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/dev/ukb-d-import/processed/ukb-d-20411_0/ukb-d-20411_0.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-20411_0/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 16:38:14 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-20411_0/ukb-d-20411_0.vcf.gz ...
Read summary statistics for 13571586 SNPs.
Dropped 12695 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, 1283502 SNPs remain.
After merging with regression SNP LD, 1283502 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0093 (0.0038)
Lambda GC: 1.0344
Mean Chi^2: 1.0305
Intercept: 1.009 (0.006)
Ratio: 0.2969 (0.1979)
Analysis finished at Mon Nov 25 16:40:25 2019
Total time elapsed: 2.0m:11.85s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9521,
    "inflation_factor": 1.0228,
    "mean_EFFECT": -0,
    "n": 118002,
    "n_snps": 13571586,
    "n_clumped_hits": 2,
    "n_p_sig": 4,
    "n_mono": 0,
    "n_ns": 1251353,
    "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": 564603,
    "n_est": 118430.7342,
    "ratio_se_n": 1.0018,
    "mean_diff": -0.0001,
    "ratio_diff": 3321.8136,
    "sd_y_est1": 0.2018,
    "sd_y_est2": 0.2022,
    "r2_sum1": 0,
    "r2_sum2": 0.0006,
    "r2_sum3": 0.0006,
    "r2_sum4": 0.0006,
    "ldsc_nsnp_merge_refpanel_ld": 1283502,
    "ldsc_nsnp_merge_regression_ld": 1283502,
    "ldsc_observed_scale_h2_beta": 0.0093,
    "ldsc_observed_scale_h2_se": 0.0038,
    "ldsc_intercept_beta": 1.009,
    "ldsc_intercept_se": 0.006,
    "ldsc_lambda_gc": 1.0344,
    "ldsc_mean_chisq": 1.0305,
    "ldsc_ratio": 0.2951
}

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 13559555 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 100 0 57056 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 342 0 33331 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 9.051449e+00 6.185087e+00 1.00000e+00 4.000000e+00 8.000000e+00 1.300000e+01 2.300000e+01 ▇▅▅▂▂
numeric POS 0 1.0000000 NA NA NA NA NA 7.902494e+07 5.591561e+07 3.02000e+02 3.292817e+07 7.013899e+07 1.148582e+08 2.492309e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA -1.410000e-05 4.849200e-03 -7.18807e-02 -1.266800e-03 1.480000e-05 1.315500e-03 4.915950e-02 ▁▁▇▅▁
numeric SE 0 1.0000000 NA NA NA NA NA 3.480700e-03 3.355200e-03 6.87900e-04 9.934000e-04 1.828300e-03 4.997800e-03 1.672620e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.966934e-01 2.896464e-01 0.00000e+00 2.448268e-01 4.951582e-01 7.479972e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.966920e-01 2.896472e-01 0.00000e+00 2.448244e-01 4.951567e-01 7.479961e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA 1.909972e-01 2.544492e-01 1.00000e-03 7.483500e-03 6.026560e-02 2.956750e-01 9.990000e-01 ▇▂▁▁▁
numeric AF_reference 564603 0.9583982 NA NA NA NA NA 1.964727e-01 2.471290e-01 0.00000e+00 5.790700e-03 8.446490e-02 3.083070e-01 1.000000e+00 ▇▂▁▁▁
numeric N 0 1.0000000 NA NA NA NA NA 1.180020e+05 0.000000e+00 1.18002e+05 1.180020e+05 1.180020e+05 1.180020e+05 1.180020e+05 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 692794 rs530212009 CA C 0.0009820 0.0014466 0.4972308 0.4972303 0.1117250 0.1894970 118002
1 693731 rs12238997 A G 0.0014141 0.0013700 0.3019639 0.3019625 0.1167420 0.1417730 118002
1 707522 rs371890604 G C 0.0003469 0.0015372 0.8214840 0.8214841 0.0983656 0.1293930 118002
1 717587 rs144155419 G A 0.0046085 0.0036845 0.2110129 0.2110102 0.0157540 0.0045926 118002
1 723329 rs189787166 A T -0.0083080 0.0107327 0.4388863 0.4388845 0.0017785 0.0003994 118002
1 730087 rs148120343 T C -0.0013419 0.0019060 0.4814099 0.4814096 0.0569247 0.0127796 118002
1 731718 rs142557973 T C 0.0011569 0.0012999 0.3734642 0.3734599 0.1226680 0.1543530 118002
1 732032 rs61770163 A C 0.0010419 0.0013854 0.4520204 0.4520178 0.1222730 0.1555510 118002
1 734349 rs141242758 T C 0.0011398 0.0013006 0.3807983 0.3807967 0.1219240 0.1525560 118002
1 740284 rs61770167 C T 0.0048370 0.0059371 0.4152398 0.4152384 0.0058595 0.0023962 118002
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T -0.0035905 0.0015283 0.0188105 0.0188093 0.0554993 0.0309934 118002
23 154923374 rs111332691 T A 0.0016408 0.0016662 0.3247633 0.3247587 0.0447620 0.0116556 118002
23 154925045 rs509981 C T 0.0005616 0.0008023 0.4839305 0.4839300 0.2447160 0.3634440 118002
23 154925895 rs538470 C T 0.0006395 0.0008204 0.4356633 0.4356605 0.2411370 0.3634440 118002
23 154927581 rs644138 G A -0.0003630 0.0007549 0.6306480 0.6306476 0.3005770 0.4635760 118002
23 154929412 rs557132 C T 0.0005677 0.0008025 0.4793315 0.4793306 0.2445910 0.3568210 118002
23 154929637 rs35185538 CT C -0.0001213 0.0008383 0.8849320 0.8849323 0.2288060 0.3011920 118002
23 154929952 rs4012982 CAA C 0.0003785 0.0008446 0.6540817 0.6540799 0.2386720 0.3165560 118002
23 154930230 rs781880 A G 0.0006476 0.0008023 0.4195483 0.4195468 0.2450140 0.3618540 118002
23 154930487 rs781879 T A -0.0008167 0.0027269 0.7645572 0.7645570 0.0196756 0.1263580 118002

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.111725 ES:SE:LP:AF:SS:ID   0.000982044:0.00144662:0.303442:0.111725:118002:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.116742 ES:SE:LP:AF:SS:ID   0.00141414:0.00136998:0.520045:0.116742:118002:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0983656    ES:SE:LP:AF:SS:ID   0.000346853:0.00153722:0.0854009:0.0983656:118002:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.015754 ES:SE:LP:AF:SS:ID   0.00460852:0.00368448:0.675691:0.015754:118002:rs144155419
1   723329  rs189787166 A   T   .   PASS    AF=0.00177852   ES:SE:LP:AF:SS:ID   -0.00830795:0.0107327:0.357648:0.00177852:118002:rs189787166
1   730087  rs148120343 T   C   .   PASS    AF=0.0569247    ES:SE:LP:AF:SS:ID   -0.00134193:0.00190605:0.317485:0.0569247:118002:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122668 ES:SE:LP:AF:SS:ID   0.00115689:0.00129986:0.427751:0.122668:118002:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.122273 ES:SE:LP:AF:SS:ID   0.00104192:0.00138543:0.344842:0.122273:118002:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.121924 ES:SE:LP:AF:SS:ID   0.00113984:0.00130055:0.419305:0.121924:118002:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.00585949   ES:SE:LP:AF:SS:ID   0.00483703:0.00593712:0.381701:0.00585949:118002:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.00183332   ES:SE:LP:AF:SS:ID   -0.0161392:0.0113904:0.80545:0.00183332:118002:rs112573343
1   749963  rs529266287 T   TAA .   PASS    AF=0.869    ES:SE:LP:AF:SS:ID   -0.000862212:0.00128149:0.300109:0.869:118002:rs529266287
1   750230  rs190826124 G   C   .   PASS    AF=0.00158573   ES:SE:LP:AF:SS:ID   0.00201077:0.011393:0.065548:0.00158573:118002:rs190826124
1   751343  rs28544273  T   A   .   PASS    AF=0.123707 ES:SE:LP:AF:SS:ID   0.00120727:0.00126967:0.46638:0.123707:118002:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.143341 ES:SE:LP:AF:SS:ID   0.000945828:0.00125571:0.345517:0.143341:118002:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.123828 ES:SE:LP:AF:SS:ID   0.00118505:0.00126791:0.455967:0.123828:118002:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.869757 ES:SE:LP:AF:SS:ID   -0.000660851:0.00123473:0.227312:0.869757:118002:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.874545 ES:SE:LP:AF:SS:ID   -0.000770491:0.00125552:0.268069:0.874545:118002:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.129703 ES:SE:LP:AF:SS:ID   0.000908685:0.00123819:0.334399:0.129703:118002:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.036546 ES:SE:LP:AF:SS:ID   -7.74101e-05:0.00224814:0.0120961:0.036546:118002:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.86935  ES:SE:LP:AF:SS:ID   -0.000778971:0.00123356:0.277591:0.86935:118002:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869422 ES:SE:LP:AF:SS:ID   -0.000796948:0.00123401:0.285336:0.869422:118002:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.86935  ES:SE:LP:AF:SS:ID   -0.00076152:0.00123352:0.270023:0.86935:118002:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00511994   ES:SE:LP:AF:SS:ID   0.00010257:0.00630825:0.00567097:0.00511994:118002:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00508903   ES:SE:LP:AF:SS:ID   -5.33972e-05:0.00632215:0.00293662:0.00508903:118002:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.0056895    ES:SE:LP:AF:SS:ID   0.00878831:0.0060786:0.829029:0.0056895:118002:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.869423 ES:SE:LP:AF:SS:ID   -0.000711701:0.00123125:0.249303:0.869423:118002:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.126979 ES:SE:LP:AF:SS:ID   0.000796448:0.00125686:0.278773:0.126979:118002:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.868943 ES:SE:LP:AF:SS:ID   -0.000609334:0.00122801:0.207778:0.868943:118002:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.868344 ES:SE:LP:AF:SS:ID   -0.000840511:0.00122755:0.306686:0.868344:118002:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869132 ES:SE:LP:AF:SS:ID   -0.000720409:0.00122924:0.253495:0.869132:118002:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869139 ES:SE:LP:AF:SS:ID   -0.000722011:0.00122932:0.254155:0.869139:118002:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869142 ES:SE:LP:AF:SS:ID   -0.000721676:0.00122933:0.254011:0.869142:118002:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869577 ES:SE:LP:AF:SS:ID   -0.00071266:0.00123248:0.249408:0.869577:118002:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.0995897    ES:SE:LP:AF:SS:ID   -0.000184095:0.00143286:0.0468359:0.0995897:118002:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.0065479    ES:SE:LP:AF:SS:ID   0.00908024:0.00551945:1.00024:0.0065479:118002:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.873646 ES:SE:LP:AF:SS:ID   -0.000812441:0.00125142:0.287181:0.873646:118002:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.862925 ES:SE:LP:AF:SS:ID   -0.000790577:0.00122724:0.284455:0.862925:118002:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.868327 ES:SE:LP:AF:SS:ID   -0.000972096:0.00123907:0.363787:0.868327:118002:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.865338 ES:SE:LP:AF:SS:ID   -0.000752048:0.00123926:0.264443:0.865338:118002:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0997644    ES:SE:LP:AF:SS:ID   0.00163022:0.00148083:0.567111:0.0997644:118002:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.870635 ES:SE:LP:AF:SS:ID   -0.00103212:0.00125391:0.386749:0.870635:118002:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.870635 ES:SE:LP:AF:SS:ID   -0.00103214:0.00125391:0.386759:0.870635:118002:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.870634 ES:SE:LP:AF:SS:ID   -0.0010317:0.00125392:0.386545:0.870634:118002:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.870956 ES:SE:LP:AF:SS:ID   -0.00098491:0.00125444:0.364139:0.870956:118002:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.126564 ES:SE:LP:AF:SS:ID   0.00111009:0.00125551:0.424114:0.126564:118002:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.10635  ES:SE:LP:AF:SS:ID   0.000204642:0.00136439:0.055135:0.10635:118002:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.854373 ES:SE:LP:AF:SS:ID   -0.00102515:0.00122693:0.394251:0.854373:118002:rs2519015
1   767393  rs538667473 A   C   .   PASS    AF=0.0016903    ES:SE:LP:AF:SS:ID   -0.00933689:0.0107804:0.412921:0.0016903:118002:rs538667473
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.839097 ES:SE:LP:AF:SS:ID   -0.00285657:0.00121564:1.72625:0.839097:118002:rs376645387