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-8798/UKB-b-8798_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-8798/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:42:05 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-8798/UKB-b-8798_data.vcf.gz ...
Read summary statistics for 5798746 SNPs.
Dropped 2476 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, 1199760 SNPs remain.
After merging with regression SNP LD, 1199760 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0066 (0.0011)
Lambda GC: 1.078
Mean Chi^2: 1.0849
Intercept: 1.0226 (0.0068)
Ratio: 0.2656 (0.0805)
Analysis finished at Thu Oct 17 14:43:16 2019
Total time elapsed: 1.0m:10.72s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9216,
    "inflation_factor": 1.0966,
    "mean_EFFECT": 1.7452e-06,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 1,
    "n_p_sig": 7,
    "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": 51860,
    "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": 1199760,
    "ldsc_nsnp_merge_regression_ld": 1199760,
    "ldsc_observed_scale_h2_beta": 0.0066,
    "ldsc_observed_scale_h2_se": 0.0011,
    "ldsc_intercept_beta": 1.0226,
    "ldsc_intercept_se": 0.0068,
    "ldsc_lambda_gc": 1.078,
    "ldsc_mean_chisq": 1.0849,
    "ldsc_ratio": 0.2662
}

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 5796286 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 5798746 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 8.670084e+00 5.762501e+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 7.859717e+07 5.654195e+07 828.0000000 3.199558e+07 6.904246e+07 1.145166e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 1.700000e-06 4.477000e-04 -0.0031617 -2.695000e-04 5.000000e-07 2.703000e-04 3.511600e-03 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 4.103000e-04 1.276000e-04 0.0002768 3.047000e-04 3.614000e-04 4.860000e-04 1.327200e-03 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.868477e-01 2.922250e-01 0.0000000 2.300001e-01 4.799997e-01 7.400005e-01 1.000000e+00 ▇▇▇▆▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.868443e-01 2.922003e-01 0.0000000 2.295202e-01 4.831583e-01 7.397882e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 3.194624e-01 2.510700e-01 0.0388290 1.059200e-01 2.406680e-01 4.856340e-01 9.611710e-01 ▇▃▂▂▁
numeric AF_reference 51860 0.9910567 NA NA NA NA NA NA NA 3.154487e-01 2.447048e-01 0.0000000 1.142170e-01 2.472040e-01 4.756390e-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 0.0001318 0.0005094 0.8000000 0.7957940 0.623765 0.7821490 NA
1 54676 rs2462492 C T 0.0000738 0.0005047 0.8800001 0.8836915 0.400401 NA NA
1 86028 rs114608975 T C -0.0006549 0.0008068 0.4199997 0.4169485 0.103556 0.0277556 NA
1 91536 rs6702460 G T 0.0001297 0.0004969 0.7899998 0.7940988 0.456846 0.4207270 NA
1 234313 rs8179466 C T 0.0021375 0.0009798 0.0290001 0.0291362 0.074506 NA NA
1 534192 rs6680723 C T -0.0001955 0.0005676 0.7300002 0.7305105 0.240959 NA NA
1 546697 rs12025928 A G -0.0005585 0.0007081 0.4299995 0.4302970 0.913475 NA NA
1 693731 rs12238997 A G -0.0003451 0.0004756 0.4700002 0.4680646 0.116329 0.1417730 NA
1 705882 rs72631875 G A -0.0006760 0.0006970 0.3300000 0.3321128 0.067288 0.0315495 NA
1 706368 rs55727773 A G -0.0000314 0.0003523 0.9299999 0.9290782 0.515645 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51218377 rs2519461 G C 0.0004857 0.0005506 0.3800004 0.3776860 0.073622 0.0826677 NA
22 51219006 rs28729663 G A 0.0003365 0.0004250 0.4299995 0.4285152 0.137950 0.2052720 NA
22 51219387 rs9616832 T C 0.0005558 0.0005517 0.3100002 0.3137450 0.073744 0.0654952 NA
22 51219704 rs147475742 G A 0.0006362 0.0007393 0.3900004 0.3895009 0.041954 0.0473243 NA
22 51221190 rs369304721 G A 0.0011747 0.0007381 0.1100001 0.1114999 0.049731 NA NA
22 51221731 rs115055839 T C 0.0004848 0.0005521 0.3800004 0.3798766 0.073235 0.0625000 NA
22 51222100 rs114553188 G T 0.0000930 0.0006500 0.8900000 0.8861785 0.054460 0.0880591 NA
22 51223637 rs375798137 G A 0.0000291 0.0006531 0.9599999 0.9645125 0.054089 0.0788738 NA
22 51229805 rs9616985 T C 0.0004813 0.0005541 0.3900004 0.3850417 0.073071 0.0730831 NA
22 51237063 rs3896457 T C 0.0007893 0.0003389 0.0200000 0.0198553 0.297974 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623765 ES:SE:LP:AF:ID  0.000131828:0.000509393:0.09691:0.623765:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400401 ES:SE:LP:AF:ID  7.3826e-05:0.000504651:0.0555173:0.400401:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103556 ES:SE:LP:AF:ID  -0.000654927:0.000806832:0.376751:0.103556:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456846 ES:SE:LP:AF:ID  0.000129684:0.000496889:0.102373:0.456846:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074506 ES:SE:LP:AF:ID  0.00213746:0.00097975:1.5376:0.074506:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240959 ES:SE:LP:AF:ID  -0.000195501:0.00056758:0.136677:0.240959:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913475 ES:SE:LP:AF:ID  -0.000558455:0.000708085:0.366532:0.913475:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116329 ES:SE:LP:AF:ID  -0.000345146:0.000475649:0.327902:0.116329:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067288 ES:SE:LP:AF:ID  -0.000676006:0.000697009:0.481486:0.067288:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515645 ES:SE:LP:AF:ID  -3.13601e-05:0.000352342:0.0315171:0.515645:rs12029736
1   722670  rs116030099 T   C   .   PASS    AF=0.1012   ES:SE:LP:AF:ID  0.00032444:0.000581343:0.236572:0.1012:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959091 ES:SE:LP:AF:ID  -0.00067967:0.000769492:0.420216:0.959091:rs2977670
1   725060  rs865924913 A   T   .   PASS    AF=0.053255 ES:SE:LP:AF:ID  0.00086586:0.00111129:0.356547:0.053255:rs865924913
1   729679  rs4951859   C   G   .   PASS    AF=0.843204 ES:SE:LP:AF:ID  0.000264485:0.000412211:0.283997:0.843204:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055912 ES:SE:LP:AF:ID  -0.000409861:0.000667437:0.267606:0.055912:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122312 ES:SE:LP:AF:ID  -0.000428678:0.0004512:0.468521:0.122312:rs58276399
1   734349  rs141242758 T   C   .   PASS    AF=0.121554 ES:SE:LP:AF:ID  -0.000377683:0.00045139:0.39794:0.121554:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132335 ES:SE:LP:AF:ID  -0.000195858:0.00044489:0.180456:0.132335:rs79010578
1   752566  rs3094315   G   A   .   PASS    AF=0.838945 ES:SE:LP:AF:ID  0.000196931:0.000399199:0.207608:0.838945:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838573 ES:SE:LP:AF:ID  0.000203634:0.00039877:0.21467:0.838573:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869776 ES:SE:LP:AF:ID  0.000406426:0.000427894:0.468521:0.869776:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129876 ES:SE:LP:AF:ID  -0.00034477:0.00042877:0.376751:0.129876:rs2073813
1   754182  rs3131969   A   G   .   PASS    AF=0.869117 ES:SE:LP:AF:ID  0.00038427:0.000427055:0.431798:0.869117:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869215 ES:SE:LP:AF:ID  0.000379459:0.000427225:0.431798:0.869215:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.869121 ES:SE:LP:AF:ID  0.000384324:0.000427047:0.431798:0.869121:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.838026 ES:SE:LP:AF:ID  0.000162718:0.000397663:0.167491:0.838026:rs3115859
1   754964  rs3131966   C   T   .   PASS    AF=0.838657 ES:SE:LP:AF:ID  0.000137458:0.00039878:0.136677:0.838657:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.83977  ES:SE:LP:AF:ID  0.000167423:0.000404173:0.167491:0.83977:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.8694   ES:SE:LP:AF:ID  0.000353554:0.000426553:0.387216:0.8694:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.868947 ES:SE:LP:AF:ID  0.000332444:0.00042548:0.366532:0.868947:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867898 ES:SE:LP:AF:ID  0.000301915:0.000424664:0.318759:0.867898:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.86909  ES:SE:LP:AF:ID  0.000314799:0.000425828:0.337242:0.86909:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869098 ES:SE:LP:AF:ID  0.000314287:0.000425861:0.337242:0.869098:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869106 ES:SE:LP:AF:ID  0.000313162:0.000425871:0.337242:0.869106:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869584 ES:SE:LP:AF:ID  0.00037255:0.00042704:0.420216:0.869584:rs3131954
1   760912  rs1048488   C   T   .   PASS    AF=0.838306 ES:SE:LP:AF:ID  0.000125818:0.000396908:0.124939:0.838306:rs1048488
1   761147  rs3115850   T   C   .   PASS    AF=0.838427 ES:SE:LP:AF:ID  0.000140828:0.000397189:0.142668:0.838427:rs3115850
1   761732  rs2286139   C   T   .   PASS    AF=0.862254 ES:SE:LP:AF:ID  0.000344671:0.00042433:0.376751:0.862254:rs2286139
1   763394  rs3115847   G   A   .   PASS    AF=0.706753 ES:SE:LP:AF:ID  0.000295474:0.000413088:0.327902:0.706753:rs3115847
1   766007  rs61768174  A   C   .   PASS    AF=0.105145 ES:SE:LP:AF:ID  -0.000299026:0.000475869:0.275724:0.105145:rs61768174
1   768253  rs2977608   A   C   .   PASS    AF=0.761297 ES:SE:LP:AF:ID  -9.14545e-05:0.000337152:0.102373:0.761297:rs2977608
1   768448  rs12562034  G   A   .   PASS    AF=0.10649  ES:SE:LP:AF:ID  0.000539867:0.000464699:0.60206:0.10649:rs12562034
1   769223  rs60320384  C   G   .   PASS    AF=0.129581 ES:SE:LP:AF:ID  -0.000232149:0.000428514:0.229148:0.129581:rs60320384
1   771823  rs2977605   T   C   .   PASS    AF=0.868906 ES:SE:LP:AF:ID  0.000296882:0.000426225:0.309804:0.868906:rs2977605
1   771967  rs59066358  G   A   .   PASS    AF=0.129682 ES:SE:LP:AF:ID  -0.000279405:0.000428237:0.29243:0.129682:rs59066358
1   772755  rs2905039   A   C   .   PASS    AF=0.868916 ES:SE:LP:AF:ID  0.00030349:0.000426233:0.318759:0.868916:rs2905039
1   776546  rs12124819  A   G   .   PASS    AF=0.265385 ES:SE:LP:AF:ID  -0.000113577:0.000376611:0.119186:0.265385:rs12124819
1   777122  rs2980319   A   T   .   PASS    AF=0.870039 ES:SE:LP:AF:ID  0.000211295:0.000427102:0.207608:0.870039:rs2980319
1   777232  rs112618790 C   T   .   PASS    AF=0.095141 ES:SE:LP:AF:ID  0.000445697:0.000495004:0.431798:0.095141:rs112618790
1   778745  rs1055606   A   G   .   PASS    AF=0.12858  ES:SE:LP:AF:ID  -0.000197252:0.000428789:0.187087:0.12858:rs1055606