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-19950/UKB-b-19950_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19950/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-19950/UKB-b-19950_data.vcf.gz ...
Read summary statistics for 9165801 SNPs.
Dropped 9498 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, 1287608 SNPs remain.
After merging with regression SNP LD, 1287608 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0262 (0.0017)
Lambda GC: 1.3127
Mean Chi^2: 1.3415
Intercept: 1.1078 (0.008)
Ratio: 0.3158 (0.0235)
Analysis finished at Thu Oct 17 14:43:45 2019
Total time elapsed: 1.0m:39.95s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9482,
    "inflation_factor": 1.2544,
    "mean_EFFECT": 0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 6,
    "n_p_sig": 122,
    "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": 102150,
    "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": 1287608,
    "ldsc_nsnp_merge_regression_ld": 1287608,
    "ldsc_observed_scale_h2_beta": 0.0262,
    "ldsc_observed_scale_h2_se": 0.0017,
    "ldsc_intercept_beta": 1.1078,
    "ldsc_intercept_se": 0.008,
    "ldsc_lambda_gc": 1.3127,
    "ldsc_mean_chisq": 1.3415,
    "ldsc_ratio": 0.3157
}

Flags

name value
af_correlation FALSE
inflation_factor TRUE
n TRUE
is_snpid_non_unique FALSE
mean_EFFECT_nonfinite FALSE
mean_EFFECT_05 FALSE
mean_EFFECT_01 FALSE
mean_chisq TRUE
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 9156350 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 9165801 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 8.638714e+00 5.755746e+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 7.880042e+07 5.632123e+07 828.0000000 3.247276e+07 6.937501e+07 1.145290e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 2.160000e-05 3.394700e-03 -0.0388801 -1.320400e-03 7.700000e-06 1.348400e-03 3.694590e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 2.520900e-03 1.918700e-03 0.0008790 1.052700e-03 1.638500e-03 3.459700e-03 2.138250e-02 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.647545e-01 2.982623e-01 0.0000000 2.000000e-01 4.500005e-01 7.199992e-01 1.000000e+00 ▇▆▆▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.647569e-01 2.982377e-01 0.0000000 1.973891e-01 4.517481e-01 7.233001e-01 9.999999e-01 ▇▆▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.167130e-01 2.581604e-01 0.0028780 1.882900e-02 9.661300e-02 3.412210e-01 9.971220e-01 ▇▂▁▁▁
numeric AF_reference 102150 0.9888553 NA NA NA NA NA NA NA 2.173313e-01 2.500077e-01 0.0000000 1.597440e-02 1.146170e-01 3.402560e-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 0.0001236 0.0016168 0.9400001 0.9390540 0.623765 0.7821490 NA
1 54676 rs2462492 C T 0.0002195 0.0016017 0.8900000 0.8910033 0.400401 NA NA
1 86028 rs114608975 T C 0.0004304 0.0025608 0.8700001 0.8665175 0.103556 0.0277556 NA
1 91536 rs6702460 G T 0.0015387 0.0015771 0.3300000 0.3292372 0.456846 0.4207270 NA
1 234313 rs8179466 C T -0.0004533 0.0031097 0.8800001 0.8841144 0.074506 NA NA
1 534192 rs6680723 C T 0.0015504 0.0018015 0.3900004 0.3894385 0.240959 NA NA
1 546697 rs12025928 A G 0.0005228 0.0022474 0.8200001 0.8160534 0.913475 NA NA
1 693731 rs12238997 A G 0.0003018 0.0015097 0.8400000 0.8415272 0.116329 0.1417730 NA
1 705882 rs72631875 G A -0.0014576 0.0022123 0.5099998 0.5099952 0.067288 0.0315495 NA
1 706368 rs55727773 A G -0.0004721 0.0011183 0.6700003 0.6728978 0.515645 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0002025 0.0013500 0.8800001 0.8807725 0.137950 0.2052720 NA
22 51219387 rs9616832 T C 0.0000506 0.0017523 0.9800000 0.9769585 0.073744 0.0654952 NA
22 51219704 rs147475742 G A -0.0013302 0.0023481 0.5700002 0.5710646 0.041954 0.0473243 NA
22 51221190 rs369304721 G A -0.0015493 0.0023443 0.5099998 0.5086732 0.049731 NA NA
22 51221731 rs115055839 T C -0.0000763 0.0017534 0.9699999 0.9652719 0.073235 0.0625000 NA
22 51222100 rs114553188 G T -0.0007354 0.0020643 0.7199992 0.7216579 0.054460 0.0880591 NA
22 51223637 rs375798137 G A -0.0008081 0.0020742 0.6999999 0.6968476 0.054089 0.0788738 NA
22 51229805 rs9616985 T C -0.0000444 0.0017597 0.9800000 0.9798575 0.073071 0.0730831 NA
22 51232488 rs376461333 A G -0.0027769 0.0041456 0.5000000 0.5029539 0.020042 NA NA
22 51237063 rs3896457 T C 0.0012337 0.0010763 0.2500000 0.2516973 0.297974 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623765 ES:SE:LP:AF:ID  0.000123618:0.00161679:0.0268721:0.623765:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400401 ES:SE:LP:AF:ID  0.000219494:0.00160174:0.05061:0.400401:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103556 ES:SE:LP:AF:ID  0.000430437:0.00256085:0.0604807:0.103556:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456846 ES:SE:LP:AF:ID  0.0015387:0.0015771:0.481486:0.456846:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074506 ES:SE:LP:AF:ID  -0.000453253:0.00310968:0.0555173:0.074506:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240959 ES:SE:LP:AF:ID  0.00155041:0.00180147:0.408935:0.240959:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913475 ES:SE:LP:AF:ID  0.000522806:0.00224743:0.0861861:0.913475:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116329 ES:SE:LP:AF:ID  0.000301848:0.00150969:0.0757207:0.116329:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067288 ES:SE:LP:AF:ID  -0.00145755:0.00221228:0.29243:0.067288:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515645 ES:SE:LP:AF:ID  -0.000472126:0.00111832:0.173925:0.515645:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033009 ES:SE:LP:AF:ID  -0.000898652:0.00281918:0.124939:0.033009:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036624 ES:SE:LP:AF:ID  -0.000808259:0.00256079:0.124939:0.036624:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036741 ES:SE:LP:AF:ID  -0.000925556:0.00255111:0.142668:0.036741:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036439 ES:SE:LP:AF:ID  -0.000876005:0.00256951:0.136677:0.036439:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016409 ES:SE:LP:AF:ID  0.0012231:0.00395634:0.119186:0.016409:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.03698  ES:SE:LP:AF:ID  -0.00109839:0.00254099:0.173925:0.03698:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037077 ES:SE:LP:AF:ID  -0.00123457:0.00253228:0.200659:0.037077:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.1012   ES:SE:LP:AF:ID  0.00264553:0.00184516:0.823909:0.1012:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959091 ES:SE:LP:AF:ID  0.00110111:0.00244233:0.187087:0.959091:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.03145  ES:SE:LP:AF:ID  0.00177442:0.00443405:0.161151:0.03145:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053255 ES:SE:LP:AF:ID  -0.006006:0.00352719:1.05061:0.053255:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036594 ES:SE:LP:AF:ID  -0.00107091:0.00254871:0.173925:0.036594:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.03691  ES:SE:LP:AF:ID  -0.000919731:0.00252551:0.142668:0.03691:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843204 ES:SE:LP:AF:ID  9.39832e-05:0.00130834:0.0268721:0.843204:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055912 ES:SE:LP:AF:ID  -0.000827143:0.00211841:0.154902:0.055912:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122312 ES:SE:LP:AF:ID  0.000341056:0.00143209:0.091515:0.122312:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025711 ES:SE:LP:AF:ID  0.00137487:0.00352282:0.154902:0.025711:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121554 ES:SE:LP:AF:ID  0.000406594:0.00143269:0.107905:0.121554:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132335 ES:SE:LP:AF:ID  0.000120694:0.00141206:0.0315171:0.132335:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011133 ES:SE:LP:AF:ID  -0.00435368:0.00513435:0.39794:0.011133:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005698 ES:SE:LP:AF:ID  -0.00883995:0.00662857:0.744727:0.005698:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.036825 ES:SE:LP:AF:ID  -0.000872763:0.00249996:0.136677:0.036825:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838945 ES:SE:LP:AF:ID  0.000274984:0.00126704:0.0809219:0.838945:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838573 ES:SE:LP:AF:ID  0.000135716:0.00126568:0.0409586:0.838573:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869776 ES:SE:LP:AF:ID  0.000144725:0.00135811:0.0362122:0.869776:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129876 ES:SE:LP:AF:ID  -1.4598e-05:0.00136089:0.00436481:0.129876:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037336 ES:SE:LP:AF:ID  -0.000745308:0.00245758:0.119186:0.037336:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.03758  ES:SE:LP:AF:ID  -0.000775476:0.00244205:0.124939:0.03758:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869117 ES:SE:LP:AF:ID  0.000105677:0.00135545:0.0268721:0.869117:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869215 ES:SE:LP:AF:ID  0.000159283:0.00135599:0.0409586:0.869215:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037538 ES:SE:LP:AF:ID  -0.000614533:0.00245261:0.09691:0.037538:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869121 ES:SE:LP:AF:ID  6.88763e-05:0.00135543:0.0177288:0.869121:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005121 ES:SE:LP:AF:ID  -0.00345195:0.00696006:0.207608:0.005121:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005087 ES:SE:LP:AF:ID  -0.00339383:0.00697832:0.200659:0.005087:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.838026 ES:SE:LP:AF:ID  0.000238106:0.00126216:0.0705811:0.838026:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.03755  ES:SE:LP:AF:ID  -0.000637658:0.00245608:0.09691:0.03755:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838657 ES:SE:LP:AF:ID  0.000225182:0.00126571:0.0655015:0.838657:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013772 ES:SE:LP:AF:ID  -0.00220296:0.00441841:0.207608:0.013772:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005547 ES:SE:LP:AF:ID  -0.011135:0.00681704:1:0.005547:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.83977  ES:SE:LP:AF:ID  0.000202321:0.00128283:0.0604807:0.83977:rs3131965