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-15025/UKB-b-15025_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-15025/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:43:52 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-15025/UKB-b-15025_data.vcf.gz ...
Read summary statistics for 4484834 SNPs.
Dropped 1025 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, 1038186 SNPs remain.
After merging with regression SNP LD, 1038186 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0016 (0.0011)
Lambda GC: 1.0238
Mean Chi^2: 1.0303
Intercept: 1.0149 (0.0079)
Ratio: 0.4923 (0.2594)
Analysis finished at Thu Oct 17 14:44:45 2019
Total time elapsed: 53.65s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.8925,
    "inflation_factor": 1,
    "mean_EFFECT": -1.9618e-06,
    "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": 37323,
    "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": 1038186,
    "ldsc_nsnp_merge_regression_ld": 1038186,
    "ldsc_observed_scale_h2_beta": 0.0016,
    "ldsc_observed_scale_h2_se": 0.0011,
    "ldsc_intercept_beta": 1.0149,
    "ldsc_intercept_se": 0.0079,
    "ldsc_lambda_gc": 1.0238,
    "ldsc_mean_chisq": 1.0303,
    "ldsc_ratio": 0.4917
}

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 1.000000 3 58 0 4483816 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 4484834 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 8.657462e+00 5.765366e+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 7.859172e+07 5.672575e+07 828.0000000 3.172702e+07 6.894466e+07 1.147283e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.000000 NA NA NA NA NA NA NA -2.000000e-06 2.379000e-04 -0.0016591 -1.558000e-04 -1.700000e-06 1.521000e-04 1.668900e-03 ▁▁▇▁▁
numeric SE 0 1.000000 NA NA NA NA NA NA NA 2.308000e-04 4.050000e-05 0.0001821 1.960000e-04 2.169000e-04 2.576000e-04 6.661000e-04 ▇▂▁▁▁
numeric PVAL 0 1.000000 NA NA NA NA NA NA NA 4.959898e-01 2.900778e-01 0.0000002 2.399999e-01 5.000000e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.000000 NA NA NA NA NA NA NA 4.959894e-01 2.900512e-01 0.0000002 2.437891e-01 4.955285e-01 7.477673e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.000000 NA NA NA NA NA NA NA 3.735611e-01 2.266026e-01 0.0902760 1.771000e-01 3.170760e-01 5.369760e-01 9.097240e-01 ▇▅▃▂▂
numeric AF_reference 37323 0.991678 NA NA NA NA NA NA NA 3.648302e-01 2.265331e-01 0.0000000 1.779150e-01 3.162940e-01 5.253590e-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 -0.0004055 0.0003352 0.2300001 0.2263170 0.623763 0.7821490 NA
1 54676 rs2462492 C T 0.0001241 0.0003320 0.7099994 0.7085235 0.400401 NA NA
1 86028 rs114608975 T C 0.0004456 0.0005309 0.4000000 0.4013063 0.103556 0.0277556 NA
1 91536 rs6702460 G T -0.0001062 0.0003269 0.7499995 0.7452902 0.456851 0.4207270 NA
1 534192 rs6680723 C T -0.0000629 0.0003735 0.8700001 0.8661694 0.240960 NA NA
1 693731 rs12238997 A G -0.0004881 0.0003130 0.1199999 0.1188613 0.116325 0.1417730 NA
1 706368 rs55727773 A G -0.0003098 0.0002318 0.1800002 0.1814190 0.515650 0.2751600 NA
1 722670 rs116030099 T C 0.0015118 0.0003825 0.0000770 0.0000774 0.101199 0.0413339 NA
1 729679 rs4951859 C G 0.0005711 0.0002712 0.0350002 0.0352494 0.843212 0.6399760 NA
1 731718 rs142557973 T C -0.0004144 0.0002969 0.1600000 0.1627921 0.122307 0.1543530 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51198027 rs34939255 A G 0.0002135 0.0002312 0.3599996 0.3557269 0.254557 0.0984425 NA
22 51208537 rs72619593 G A 0.0001835 0.0003090 0.5500004 0.5527378 0.120739 0.1142170 NA
22 51210289 rs112565862 C T -0.0002821 0.0003078 0.3599996 0.3594015 0.129955 0.1018370 NA
22 51211106 rs9628250 T C 0.0002478 0.0002292 0.2800000 0.2796851 0.271547 0.1671330 NA
22 51211392 rs3888396 T C -0.0002339 0.0003050 0.4400003 0.4432591 0.132635 0.1641370 NA
22 51212875 rs2238837 A C -0.0001366 0.0002178 0.5300002 0.5304782 0.331455 0.3724040 NA
22 51213613 rs34726907 C T -0.0001223 0.0002869 0.6700003 0.6698420 0.127816 0.1727240 NA
22 51216564 rs9616970 T C -0.0001207 0.0002857 0.6700003 0.6727279 0.128330 0.1563500 NA
22 51219006 rs28729663 G A -0.0000394 0.0002797 0.8900000 0.8879422 0.137953 0.2052720 NA
22 51237063 rs3896457 T C -0.0001336 0.0002230 0.5500004 0.5488925 0.297971 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623763 ES:SE:LP:AF:ID  -0.00040552:0.000335168:0.638272:0.623763:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400401 ES:SE:LP:AF:ID  0.000124132:0.000332046:0.148742:0.400401:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103556 ES:SE:LP:AF:ID  0.00044556:0.000530877:0.39794:0.103556:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456851 ES:SE:LP:AF:ID  -0.00010621:0.000326945:0.124939:0.456851:rs6702460
1   534192  rs6680723   C   T   .   PASS    AF=0.24096  ES:SE:LP:AF:ID  -6.29366e-05:0.000373453:0.0604807:0.24096:rs6680723
1   693731  rs12238997  A   G   .   PASS    AF=0.116325 ES:SE:LP:AF:ID  -0.000488102:0.000312972:0.920819:0.116325:rs12238997
1   706368  rs12029736  A   G   .   PASS    AF=0.51565  ES:SE:LP:AF:ID  -0.000309824:0.000231835:0.744727:0.51565:rs12029736
1   722670  rs116030099 T   C   .   PASS    AF=0.101199 ES:SE:LP:AF:ID  0.00151177:0.000382513:4.11351:0.101199:rs116030099
1   729679  rs4951859   C   G   .   PASS    AF=0.843212 ES:SE:LP:AF:ID  0.000571072:0.000271231:1.45593:0.843212:rs4951859
1   731718  rs58276399  T   C   .   PASS    AF=0.122307 ES:SE:LP:AF:ID  -0.000414375:0.000296885:0.79588:0.122307:rs58276399
1   734349  rs141242758 T   C   .   PASS    AF=0.121549 ES:SE:LP:AF:ID  -0.000442144:0.00029701:0.853872:0.121549:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.13233  ES:SE:LP:AF:ID  -0.000715661:0.000292733:1.85387:0.13233:rs79010578
1   752566  rs3094315   G   A   .   PASS    AF=0.838951 ES:SE:LP:AF:ID  0.000565035:0.000262668:1.50864:0.838951:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.83858  ES:SE:LP:AF:ID  0.000563544:0.000262385:1.49485:0.83858:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869781 ES:SE:LP:AF:ID  0.000423644:0.000281549:0.886057:0.869781:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129871 ES:SE:LP:AF:ID  -0.000395243:0.000282126:0.79588:0.129871:rs2073813
1   754182  rs3131969   A   G   .   PASS    AF=0.869123 ES:SE:LP:AF:ID  0.000435468:0.000280998:0.920819:0.869123:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869221 ES:SE:LP:AF:ID  0.000430418:0.00028111:0.886057:0.869221:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.869126 ES:SE:LP:AF:ID  0.000434683:0.000280992:0.920819:0.869126:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.838033 ES:SE:LP:AF:ID  0.000574463:0.000261657:1.55284:0.838033:rs3115859
1   754964  rs3131966   C   T   .   PASS    AF=0.838664 ES:SE:LP:AF:ID  0.000574549:0.000262392:1.5376:0.838664:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.839777 ES:SE:LP:AF:ID  0.000624139:0.000265941:1.72125:0.839777:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.869405 ES:SE:LP:AF:ID  0.000449242:0.000280667:0.958607:0.869405:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.868952 ES:SE:LP:AF:ID  0.000423368:0.000279961:0.886057:0.868952:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867905 ES:SE:LP:AF:ID  0.000460979:0.000279425:1.00436:0.867905:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869095 ES:SE:LP:AF:ID  0.000445941:0.000280191:0.958607:0.869095:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869104 ES:SE:LP:AF:ID  0.000445764:0.000280212:0.958607:0.869104:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869112 ES:SE:LP:AF:ID  0.000445983:0.000280219:0.958607:0.869112:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869589 ES:SE:LP:AF:ID  0.000446107:0.000280988:0.958607:0.869589:rs3131954
1   760912  rs1048488   C   T   .   PASS    AF=0.838313 ES:SE:LP:AF:ID  0.000570832:0.00026116:1.5376:0.838313:rs1048488
1   761147  rs3115850   T   C   .   PASS    AF=0.838434 ES:SE:LP:AF:ID  0.000567546:0.000261345:1.52288:0.838434:rs3115850
1   761732  rs2286139   C   T   .   PASS    AF=0.862261 ES:SE:LP:AF:ID  0.00049543:0.000279205:1.11919:0.862261:rs2286139
1   763394  rs3115847   G   A   .   PASS    AF=0.706753 ES:SE:LP:AF:ID  0.000278034:0.000271804:0.508638:0.706753:rs3115847
1   766007  rs61768174  A   C   .   PASS    AF=0.105142 ES:SE:LP:AF:ID  -0.000550604:0.000313116:1.10237:0.105142:rs61768174
1   768253  rs2977608   A   C   .   PASS    AF=0.761304 ES:SE:LP:AF:ID  0.000267273:0.000221841:0.638272:0.761304:rs2977608
1   768448  rs12562034  G   A   .   PASS    AF=0.106488 ES:SE:LP:AF:ID  -4.15154e-05:0.000305764:0.05061:0.106488:rs12562034
1   769223  rs60320384  C   G   .   PASS    AF=0.129576 ES:SE:LP:AF:ID  -0.000395553:0.000281957:0.79588:0.129576:rs60320384
1   771823  rs2977605   T   C   .   PASS    AF=0.868911 ES:SE:LP:AF:ID  0.000434582:0.000280451:0.920819:0.868911:rs2977605
1   771967  rs59066358  G   A   .   PASS    AF=0.129677 ES:SE:LP:AF:ID  -0.000404434:0.000281775:0.823909:0.129677:rs59066358
1   772755  rs2905039   A   C   .   PASS    AF=0.868921 ES:SE:LP:AF:ID  0.000433498:0.000280457:0.920819:0.868921:rs2905039
1   776546  rs12124819  A   G   .   PASS    AF=0.26539  ES:SE:LP:AF:ID  0.000201448:0.0002478:0.376751:0.26539:rs12124819
1   777122  rs2980319   A   T   .   PASS    AF=0.870044 ES:SE:LP:AF:ID  0.00045645:0.000281029:1:0.870044:rs2980319
1   777232  rs112618790 C   T   .   PASS    AF=0.095139 ES:SE:LP:AF:ID  5.54644e-06:0.000325701:0.00436481:0.095139:rs112618790
1   778745  rs1055606   A   G   .   PASS    AF=0.128576 ES:SE:LP:AF:ID  -0.000430364:0.000282138:0.886057:0.128576:rs1055606
1   779322  rs4040617   A   G   .   PASS    AF=0.128873 ES:SE:LP:AF:ID  -0.000432975:0.00028166:0.920819:0.128873:rs4040617
1   780785  rs2977612   T   A   .   PASS    AF=0.868786 ES:SE:LP:AF:ID  0.000446685:0.00028028:0.958607:0.868786:rs2977612
1   781845  rs61768199  A   G   .   PASS    AF=0.10187  ES:SE:LP:AF:ID  -0.00058789:0.000317584:1.19382:0.10187:rs61768199
1   782981  rs6594026   C   T   .   PASS    AF=0.129513 ES:SE:LP:AF:ID  -0.000403024:0.000281569:0.823909:0.129513:rs6594026
1   785050  rs2905062   G   A   .   PASS    AF=0.868539 ES:SE:LP:AF:ID  0.000449439:0.000280214:0.958607:0.868539:rs2905062
1   785989  rs2980300   T   C   .   PASS    AF=0.86848  ES:SE:LP:AF:ID  0.000456578:0.000280389:1:0.86848:rs2980300