AF:ID  -0.000270587:0.000280617:0.481486:0.240959:rs6680723
1   706368  rs12029736  A   G   .   PASS    AF=0.515645 ES:SE:LP:AF:ID  -0.000249229:0.000174201:0.823909:0.515645:rs12029736
1   752566  rs3094315   G   A   .   PASS    AF=0.838945 ES:SE:LP:AF:ID  -0.00045019:0.000197368:1.63827:0.838945:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838573 ES:SE:LP:AF:ID  -0.000449376:0.000197155:1.63827:0.838573:rs3131972
1   754503  rs3115859   G   A   .   PASS    AF=0.838026 ES:SE:LP:AF:ID  -0.000440967:0.000196608:1.60206:0.838026:rs3115859
1   754964  rs3131966   C   T   .   PASS    AF=0.838657 ES:SE:LP:AF:ID  -0.000447294:0.000197161:1.63827:0.838657:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.83977  ES:SE:LP:AF:ID  -0.000474078:0.000199827:1.74473:0.83977:rs3131965
1   760912  rs1048488   C   T   .   PASS    AF=0.838306 ES:SE:LP:AF:ID  -0.000442722:0.000196235:1.61979:0.838306:rs1048488
1   761147  rs3115850   T   C   .   PASS    AF=0.838427 ES:SE:LP:AF:ID  -0.000447942:0.000196374:1.63827:0.838427:rs3115850
1   763394  rs3115847   G   A   .   PASS    AF=0.706753 ES:SE:LP:AF:ID  -0.000372829:0.000204234:1.16749:0.706753:rs3115847
1   768253  rs2977608   A   C   .   PASS    AF=0.761297 ES:SE:LP:AF:ID  -8.11012e-05:0.000166691:0.200659:0.761297:rs2977608
1   776546  rs12124819  A   G   .   PASS    AF=0.265385 ES:SE:LP:AF:ID  6.61838e-05:0.0001862:0.142668:0.265385:rs12124819
1   798400  rs10900604  A   G   .   PASS    AF=0.206591 ES:SE:LP:AF:ID  6.70442e-05:0.000177825:0.148742:0.206591:rs10900604
1   798959  rs11240777  G   A   .   PASS    AF=0.20642  ES:SE:LP:AF:ID  6.54945e-05:0.000177901:0.148742:0.20642:rs11240777
1   808631  rs11240779  G   A   .   PASS    AF=0.772619 ES:SE:LP:AF:ID  5.9561e-06:0.000169234:0.0132283:0.772619:rs11240779
1   808928  rs11240780  C   T   .   PASS    AF=0.772847 ES:SE:LP:AF:ID  3.31572e-06:0.000169519:0.00877392:0.772847:rs11240780
1   814495  rs74461805  C   A   .   PASS    AF=0.340396 ES:SE:LP:AF:ID  -4.73253e-06:0.00023886:0.00877392:0.340396:rs74461805
1   830181  rs28444699  A   G   .   PASS    AF=0.697255 ES:SE:LP:AF:ID  -0.000261947:0.00015981:1:0.697255:rs28444699
1   831489  rs4970385   C   T   .   PASS    AF=0.705397 ES:SE:LP:AF:ID  -0.000237674:0.000156918:0.886057:0.705397:rs4970385
1   831909  rs9697642   C   T   .   PASS    AF=0.705442 ES:SE:LP:AF:ID  -0.000239867:0.000156912:0.886057:0.705442:rs9697642
1   832066  rs9697380   G   C   .   PASS    AF=0.705627 ES:SE:LP:AF:ID  -0.000242499:0.00015692:0.920819:0.705627:rs9697380
1   832318  rs4500250   C   A   .   PASS    AF=0.705655 ES:SE:LP:AF:ID  -0.000241325:0.000156936:0.920819:0.705655:rs4500250
1   832398  rs4553118   T   C   .   PASS    AF=0.730129 ES:SE:LP:AF:ID  -0.000278766:0.000161212:1.07572:0.730129:rs4553118
1   832918  rs28765502  T   C   .   PASS    AF=0.294377 ES:SE:LP:AF:ID  0.000240953:0.000156929:0.920819:0.294377:rs28765502
1   833223  rs13303211  C   T   .   PASS    AF=0.236703 ES:SE:LP:AF:ID  0.000222323:0.000167075:0.744727:0.236703:rs13303211
1   833302  rs28752186  C   T   .   PASS    AF=0.236691 ES:SE:LP:AF:ID  0.000223449:0.000167077:0.744727:0.236691:rs28752186
1   833641  rs28594623  T   C   .   PASS    AF=0.23975  ES:SE:LP:AF:ID  0.000261591:0.000166541:0.920819:0.23975:rs28594623
1   833824  rs28484835  T   C   .   PASS    AF=0.236693 ES:SE:LP:AF:ID  0.000223198:0.000167075:0.744727:0.236693:rs28484835
1   833927  rs28593608  T   C   .   PASS    AF=0.212421 ES:SE:LP:AF:ID  0.000259953:0.000173652:0.886057:0.212421:rs28593608
1   834198  rs28385272  T   C   .   PASS    AF=0.212316 ES:SE:LP:AF:ID  0.000261147:0.000173683:0.886057:0.212316:rs28385272
1   834832  rs4411087   G   C   .   PASS    AF=0.237178 ES:SE:LP:AF:ID  0.000240463:0.000166947:0.823909:0.237178:rs4411087
1   834928  rs4422949   A   G   .   PASS    AF=0.212962 ES:SE:LP:AF:ID  0.000282869:0.000173435:1:0.212962:rs4422949
1   834999  rs28570054  G   A   .   PASS    AF=0.212924 ES:SE:LP:AF:ID  0.000285848:0.000173471:1.00436:0.212924:rs28570054
1   835499  rs4422948   A   G   .   PASS    AF=0.241162 ES:SE:LP:AF:ID  0.000239865:0.000165782:0.823909:0.241162:rs4422948
1   836529  rs28731045  C   G   .   PASS    AF=0.213543 ES:SE:LP:AF:ID  0.000299665:0.000173216:1.07572:0.213543:rs28731045
1   836896  rs28705752  T   C   .   PASS    AF=0.269511 ES:SE:LP:AF:ID  0.000269141:0.000159967:1.03621:0.269511:rs28705752
1   836924  rs72890788  G   A   .   PASS    AF=0.213513 ES:SE:LP:AF:ID  0.000299932:0.000173237:1.08092:0.213513:rs72890788
1   838387  rs4970384   T   C   .   PASS    AF=0.214587 ES:SE:LP:AF:ID  0.000290297:0.000172903:1.03152:0.214587:rs4970384
1   838555  rs4970383   C   A   .   PASS    AF=0.246204 ES:SE:LP:AF:ID  0.000295769:0.000164642:1.14267:0.246204:rs4970383
1   839103  rs28562941  A   G   .   PASS    AF=0.270019 ES:SE:LP:AF:ID  0.000252379:0.00016008:0.958607:0.270019:rs28562941
1   840753  rs4970382   T   C   .   PASS    AF=0.400124 ES:SE:LP:AF:ID  0.000275665:0.000144743:1.24413:0.400124:rs4970382
1   841085  rs1574243   C   G   .   PASS    AF=0.237102 ES:SE:LP:AF:ID  0.000358276:0.000168124:1.48149:0.237102:rs1574243
1   842013  rs7419119   T   G   .   PASS    AF=0.215391 ES:SE:LP:AF:ID  0.000321506:0.000173019:1.20066:0.215391:rs7419119
1   842362  rs28540380  C   T   .   PASS    AF=0.235323 ES:SE:LP:AF:ID  0.00025967:0.000170643:0.886057:0.235323:rs28540380
1   843405  rs11516185  A   G   .   PASS    AF=0.362606 ES:SE:LP:AF:ID  -0.000288914:0.000179678:0.958607:0.362606:rs11516185
1   844300  rs61769713  C   G   .   PASS    AF=0.818811 ES:SE:LP:AF:ID  -0.000221387:0.000184665:0.638272:0.818811:rs61769713
1   845283  rs7366404   G   T   .   PASS    AF=0.814497 ES:SE:LP:AF:ID  -0.000193965:0.000183037:0.537602:0.814497:rs7366404