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_3872.vcf.gz --id UKB-b:16927 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_3872.txt.gz --cohort_controls 33147 --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-16927/UKB-b-16927_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-16927/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:45:22 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-16927/UKB-b-16927_data.vcf.gz ...
Read summary statistics for 7655074 SNPs.
Dropped 5451 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, 1278657 SNPs remain.
After merging with regression SNP LD, 1278657 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0767 (0.0157)
Lambda GC: 1.0723
Mean Chi^2: 1.0749
Intercept: 1.0246 (0.0066)
Ratio: 0.329 (0.088)
Analysis finished at Thu Oct 17 14:46:40 2019
Total time elapsed: 1.0m:17.64s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9409,
    "inflation_factor": 1.0475,
    "mean_EFFECT": -0.0001,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 1,
    "n_p_sig": 1,
    "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": 71175,
    "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": 1278657,
    "ldsc_nsnp_merge_regression_ld": 1278657,
    "ldsc_observed_scale_h2_beta": 0.0767,
    "ldsc_observed_scale_h2_se": 0.0157,
    "ldsc_intercept_beta": 1.0246,
    "ldsc_intercept_se": 0.0066,
    "ldsc_lambda_gc": 1.0723,
    "ldsc_mean_chisq": 1.0749,
    "ldsc_ratio": 0.3284
}

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 7649647 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 7655074 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 8.661423e+00 5.764183e+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 7.868370e+07 5.644273e+07 828.0000000 3.220706e+07 6.913107e+07 1.145743e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -5.820000e-05 1.772570e-02 -0.1784350 -8.718700e-03 -2.160000e-05 8.660600e-03 1.602840e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.520340e-02 8.447900e-03 0.0074102 8.519900e-03 1.153250e-02 1.970410e-02 9.364830e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.904545e-01 2.909304e-01 0.0000000 2.399999e-01 4.899999e-01 7.400005e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.904538e-01 2.909049e-01 0.0000000 2.366336e-01 4.866851e-01 7.424489e-01 9.999998e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.554158e-01 2.608420e-01 0.0105600 4.260200e-02 1.506290e-01 4.036300e-01 9.894390e-01 ▇▂▂▁▁
numeric AF_reference 71175 0.9907022 NA NA NA NA NA NA NA 2.544099e-01 2.526828e-01 0.0000000 4.672520e-02 1.649360e-01 3.979630e-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 -0.0230133 0.0137073 0.0929994 0.0931699 0.622140 0.7821490 NA
1 54676 rs2462492 C T 0.0309165 0.0135506 0.0230001 0.0225154 0.400397 NA NA
1 86028 rs114608975 T C -0.0027498 0.0217638 0.9000000 0.8994566 0.103220 0.0277556 NA
1 91536 rs6702460 G T 0.0181303 0.0133590 0.1700000 0.1747304 0.456587 0.4207270 NA
1 234313 rs8179466 C T 0.0129502 0.0262124 0.6200004 0.6212718 0.074500 NA NA
1 534192 rs6680723 C T 0.0043227 0.0152698 0.7800007 0.7771077 0.241275 NA NA
1 546697 rs12025928 A G -0.0114327 0.0188759 0.5400003 0.5447292 0.912619 NA NA
1 693731 rs12238997 A G 0.0054738 0.0127727 0.6700003 0.6682451 0.116927 0.1417730 NA
1 705882 rs72631875 G A 0.0057421 0.0184425 0.7600007 0.7555322 0.068484 0.0315495 NA
1 706368 rs55727773 A G 0.0079810 0.0094255 0.4000000 0.3971392 0.514009 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0194695 0.0114902 0.0899995 0.0901810 0.135645 0.2052720 NA
22 51219387 rs9616832 T C 0.0253844 0.0149080 0.0890000 0.0886173 0.072757 0.0654952 NA
22 51219704 rs147475742 G A 0.0136256 0.0199791 0.5000000 0.4952436 0.041143 0.0473243 NA
22 51221190 rs369304721 G A 0.0272845 0.0199971 0.1700000 0.1724345 0.048988 NA NA
22 51221731 rs115055839 T C 0.0260557 0.0149261 0.0810009 0.0808723 0.072196 0.0625000 NA
22 51222100 rs114553188 G T 0.0029811 0.0175933 0.8700001 0.8654478 0.053401 0.0880591 NA
22 51223637 rs375798137 G A 0.0040593 0.0176763 0.8200001 0.8183660 0.053066 0.0788738 NA
22 51229805 rs9616985 T C 0.0248481 0.0149772 0.0969996 0.0971033 0.072081 0.0730831 NA
22 51232488 rs376461333 A G 0.0026286 0.0364409 0.9400001 0.9424963 0.019122 NA NA
22 51237063 rs3896457 T C 0.0000639 0.0090746 0.9900000 0.9943788 0.296619 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.62214  ES:SE:LP:AF:ID  -0.0230133:0.0137073:1.03152:0.62214:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400397 ES:SE:LP:AF:ID  0.0309165:0.0135506:1.63827:0.400397:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.10322  ES:SE:LP:AF:ID  -0.00274981:0.0217638:0.0457575:0.10322:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456587 ES:SE:LP:AF:ID  0.0181303:0.013359:0.769551:0.456587:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.0745   ES:SE:LP:AF:ID  0.0129502:0.0262124:0.207608:0.0745:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241275 ES:SE:LP:AF:ID  0.00432273:0.0152698:0.107905:0.241275:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912619 ES:SE:LP:AF:ID  -0.0114327:0.0188759:0.267606:0.912619:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116927 ES:SE:LP:AF:ID  0.00547384:0.0127727:0.173925:0.116927:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.068484 ES:SE:LP:AF:ID  0.00574213:0.0184425:0.119186:0.068484:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.514009 ES:SE:LP:AF:ID  0.00798097:0.00942551:0.39794:0.514009:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.032364 ES:SE:LP:AF:ID  0.0130459:0.024106:0.229148:0.032364:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036039 ES:SE:LP:AF:ID  0.0104025:0.0218081:0.200659:0.036039:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036163 ES:SE:LP:AF:ID  0.0151076:0.0217263:0.309804:0.036163:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.035856 ES:SE:LP:AF:ID  0.0143363:0.0218849:0.29243:0.035856:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016319 ES:SE:LP:AF:ID  0.0476458:0.0336386:0.79588:0.016319:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036393 ES:SE:LP:AF:ID  0.0105715:0.0216292:0.200659:0.036393:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.036439 ES:SE:LP:AF:ID  0.00890471:0.0215739:0.167491:0.036439:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101122 ES:SE:LP:AF:ID  -0.0219492:0.0154958:0.79588:0.101122:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959286 ES:SE:LP:AF:ID  -0.000813203:0.0206503:0.0132283:0.959286:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031499 ES:SE:LP:AF:ID  -0.070367:0.0374788:1.22185:0.031499:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053248 ES:SE:LP:AF:ID  0.0150037:0.0297797:0.21467:0.053248:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.03602  ES:SE:LP:AF:ID  0.00496429:0.0216867:0.0861861:0.03602:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036406 ES:SE:LP:AF:ID  0.00766345:0.0214719:0.142668:0.036406:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.842759 ES:SE:LP:AF:ID  -0.0062609:0.0110524:0.244125:0.842759:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055836 ES:SE:LP:AF:ID  0.0006772:0.0179264:0.0132283:0.055836:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.12297  ES:SE:LP:AF:ID  0.00391728:0.012093:0.124939:0.12297:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.024596 ES:SE:LP:AF:ID  -0.0249433:0.0307343:0.376751:0.024596:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122255 ES:SE:LP:AF:ID  0.00471505:0.0120876:0.154902:0.122255:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132507 ES:SE:LP:AF:ID  0.00210459:0.0119041:0.0655015:0.132507:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.010939 ES:SE:LP:AF:ID  -0.0151307:0.0439784:0.136677:0.010939:rs181876450
1   752478  rs146277091 G   A   .   PASS    AF=0.036302 ES:SE:LP:AF:ID  0.00787775:0.0212508:0.148742:0.036302:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838034 ES:SE:LP:AF:ID  -0.00295161:0.0106732:0.107905:0.838034:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.837643 ES:SE:LP:AF:ID  -0.00283397:0.0106642:0.102373:0.837643:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868425 ES:SE:LP:AF:ID  0.000586481:0.0114184:0.0177288:0.868425:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131045 ES:SE:LP:AF:ID  -0.00296067:0.0114534:0.09691:0.131045:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.036739 ES:SE:LP:AF:ID  -8.66938e-05:0.0209109:-0:0.036739:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.036988 ES:SE:LP:AF:ID  0.00173255:0.0207762:0.0315171:0.036988:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867765 ES:SE:LP:AF:ID  0.00172308:0.0113995:0.0555173:0.867765:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.867827 ES:SE:LP:AF:ID  0.00204422:0.0114015:0.0655015:0.867827:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.036896 ES:SE:LP:AF:ID  0.000793118:0.0208781:0.0132283:0.036896:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.867795 ES:SE:LP:AF:ID  0.00181502:0.0113992:0.0604807:0.867795:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.837138 ES:SE:LP:AF:ID  -0.00124844:0.0106354:0.0409586:0.837138:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.036883 ES:SE:LP:AF:ID  0.00103876:0.0209125:0.0177288:0.036883:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.837877 ES:SE:LP:AF:ID  -0.000405456:0.0106692:0.0132283:0.837877:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013128 ES:SE:LP:AF:ID  0.0498711:0.0385413:0.69897:0.013128:rs181660517
1   755775  rs3131965   A   G   .   PASS    AF=0.839197 ES:SE:LP:AF:ID  -0.000479482:0.0108184:0.0177288:0.839197:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.868192 ES:SE:LP:AF:ID  0.00154109:0.0113873:0.05061:0.868192:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.867767 ES:SE:LP:AF:ID  0.00229564:0.0113608:0.0757207:0.867767:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.866714 ES:SE:LP:AF:ID  0.00336574:0.011347:0.113509:0.866714:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.867902 ES:SE:LP:AF:ID  0.00264356:0.0113709:0.0861861:0.867902:rs4951929