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-30130_irnt/ukb-d-30130_irnt.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-30130_irnt/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 14:57:32 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-30130_irnt/ukb-d-30130_irnt.vcf.gz ...
Read summary statistics for 13586291 SNPs.
Dropped 12710 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: 0.1913 (0.0192)
Lambda GC: 1.537
Mean Chi^2: 2.4582
Intercept: 1.1995 (0.0233)
Ratio: 0.1368 (0.016)
Analysis finished at Mon Nov 25 14:59:53 2019
Total time elapsed: 2.0m:21.9s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9521,
    "inflation_factor": 1.3426,
    "mean_EFFECT": -0,
    "n": 349856,
    "n_snps": 13586291,
    "n_clumped_hits": 348,
    "n_p_sig": 77741,
    "n_mono": 0,
    "n_ns": 1251790,
    "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": 569188,
    "n_est": 351181.9444,
    "ratio_se_n": 1.0019,
    "mean_diff": 2.4842e-07,
    "ratio_diff": 0.3648,
    "sd_y_est1": 0.9711,
    "sd_y_est2": 0.9729,
    "r2_sum1": 0.1192,
    "r2_sum2": 0.1264,
    "r2_sum3": 0.1259,
    "r2_sum4": 0.1246,
    "ldsc_nsnp_merge_refpanel_ld": 1283504,
    "ldsc_nsnp_merge_regression_ld": 1283504,
    "ldsc_observed_scale_h2_beta": 0.1913,
    "ldsc_observed_scale_h2_se": 0.0192,
    "ldsc_intercept_beta": 1.1995,
    "ldsc_intercept_se": 0.0233,
    "ldsc_lambda_gc": 1.537,
    "ldsc_mean_chisq": 2.4582,
    "ldsc_ratio": 0.1368
}

Flags

name value
af_correlation FALSE
inflation_factor TRUE
n FALSE
is_snpid_non_unique TRUE
mean_EFFECT_nonfinite FALSE
mean_EFFECT_05 FALSE
mean_EFFECT_01 FALSE
mean_chisq TRUE
n_p_sig TRUE
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 13574245 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 100 0 57080 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 342 0 33337 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 9.051330e+00 6.184986e+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 7.902511e+07 5.591484e+07 3.02000e+02 3.293100e+07 7.013867e+07 1.148588e+08 2.492309e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA -1.550000e-05 1.459750e-02 -5.90019e-01 -4.370600e-03 -1.850000e-05 4.371700e-03 4.864800e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 9.755100e-03 9.410100e-03 1.90810e-03 2.777300e-03 5.117900e-03 1.401880e-02 4.776410e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.503523e-01 3.039391e-01 0.00000e+00 1.745119e-01 4.344942e-01 7.140703e-01 1.000000e+00 ▇▆▅▅▅
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.503518e-01 3.039393e-01 0.00000e+00 1.745112e-01 4.344942e-01 7.140704e-01 1.000000e+00 ▇▆▅▅▅
numeric AF 0 1.0000000 NA NA NA NA NA 1.908058e-01 2.544033e-01 1.00000e-03 7.430400e-03 6.003380e-02 2.953175e-01 9.989990e-01 ▇▂▁▁▁
numeric AF_reference 569188 0.9581057 NA NA NA NA NA 1.963354e-01 2.471013e-01 0.00000e+00 5.591000e-03 8.426520e-02 3.081070e-01 1.000000e+00 ▇▂▁▁▁
numeric N 0 1.0000000 NA NA NA NA NA 3.498560e+05 0.000000e+00 3.49856e+05 3.498560e+05 3.498560e+05 3.498560e+05 3.498560e+05 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 692794 rs530212009 CA C -0.0031781 0.0040644 0.4342482 0.4342471 0.1106480 0.1894970 349856
1 693731 rs12238997 A G 0.0017497 0.0038406 0.6486897 0.6486884 0.1158220 0.1417730 349856
1 707522 rs371890604 G C 0.0027882 0.0043192 0.5185791 0.5185789 0.0972503 0.1293930 349856
1 717587 rs144155419 G A -0.0006516 0.0102916 0.9495211 0.9495205 0.0157180 0.0045926 349856
1 723329 rs189787166 A T -0.0583074 0.0305506 0.0563210 0.0563199 0.0017201 0.0003994 349856
1 730087 rs148120343 T C -0.0043841 0.0053483 0.4123766 0.4123768 0.0564790 0.0127796 349856
1 731718 rs142557973 T C 0.0013028 0.0036422 0.7205714 0.7205703 0.1217450 0.1543530 349856
1 732032 rs61770163 A C 0.0024834 0.0038857 0.5227427 0.5227434 0.1211440 0.1555510 349856
1 734349 rs141242758 T C 0.0011620 0.0036442 0.7498390 0.7498389 0.1209750 0.1525560 349856
1 740284 rs61770167 C T -0.0020677 0.0166715 0.9012930 0.9012932 0.0057856 0.0023962 349856
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T 0.0090609 0.0042062 0.0312255 0.0312247 0.0561631 0.0309934 349856
23 154923374 rs111332691 T A -0.0084794 0.0046240 0.0666868 0.0666865 0.0447941 0.0116556 349856
23 154925045 rs509981 C T 0.0016944 0.0022253 0.4463947 0.4463929 0.2454420 0.3634440 349856
23 154925895 rs538470 C T 0.0016918 0.0022762 0.4573261 0.4573272 0.2417880 0.3634440 349856
23 154927581 rs644138 G A 0.0036808 0.0020925 0.0785652 0.0785639 0.3020000 0.4635760 349856
23 154929412 rs557132 C T 0.0017025 0.0022260 0.4443693 0.4443683 0.2452960 0.3568210 349856
23 154929637 rs35185538 CT C 0.0022289 0.0023234 0.3373961 0.3373963 0.2295270 0.3011920 349856
23 154929952 rs4012982 CAA C 0.0018999 0.0023412 0.4170777 0.4170761 0.2392510 0.3165560 349856
23 154930230 rs781880 A G 0.0018358 0.0022256 0.4094577 0.4094576 0.2457070 0.3618540 349856
23 154930487 rs781879 T A -0.0053872 0.0076398 0.4807209 0.4807202 0.0195212 0.1263580 349856

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.110648 ES:SE:LP:AF:SS:ID   -0.00317812:0.00406438:0.362262:0.110648:349856:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.115822 ES:SE:LP:AF:SS:ID   0.00174972:0.0038406:0.187963:0.115822:349856:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0972503    ES:SE:LP:AF:SS:ID   0.00278818:0.00431916:0.285185:0.0972503:349856:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.015718 ES:SE:LP:AF:SS:ID   -0.00065155:0.0102916:0.0224954:0.015718:349856:rs144155419
1   723329  rs189787166 A   T   .   PASS    AF=0.00172006   ES:SE:LP:AF:SS:ID   -0.0583074:0.0305506:1.24933:0.00172006:349856:rs189787166
1   730087  rs148120343 T   C   .   PASS    AF=0.056479 ES:SE:LP:AF:SS:ID   -0.00438409:0.00534829:0.384706:0.056479:349856:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.121745 ES:SE:LP:AF:SS:ID   0.00130281:0.00364222:0.142323:0.121745:349856:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.121144 ES:SE:LP:AF:SS:ID   0.00248345:0.00388572:0.281712:0.121144:349856:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.120975 ES:SE:LP:AF:SS:ID   0.00116195:0.00364417:0.125032:0.120975:349856:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.00578559   ES:SE:LP:AF:SS:ID   -0.00206773:0.0166715:0.045134:0.00578559:349856:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.00188274   ES:SE:LP:AF:SS:ID   -0.0513215:0.0314847:0.986775:0.00188274:349856:rs112573343
1   749963  rs529266287 T   TAA .   PASS    AF=0.869759 ES:SE:LP:AF:SS:ID   -0.00184837:0.00359296:0.216852:0.869759:349856:rs529266287
1   750230  rs190826124 G   C   .   PASS    AF=0.00153176   ES:SE:LP:AF:SS:ID   -0.0307418:0.0325062:0.463074:0.00153176:349856:rs190826124
1   751343  rs28544273  T   A   .   PASS    AF=0.122895 ES:SE:LP:AF:SS:ID   0.00188022:0.00355809:0.223882:0.122895:349856:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.142708 ES:SE:LP:AF:SS:ID   0.0029009:0.00351705:0.387766:0.142708:349856:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.123012 ES:SE:LP:AF:SS:ID   0.00176:0.00355311:0.207356:0.123012:349856:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.870892 ES:SE:LP:AF:SS:ID   -0.00153858:0.00346234:0.182585:0.870892:349856:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.875492 ES:SE:LP:AF:SS:ID   -0.00175501:0.00351983:0.208971:0.875492:349856:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.128631 ES:SE:LP:AF:SS:ID   0.00197291:0.00347096:0.244307:0.128631:349856:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0363478    ES:SE:LP:AF:SS:ID   -0.000548714:0.00630036:0.0312379:0.0363478:349856:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.870485 ES:SE:LP:AF:SS:ID   -0.00183481:0.0034587:0.22492:0.870485:349856:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.870587 ES:SE:LP:AF:SS:ID   -0.00185173:0.00346022:0.227276:0.870587:349856:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.870481 ES:SE:LP:AF:SS:ID   -0.00181245:0.00345856:0.221671:0.870481:349856:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00504553   ES:SE:LP:AF:SS:ID   0.00940441:0.0177586:0.224454:0.00504553:349856:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00501236   ES:SE:LP:AF:SS:ID   0.00968504:0.0178059:0.231735:0.00501236:349856:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.00558059   ES:SE:LP:AF:SS:ID   0.00629273:0.0172065:0.145952:0.00558059:349856:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.870565 ES:SE:LP:AF:SS:ID   -0.00161638:0.0034525:0.194051:0.870565:349856:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.125973 ES:SE:LP:AF:SS:ID   0.00190222:0.00352308:0.229703:0.125973:349856:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.870129 ES:SE:LP:AF:SS:ID   -0.00140373:0.00344418:0.165203:0.870129:349856:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.869408 ES:SE:LP:AF:SS:ID   -0.00189129:0.00344091:0.234658:0.869408:349856:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.870284 ES:SE:LP:AF:SS:ID   -0.00172795:0.00344716:0.21029:0.870284:349856:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.87029  ES:SE:LP:AF:SS:ID   -0.00172571:0.00344741:0.20995:0.87029:349856:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.870298 ES:SE:LP:AF:SS:ID   -0.00173014:0.00344752:0.21058:0.870298:349856:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.87074  ES:SE:LP:AF:SS:ID   -0.00156437:0.00345629:0.186536:0.87074:349856:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.0987996    ES:SE:LP:AF:SS:ID   0.00199354:0.00401763:0.207781:0.0987996:349856:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.00647264   ES:SE:LP:AF:SS:ID   -0.00246116:0.0155445:0.0583907:0.00647264:349856:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.874649 ES:SE:LP:AF:SS:ID   -0.00203755:0.00350794:0.250766:0.874649:349856:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.864035 ES:SE:LP:AF:SS:ID   -0.000964902:0.00343994:0.10841:0.864035:349856:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.869419 ES:SE:LP:AF:SS:ID   -0.00197533:0.00347336:0.244465:0.869419:349856:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.866376 ES:SE:LP:AF:SS:ID   -0.000775979:0.00347415:0.0844646:0.866376:349856:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0987131    ES:SE:LP:AF:SS:ID   0.00230029:0.00416063:0.236308:0.0987131:349856:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.87156  ES:SE:LP:AF:SS:ID   -0.00187624:0.00351372:0.226683:0.87156:349856:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.87156  ES:SE:LP:AF:SS:ID   -0.0018761:0.00351371:0.226662:0.87156:349856:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.87156  ES:SE:LP:AF:SS:ID   -0.00187047:0.00351378:0.225847:0.87156:349856:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.871932 ES:SE:LP:AF:SS:ID   -0.001976:0.00351578:0.241019:0.871932:349856:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.125555 ES:SE:LP:AF:SS:ID   0.00205987:0.00351853:0.253167:0.125555:349856:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.105385 ES:SE:LP:AF:SS:ID   0.00166866:0.00383122:0.178376:0.105385:349856:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.855371 ES:SE:LP:AF:SS:ID   -0.000503817:0.00343898:0.0537812:0.855371:349856:rs2519015
1   767393  rs538667473 A   C   .   PASS    AF=0.00164256   ES:SE:LP:AF:SS:ID   -0.0565957:0.0306037:1.19102:0.00164256:349856:rs538667473
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.838282 ES:SE:LP:AF:SS:ID   -0.00158705:0.00338722:0.194228:0.838282:349856:rs376645387