Summary

Summary {data-width=650}

Manhattan plot

manhattan_plot

QQ plot

qq_plot

AF plot

af_plot

P-Z plot

pz_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/dev/ukb-d-import/processed/ukb-d-20552_2/ukb-d-20552_2.vcf.gz \
--ref-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-20552_2/ldsc.txt \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ 

Beginning analysis at Mon Nov 25 16:51:46 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-20552_2/ukb-d-20552_2.vcf.gz ...
Read summary statistics for 9619068 SNPs.
Dropped 7224 SNPs with duplicated rs numbers.
Reading reference panel LD Score from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/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 /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/[1-22] ...
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 1274627 SNPs remain.
After merging with regression SNP LD, 1274627 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0056 (0.0034)
Lambda GC: 1.0304
Mean Chi^2: 1.017
Intercept: 1.004 (0.006)
Ratio: 0.2387 (0.3526)
Analysis finished at Mon Nov 25 16:53:21 2019
Total time elapsed: 1.0m:34.83s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9396,
    "inflation_factor": 1.0274,
    "mean_EFFECT": -1.9895e-06,
    "n": 116746,
    "n_snps": 9619068,
    "n_clumped_hits": 1,
    "n_p_sig": 1,
    "n_mono": 0,
    "n_ns": 1058672,
    "n_mac": 0,
    "is_snpid_unique": false,
    "n_miss_EFFECT": 0,
    "n_miss_SE": 0,
    "n_miss_PVAL": 0,
    "n_miss_AF": 0,
    "n_miss_AF_reference": 191942,
    "n_est": 116744.4957,
    "ratio_se_n": 1,
    "mean_diff": -0,
    "ratio_diff": 90.4577,
    "sd_y_est1": 0.1015,
    "sd_y_est2": 0.1015,
    "r2_sum1": 2.6623e-06,
    "r2_sum2": 0.0003,
    "r2_sum3": 0.0003,
    "r2_sum4": 0.0003,
    "ldsc_nsnp_merge_refpanel_ld": 1274627,
    "ldsc_nsnp_merge_regression_ld": 1274627,
    "ldsc_observed_scale_h2_beta": 0.0056,
    "ldsc_observed_scale_h2_se": 0.0034,
    "ldsc_intercept_beta": 1.004,
    "ldsc_intercept_se": 0.006,
    "ldsc_lambda_gc": 1.0304,
    "ldsc_mean_chisq": 1.017,
    "ldsc_ratio": 0.2353
}

Flags

name value
af_correlation FALSE
inflation_factor FALSE
n FALSE
is_snpid_non_unique TRUE
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 numeric.mean numeric.sd numeric.p0 numeric.p25 numeric.p50 numeric.p75 numeric.p100 numeric.hist
character ID 0 1.0000000 3 94 0 9612392 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 98 0 46846 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 342 0 29599 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 9.079585e+00 6.175251e+00 1.00000e+00 4.000000e+00 8.000000e+00 1.300000e+01 2.300000e+01 <U+2587><U+2585><U+2585><U+2582><U+2582>
numeric POS 0 1.0000000 NA NA NA NA NA 7.873016e+07 5.611727e+07 3.02000e+02 3.242351e+07 6.960996e+07 1.147401e+08 2.492297e+08 <U+2587><U+2586><U+2585><U+2582><U+2581>
numeric EFFECT 0 1.0000000 NA NA NA NA NA -2.000000e-06 9.356000e-04 -8.39630e-03 -4.632000e-04 -7.400000e-06 4.483000e-04 1.120110e-02 <U+2581><U+2582><U+2587><U+2581><U+2581>
numeric SE 0 1.0000000 NA NA NA NA NA 8.119000e-04 4.560000e-04 3.48300e-04 4.596000e-04 6.102000e-04 1.036500e-03 2.522500e-03 <U+2587><U+2582><U+2582><U+2581><U+2581>
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.961479e-01 2.892685e-01 0.00000e+00 2.447851e-01 4.941810e-01 7.464728e-01 1.000000e+00 <U+2587><U+2587><U+2587><U+2587><U+2587>
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.961465e-01 2.892693e-01 0.00000e+00 2.447832e-01 4.941802e-01 7.464723e-01 9.999998e-01 <U+2587><U+2587><U+2587><U+2587><U+2587>
numeric AF 0 1.0000000 NA NA NA NA NA 2.635964e-01 2.628377e-01 1.02796e-02 4.566320e-02 1.622680e-01 4.192010e-01 9.897200e-01 <U+2587><U+2582><U+2582><U+2581><U+2581>
numeric AF_reference 191942 0.9800457 NA NA NA NA NA 2.630406e-01 2.536419e-01 0.00000e+00 5.191690e-02 1.773160e-01 4.145370e-01 1.000000e+00 <U+2587><U+2583><U+2582><U+2581><U+2581>
numeric N 0 1.0000000 NA NA NA NA NA 1.167460e+05 0.000000e+00 1.16746e+05 1.167460e+05 1.167460e+05 1.167460e+05 1.167460e+05 <U+2581><U+2581><U+2587><U+2581><U+2581>

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 692794 rs530212009 CA C -0.0007684 0.0007323 0.2940492 0.2940467 0.1117380 0.1894970 116746
1 693731 rs12238997 A G -0.0012108 0.0006937 0.0808872 0.0808838 0.1167610 0.1417730 116746
1 707522 rs371890604 G C -0.0008437 0.0007785 0.2784659 0.2784631 0.0983468 0.1293930 116746
1 717587 rs144155419 G A -0.0020263 0.0018629 0.2767158 0.2767125 0.0157960 0.0045926 116746
1 730087 rs148120343 T C -0.0005001 0.0009654 0.6044634 0.6044631 0.0569136 0.0127796 116746
1 731718 rs142557973 T C -0.0007824 0.0006581 0.2345379 0.2345361 0.1226930 0.1543530 116746
1 732032 rs61770163 A C -0.0007804 0.0007016 0.2660511 0.2660484 0.1222720 0.1555510 116746
1 734349 rs141242758 T C -0.0007565 0.0006585 0.2506161 0.2506135 0.1219490 0.1525560 116746
1 749963 rs529266287 T TAA 0.0004705 0.0006489 0.4684195 0.4684168 0.8689790 0.7641770 116746
1 751343 rs28544273 T A -0.0006386 0.0006429 0.3205391 0.3205370 0.1237060 0.2426120 116746
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T 0.0019645 0.0007738 0.0111314 0.0111302 0.0554682 0.0309934 116746
23 154923374 rs111332691 T A 0.0001242 0.0008432 0.8828561 0.8828563 0.0447981 0.0116556 116746
23 154925045 rs509981 C T -0.0003924 0.0004062 0.3340519 0.3340498 0.2447810 0.3634440 116746
23 154925895 rs538470 C T -0.0003136 0.0004154 0.4502918 0.4502897 0.2411990 0.3634440 116746
23 154927581 rs644138 G A 0.0001178 0.0003822 0.7578635 0.7578622 0.3006130 0.4635760 116746
23 154929412 rs557132 C T -0.0003888 0.0004063 0.3385540 0.3385518 0.2446550 0.3568210 116746
23 154929637 rs35185538 CT C -0.0001934 0.0004245 0.6486494 0.6486496 0.2288100 0.3011920 116746
23 154929952 rs4012982 CAA C -0.0006402 0.0004276 0.1343399 0.1343371 0.2387680 0.3165560 116746
23 154930230 rs781880 A G -0.0003784 0.0004062 0.3515523 0.3515499 0.2450820 0.3618540 116746
23 154930487 rs781879 T A 0.0022102 0.0013822 0.1098070 0.1098029 0.0196508 0.1263580 116746

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.111738 ES:SE:LP:AF:SS:ID   -0.000768418:0.000732325:0.53158:0.111738:116746:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.116761 ES:SE:LP:AF:SS:ID   -0.00121085:0.000693666:1.09212:0.116761:116746:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0983468    ES:SE:LP:AF:SS:ID   -0.000843717:0.000778496:0.555228:0.0983468:116746:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.015796 ES:SE:LP:AF:SS:ID   -0.00202634:0.0018629:0.557966:0.015796:116746:rs144155419
1   730087  rs148120343 T   C   .   PASS    AF=0.0569136    ES:SE:LP:AF:SS:ID   -0.000500071:0.000965401:0.21863:0.0569136:116746:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122693 ES:SE:LP:AF:SS:ID   -0.00078236:0.000658134:0.629787:0.122693:116746:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.122272 ES:SE:LP:AF:SS:ID   -0.000780359:0.00070163:0.575035:0.122272:116746:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.121949 ES:SE:LP:AF:SS:ID   -0.000756504:0.000658482:0.600991:0.121949:116746:rs141242758
1   749963  rs529266287 T   TAA .   PASS    AF=0.868979 ES:SE:LP:AF:SS:ID   0.00047046:0.000648859:0.329365:0.868979:116746:rs529266287
1   751343  rs28544273  T   A   .   PASS    AF=0.123706 ES:SE:LP:AF:SS:ID   -0.000638617:0.000642889:0.494119:0.123706:116746:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.143383 ES:SE:LP:AF:SS:ID   -0.000535276:0.00063581:0.398095:0.143383:116746:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.123828 ES:SE:LP:AF:SS:ID   -0.000599898:0.000641999:0.455823:0.123828:116746:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.869732 ES:SE:LP:AF:SS:ID   0.00063519:0.000625125:0.509223:0.869732:116746:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.874543 ES:SE:LP:AF:SS:ID   0.00065564:0.000635684:0.519479:0.874543:116746:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.129734 ES:SE:LP:AF:SS:ID   -0.000605483:0.000626868:0.47612:0.129734:116746:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0365537    ES:SE:LP:AF:SS:ID   -0.000186259:0.00113815:0.0604773:0.0365537:116746:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869323 ES:SE:LP:AF:SS:ID   0.000650899:0.000624526:0.526793:0.869323:116746:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869395 ES:SE:LP:AF:SS:ID   0.000642148:0.00062476:0.517082:0.869395:116746:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.869322 ES:SE:LP:AF:SS:ID   0.000649822:0.000624509:0.525645:0.869322:116746:rs3131967
1   755890  rs3115858   A   T   .   PASS    AF=0.869388 ES:SE:LP:AF:SS:ID   0.000633111:0.000623345:0.508934:0.869388:116746:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.127007 ES:SE:LP:AF:SS:ID   -0.000557335:0.000636363:0.418923:0.127007:116746:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.868903 ES:SE:LP:AF:SS:ID   0.000626844:0.000621689:0.504016:0.868903:116746:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.868299 ES:SE:LP:AF:SS:ID   0.000668536:0.000621448:0.549702:0.868299:116746:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869095 ES:SE:LP:AF:SS:ID   0.000639138:0.000622312:0.516548:0.869095:116746:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869101 ES:SE:LP:AF:SS:ID   0.000638199:0.000622356:0.515487:0.869101:116746:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869104 ES:SE:LP:AF:SS:ID   0.000639982:0.000622359:0.517408:0.869104:116746:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869542 ES:SE:LP:AF:SS:ID   0.000623555:0.000623965:0.498077:0.869542:116746:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.099572 ES:SE:LP:AF:SS:ID   -0.000744481:0.000725637:0.515831:0.099572:116746:rs10157329
1   759837  rs3115851   T   A   .   PASS    AF=0.873634 ES:SE:LP:AF:SS:ID   0.00063833:0.000633616:0.503449:0.873634:116746:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.86289  ES:SE:LP:AF:SS:ID   0.000665968:0.000621319:0.547011:0.86289:116746:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.868299 ES:SE:LP:AF:SS:ID   0.000687967:0.000627314:0.564186:0.868299:116746:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.865294 ES:SE:LP:AF:SS:ID   0.000592232:0.000627413:0.461918:0.865294:116746:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0997639    ES:SE:LP:AF:SS:ID   -0.0010019:0.000749781:0.741202:0.0997639:116746:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.870619 ES:SE:LP:AF:SS:ID   0.000696306:0.000634895:0.564215:0.870619:116746:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.870619 ES:SE:LP:AF:SS:ID   0.000696311:0.000634895:0.564219:0.870619:116746:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.870618 ES:SE:LP:AF:SS:ID   0.000696093:0.000634896:0.563979:0.870618:116746:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.870937 ES:SE:LP:AF:SS:ID   0.000774405:0.000635161:0.652161:0.870937:116746:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.126594 ES:SE:LP:AF:SS:ID   -0.000570489:0.000635649:0.432434:0.126594:116746:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.10633  ES:SE:LP:AF:SS:ID   -0.000617735:0.000690999:0.430231:0.10633:116746:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.854304 ES:SE:LP:AF:SS:ID   0.000535191:0.000621172:0.410141:0.854304:116746:rs2519015
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.839004 ES:SE:LP:AF:SS:ID   -0.000140626:0.000615491:0.0865703:0.839004:116746:rs376645387
1   768253  rs2977608   A   C   .   PASS    AF=0.762921 ES:SE:LP:AF:SS:ID   0.000131164:0.000493332:0.102188:0.762921:116746:rs2977608
1   768448  rs12562034  G   A   .   PASS    AF=0.105297 ES:SE:LP:AF:SS:ID   0.000477873:0.000682477:0.315333:0.105297:116746:rs12562034
1   769138  rs59306077  CAT C   .   PASS    AF=0.130025 ES:SE:LP:AF:SS:ID   -0.000563491:0.000626547:0.433606:0.130025:116746:rs762168062
1   769223  rs60320384  C   G   .   PASS    AF=0.129719 ES:SE:LP:AF:SS:ID   -0.00058058:0.000625804:0.451553:0.129719:116746:rs60320384
1   769963  rs7518545   G   A   .   PASS    AF=0.10454  ES:SE:LP:AF:SS:ID   0.000529245:0.000687829:0.354939:0.10454:116746:rs7518545
1   770886  rs371458725 G   A   .   PASS    AF=0.103591 ES:SE:LP:AF:SS:ID   0.000591025:0.000693522:0.404392:0.103591:116746:rs371458725
1   771410  rs2519006   C   T   .   PASS    AF=0.829153 ES:SE:LP:AF:SS:ID   0.000149984:0.000610525:0.0936957:0.829153:116746:rs2519006
1   771823  rs2977605   T   C   .   PASS    AF=0.868953 ES:SE:LP:AF:SS:ID   0.000626625:0.000622871:0.502511:0.868953:116746:rs2977605
1   771967  rs59066358  G   A   .   PASS    AF=0.129804 ES:SE:LP:AF:SS:ID   -0.000598834:0.000625451:0.47064:0.129804:116746:rs59066358