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-1163/ieu-a-1163.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-1163/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 08:55:40 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-1163/ieu-a-1163.vcf.gz ...
Read summary statistics for 8680609 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, 1283563 SNPs remain.
After merging with regression SNP LD, 1283563 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: -0.1415 (0.0629)
Lambda GC: 1.0121
Mean Chi^2: 1.018
Intercept: 1.0372 (0.0064)
Ratio: 2.0668 (0.3561)
Analysis finished at Wed Feb  5 08:57:06 2020
Total time elapsed: 1.0m:26.77s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9444,
    "inflation_factor": 1.0302,
    "mean_EFFECT": -0.0509,
    "n": 6881,
    "n_snps": 8680609,
    "n_clumped_hits": 1,
    "n_p_sig": 1,
    "n_mono": 0,
    "n_ns": 3348,
    "n_mac": 0,
    "is_snpid_unique": true,
    "n_miss_EFFECT": 0,
    "n_miss_SE": 0,
    "n_miss_PVAL": 0,
    "n_miss_AF": 0,
    "n_miss_AF_reference": 242595,
    "n_est": 6782.1563,
    "ratio_se_n": 0.9928,
    "mean_diff": 0.0562,
    "ratio_diff": 2.5552,
    "sd_y_est1": 3.0786,
    "sd_y_est2": 3.0564,
    "r2_sum1": 0.0471,
    "r2_sum2": 0.005,
    "r2_sum3": 0.005,
    "r2_sum4": 0.0053,
    "ldsc_nsnp_merge_refpanel_ld": 1283563,
    "ldsc_nsnp_merge_regression_ld": 1283563,
    "ldsc_observed_scale_h2_beta": "NA",
    "ldsc_observed_scale_h2_se": "NA",
    "ldsc_intercept_beta": 1.0372,
    "ldsc_intercept_se": 0.0064,
    "ldsc_lambda_gc": 1.0121,
    "ldsc_mean_chisq": 1.018,
    "ldsc_ratio": 2.0667
}

Flags

name value
af_correlation FALSE
inflation_factor FALSE
n TRUE
is_snpid_non_unique FALSE
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 TRUE
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 59 0 8680607 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 26 0 540 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 41 0 288 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 9.041882e+00 6.124581e+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 7.860405e+07 5.621433e+07 1.01330e+04 3.235046e+07 6.952116e+07 1.145956e+08 2.492225e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA -5.086910e-02 7.316327e+01 -8.77524e+04 -4.962840e-02 8.182300e-03 7.132270e-02 6.768810e+00 ▁▁▁▁▇
numeric SE 0 1.0000000 NA NA NA NA NA 1.580973e-01 4.623594e+01 3.08485e-02 5.783810e-02 8.070390e-02 1.519680e-01 6.997730e+04 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.950589e-01 2.899641e-01 0.00000e+00 2.425180e-01 4.936066e-01 7.461532e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.950589e-01 2.899641e-01 0.00000e+00 2.425179e-01 4.936071e-01 7.461547e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA 2.480562e-01 2.613316e-01 1.00001e-02 3.655270e-02 1.398470e-01 3.921350e-01 9.899990e-01 ▇▂▂▁▁
numeric AF_reference 242595 0.9720532 NA NA NA NA NA 2.516472e-01 2.550511e-01 0.00000e+00 4.213260e-02 1.589460e-01 3.953670e-01 1.000000e+00 ▇▃▂▁▁
numeric N 0 1.0000000 NA NA NA NA NA 6.881000e+03 0.000000e+00 6.88100e+03 6.881000e+03 6.881000e+03 6.881000e+03 6.881000e+03 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 88188 rs148331237 C A -0.1011540 0.450811 0.8224600 0.8224599 0.0118080 0.0033946 6881
1 767811 rs140378911 G C 0.2244370 0.371081 0.5452994 0.5452990 0.0220691 NA 6881
1 810777 rs143204076 C G -0.0142371 0.353163 0.9678430 0.9678435 0.0156224 0.0053914 6881
1 823790 rs143626389 G A -0.3572850 0.305174 0.2416957 0.2416959 0.0249697 0.0101837 6881
1 834830 rs116452738 G A -0.2403780 0.333133 0.4705611 0.4705603 0.0232119 0.0069888 6881
1 837214 rs72631888 G C 0.6166800 0.430897 0.1523860 0.1523861 0.0107468 0.0029952 6881
1 839682 rs189624598 G A -0.0327276 0.272502 0.9044041 0.9044036 0.0254063 0.0039936 6881
1 848223 rs4246504 G A 0.0616649 0.265817 0.8165510 0.8165516 0.0220149 0.0081869 6881
1 851390 rs72631889 G T -0.2905400 0.264584 0.2721611 0.2721603 0.0319425 0.0115815 6881
1 870873 rs114397781 G A -0.5060750 0.430508 0.2397828 0.2397828 0.0107572 0.0043930 6881
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154921127 rs58486544 T C -0.1086530 0.1477940 0.4622362 0.4622389 0.0512807 0.1451660 6881
23 154923311 rs141127553 C T -0.1318620 0.1601440 0.4102835 0.4102826 0.0441006 0.0309934 6881
23 154925045 rs509981 C T -0.0060320 0.0580755 0.9172759 0.9172761 0.2489410 0.3634440 6881
23 154925895 rs538470 C T -0.0205950 0.0608961 0.7352144 0.7352132 0.2463230 0.3634440 6881
23 154926315 rs145589925 A C -0.3716340 0.5760070 0.5188036 0.5188040 0.0109121 0.0037086 6881
23 154927185 rs185685661 T C 0.0388293 0.0999471 0.6976474 0.6976472 0.1423390 0.1796030 6881
23 154927199 rs645904 C T -0.0045917 0.0581794 0.9370941 0.9370938 0.2479130 0.3674170 6881
23 154927581 rs644138 G A -0.0276662 0.0571227 0.6281524 0.6281522 0.2971330 0.4635760 6881
23 154929412 rs557132 C T -0.0036694 0.0581512 0.9496859 0.9496863 0.2470640 0.3568210 6881
23 154930230 rs781880 A G -0.0283976 0.0641779 0.6581396 0.6581401 0.2476340 0.3618540 6881

bcf preview

1   88188   rs148331237 C   A   .   PASS    AF=0.011808 ES:SE:LP:AF:SS:ID   -0.101154:0.450811:0.0848852:0.011808:6881:rs148331237
1   767811  rs140378911 G   C   .   PASS    AF=0.0220691    ES:SE:LP:AF:SS:ID   0.224437:0.371081:0.263365:0.0220691:6881:rs140378911
1   810777  rs143204076 C   G   .   PASS    AF=0.0156224    ES:SE:LP:AF:SS:ID   -0.0142371:0.353163:0.0141951:0.0156224:6881:rs143204076
1   823790  rs143626389 G   A   .   PASS    AF=0.0249697    ES:SE:LP:AF:SS:ID   -0.357285:0.305174:0.616731:0.0249697:6881:rs143626389
1   834830  rs116452738 G   A   .   PASS    AF=0.0232119    ES:SE:LP:AF:SS:ID   -0.240378:0.333133:0.327384:0.0232119:6881:rs116452738
1   837214  rs72631888  G   C   .   PASS    AF=0.0107468    ES:SE:LP:AF:SS:ID   0.61668:0.430897:0.817055:0.0107468:6881:rs72631888
1   839682  rs189624598 G   A   .   PASS    AF=0.0254063    ES:SE:LP:AF:SS:ID   -0.0327276:0.272502:0.0436375:0.0254063:6881:rs189624598
1   848223  rs4246504   G   A   .   PASS    AF=0.0220149    ES:SE:LP:AF:SS:ID   0.0616649:0.265817:0.0880167:0.0220149:6881:rs4246504
1   851390  rs72631889  G   T   .   PASS    AF=0.0319425    ES:SE:LP:AF:SS:ID   -0.29054:0.264584:0.565174:0.0319425:6881:rs72631889
1   870873  rs114397781 G   A   .   PASS    AF=0.0107572    ES:SE:LP:AF:SS:ID   -0.506075:0.430508:0.620182:0.0107572:6881:rs114397781
1   871800  rs7520955   G   A   .   PASS    AF=0.0233245    ES:SE:LP:AF:SS:ID   -0.0597297:0.335284:0.0662051:0.0233245:6881:rs7520955
1   872467  rs138192888 C   G   .   PASS    AF=0.0114732    ES:SE:LP:AF:SS:ID   0.286338:0.396046:0.328193:0.0114732:6881:rs138192888
1   879911  rs143853699 G   A   .   PASS    AF=0.0195424    ES:SE:LP:AF:SS:ID   0.548479:0.321312:1.05639:0.0195424:6881:rs143853699
1   881918  rs35471880  G   A   .   PASS    AF=0.0517897    ES:SE:LP:AF:SS:ID   0.155215:0.197182:0.365336:0.0517897:6881:rs35471880
1   885765  rs114631528 G   A   .   PASS    AF=0.0150219    ES:SE:LP:AF:SS:ID   0.189246:0.495727:0.153265:0.0150219:6881:rs114631528
1   886409  rs77573237  G   C   .   PASS    AF=0.0344582    ES:SE:LP:AF:SS:ID   0.0110946:0.255349:0.0153179:0.0344582:6881:rs77573237
1   892569  rs41285806  C   T   .   PASS    AF=0.0108324    ES:SE:LP:AF:SS:ID   -0.504189:0.427335:0.623308:0.0108324:6881:rs41285806
1   894519  rs115438739 G   A   .   PASS    AF=0.0244802    ES:SE:LP:AF:SS:ID   -0.46602:0.307948:0.885382:0.0244802:6881:rs115438739
1   903245  rs28690976  A   G   .   PASS    AF=0.528112 ES:SE:LP:AF:SS:ID   0.0447059:0.0899553:0.208166:0.528112:6881:rs28690976
1   908929  rs142080242 C   T   .   PASS    AF=0.0100581    ES:SE:LP:AF:SS:ID   0.685454:0.492953:0.784162:0.0100581:6881:rs142080242
1   909221  rs62639985  T   C   .   PASS    AF=0.0346013    ES:SE:LP:AF:SS:ID   0.0703855:0.222648:0.123837:0.0346013:6881:rs62639985
1   913889  rs2340596   G   A   .   PASS    AF=0.574172 ES:SE:LP:AF:SS:ID   -0.0538838:0.0786939:0.306698:0.574172:6881:rs2340596
1   914048  rs189428827 G   A   .   PASS    AF=0.010058 ES:SE:LP:AF:SS:ID   0.684039:0.492494:0.782898:0.010058:6881:rs189428827
1   914333  rs13302979  C   G   .   PASS    AF=0.588585 ES:SE:LP:AF:SS:ID   -0.0290126:0.0786608:0.147366:0.588585:6881:rs13302979
1   914852  rs13303368  G   C   .   PASS    AF=0.572722 ES:SE:LP:AF:SS:ID   -0.0613013:0.0786451:0.360808:0.572722:6881:rs13303368
1   914940  rs13303033  T   C   .   PASS    AF=0.560638 ES:SE:LP:AF:SS:ID   -0.0367282:0.0777034:0.196238:0.560638:6881:rs13303033
1   916549  rs6660139   A   G   .   PASS    AF=0.74879  ES:SE:LP:AF:SS:ID   -0.08786:0.0943045:0.454062:0.74879:6881:rs6660139
1   916834  rs6694632   G   A   .   PASS    AF=0.594693 ES:SE:LP:AF:SS:ID   -0.0181599:0.0775823:0.0888802:0.594693:6881:rs6694632
1   917640  rs41285816  G   A   .   PASS    AF=0.210691 ES:SE:LP:AF:SS:ID   -0.0103129:0.101575:0.0366235:0.210691:6881:rs41285816
1   918384  rs13303118  G   T   .   PASS    AF=0.566047 ES:SE:LP:AF:SS:ID   -0.0446122:0.0764895:0.252024:0.566047:6881:rs13303118
1   918573  rs2341354   A   G   .   PASS    AF=0.589441 ES:SE:LP:AF:SS:ID   -0.0157667:0.0768154:0.077081:0.589441:6881:rs2341354
1   919501  rs4970414   G   T   .   PASS    AF=0.591425 ES:SE:LP:AF:SS:ID   0.021126:0.0818131:0.0989587:0.591425:6881:rs4970414
1   920503  rs28896913  G   A   .   PASS    AF=0.149718 ES:SE:LP:AF:SS:ID   -0.127716:0.120319:0.539896:0.149718:6881:rs28896913
1   920640  rs28869591  C   T   .   PASS    AF=0.166206 ES:SE:LP:AF:SS:ID   -0.0829423:0.115002:0.327189:0.166206:6881:rs28869591
1   920977  rs55692389  T   C   .   PASS    AF=0.161974 ES:SE:LP:AF:SS:ID   -0.0884483:0.115401:0.353192:0.161974:6881:rs55692389
1   921245  rs148047438 C   T   .   PASS    AF=0.0140011    ES:SE:LP:AF:SS:ID   0.350014:0.392649:0.428634:0.0140011:6881:rs148047438
1   921660  rs6703008   C   T   .   PASS    AF=0.202477 ES:SE:LP:AF:SS:ID   -0.0586487:0.0995092:0.255232:0.202477:6881:rs6703008
1   921716  rs13303278  C   A   .   PASS    AF=0.524784 ES:SE:LP:AF:SS:ID   0.0500845:0.075833:0.293316:0.524784:6881:rs13303278
1   922009  rs6693747   G   A   .   PASS    AF=0.19932  ES:SE:LP:AF:SS:ID   -0.0919837:0.100648:0.442777:0.19932:6881:rs6693747
1   922011  rs185011518 G   A   .   PASS    AF=0.0121698    ES:SE:LP:AF:SS:ID   -0.11402:0.419741:0.104634:0.0121698:6881:rs185011518
1   922483  rs114583033 T   C   .   PASS    AF=0.162093 ES:SE:LP:AF:SS:ID   -0.0888612:0.115316:0.355611:0.162093:6881:rs114583033
1   923457  rs147869918 C   T   .   PASS    AF=0.0109439    ES:SE:LP:AF:SS:ID   0.830788:0.417446:1.33187:0.0109439:6881:rs147869918
1   924111  rs28447224  T   A   .   PASS    AF=0.220386 ES:SE:LP:AF:SS:ID   -0.122845:0.0992384:0.666029:0.220386:6881:rs28447224
1   924528  rs34712273  C   A   .   PASS    AF=0.564323 ES:SE:LP:AF:SS:ID   0.0719776:0.0738265:0.482037:0.564323:6881:rs34712273
1   926968  rs140144962 C   T   .   PASS    AF=0.159705 ES:SE:LP:AF:SS:ID   -0.105276:0.116282:0.437374:0.159705:6881:rs140144962
1   927741  rs6665587   G   A   .   PASS    AF=0.204773 ES:SE:LP:AF:SS:ID   -0.110889:0.0999781:0.572886:0.204773:6881:rs6665587
1   928416  rs111754459 G   A   .   PASS    AF=0.21827  ES:SE:LP:AF:SS:ID   -0.124947:0.0987121:0.686992:0.21827:6881:rs111754459
1   929550  rs138560419 A   C   .   PASS    AF=0.0141445    ES:SE:LP:AF:SS:ID   0.238695:0.359828:0.294906:0.0141445:6881:rs138560419
1   930329  rs3128108   C   T   .   PASS    AF=0.215342 ES:SE:LP:AF:SS:ID   -0.108871:0.0989679:0.566542:0.215342:6881:rs3128108
1   930377  rs3128109   C   T   .   PASS    AF=0.215344 ES:SE:LP:AF:SS:ID   -0.108905:0.0989663:0.566794:0.215344:6881:rs3128109