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-22617_4150/ukb-d-22617_4150.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-22617_4150/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 14:55:59 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-22617_4150/ukb-d-22617_4150.vcf.gz ...
Read summary statistics for 11659378 SNPs.
Dropped 9985 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, 1282786 SNPs remain.
After merging with regression SNP LD, 1282786 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0107 (0.0053)
Lambda GC: 1.0205
Mean Chi^2: 1.0182
Intercept: 0.9986 (0.007)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Mon Nov 25 14:57:54 2019
Total time elapsed: 1.0m:54.35s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9481,
    "inflation_factor": 1.0063,
    "mean_EFFECT": -6.4297e-07,
    "n": 91149,
    "n_snps": 11659378,
    "n_clumped_hits": 0,
    "n_p_sig": 0,
    "n_mono": 0,
    "n_ns": 1169182,
    "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": 254257,
    "n_est": 91368.9445,
    "ratio_se_n": 1.0012,
    "mean_diff": -3.9799e-06,
    "ratio_diff": 2.6708,
    "sd_y_est1": 0.205,
    "sd_y_est2": 0.2052,
    "r2_sum1": 0,
    "r2_sum2": 0,
    "r2_sum3": 0,
    "r2_sum4": 0,
    "ldsc_nsnp_merge_refpanel_ld": 1282786,
    "ldsc_nsnp_merge_regression_ld": 1282786,
    "ldsc_observed_scale_h2_beta": 0.0107,
    "ldsc_observed_scale_h2_se": 0.0053,
    "ldsc_intercept_beta": 0.9986,
    "ldsc_intercept_se": 0.007,
    "ldsc_lambda_gc": 1.0205,
    "ldsc_mean_chisq": 1.0182,
    "ldsc_ratio": -0.0769
}

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 11650003 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 100 0 52615 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 342 0 31857 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 9.072344e+00 6.184417e+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 7.886320e+07 5.599024e+07 3.02000e+02 3.265989e+07 6.987932e+07 1.147316e+08 2.492309e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA -6.000000e-07 3.463200e-03 -3.62927e-02 -1.262500e-03 -1.580000e-05 1.219600e-03 4.537790e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 2.711800e-03 2.149700e-03 7.93500e-04 1.095500e-03 1.689000e-03 3.703800e-03 1.318070e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.986760e-01 2.891345e-01 2.00000e-07 2.477411e-01 4.986491e-01 7.488486e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.986743e-01 2.891355e-01 2.00000e-07 2.477385e-01 4.986470e-01 7.488470e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA 2.206277e-01 2.603180e-01 3.10790e-03 1.798290e-02 1.009100e-01 3.518918e-01 9.968920e-01 ▇▂▁▁▁
numeric AF_reference 254257 0.9781929 NA NA NA NA NA 2.214958e-01 2.516452e-01 0.00000e+00 1.497600e-02 1.198080e-01 3.526360e-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 -0.0027660 0.0016784 0.0993459 0.0993421 0.1114870 0.1894970 91149
1 693731 rs12238997 A G -0.0015640 0.0015896 0.3251726 0.3251700 0.1164590 0.1417730 91149
1 707522 rs371890604 G C -0.0019807 0.0017864 0.2675149 0.2675128 0.0978765 0.1293930 91149
1 717587 rs144155419 G A -0.0052814 0.0042583 0.2148760 0.2148733 0.0157837 0.0045926 91149
1 730087 rs148120343 T C -0.0035034 0.0022033 0.1118230 0.1118190 0.0569973 0.0127796 91149
1 731718 rs142557973 T C -0.0021949 0.0015084 0.1456431 0.1456409 0.1223590 0.1543530 91149
1 732032 rs61770163 A C -0.0024655 0.0016083 0.1252951 0.1252935 0.1220470 0.1555510 91149
1 734349 rs141242758 T C -0.0021870 0.0015096 0.1474161 0.1474110 0.1215880 0.1525560 91149
1 740284 rs61770167 C T 0.0048694 0.0069315 0.4823675 0.4823658 0.0057444 0.0023962 91149
1 749963 rs529266287 T TAA 0.0017762 0.0014849 0.2316360 0.2316320 0.8689170 0.7641770 91149
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T -0.0003362 0.0017658 0.8490141 0.8490140 0.0552869 0.0309934 91149
23 154923374 rs111332691 T A 0.0010619 0.0019217 0.5805385 0.5805391 0.0448387 0.0116556 91149
23 154925045 rs509981 C T 0.0003034 0.0009261 0.7432347 0.7432346 0.2444670 0.3634440 91149
23 154925895 rs538470 C T 0.0003501 0.0009468 0.7115854 0.7115859 0.2409490 0.3634440 91149
23 154927581 rs644138 G A 0.0002016 0.0008710 0.8169670 0.8169662 0.3001640 0.4635760 91149
23 154929412 rs557132 C T 0.0002924 0.0009264 0.7522393 0.7522376 0.2443160 0.3568210 91149
23 154929637 rs35185538 CT C -0.0000593 0.0009673 0.9511211 0.9511212 0.2286640 0.3011920 91149
23 154929952 rs4012982 CAA C 0.0002005 0.0009750 0.8370629 0.8370628 0.2382530 0.3165560 91149
23 154930230 rs781880 A G 0.0002222 0.0009261 0.8103970 0.8103965 0.2447660 0.3618540 91149
23 154930487 rs781879 T A 0.0000702 0.0031407 0.9821750 0.9821746 0.0196632 0.1263580 91149

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.111487 ES:SE:LP:AF:SS:ID   -0.00276604:0.00167837:1.00285:0.111487:91149:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.116459 ES:SE:LP:AF:SS:ID   -0.001564:0.00158961:0.487886:0.116459:91149:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0978765    ES:SE:LP:AF:SS:ID   -0.00198072:0.00178635:0.572652:0.0978765:91149:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.0157837    ES:SE:LP:AF:SS:ID   -0.00528143:0.00425827:0.667812:0.0157837:91149:rs144155419
1   730087  rs148120343 T   C   .   PASS    AF=0.0569973    ES:SE:LP:AF:SS:ID   -0.00350337:0.00220328:0.951469:0.0569973:91149:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122359 ES:SE:LP:AF:SS:ID   -0.00219492:0.00150843:0.83671:0.122359:91149:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.122047 ES:SE:LP:AF:SS:ID   -0.00246547:0.00160834:0.902066:0.122047:91149:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.121588 ES:SE:LP:AF:SS:ID   -0.00218697:0.00150957:0.831455:0.121588:91149:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.00574438   ES:SE:LP:AF:SS:ID   0.00486939:0.00693149:0.316622:0.00574438:91149:rs61770167
1   749963  rs529266287 T   TAA .   PASS    AF=0.868917 ES:SE:LP:AF:SS:ID   0.00177621:0.00148492:0.635194:0.868917:91149:rs529266287
1   751343  rs28544273  T   A   .   PASS    AF=0.123699 ES:SE:LP:AF:SS:ID   -0.00173455:0.00147219:0.622118:0.123699:91149:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.143202 ES:SE:LP:AF:SS:ID   -0.00166011:0.00145748:0.593983:0.143202:91149:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.123834 ES:SE:LP:AF:SS:ID   -0.00174076:0.00147006:0.626428:0.123834:91149:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.869892 ES:SE:LP:AF:SS:ID   0.00195778:0.00143243:0.765217:0.869892:91149:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.874553 ES:SE:LP:AF:SS:ID   0.00158305:0.00145566:0.557812:0.874553:91149:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.129529 ES:SE:LP:AF:SS:ID   -0.0019893:0.00143692:0.779283:0.129529:91149:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0358205    ES:SE:LP:AF:SS:ID   -0.00130078:0.00262513:0.207439:0.0358205:91149:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869539 ES:SE:LP:AF:SS:ID   0.00188943:0.00143123:0.728646:0.869539:91149:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869616 ES:SE:LP:AF:SS:ID   0.00186476:0.00143186:0.714886:0.869616:91149:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.869535 ES:SE:LP:AF:SS:ID   0.00188345:0.00143117:0.72545:0.869535:91149:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00531588   ES:SE:LP:AF:SS:ID   0.00459155:0.00714945:0.283388:0.00531588:91149:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00528125   ES:SE:LP:AF:SS:ID   0.00409137:0.00716722:0.245571:0.00528125:91149:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.0056599    ES:SE:LP:AF:SS:ID   0.00107455:0.00704578:0.0561174:0.0056599:91149:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.86952  ES:SE:LP:AF:SS:ID   0.00185807:0.00142822:0.713827:0.86952:91149:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.126911 ES:SE:LP:AF:SS:ID   -0.00185755:0.00145797:0.693273:0.126911:91149:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.86905  ES:SE:LP:AF:SS:ID   0.0018196:0.00142453:0.695755:0.86905:91149:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.868436 ES:SE:LP:AF:SS:ID   0.00172897:0.00142362:0.648658:0.868436:91149:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869199 ES:SE:LP:AF:SS:ID   0.00181302:0.00142582:0.691372:0.869199:91149:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869205 ES:SE:LP:AF:SS:ID   0.00181319:0.00142591:0.691397:0.869205:91149:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869207 ES:SE:LP:AF:SS:ID   0.00181488:0.00142592:0.69229:0.869207:91149:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869687 ES:SE:LP:AF:SS:ID   0.00190209:0.00142974:0.736605:0.869687:91149:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.0991787    ES:SE:LP:AF:SS:ID   -0.00151532:0.00166387:0.440755:0.0991787:91149:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.00649624   ES:SE:LP:AF:SS:ID   -0.00372659:0.00641909:0.250615:0.00649624:91149:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.873682 ES:SE:LP:AF:SS:ID   0.00162979:0.00145091:0.582826:0.873682:91149:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.863058 ES:SE:LP:AF:SS:ID   0.00191092:0.00142323:0.746219:0.863058:91149:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.868472 ES:SE:LP:AF:SS:ID   0.00188268:0.00143735:0.720662:0.868472:91149:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.865369 ES:SE:LP:AF:SS:ID   0.00196763:0.00143762:0.76674:0.865369:91149:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0995228    ES:SE:LP:AF:SS:ID   -0.00177922:0.00172056:0.521296:0.0995228:91149:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.870657 ES:SE:LP:AF:SS:ID   0.00196321:0.00145399:0.752154:0.870657:91149:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.870657 ES:SE:LP:AF:SS:ID   0.00196325:0.00145399:0.752174:0.870657:91149:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.870654 ES:SE:LP:AF:SS:ID   0.00196284:0.001454:0.751948:0.870654:91149:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.871019 ES:SE:LP:AF:SS:ID   0.00187862:0.00145467:0.706516:0.871019:91149:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.126515 ES:SE:LP:AF:SS:ID   -0.00185168:0.00145629:0.69132:0.126515:91149:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.106037 ES:SE:LP:AF:SS:ID   -0.00131276:0.00158301:0.390462:0.106037:91149:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.85438  ES:SE:LP:AF:SS:ID   0.00225681:0.00142268:0.948184:0.85438:91149:rs2519015
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.839153 ES:SE:LP:AF:SS:ID   -0.000371196:0.00140569:0.101424:0.839153:91149:rs376645387
1   768253  rs2977608   A   C   .   PASS    AF=0.763195 ES:SE:LP:AF:SS:ID   0.000779643:0.00113024:0.309519:0.763195:91149:rs2977608
1   768448  rs12562034  G   A   .   PASS    AF=0.105092 ES:SE:LP:AF:SS:ID   0.000748449:0.00155749:0.200082:0.105092:91149:rs12562034
1   768819  rs12562811  C   T   .   PASS    AF=0.00772317   ES:SE:LP:AF:SS:ID   0.00324907:0.00577102:0.241514:0.00772317:91149:rs12562811
1   769138  rs59306077  CAT C   .   PASS    AF=0.129904 ES:SE:LP:AF:SS:ID   -0.00183931:0.00143595:0.698464:0.129904:91149:rs762168062