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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    "gwas_harmonisation_command": "--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/vcf_09_19b/bgzip_vcf/data.batch_20004_1534.vcf.gz --id UKB-b:1917 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20004_1534.txt.gz --cohort_cases 2033 --cohort_controls 460900 --ref /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/human_g1k_v37.fasta --json /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/ukb_gwas.json; 1.1.1",
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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-1917/UKB-b-1917_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-1917/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:41:01 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-1917/UKB-b-1917_data.vcf.gz ...
Read summary statistics for 3225637 SNPs.
Dropped 456 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, 796816 SNPs remain.
After merging with regression SNP LD, 796816 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0019 (0.0011)
Lambda GC: 1.0209
Mean Chi^2: 1.0148
Intercept: 0.9948 (0.0098)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Thu Oct 17 14:41:44 2019
Total time elapsed: 42.74s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.8337,
    "inflation_factor": 1.0475,
    "mean_EFFECT": 3.07e-07,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 0,
    "n_p_sig": 0,
    "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": 25796,
    "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": 796816,
    "ldsc_nsnp_merge_regression_ld": 796816,
    "ldsc_observed_scale_h2_beta": 0.0019,
    "ldsc_observed_scale_h2_se": 0.0011,
    "ldsc_intercept_beta": 0.9948,
    "ldsc_intercept_se": 0.0098,
    "ldsc_lambda_gc": 1.0209,
    "ldsc_mean_chisq": 1.0148,
    "ldsc_ratio": -0.3514
}
 

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 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 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 3225184 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 3225637 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 8.661137e+00 5.772083e+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 7.853012e+07 5.669975e+07 828.0000000 3.159544e+07 6.891619e+07 1.147697e+08 2.492013e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 3.000000e-07 1.542000e-04 -0.0007500 -1.028000e-04 3.000000e-07 1.035000e-04 7.786000e-04 ▁▂▇▂▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.520000e-04 1.340000e-05 0.0001326 1.404000e-04 1.479000e-04 1.615000e-04 3.279000e-04 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.964139e-01 2.894913e-01 0.0000012 2.399999e-01 4.899999e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.964132e-01 2.894641e-01 0.0000012 2.449850e-01 4.949370e-01 7.467135e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 4.262434e-01 1.847295e-01 0.1721600 2.642990e-01 3.919230e-01 5.692450e-01 8.278400e-01 ▇▆▅▃▃
numeric AF_reference 25796 0.9920028 NA NA NA NA NA NA NA 4.113446e-01 1.977528e-01 0.0000000 2.513980e-01 3.843850e-01 5.563100e-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 0.0002745 0.0002440 0.2599998 0.2605873 0.623763 0.7821490 NA
1 54676 rs2462492 C T 0.0000836 0.0002418 0.7300002 0.7294342 0.400401 NA NA
1 91536 rs6702460 G T 0.0002456 0.0002380 0.2999998 0.3020966 0.456851 0.4207270 NA
1 534192 rs6680723 C T -0.0000095 0.0002719 0.9699999 0.9722392 0.240960 NA NA
1 706368 rs55727773 A G 0.0000627 0.0001688 0.7099994 0.7100985 0.515650 0.2751600 NA
1 763394 rs369924889 G A -0.0001282 0.0001979 0.5199996 0.5172211 0.706753 0.6176120 NA
1 768253 rs2977608 A C 0.0001826 0.0001615 0.2599998 0.2581414 0.761304 0.4894170 NA
1 776546 rs12124819 A G 0.0000639 0.0001804 0.7199992 0.7230875 0.265390 0.0756789 NA
1 798400 rs10900604 A G -0.0003132 0.0001723 0.0690001 0.0691612 0.206580 0.4105430 NA
1 798959 rs11240777 G A -0.0003100 0.0001724 0.0719996 0.0721662 0.206409 0.4099440 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51180501 rs5770999 T C 0.0001538 0.0001589 0.3300000 0.3330741 0.713658 0.6369810 NA
22 51181919 rs9616825 G C 0.0001116 0.0001580 0.4799997 0.4802759 0.695471 0.6194090 NA
22 51182485 rs6009961 A G 0.0001385 0.0001594 0.3800004 0.3846510 0.715505 0.6383790 NA
22 51186143 rs2879914 T C -0.0000070 0.0001478 0.9599999 0.9623008 0.381826 0.2733630 NA
22 51186228 rs3865766 C T 0.0000638 0.0001440 0.6600001 0.6577060 0.451063 0.4532750 NA
22 51197266 rs61290853 A G -0.0000246 0.0001487 0.8700001 0.8687734 0.386333 0.4229230 NA
22 51198027 rs34939255 A G 0.0000566 0.0001683 0.7400005 0.7366678 0.254557 0.0984425 NA
22 51211106 rs9628250 T C -0.0000125 0.0001669 0.9400001 0.9403638 0.271547 0.1671330 NA
22 51212875 rs2238837 A C 0.0001237 0.0001586 0.4400003 0.4352513 0.331455 0.3724040 NA
22 51237063 rs3896457 T C -0.0000985 0.0001623 0.5400003 0.5440605 0.297971 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623763 ES:SE:LP:AF:ID  0.000274537:0.000244032:0.585027:0.623763:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400401 ES:SE:LP:AF:ID  8.36192e-05:0.000241759:0.136677:0.400401:rs2462492
1   91536   rs6702460   G   T   .   PASS    AF=0.456851 ES:SE:LP:AF:ID  0.00024565:0.000238045:0.522879:0.456851:rs6702460
1   534192  rs6680723   C   T   .   PASS    AF=0.24096  ES:SE:LP:AF:ID  -9.46235e-06:0.000271906:0.0132283:0.24096:rs6680723
1   706368  rs12029736  A   G   .   PASS    AF=0.51565  ES:SE:LP:AF:ID  6.27455e-05:0.000168796:0.148742:0.51565:rs12029736
1   763394  rs3115847   G   A   .   PASS    AF=0.706753 ES:SE:LP:AF:ID  -0.000128165:0.000197897:0.283997:0.706753:rs3115847
1   768253  rs2977608   A   C   .   PASS    AF=0.761304 ES:SE:LP:AF:ID  0.000182646:0.00016152:0.585027:0.761304:rs2977608
1   776546  rs12124819  A   G   .   PASS    AF=0.26539  ES:SE:LP:AF:ID  6.39292e-05:0.00018042:0.142668:0.26539:rs12124819
1   798400  rs10900604  A   G   .   PASS    AF=0.20658  ES:SE:LP:AF:ID  -0.000313152:0.000172311:1.16115:0.20658:rs10900604
1   798959  rs11240777  G   A   .   PASS    AF=0.206409 ES:SE:LP:AF:ID  -0.00030996:0.000172385:1.14267:0.206409:rs11240777
1   808631  rs11240779  G   A   .   PASS    AF=0.772626 ES:SE:LP:AF:ID  0.000253972:0.000163984:0.920819:0.772626:rs11240779
1   808928  rs11240780  C   T   .   PASS    AF=0.772854 ES:SE:LP:AF:ID  0.000235282:0.000164261:0.823909:0.772854:rs11240780
1   814495  rs74461805  C   A   .   PASS    AF=0.340397 ES:SE:LP:AF:ID  6.35843e-05:0.000231453:0.107905:0.340397:rs74461805
1   830181  rs28444699  A   G   .   PASS    AF=0.697259 ES:SE:LP:AF:ID  -0.000177792:0.000154849:0.60206:0.697259:rs28444699
1   831489  rs4970385   C   T   .   PASS    AF=0.705403 ES:SE:LP:AF:ID  -6.70464e-05:0.000152047:0.180456:0.705403:rs4970385
1   831909  rs9697642   C   T   .   PASS    AF=0.705448 ES:SE:LP:AF:ID  -6.52924e-05:0.000152042:0.173925:0.705448:rs9697642
1   832066  rs9697380   G   C   .   PASS    AF=0.705634 ES:SE:LP:AF:ID  -6.61659e-05:0.000152049:0.180456:0.705634:rs9697380
1   832318  rs4500250   C   A   .   PASS    AF=0.705662 ES:SE:LP:AF:ID  -6.35121e-05:0.000152065:0.167491:0.705662:rs4500250
1   832398  rs4553118   T   C   .   PASS    AF=0.730134 ES:SE:LP:AF:ID  -0.000144428:0.000156209:0.443698:0.730134:rs4553118
1   832918  rs28765502  T   C   .   PASS    AF=0.294371 ES:SE:LP:AF:ID  6.38511e-05:0.000152058:0.173925:0.294371:rs28765502
1   833223  rs13303211  C   T   .   PASS    AF=0.236696 ES:SE:LP:AF:ID  5.01012e-05:0.00016189:0.119186:0.236696:rs13303211
1   833302  rs28752186  C   T   .   PASS    AF=0.236684 ES:SE:LP:AF:ID  5.04054e-05:0.000161892:0.119186:0.236684:rs28752186
1   833641  rs28594623  T   C   .   PASS    AF=0.239743 ES:SE:LP:AF:ID  5.89407e-05:0.000161373:0.148742:0.239743:rs28594623
1   833824  rs28484835  T   C   .   PASS    AF=0.236686 ES:SE:LP:AF:ID  5.06597e-05:0.00016189:0.124939:0.236686:rs28484835
1   833927  rs28593608  T   C   .   PASS    AF=0.212416 ES:SE:LP:AF:ID  0.000135311:0.000168263:0.376751:0.212416:rs28593608
1   834198  rs28385272  T   C   .   PASS    AF=0.212311 ES:SE:LP:AF:ID  0.000144694:0.000168292:0.408935:0.212311:rs28385272
1   834832  rs4411087   G   C   .   PASS    AF=0.237171 ES:SE:LP:AF:ID  5.39267e-05:0.000161766:0.130768:0.237171:rs4411087
1   834928  rs4422949   A   G   .   PASS    AF=0.212956 ES:SE:LP:AF:ID  0.000146263:0.000168053:0.420216:0.212956:rs4422949
1   834999  rs28570054  G   A   .   PASS    AF=0.212918 ES:SE:LP:AF:ID  0.00014297:0.000168088:0.39794:0.212918:rs28570054
1   835499  rs4422948   A   G   .   PASS    AF=0.241155 ES:SE:LP:AF:ID  3.93013e-06:0.000160637:0.00877392:0.241155:rs4422948
1   836529  rs28731045  C   G   .   PASS    AF=0.213538 ES:SE:LP:AF:ID  0.000129439:0.00016784:0.356547:0.213538:rs28731045
1   836896  rs28705752  T   C   .   PASS    AF=0.269503 ES:SE:LP:AF:ID  0.000119452:0.000155002:0.356547:0.269503:rs28705752
1   836924  rs72890788  G   A   .   PASS    AF=0.213508 ES:SE:LP:AF:ID  0.000130514:0.00016786:0.356547:0.213508:rs72890788
1   838387  rs4970384   T   C   .   PASS    AF=0.214583 ES:SE:LP:AF:ID  0.00014299:0.000167537:0.408935:0.214583:rs4970384
1   838555  rs4970383   C   A   .   PASS    AF=0.246197 ES:SE:LP:AF:ID  0.000203598:0.000159533:0.69897:0.246197:rs4970383
1   839103  rs28562941  A   G   .   PASS    AF=0.27001  ES:SE:LP:AF:ID  0.00015196:0.000155112:0.481486:0.27001:rs28562941
1   840753  rs4970382   T   C   .   PASS    AF=0.400106 ES:SE:LP:AF:ID  0.0002431:0.000140251:1.08092:0.400106:rs4970382
1   841085  rs1574243   C   G   .   PASS    AF=0.237094 ES:SE:LP:AF:ID  0.000257066:0.000162908:0.958607:0.237094:rs1574243
1   842013  rs7419119   T   G   .   PASS    AF=0.215384 ES:SE:LP:AF:ID  0.000144963:0.000167649:0.408935:0.215384:rs7419119
1   842362  rs28540380  C   T   .   PASS    AF=0.235321 ES:SE:LP:AF:ID  0.000155331:0.000165345:0.455932:0.235321:rs28540380
1   843405  rs11516185  A   G   .   PASS    AF=0.362599 ES:SE:LP:AF:ID  -0.000200494:0.000174106:0.60206:0.362599:rs11516185
1   844300  rs61769713  C   G   .   PASS    AF=0.818815 ES:SE:LP:AF:ID  0.000222369:0.000178932:0.677781:0.818815:rs61769713
1   845283  rs7366404   G   T   .   PASS    AF=0.814502 ES:SE:LP:AF:ID  0.000204272:0.000177357:0.60206:0.814502:rs7366404
1   845635  rs117086422 C   T   .   PASS    AF=0.20543  ES:SE:LP:AF:ID  0.000182055:0.0001705:0.537602:0.20543:rs117086422
1   845938  rs57760052  G   A   .   PASS    AF=0.210865 ES:SE:LP:AF:ID  0.000170988:0.000168859:0.508638:0.210865:rs57760052
1   846078  rs28612348  C   T   .   PASS    AF=0.196786 ES:SE:LP:AF:ID  0.000127642:0.000173113:0.337242:0.196786:rs28612348
1   846338  rs4970334   A   G   .   PASS    AF=0.813832 ES:SE:LP:AF:ID  0.000203735:0.000177206:0.60206:0.813832:rs4970334
1   846398  rs58781670  G   A   .   PASS    AF=0.204448 ES:SE:LP:AF:ID  0.000202827:0.000171083:0.619789:0.204448:rs58781670
1   846489  rs4970333   T   C   .   PASS    AF=0.813925 ES:SE:LP:AF:ID  0.000201513:0.000177328:0.585027:0.813925:rs4970333
1   846808  rs4475691   C   T   .   PASS    AF=0.198378 ES:SE:LP:AF:ID  0.000119927:0.000172621:0.309804:0.198378:rs4475691