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-22615_1/ukb-d-22615_1.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-22615_1/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 16:37:14 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-22615_1/ukb-d-22615_1.vcf.gz ...
Read summary statistics for 13566609 SNPs.
Dropped 12684 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: 0.0272 (0.0055)
Lambda GC: 1.0449
Mean Chi^2: 1.052
Intercept: 1.0043 (0.0061)
Ratio: 0.083 (0.117)
Analysis finished at Mon Nov 25 16:39:32 2019
Total time elapsed: 2.0m:17.55s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9521,
    "inflation_factor": 1.0343,
    "mean_EFFECT": 0,
    "n": 89046,
    "n_snps": 13566609,
    "n_clumped_hits": 0,
    "n_p_sig": 3,
    "n_mono": 0,
    "n_ns": 1251210,
    "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": 563091,
    "n_est": 89379.127,
    "ratio_se_n": 1.0019,
    "mean_diff": 0.0001,
    "ratio_diff": 238.8189,
    "sd_y_est1": 0.341,
    "sd_y_est2": 0.3416,
    "r2_sum1": 0,
    "r2_sum2": 0,
    "r2_sum3": 0,
    "r2_sum4": 0,
    "ldsc_nsnp_merge_refpanel_ld": 1283502,
    "ldsc_nsnp_merge_regression_ld": 1283502,
    "ldsc_observed_scale_h2_beta": 0.0272,
    "ldsc_observed_scale_h2_se": 0.0055,
    "ldsc_intercept_beta": 1.0043,
    "ldsc_intercept_se": 0.0061,
    "ldsc_lambda_gc": 1.0449,
    "ldsc_mean_chisq": 1.052,
    "ldsc_ratio": 0.0827
}

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 13554589 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 100 0 57046 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 342 0 33332 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 9.051338e+00 6.184964e+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 7.902426e+07 5.591642e+07 3.02000e+02 3.292745e+07 7.013474e+07 1.148612e+08 2.492309e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA 3.190000e-05 9.452800e-03 -1.15072e-01 -2.532200e-03 -3.100000e-06 2.529700e-03 1.300550e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 6.763100e-03 6.517700e-03 1.33260e-03 1.932400e-03 3.554100e-03 9.711500e-03 3.286230e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.942982e-01 2.902074e-01 0.00000e+00 2.419909e-01 4.927368e-01 7.453084e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.942964e-01 2.902085e-01 0.00000e+00 2.419877e-01 4.927348e-01 7.453086e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA 1.910707e-01 2.544751e-01 1.00000e-03 7.500000e-03 6.029930e-02 2.957910e-01 9.990000e-01 ▇▂▁▁▁
numeric AF_reference 563091 0.9584943 NA NA NA NA NA 1.965261e-01 2.471458e-01 0.00000e+00 5.790700e-03 8.446490e-02 3.085060e-01 1.000000e+00 ▇▂▁▁▁
numeric N 0 1.0000000 NA NA NA NA NA 8.904600e+04 0.000000e+00 8.90460e+04 8.904600e+04 8.904600e+04 8.904600e+04 8.904600e+04 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 692794 rs530212009 CA C -0.0016901 0.0028214 0.5491462 0.5491451 0.1115480 0.1894970 89046
1 693731 rs12238997 A G -0.0013450 0.0026722 0.6147337 0.6147306 0.1165110 0.1417730 89046
1 707522 rs371890604 G C -0.0015122 0.0030025 0.6145115 0.6145095 0.0980287 0.1293930 89046
1 717587 rs144155419 G A -0.0110982 0.0071677 0.1215380 0.1215360 0.0157549 0.0045926 89046
1 723329 rs189787166 A T -0.0160423 0.0209364 0.4435361 0.4435337 0.0017713 0.0003994 89046
1 730087 rs148120343 T C -0.0020309 0.0037034 0.5834155 0.5834151 0.0570994 0.0127796 89046
1 731718 rs142557973 T C -0.0012703 0.0025350 0.6162983 0.6162962 0.1224730 0.1543530 89046
1 732032 rs61770163 A C -0.0016567 0.0027037 0.5400575 0.5400555 0.1221180 0.1555510 89046
1 734349 rs141242758 T C -0.0013996 0.0025370 0.5811564 0.5811556 0.1216990 0.1525560 89046
1 740284 rs61770167 C T 0.0016163 0.0116016 0.8892009 0.8892003 0.0057916 0.0023962 89046
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T 0.0009090 0.0029654 0.7591997 0.7591983 0.0552906 0.0309934 89046
23 154923374 rs111332691 T A -0.0017206 0.0032299 0.5942347 0.5942325 0.0447690 0.0116556 89046
23 154925045 rs509981 C T -0.0013654 0.0015553 0.3799890 0.3799876 0.2447040 0.3634440 89046
23 154925895 rs538470 C T -0.0014241 0.0015899 0.3704230 0.3704195 0.2412220 0.3634440 89046
23 154927581 rs644138 G A -0.0008087 0.0014628 0.5803941 0.5803917 0.3004000 0.4635760 89046
23 154929412 rs557132 C T -0.0014120 0.0015557 0.3640667 0.3640632 0.2445590 0.3568210 89046
23 154929637 rs35185538 CT C -0.0015120 0.0016243 0.3519298 0.3519247 0.2289410 0.3011920 89046
23 154929952 rs4012982 CAA C -0.0018118 0.0016375 0.2685412 0.2685379 0.2384600 0.3165560 89046
23 154930230 rs781880 A G -0.0013634 0.0015552 0.3806738 0.3806693 0.2450200 0.3618540 89046
23 154930487 rs781879 T A -0.0024186 0.0052688 0.6462001 0.6461987 0.0196666 0.1263580 89046

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.111548 ES:SE:LP:AF:SS:ID   -0.00169013:0.00282139:0.260312:0.111548:89046:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.116511 ES:SE:LP:AF:SS:ID   -0.00134503:0.00267225:0.211313:0.116511:89046:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0980287    ES:SE:LP:AF:SS:ID   -0.0015122:0.0030025:0.21147:0.0980287:89046:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.0157549    ES:SE:LP:AF:SS:ID   -0.0110982:0.00716772:0.915288:0.0157549:89046:rs144155419
1   723329  rs189787166 A   T   .   PASS    AF=0.0017713    ES:SE:LP:AF:SS:ID   -0.0160423:0.0209364:0.353071:0.0017713:89046:rs189787166
1   730087  rs148120343 T   C   .   PASS    AF=0.0570994    ES:SE:LP:AF:SS:ID   -0.00203094:0.00370337:0.234022:0.0570994:89046:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122473 ES:SE:LP:AF:SS:ID   -0.00127032:0.00253503:0.210209:0.122473:89046:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.122118 ES:SE:LP:AF:SS:ID   -0.00165666:0.00270374:0.26756:0.122118:89046:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.121699 ES:SE:LP:AF:SS:ID   -0.00139964:0.00253697:0.235707:0.121699:89046:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.00579165   ES:SE:LP:AF:SS:ID   0.00161629:0.0116016:0.0510001:0.00579165:89046:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.00184267   ES:SE:LP:AF:SS:ID   0.0118731:0.0221372:0.227881:0.00184267:89046:rs112573343
1   749963  rs529266287 T   TAA .   PASS    AF=0.868828 ES:SE:LP:AF:SS:ID   -2.6869e-05:0.00249585:0.00374627:0.868828:89046:rs529266287
1   750230  rs190826124 G   C   .   PASS    AF=0.00160763   ES:SE:LP:AF:SS:ID   -0.0390018:0.0219974:1.11788:0.00160763:89046:rs190826124
1   751343  rs28544273  T   A   .   PASS    AF=0.123815 ES:SE:LP:AF:SS:ID   -0.000754165:0.00247423:0.118893:0.123815:89046:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.143355 ES:SE:LP:AF:SS:ID   -0.000181171:0.0024492:0.0263951:0.143355:89046:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.123953 ES:SE:LP:AF:SS:ID   -0.000808335:0.00247063:0.128699:0.123953:89046:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.86978  ES:SE:LP:AF:SS:ID   0.000373186:0.00240721:0.0571:0.86978:89046:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.874418 ES:SE:LP:AF:SS:ID   0.000900283:0.00244639:0.146989:0.874418:89046:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.129636 ES:SE:LP:AF:SS:ID   -0.000114143:0.00241485:0.0166895:0.129636:89046:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0357568    ES:SE:LP:AF:SS:ID   0.0132496:0.00441703:2.56807:0.0357568:89046:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869424 ES:SE:LP:AF:SS:ID   0.000275253:0.00240519:0.0414896:0.869424:89046:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869502 ES:SE:LP:AF:SS:ID   0.000316755:0.00240625:0.0480455:0.869502:89046:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.869419 ES:SE:LP:AF:SS:ID   0.000279873:0.00240509:0.0422194:0.869419:89046:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00530338   ES:SE:LP:AF:SS:ID   0.00888808:0.0120348:0.337061:0.00530338:89046:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00526923   ES:SE:LP:AF:SS:ID   0.00890943:0.0120648:0.337022:0.00526923:89046:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.00562576   ES:SE:LP:AF:SS:ID   0.00374135:0.0118818:0.12329:0.00562576:89046:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.869412 ES:SE:LP:AF:SS:ID   0.000415556:0.00240016:0.0642187:0.869412:89046:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.12704  ES:SE:LP:AF:SS:ID   -0.000134412:0.00245014:0.0194281:0.12704:89046:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.868933 ES:SE:LP:AF:SS:ID   0.000361373:0.00239387:0.0555124:0.868933:89046:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.868317 ES:SE:LP:AF:SS:ID   0.000334303:0.00239233:0.0511637:0.868317:89046:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869088 ES:SE:LP:AF:SS:ID   0.000395356:0.00239609:0.0610082:0.869088:89046:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869094 ES:SE:LP:AF:SS:ID   0.000397342:0.00239624:0.0613302:0.869094:89046:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869096 ES:SE:LP:AF:SS:ID   0.000395809:0.00239626:0.0610782:0.869096:89046:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869577 ES:SE:LP:AF:SS:ID   0.000468655:0.00240271:0.0729624:0.869577:89046:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.0993312    ES:SE:LP:AF:SS:ID   -0.000628118:0.00279608:0.0849919:0.0993312:89046:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.00646161   ES:SE:LP:AF:SS:ID   0.00912136:0.0108246:0.398566:0.00646161:89046:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.873557 ES:SE:LP:AF:SS:ID   0.000682851:0.00243832:0.108217:0.873557:89046:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.862943 ES:SE:LP:AF:SS:ID   -0.000233954:0.00239171:0.0352333:0.862943:89046:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.868353 ES:SE:LP:AF:SS:ID   0.000307512:0.00241537:0.0463896:0.868353:89046:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.865248 ES:SE:LP:AF:SS:ID   0.000446723:0.00241595:0.0688967:0.865248:89046:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0995894    ES:SE:LP:AF:SS:ID   -0.000540131:0.00289272:0.0696216:0.0995894:89046:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.87054  ES:SE:LP:AF:SS:ID   0.0006145:0.00244348:0.096129:0.87054:89046:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.87054  ES:SE:LP:AF:SS:ID   0.000614129:0.00244348:0.0960656:0.87054:89046:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.870537 ES:SE:LP:AF:SS:ID   0.0006149:0.00244349:0.0961973:0.870537:89046:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.870906 ES:SE:LP:AF:SS:ID   0.00057301:0.00244466:0.0890124:0.870906:89046:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.126637 ES:SE:LP:AF:SS:ID   -0.000455334:0.00244736:0.069354:0.126637:89046:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.106215 ES:SE:LP:AF:SS:ID   0.000387245:0.00266:0.0534235:0.106215:89046:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.854265 ES:SE:LP:AF:SS:ID   0.000184133:0.00239111:0.0275111:0.854265:89046:rs2519015
1   767393  rs538667473 A   C   .   PASS    AF=0.00168382   ES:SE:LP:AF:SS:ID   -0.0180865:0.0210007:0.409924:0.00168382:89046:rs538667473
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.839406 ES:SE:LP:AF:SS:ID   0.00106134:0.00236543:0.184648:0.839406:89046:rs376645387