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/ieu-a-import/processed/ieu-a-1130/ieu-a-1130.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/ieu-a-import/processed/ieu-a-1130/ldsc.txt \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ 

Beginning analysis at Wed Feb  5 09:22:30 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-1130/ieu-a-1130.vcf.gz ...
Read summary statistics for 10166262 SNPs.
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, 1220160 SNPs remain.
After merging with regression SNP LD, 1220160 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.1401 (0.0126)
Lambda GC: 1.205
Mean Chi^2: 1.2962
Intercept: 1.0549 (0.0089)
Ratio: 0.1855 (0.0302)
Analysis finished at Wed Feb  5 09:24:08 2020
Total time elapsed: 1.0m:38.33s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9535,
    "inflation_factor": 1.1568,
    "mean_EFFECT": -0,
    "n": 89677,
    "n_snps": 10166262,
    "n_clumped_hits": 47,
    "n_p_sig": 5014,
    "n_mono": 0,
    "n_ns": 619877,
    "n_mac": 2,
    "is_snpid_unique": false,
    "n_miss_EFFECT": 0,
    "n_miss_SE": 0,
    "n_miss_PVAL": 0,
    "n_miss_AF": 0,
    "n_miss_AF_reference": 131469,
    "n_est": 87468.034,
    "ratio_se_n": 0.9876,
    "mean_diff": 0.0001,
    "ratio_diff": 1.2427,
    "sd_y_est1": 2.315,
    "sd_y_est2": 2.2863,
    "r2_sum1": 0.1888,
    "r2_sum2": 0.0352,
    "r2_sum3": 0.0361,
    "r2_sum4": 0.0449,
    "ldsc_nsnp_merge_refpanel_ld": 1220160,
    "ldsc_nsnp_merge_regression_ld": 1220160,
    "ldsc_observed_scale_h2_beta": 0.1401,
    "ldsc_observed_scale_h2_se": 0.0126,
    "ldsc_intercept_beta": 1.0549,
    "ldsc_intercept_se": 0.0089,
    "ldsc_lambda_gc": 1.205,
    "ldsc_mean_chisq": 1.2962,
    "ldsc_ratio": 0.1853
}

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 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 47 0 10166260 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 99 0 30496 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 103 0 16152 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 8.953993e+00 6.300657e+00 1.00000e+00 4.000000e+00 7.000000e+00 1.400000e+01 2.400000e+01 ▇▆▂▃▂
numeric POS 0 1.0000000 NA NA NA NA NA 7.912658e+07 5.684504e+07 8.28000e+02 3.226754e+07 6.948198e+07 1.156574e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA -4.270000e-05 4.044140e-02 -1.79471e+00 -1.483000e-02 0.000000e+00 1.482000e-02 2.871970e+00 ▁▇▁▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 3.010130e-02 2.470500e-02 9.58000e-03 1.236000e-02 1.867000e-02 4.084000e-02 2.936010e+00 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.761704e-01 2.954301e-01 0.00000e+00 2.141401e-01 4.681705e-01 7.323404e-01 1.000000e+00 ▇▇▆▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.761703e-01 2.954300e-01 0.00000e+00 2.141354e-01 4.681768e-01 7.323255e-01 1.000000e+00 ▇▇▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA 2.290846e-01 2.639213e-01 1.43000e-05 2.165400e-02 1.098540e-01 3.652968e-01 9.998480e-01 ▇▂▁▁▁
numeric AF_reference 131469 0.9870681 NA NA NA NA NA 2.275589e-01 2.512132e-01 1.99700e-04 2.456070e-02 1.263980e-01 3.582270e-01 9.996010e-01 ▇▂▂▁▁
numeric N 0 1.0000000 NA NA NA NA NA 8.967700e+04 0.000000e+00 8.96770e+04 8.967700e+04 8.967700e+04 8.967700e+04 8.967700e+04 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 10616 rs376342519 CCGCCGTTGCAAAGGCGCGCCG C 0.01915 0.13120 0.8839301 0.8839527 0.9947420 0.9930110 89677
1 14933 rs199856693 G A 0.00739 0.04537 0.8706901 0.8706106 0.0389174 0.0283546 89677
1 15774 rs374029747 G A 0.12825 0.12189 0.2927298 0.2927178 0.0069097 0.0119808 89677
1 16949 rs199745162 A C -0.07171 0.07617 0.3464296 0.3464760 0.0155154 0.0139776 89677
1 51479 rs116400033 T A -0.01575 0.02021 0.4358499 0.4357929 0.2231700 0.1281950 89677
1 52185 rs201374420 TTAA T -0.03703 0.09584 0.6991897 0.6992204 0.0065417 0.0053914 89677
1 54490 rs141149254 G A -0.02416 0.02329 0.2995601 0.2995704 0.1597710 0.0960463 89677
1 55164 rs3091274 C A 0.05054 0.05790 0.3827199 0.3827262 0.9788040 0.9233230 89677
1 55326 rs3107975 T C -0.02776 0.05049 0.5824505 0.5824484 0.0286485 0.0459265 89677
1 55545 rs28396308 C T 0.01019 0.02084 0.6249093 0.6248675 0.2396130 0.2392170 89677
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154925045 rs509981 C T -0.00012 0.01147 0.9919100 0.9916526 0.2381790 0.3634440 89677
23 154925895 rs538470 C T 0.00034 0.01151 0.9767200 0.9764343 0.2461470 0.3634440 89677
23 154927185 rs185685661 T C -0.01040 0.02201 0.6367706 0.6365610 0.1104930 0.1796030 89677
23 154927199 rs645904 C T 0.00027 0.01149 0.9810800 0.9812525 0.2396350 0.3674170 89677
23 154927581 rs644138 G A 0.00284 0.01113 0.7983900 0.7985947 0.2888270 0.4635760 89677
23 154928151 rs144607509 C T -0.03942 0.07212 0.5846595 0.5846611 0.0225172 0.0084768 89677
23 154929412 rs557132 C T -0.00004 0.01148 0.9974400 0.9972199 0.2377850 0.3568210 89677
23 154929952 rs4012982 CAA C -0.00565 0.01679 0.7364004 0.7364864 0.2084070 0.3165560 89677
23 154930230 rs781880 A G -0.00009 0.01148 0.9939700 0.9937449 0.2376560 0.3618540 89677
24 13537468 rs7203107 A G 0.05035 0.06473 0.4366103 0.4366595 0.9832890 NA 89677

bcf preview

1   10616   rs376342519 CCGCCGTTGCAAAGGCGCGCCG  C   .   PASS    AF=0.994742 ES:SE:LP:AF:SS:ID   0.01915:0.1312:0.0535821:0.994742:89677:rs376342519
1   14933   rs199856693 G   A   .   PASS    AF=0.0389174    ES:SE:LP:AF:SS:ID   0.00739:0.04537:0.0601364:0.0389174:89677:rs199856693
1   15774   rs374029747 G   A   .   PASS    AF=0.00690967   ES:SE:LP:AF:SS:ID   0.12825:0.12189:0.533533:0.00690967:89677:rs374029747
1   16949   rs199745162 A   C   .   PASS    AF=0.0155154    ES:SE:LP:AF:SS:ID   -0.07171:0.07617:0.460385:0.0155154:89677:rs199745162
1   51479   rs116400033 T   A   .   PASS    AF=0.22317  ES:SE:LP:AF:SS:ID   -0.01575:0.02021:0.360663:0.22317:89677:rs116400033
1   52185   rs201374420 TTAA    T   .   PASS    AF=0.0065417    ES:SE:LP:AF:SS:ID   -0.03703:0.09584:0.155405:0.0065417:89677:rs201374420
1   54490   rs141149254 G   A   .   PASS    AF=0.159771 ES:SE:LP:AF:SS:ID   -0.02416:0.02329:0.523516:0.159771:89677:rs141149254
1   55164   rs3091274   C   A   .   PASS    AF=0.978804 ES:SE:LP:AF:SS:ID   0.05054:0.0579:0.417119:0.978804:89677:rs3091274
1   55326   rs3107975   T   C   .   PASS    AF=0.0286485    ES:SE:LP:AF:SS:ID   -0.02776:0.05049:0.234741:0.0286485:89677:rs3107975
1   55545   rs28396308  C   T   .   PASS    AF=0.239613 ES:SE:LP:AF:SS:ID   0.01019:0.02084:0.204183:0.239613:89677:rs28396308
1   57292   rs201418760 C   T   .   PASS    AF=0.0277029    ES:SE:LP:AF:SS:ID   -0.0277:0.05055:0.233825:0.0277029:89677:rs201418760
1   58814   rs114420996 G   A   .   PASS    AF=0.0984317    ES:SE:LP:AF:SS:ID   0.00412:0.03103:0.0485022:0.0984317:89677:rs114420996
1   61743   rs184286948 G   C   .   PASS    AF=0.00808518   ES:SE:LP:AF:SS:ID   -0.05171:0.09848:0.222167:0.00808518:89677:rs184286948
1   61920   rs62637820  G   A   .   PASS    AF=0.0208939    ES:SE:LP:AF:SS:ID   -0.01157:0.07073:0.0604458:0.0208939:89677:rs62637820
1   63093   rs200092917 G   A   .   PASS    AF=0.024919 ES:SE:LP:AF:SS:ID   0.01851:0.056:0.130152:0.024919:89677:rs200092917
1   64649   rs181431124 A   C   .   PASS    AF=0.018072 ES:SE:LP:AF:SS:ID   0.01811:0.07499:0.0919656:0.018072:89677:rs181431124
1   66219   rs181028663 A   T   .   PASS    AF=0.0127579    ES:SE:LP:AF:SS:ID   0.0244:0.08117:0.117094:0.0127579:89677:rs181028663
1   68082   rs367789441 T   C   .   PASS    AF=0.0693518    ES:SE:LP:AF:SS:ID   -0.00279:0.03384:0.0295601:0.0693518:89677:rs367789441
1   69428   rs140739101 T   G   .   PASS    AF=0.0377403    ES:SE:LP:AF:SS:ID   -0.02712:0.04581:0.256576:0.0377403:89677:rs140739101
1   69761   rs200505207 A   T   .   PASS    AF=0.0731287    ES:SE:LP:AF:SS:ID   -0.00817:0.03338:0.0933041:0.0731287:89677:rs200505207
1   69897   rs200676709 T   C   .   PASS    AF=0.754938 ES:SE:LP:AF:SS:ID   0.00951:0.02039:0.193128:0.754938:89677:rs200676709
1   72297   rs200651397 G   GTAT    .   PASS    AF=0.00265451   ES:SE:LP:AF:SS:ID   0.20289:0.21289:0.467806:0.00265451:89677:rs200651397
1   74790   rs13328700  C   G   .   PASS    AF=0.0243877    ES:SE:LP:AF:SS:ID   -0.08104:0.05937:0.763715:0.0243877:89677:rs13328700
1   74792   rs13328684  G   A   .   PASS    AF=0.0243877    ES:SE:LP:AF:SS:ID   -0.08104:0.05937:0.763715:0.0243877:89677:rs13328684
1   76854   rs367666799 A   G   .   PASS    AF=0.0552744    ES:SE:LP:AF:SS:ID   0.05376:0.03797:0.804543:0.0552744:89677:rs367666799
1   82163   rs139113303 G   A   .   PASS    AF=0.0546521    ES:SE:LP:AF:SS:ID   0.05928:0.03791:0.928523:0.0546521:89677:rs139113303
1   82609   rs149189449 C   G   .   PASS    AF=0.0561335    ES:SE:LP:AF:SS:ID   0.04898:0.03722:0.725473:0.0561335:89677:rs149189449
1   83514   rs201754587 C   T   .   PASS    AF=0.350363 ES:SE:LP:AF:SS:ID   0.01947:0.01834:0.53996:0.350363:89677:rs201754587
1   84139   rs183605470 A   T   .   PASS    AF=0.0243281    ES:SE:LP:AF:SS:ID   0.06095:0.05679:0.547983:0.0243281:89677:rs183605470
1   86028   rs114608975 T   C   .   PASS    AF=0.0405665    ES:SE:LP:AF:SS:ID   -0.03267:0.04263:0.353155:0.0405665:89677:rs114608975
1   86065   rs116504101 G   C   .   PASS    AF=0.0591133    ES:SE:LP:AF:SS:ID   0.05827:0.03609:0.972854:0.0591133:89677:rs116504101
1   86331   rs115209712 A   G   .   PASS    AF=0.0992651    ES:SE:LP:AF:SS:ID   0.01464:0.03055:0.199407:0.0992651:89677:rs115209712
1   87021   rs188486692 T   C   .   PASS    AF=0.00832897   ES:SE:LP:AF:SS:ID   0.07346:0.09315:0.366188:0.00832897:89677:rs188486692
1   87360   rs180907504 C   T   .   PASS    AF=0.0282223    ES:SE:LP:AF:SS:ID   -0.04252:0.04887:0.41542:0.0282223:89677:rs180907504
1   87409   rs139490478 C   T   .   PASS    AF=0.0600118    ES:SE:LP:AF:SS:ID   0.05709:0.03585:0.953505:0.0600118:89677:rs139490478
1   88169   rs940550    C   T   .   PASS    AF=0.218837 ES:SE:LP:AF:SS:ID   -0.04311:0.0214:1.35693:0.218837:89677:rs940550
1   88172   rs940551    G   A   .   PASS    AF=0.0695326    ES:SE:LP:AF:SS:ID   -0.01278:0.03389:0.151146:0.0695326:89677:rs940551
1   88177   rs143215837 G   C   .   PASS    AF=0.0686377    ES:SE:LP:AF:SS:ID   -0.00932:0.03396:0.105872:0.0686377:89677:rs143215837
1   88188   rs148331237 C   A   .   PASS    AF=0.00638493   ES:SE:LP:AF:SS:ID   0.1647:0.09983:1.0045:0.00638493:89677:rs148331237
1   88236   rs186918018 C   T   .   PASS    AF=0.0140967    ES:SE:LP:AF:SS:ID   -0.0257:0.0644:0.161296:0.0140967:89677:rs186918018
1   88316   rs113759966 G   A   .   PASS    AF=0.0689114    ES:SE:LP:AF:SS:ID   -0.01515:0.03378:0.184582:0.0689114:89677:rs113759966
1   88338   rs55700207  G   A   .   PASS    AF=0.0846767    ES:SE:LP:AF:SS:ID   -0.00603:0.03351:0.0669381:0.0846767:89677:rs55700207
1   88710   rs186575039 C   G   .   PASS    AF=0.0598415    ES:SE:LP:AF:SS:ID   0.05735:0.03586:0.959556:0.0598415:89677:rs186575039
1   89599   rs375955515 A   T   .   PASS    AF=0.0598415    ES:SE:LP:AF:SS:ID   0.05735:0.03586:0.959556:0.0598415:89677:rs375955515
1   89946   rs138808727 A   T   .   PASS    AF=0.221682 ES:SE:LP:AF:SS:ID   -0.02364:0.02026:0.614126:0.221682:89677:rs138808727
1   91190   rs143856811 G   A   .   PASS    AF=0.0608869    ES:SE:LP:AF:SS:ID   0.05889:0.03546:1.01412:0.0608869:89677:rs143856811
1   91421   rs28619159  T   C   .   PASS    AF=0.0120616    ES:SE:LP:AF:SS:ID   -0.20993:0.0872:1.79409:0.0120616:89677:rs28619159
1   91515   rs376723915 A   C   .   PASS    AF=0.552369 ES:SE:LP:AF:SS:ID   0.00014:0.0175:0.00282776:0.552369:89677:rs376723915
1   92633   rs149776517 C   T   .   PASS    AF=0.0331826    ES:SE:LP:AF:SS:ID   -0.0623:0.04753:0.721384:0.0331826:89677:rs149776517
1   92858   rs147061536 G   T   .   PASS    AF=0.227226 ES:SE:LP:AF:SS:ID   -0.02746:0.02017:0.760751:0.227226:89677:rs147061536