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_41249_2003.vcf.gz --id UKB-b:19447 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41249_2003.txt.gz --cohort_cases 6228 --cohort_controls 456185 --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-19447/UKB-b-19447_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19447/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:41:22 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19447/UKB-b-19447_data.vcf.gz ...
Read summary statistics for 5250127 SNPs.
Dropped 1685 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, 1146366 SNPs remain.
After merging with regression SNP LD, 1146366 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0029 (0.0011)
Lambda GC: 1.0988
Mean Chi^2: 1.0916
Intercept: 1.0642 (0.0076)
Ratio: 0.7006 (0.0829)
Analysis finished at Thu Oct 17 14:42:25 2019
Total time elapsed: 1.0m:2.92s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9118,
    "inflation_factor": 1.0966,
    "mean_EFFECT": 5.3036e-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": 45398,
    "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": 1146366,
    "ldsc_nsnp_merge_regression_ld": 1146366,
    "ldsc_observed_scale_h2_beta": 0.0029,
    "ldsc_observed_scale_h2_se": 0.0011,
    "ldsc_intercept_beta": 1.0642,
    "ldsc_intercept_se": 0.0076,
    "ldsc_lambda_gc": 1.0988,
    "ldsc_mean_chisq": 1.0916,
    "ldsc_ratio": 0.7009
}

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 5248455 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 5250127 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 8.672966e+00 5.763784e+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 7.852790e+07 5.658911e+07 828.0000000 3.190642e+07 6.891117e+07 1.144813e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.000000 NA NA NA NA NA NA NA 5.000000e-07 3.423000e-04 -0.0024701 -2.165000e-04 -1.300000e-06 2.153000e-04 2.574900e-03 ▁▁▇▁▁
numeric SE 0 1.000000 NA NA NA NA NA NA NA 3.188000e-04 7.940000e-05 0.0002313 2.520000e-04 2.897000e-04 3.682000e-04 1.062700e-03 ▇▂▁▁▁
numeric PVAL 0 1.000000 NA NA NA NA NA NA NA 4.860432e-01 2.920448e-01 0.0000003 2.300001e-01 4.799997e-01 7.400005e-01 1.000000e+00 ▇▇▇▇▆
numeric PVAL_ztest 0 1.000000 NA NA NA NA NA NA NA 4.860448e-01 2.920189e-01 0.0000003 2.299380e-01 4.809369e-01 7.389984e-01 9.999991e-01 ▇▇▇▇▇
numeric AF 0 1.000000 NA NA NA NA NA NA NA 3.413120e-01 2.429369e-01 0.0561980 1.326290e-01 2.716030e-01 5.081260e-01 9.438020e-01 ▇▃▂▂▂
numeric AF_reference 45398 0.991353 NA NA NA NA NA NA NA 3.355921e-01 2.385136e-01 0.0000000 1.389780e-01 2.751600e-01 4.976040e-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 -0.0004739 0.0004256 0.2700001 0.2655158 0.623760 0.7821490 NA
1 54676 rs2462492 C T -0.0004812 0.0004217 0.2500000 0.2538534 0.400408 NA NA
1 86028 rs114608975 T C 0.0019916 0.0006742 0.0031000 0.0031377 0.103553 0.0277556 NA
1 91536 rs6702460 G T -0.0004897 0.0004152 0.2399999 0.2382825 0.456864 0.4207270 NA
1 234313 rs8179466 C T -0.0006844 0.0008187 0.4000000 0.4031799 0.074509 NA NA
1 534192 rs6680723 C T 0.0006715 0.0004743 0.1600000 0.1568211 0.240955 NA NA
1 546697 rs12025928 A G 0.0001643 0.0005917 0.7800007 0.7813046 0.913475 NA NA
1 693731 rs12238997 A G 0.0005400 0.0003974 0.1700000 0.1742448 0.116333 0.1417730 NA
1 705882 rs72631875 G A -0.0009075 0.0005824 0.1199999 0.1191658 0.067295 0.0315495 NA
1 706368 rs55727773 A G 0.0000116 0.0002944 0.9699999 0.9684490 0.515657 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51213613 rs34726907 C T 0.0005321 0.0003644 0.1400000 0.1442928 0.127822 0.1727240 NA
22 51216564 rs9616970 T C 0.0005538 0.0003629 0.1299999 0.1269495 0.128337 0.1563500 NA
22 51217954 rs9616974 G A 0.0006606 0.0004605 0.1499999 0.1513490 0.073317 0.0621006 NA
22 51218224 rs9616975 C A 0.0006565 0.0004607 0.1499999 0.1541040 0.073339 0.0619010 NA
22 51218377 rs2519461 G C 0.0006636 0.0004601 0.1499999 0.1492456 0.073626 0.0826677 NA
22 51219006 rs28729663 G A 0.0005105 0.0003552 0.1499999 0.1506028 0.137956 0.2052720 NA
22 51219387 rs9616832 T C 0.0006850 0.0004610 0.1400000 0.1373392 0.073750 0.0654952 NA
22 51221731 rs115055839 T C 0.0006437 0.0004613 0.1600000 0.1629189 0.073240 0.0625000 NA
22 51229805 rs9616985 T C 0.0006536 0.0004630 0.1600000 0.1580587 0.073075 0.0730831 NA
22 51237063 rs3896457 T C 0.0003637 0.0002832 0.2000000 0.1990424 0.297982 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.62376  ES:SE:LP:AF:ID  -0.000473926:0.000425638:0.568636:0.62376:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400408 ES:SE:LP:AF:ID  -0.000481165:0.00042169:0.60206:0.400408:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103553 ES:SE:LP:AF:ID  0.00199162:0.00067423:2.50864:0.103553:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456864 ES:SE:LP:AF:ID  -0.000489652:0.000415209:0.619789:0.456864:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074509 ES:SE:LP:AF:ID  -0.00068436:0.000818656:0.39794:0.074509:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240955 ES:SE:LP:AF:ID  0.000671491:0.000474269:0.79588:0.240955:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913475 ES:SE:LP:AF:ID  0.000164259:0.00059167:0.107905:0.913475:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116333 ES:SE:LP:AF:ID  0.000540006:0.000397446:0.769551:0.116333:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067295 ES:SE:LP:AF:ID  -0.000907545:0.0005824:0.920819:0.067295:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515657 ES:SE:LP:AF:ID  1.16449e-05:0.000294408:0.0132283:0.515657:rs12029736
1   722670  rs116030099 T   C   .   PASS    AF=0.10121  ES:SE:LP:AF:ID  -5.86296e-05:0.000485746:0.0457575:0.10121:rs116030099
1   729679  rs4951859   C   G   .   PASS    AF=0.843211 ES:SE:LP:AF:ID  -0.000218292:0.000344442:0.275724:0.843211:rs4951859
1   731718  rs58276399  T   C   .   PASS    AF=0.122316 ES:SE:LP:AF:ID  0.000518574:0.000377016:0.769551:0.122316:rs58276399
1   734349  rs141242758 T   C   .   PASS    AF=0.121559 ES:SE:LP:AF:ID  0.000545329:0.000377173:0.823909:0.121559:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132332 ES:SE:LP:AF:ID  0.000396052:0.000371739:0.537602:0.132332:rs79010578
1   752566  rs3094315   G   A   .   PASS    AF=0.838951 ES:SE:LP:AF:ID  -0.000262145:0.000333567:0.366532:0.838951:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838579 ES:SE:LP:AF:ID  -0.000235475:0.000333208:0.318759:0.838579:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869772 ES:SE:LP:AF:ID  -0.000486079:0.000357541:0.769551:0.869772:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129881 ES:SE:LP:AF:ID  0.000465228:0.000358272:0.721246:0.129881:rs2073813
1   754182  rs3131969   A   G   .   PASS    AF=0.869113 ES:SE:LP:AF:ID  -0.000448872:0.00035684:0.677781:0.869113:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869211 ES:SE:LP:AF:ID  -0.000451443:0.000356982:0.677781:0.869211:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.869117 ES:SE:LP:AF:ID  -0.000449239:0.000356833:0.677781:0.869117:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.838032 ES:SE:LP:AF:ID  -0.000229365:0.000332284:0.309804:0.838032:rs3115859
1   754964  rs3131966   C   T   .   PASS    AF=0.838663 ES:SE:LP:AF:ID  -0.000228523:0.000333218:0.309804:0.838663:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.839774 ES:SE:LP:AF:ID  -0.000337947:0.000337723:0.49485:0.839774:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.869397 ES:SE:LP:AF:ID  -0.000504771:0.000356421:0.79588:0.869397:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.868943 ES:SE:LP:AF:ID  -0.000487811:0.000355524:0.769551:0.868943:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867896 ES:SE:LP:AF:ID  -0.000458041:0.000354842:0.69897:0.867896:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869086 ES:SE:LP:AF:ID  -0.000500612:0.000355815:0.79588:0.869086:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869095 ES:SE:LP:AF:ID  -0.000500449:0.000355842:0.79588:0.869095:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869103 ES:SE:LP:AF:ID  -0.000500528:0.000355851:0.79588:0.869103:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869581 ES:SE:LP:AF:ID  -0.000497555:0.000356828:0.79588:0.869581:rs3131954
1   760912  rs1048488   C   T   .   PASS    AF=0.838311 ES:SE:LP:AF:ID  -0.000230202:0.000331654:0.309804:0.838311:rs1048488
1   761147  rs3115850   T   C   .   PASS    AF=0.838432 ES:SE:LP:AF:ID  -0.000233572:0.000331888:0.318759:0.838432:rs3115850
1   761732  rs2286139   C   T   .   PASS    AF=0.862252 ES:SE:LP:AF:ID  -0.000380078:0.000354562:0.552842:0.862252:rs2286139
1   763394  rs3115847   G   A   .   PASS    AF=0.706731 ES:SE:LP:AF:ID  -9.54336e-05:0.000345173:0.107905:0.706731:rs3115847
1   766007  rs61768174  A   C   .   PASS    AF=0.10515  ES:SE:LP:AF:ID  0.000704718:0.000397615:1.11919:0.10515:rs61768174
1   768253  rs2977608   A   C   .   PASS    AF=0.761321 ES:SE:LP:AF:ID  -0.000227717:0.000281726:0.376751:0.761321:rs2977608
1   768448  rs12562034  G   A   .   PASS    AF=0.106462 ES:SE:LP:AF:ID  -0.000112963:0.00038834:0.113509:0.106462:rs12562034
1   769223  rs60320384  C   G   .   PASS    AF=0.129586 ES:SE:LP:AF:ID  0.000526273:0.000358058:0.853872:0.129586:rs60320384
1   771823  rs2977605   T   C   .   PASS    AF=0.868902 ES:SE:LP:AF:ID  -0.000491926:0.000356146:0.769551:0.868902:rs2977605
1   771967  rs59066358  G   A   .   PASS    AF=0.129686 ES:SE:LP:AF:ID  0.000545685:0.000357828:0.886057:0.129686:rs59066358
1   772755  rs2905039   A   C   .   PASS    AF=0.868911 ES:SE:LP:AF:ID  -0.000495291:0.000356152:0.79588:0.868911:rs2905039
1   776546  rs12124819  A   G   .   PASS    AF=0.265392 ES:SE:LP:AF:ID  1.22308e-05:0.000314682:0.0132283:0.265392:rs12124819
1   777122  rs2980319   A   T   .   PASS    AF=0.870036 ES:SE:LP:AF:ID  -0.00052139:0.00035688:0.853872:0.870036:rs2980319
1   777232  rs112618790 C   T   .   PASS    AF=0.095129 ES:SE:LP:AF:ID  -5.93358e-05:0.00041365:0.05061:0.095129:rs112618790
1   778745  rs1055606   A   G   .   PASS    AF=0.128584 ES:SE:LP:AF:ID  0.000584035:0.000358289:1:0.128584:rs1055606
1   779322  rs4040617   A   G   .   PASS    AF=0.128881 ES:SE:LP:AF:ID  0.000585503:0.000357681:1:0.128881:rs4040617
1   780785  rs2977612   T   A   .   PASS    AF=0.868776 ES:SE:LP:AF:ID  -0.000470424:0.000355928:0.721246:0.868776:rs2977612
1   781845  rs61768199  A   G   .   PASS    AF=0.101877 ES:SE:LP:AF:ID  0.000795349:0.000403288:1.3098:0.101877:rs61768199