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-22616_0/ukb-d-22616_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-22616_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 16:37:38 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-22616_0/ukb-d-22616_0.vcf.gz ...
Read summary statistics for 13567101 SNPs.
Dropped 12681 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, 1283504 SNPs remain.
After merging with regression SNP LD, 1283504 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0188 (0.0047)
Lambda GC: 1.0267
Mean Chi^2: 1.027
Intercept: 0.9934 (0.0065)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Mon Nov 25 16:40:27 2019
Total time elapsed: 2.0m:49.45s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9521,
    "inflation_factor": 1.0208,
    "mean_EFFECT": 2.8838e-06,
    "n": 91149,
    "n_snps": 13567101,
    "n_clumped_hits": 1,
    "n_p_sig": 19,
    "n_mono": 0,
    "n_ns": 1251240,
    "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": 563245,
    "n_est": 91483.5706,
    "ratio_se_n": 1.0018,
    "mean_diff": 0,
    "ratio_diff": 9.3103,
    "sd_y_est1": 0.1302,
    "sd_y_est2": 0.1304,
    "r2_sum1": 5.6989e-06,
    "r2_sum2": 0.0003,
    "r2_sum3": 0.0003,
    "r2_sum4": 0.0003,
    "ldsc_nsnp_merge_refpanel_ld": 1283504,
    "ldsc_nsnp_merge_regression_ld": 1283504,
    "ldsc_observed_scale_h2_beta": 0.0188,
    "ldsc_observed_scale_h2_se": 0.0047,
    "ldsc_intercept_beta": 0.9934,
    "ldsc_intercept_se": 0.0065,
    "ldsc_lambda_gc": 1.0267,
    "ldsc_mean_chisq": 1.027,
    "ldsc_ratio": -0.2444
}

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 13555084 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 100 0 57045 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 342 0 33333 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 9.051336e+00 6.184954e+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 7.902471e+07 5.591640e+07 3.02000e+02 3.292784e+07 7.013475e+07 1.148619e+08 2.492309e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA 2.900000e-06 3.543600e-03 -6.39007e-02 -9.057000e-04 2.600000e-05 9.908000e-04 4.242290e-02 ▁▁▇▇▁
numeric SE 0 1.0000000 NA NA NA NA NA 2.551900e-03 2.459300e-03 5.03300e-04 7.291000e-04 1.341100e-03 3.664300e-03 1.240440e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.970286e-01 2.890181e-01 0.00000e+00 2.461597e-01 4.955677e-01 7.472123e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.970269e-01 2.890192e-01 0.00000e+00 2.461562e-01 4.955663e-01 7.472110e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA 1.910627e-01 2.544718e-01 1.00000e-03 7.498700e-03 6.029230e-02 2.957650e-01 9.990000e-01 ▇▂▁▁▁
numeric AF_reference 563245 0.9584845 NA NA NA NA NA 1.965200e-01 2.471433e-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 9.114900e+04 0.000000e+00 9.11490e+04 9.114900e+04 9.114900e+04 9.114900e+04 9.114900e+04 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 692794 rs530212009 CA C -0.0006678 0.0010647 0.5305191 0.5305163 0.1114870 0.1894970 91149
1 693731 rs12238997 A G -0.0006940 0.0010084 0.4913003 0.4913003 0.1164590 0.1417730 91149
1 707522 rs371890604 G C 0.0005235 0.0011332 0.6440788 0.6440783 0.0978765 0.1293930 91149
1 717587 rs144155419 G A -0.0028817 0.0027012 0.2860540 0.2860501 0.0157837 0.0045926 91149
1 723329 rs189787166 A T -0.0060298 0.0078770 0.4439786 0.4439772 0.0017798 0.0003994 91149
1 730087 rs148120343 T C 0.0014686 0.0013976 0.2933722 0.2933700 0.0569973 0.0127796 91149
1 731718 rs142557973 T C -0.0002629 0.0009569 0.7834946 0.7834949 0.1223590 0.1543530 91149
1 732032 rs61770163 A C -0.0001823 0.0010202 0.8581859 0.8581863 0.1220470 0.1555510 91149
1 734349 rs141242758 T C -0.0002796 0.0009576 0.7703037 0.7703032 0.1215880 0.1525560 91149
1 740284 rs61770167 C T 0.0031565 0.0043969 0.4728224 0.4728214 0.0057444 0.0023962 91149
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T -0.0003086 0.0011201 0.7829427 0.7829426 0.0552869 0.0309934 91149
23 154923374 rs111332691 T A -0.0015972 0.0012190 0.1900938 0.1900915 0.0448387 0.0116556 91149
23 154925045 rs509981 C T -0.0010136 0.0005874 0.0844617 0.0844571 0.2444670 0.3634440 91149
23 154925895 rs538470 C T -0.0012047 0.0006006 0.0448704 0.0448686 0.2409490 0.3634440 91149
23 154927581 rs644138 G A -0.0009846 0.0005525 0.0747550 0.0747521 0.3001640 0.4635760 91149
23 154929412 rs557132 C T -0.0010232 0.0005876 0.0816394 0.0816358 0.2443160 0.3568210 91149
23 154929637 rs35185538 CT C -0.0012291 0.0006136 0.0451627 0.0451599 0.2286640 0.3011920 91149
23 154929952 rs4012982 CAA C -0.0012789 0.0006185 0.0386545 0.0386515 0.2382530 0.3165560 91149
23 154930230 rs781880 A G -0.0011169 0.0005874 0.0572664 0.0572621 0.2447660 0.3618540 91149
23 154930487 rs781879 T A -0.0020470 0.0019923 0.3041978 0.3041962 0.0196632 0.1263580 91149

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.111487 ES:SE:LP:AF:SS:ID   -0.000667774:0.00106466:0.275299:0.111487:91149:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.116459 ES:SE:LP:AF:SS:ID   -0.000693989:0.00100835:0.308653:0.116459:91149:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0978765    ES:SE:LP:AF:SS:ID   0.00052352:0.00113315:0.191061:0.0978765:91149:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.0157837    ES:SE:LP:AF:SS:ID   -0.00288168:0.00270117:0.543552:0.0157837:91149:rs144155419
1   723329  rs189787166 A   T   .   PASS    AF=0.00177978   ES:SE:LP:AF:SS:ID   -0.0060298:0.007877:0.352638:0.00177978:91149:rs189787166
1   730087  rs148120343 T   C   .   PASS    AF=0.0569973    ES:SE:LP:AF:SS:ID   0.00146857:0.00139763:0.532581:0.0569973:91149:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122359 ES:SE:LP:AF:SS:ID   -0.000262914:0.00095686:0.105964:0.122359:91149:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.122047 ES:SE:LP:AF:SS:ID   -0.0001823:0.00102024:0.0664186:0.122047:91149:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.121588 ES:SE:LP:AF:SS:ID   -0.000279594:0.000957585:0.113338:0.121588:91149:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.00574438   ES:SE:LP:AF:SS:ID   0.00315651:0.00439689:0.325302:0.00574438:91149:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.00184137   ES:SE:LP:AF:SS:ID   0.0084764:0.00836513:0.507353:0.00184137:91149:rs112573343
1   749963  rs529266287 T   TAA .   PASS    AF=0.868917 ES:SE:LP:AF:SS:ID   0.000411124:0.000941944:0.178813:0.868917:91149:rs529266287
1   750230  rs190826124 G   C   .   PASS    AF=0.00161367   ES:SE:LP:AF:SS:ID   0.015155:0.00828064:1.17245:0.00161367:91149:rs190826124
1   751343  rs28544273  T   A   .   PASS    AF=0.123699 ES:SE:LP:AF:SS:ID   -0.000478437:0.000933866:0.215789:0.123699:91149:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.143202 ES:SE:LP:AF:SS:ID   -0.000801538:0.000924533:0.413454:0.143202:91149:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.123834 ES:SE:LP:AF:SS:ID   -0.000473779:0.000932515:0.213668:0.123834:91149:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.869892 ES:SE:LP:AF:SS:ID   0.000391828:0.000908646:0.176325:0.869892:91149:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.874553 ES:SE:LP:AF:SS:ID   0.000403825:0.000923382:0.179226:0.874553:91149:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.129529 ES:SE:LP:AF:SS:ID   -0.000482118:0.000911497:0.224131:0.129529:91149:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0358205    ES:SE:LP:AF:SS:ID   9.52941e-05:0.00166521:0.0202855:0.0358205:91149:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869539 ES:SE:LP:AF:SS:ID   0.000420325:0.000907887:0.191528:0.869539:91149:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869616 ES:SE:LP:AF:SS:ID   0.000432406:0.000908283:0.197893:0.869616:91149:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.869535 ES:SE:LP:AF:SS:ID   0.000420991:0.000907849:0.191892:0.869535:91149:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00531588   ES:SE:LP:AF:SS:ID   -0.00398174:0.00453514:0.420263:0.00531588:91149:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00528125   ES:SE:LP:AF:SS:ID   -0.00405873:0.00454641:0.429455:0.00528125:91149:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.0056599    ES:SE:LP:AF:SS:ID   0.0065668:0.00446933:0.848465:0.0056599:91149:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.86952  ES:SE:LP:AF:SS:ID   0.000297531:0.000905979:0.129243:0.86952:91149:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.126911 ES:SE:LP:AF:SS:ID   -0.000327003:0.000924846:0.140467:0.126911:91149:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.86905  ES:SE:LP:AF:SS:ID   0.000240451:0.000903634:0.102281:0.86905:91149:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.868436 ES:SE:LP:AF:SS:ID   0.000292273:0.000903061:0.127141:0.868436:91149:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869199 ES:SE:LP:AF:SS:ID   0.000270599:0.000904453:0.116453:0.869199:91149:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869205 ES:SE:LP:AF:SS:ID   0.000270921:0.000904512:0.116598:0.869205:91149:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869207 ES:SE:LP:AF:SS:ID   0.000273373:0.00090452:0.117773:0.869207:91149:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869687 ES:SE:LP:AF:SS:ID   0.000308994:0.00090694:0.1347:0.869687:91149:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.0991787    ES:SE:LP:AF:SS:ID   0.000287064:0.00105546:0.104778:0.0991787:91149:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.00649624   ES:SE:LP:AF:SS:ID   0.00625538:0.00407181:0.904911:0.00649624:91149:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.873682 ES:SE:LP:AF:SS:ID   0.000344157:0.000920372:0.149688:0.873682:91149:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.863058 ES:SE:LP:AF:SS:ID   0.000445893:0.00090281:0.206641:0.863058:91149:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.868472 ES:SE:LP:AF:SS:ID   0.00038688:0.000911768:0.173061:0.868472:91149:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.865369 ES:SE:LP:AF:SS:ID   0.000408365:0.000911939:0.184224:0.865369:91149:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0995228    ES:SE:LP:AF:SS:ID   -0.000669012:0.00109142:0.267691:0.0995228:91149:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.870657 ES:SE:LP:AF:SS:ID   0.000348978:0.000922328:0.151713:0.870657:91149:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.870657 ES:SE:LP:AF:SS:ID   0.000348969:0.000922328:0.151708:0.870657:91149:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.870654 ES:SE:LP:AF:SS:ID   0.00034892:0.000922331:0.151683:0.870654:91149:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.871019 ES:SE:LP:AF:SS:ID   0.000326466:0.000922759:0.140564:0.871019:91149:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.126515 ES:SE:LP:AF:SS:ID   -0.000337089:0.000923785:0.14558:0.126515:91149:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.106037 ES:SE:LP:AF:SS:ID   -0.000143716:0.00100416:0.0524707:0.106037:91149:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.85438  ES:SE:LP:AF:SS:ID   0.000444002:0.000902465:0.205701:0.85438:91149:rs2519015
1   767393  rs538667473 A   C   .   PASS    AF=0.00169214   ES:SE:LP:AF:SS:ID   -0.00488102:0.0079008:0.270255:0.00169214:91149:rs538667473
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.839153 ES:SE:LP:AF:SS:ID   0.000679363:0.000891673:0.350545:0.839153:91149:rs376645387