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-1165/ieu-a-1165.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-1165/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 08:27:13 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-1165/ieu-a-1165.vcf.gz ...
Read summary statistics for 8506274 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, 1282427 SNPs remain.
After merging with regression SNP LD, 1282427 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: -0.0061 (0.0123)
Lambda GC: 1.0203
Mean Chi^2: 1.0227
Intercept: 1.0273 (0.0059)
Ratio: 1.2004 (0.2591)
Analysis finished at Wed Feb  5 08:28:39 2020
Total time elapsed: 1.0m:26.22s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9433,
    "inflation_factor": 1.0228,
    "mean_EFFECT": 0.005,
    "n": 37954,
    "n_snps": 8506274,
    "n_clumped_hits": 1,
    "n_p_sig": 27,
    "n_mono": 0,
    "n_ns": 3338,
    "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": 236823,
    "n_est": 37070.7856,
    "ratio_se_n": 0.9883,
    "mean_diff": -0.0036,
    "ratio_diff": 34.5496,
    "sd_y_est1": 4.0437,
    "sd_y_est2": 3.9964,
    "r2_sum1": 0.0112,
    "r2_sum2": 0.0007,
    "r2_sum3": 0.0007,
    "r2_sum4": 0.0008,
    "ldsc_nsnp_merge_refpanel_ld": 1282427,
    "ldsc_nsnp_merge_regression_ld": 1282427,
    "ldsc_observed_scale_h2_beta": "NA",
    "ldsc_observed_scale_h2_se": "NA",
    "ldsc_intercept_beta": 1.0273,
    "ldsc_intercept_se": 0.0059,
    "ldsc_lambda_gc": 1.0203,
    "ldsc_mean_chisq": 1.0227,
    "ldsc_ratio": 1.2026
}

Flags

name value
af_correlation FALSE
inflation_factor FALSE
n FALSE
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 1.000000 3 59 0 8506272 0 NA NA NA NA NA NA NA NA
character REF 0 1.000000 1 26 0 541 0 NA NA NA NA NA NA NA NA
character ALT 0 1.000000 1 41 0 286 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.000000 NA NA NA NA NA 9.042460e+00 6.126470e+00 1.00000e+00 4.000000e+00 8.000000e+00 1.300000e+01 2.300000e+01 ▇▅▅▂▂
numeric POS 0 1.000000 NA NA NA NA NA 7.859971e+07 5.622916e+07 1.01330e+04 3.232951e+07 6.949357e+07 1.146200e+08 2.492225e+08 ▇▆▅▂▁
numeric EFFECT 0 1.000000 NA NA NA NA NA 4.985900e-03 7.513460e-02 -1.20666e+00 -2.961980e-02 2.526400e-03 3.636130e-02 7.996840e-01 ▁▁▆▇▁
numeric SE 0 1.000000 NA NA NA NA NA 6.240130e-02 4.084250e-02 2.40270e-03 3.230840e-02 4.435000e-02 8.082400e-02 6.062330e-01 ▇▁▁▁▁
numeric PVAL 0 1.000000 NA NA NA NA NA 4.962811e-01 2.896049e-01 0.00000e+00 2.446611e-01 4.951491e-01 7.472742e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.000000 NA NA NA NA NA 4.962811e-01 2.896049e-01 0.00000e+00 2.446610e-01 4.951494e-01 7.472737e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.000000 NA NA NA NA NA 2.523715e-01 2.614501e-01 1.00001e-02 3.973470e-02 1.462690e-01 3.989070e-01 9.899990e-01 ▇▂▂▁▁
numeric AF_reference 236823 0.972159 NA NA NA NA NA 2.558954e-01 2.551050e-01 0.00000e+00 4.612620e-02 1.649360e-01 4.017570e-01 1.000000e+00 ▇▃▂▁▁
numeric N 0 1.000000 NA NA NA NA NA 3.795400e+04 0.000000e+00 3.79540e+04 3.795400e+04 3.795400e+04 3.795400e+04 3.795400e+04 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 767811 rs140378911 G C 0.0321450 0.174215 0.8536110 0.8536107 0.0198570 NA 37954
1 810777 rs143204076 C G 0.0048781 0.200576 0.9805970 0.9805970 0.0151771 0.0053914 37954
1 823790 rs143626389 G A -0.4242730 0.164665 0.0099781 0.0099782 0.0256627 0.0101837 37954
1 834830 rs116452738 G A -0.0296735 0.191467 0.8768371 0.8768373 0.0114251 0.0069888 37954
1 837214 rs72631888 G C 0.1430920 0.222360 0.5198894 0.5198900 0.0123934 0.0029952 37954
1 839682 rs189624598 G A -0.0542686 0.148065 0.7139783 0.7139780 0.0291538 0.0039936 37954
1 848223 rs4246504 G A 0.3177760 0.151216 0.0355992 0.0355997 0.0193437 0.0081869 37954
1 851390 rs72631889 G T -0.2799450 0.149353 0.0608779 0.0608774 0.0290521 0.0115815 37954
1 870873 rs114397781 G A -0.3895240 0.215048 0.0700874 0.0700890 0.0110828 0.0043930 37954
1 871800 rs7520955 G A -0.0203930 0.186040 0.9127140 0.9127138 0.0172606 NA 37954
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154918624 rs5940558 A G 0.0212466 0.0814211 0.7941344 0.7941333 0.0507804 0.1290070 37954
23 154921127 rs58486544 T C 0.0166992 0.0803593 0.8353801 0.8353799 0.0519194 0.1451660 37954
23 154923311 rs141127553 C T 0.0352578 0.0855182 0.6801317 0.6801312 0.0447730 0.0309934 37954
23 154925045 rs509981 C T -0.0039924 0.0322574 0.9014989 0.9014988 0.2426680 0.3634440 37954
23 154925895 rs538470 C T -0.0061092 0.0337433 0.8563279 0.8563277 0.2404460 0.3634440 37954
23 154927185 rs185685661 T C -0.0330729 0.0553574 0.5502107 0.5502115 0.1373880 0.1796030 37954
23 154927199 rs645904 C T -0.0028060 0.0322554 0.9306779 0.9306779 0.2421640 0.3674170 37954
23 154927581 rs644138 G A -0.0009745 0.0315559 0.9753651 0.9753648 0.2923240 0.4635760 37954
23 154929412 rs557132 C T -0.0025794 0.0322657 0.9362821 0.9362825 0.2411750 0.3568210 37954
23 154930230 rs781880 A G -0.0013664 0.0338717 0.9678209 0.9678208 0.2389280 0.3618540 37954

bcf preview

1   767811  rs140378911 G   C   .   PASS    AF=0.019857 ES:SE:LP:AF:SS:ID   0.032145:0.174215:0.06874:0.019857:37954:rs140378911
1   810777  rs143204076 C   G   .   PASS    AF=0.0151771    ES:SE:LP:AF:SS:ID   0.00487809:0.200576:0.00850944:0.0151771:37954:rs143204076
1   823790  rs143626389 G   A   .   PASS    AF=0.0256627    ES:SE:LP:AF:SS:ID   -0.424273:0.164665:2.00095:0.0256627:37954:rs143626389
1   834830  rs116452738 G   A   .   PASS    AF=0.0114251    ES:SE:LP:AF:SS:ID   -0.0296735:0.191467:0.0570811:0.0114251:37954:rs116452738
1   837214  rs72631888  G   C   .   PASS    AF=0.0123934    ES:SE:LP:AF:SS:ID   0.143092:0.22236:0.284089:0.0123934:37954:rs72631888
1   839682  rs189624598 G   A   .   PASS    AF=0.0291538    ES:SE:LP:AF:SS:ID   -0.0542686:0.148065:0.146315:0.0291538:37954:rs189624598
1   848223  rs4246504   G   A   .   PASS    AF=0.0193437    ES:SE:LP:AF:SS:ID   0.317776:0.151216:1.44856:0.0193437:37954:rs4246504
1   851390  rs72631889  G   T   .   PASS    AF=0.0290521    ES:SE:LP:AF:SS:ID   -0.279945:0.149353:1.21554:0.0290521:37954:rs72631889
1   870873  rs114397781 G   A   .   PASS    AF=0.0110828    ES:SE:LP:AF:SS:ID   -0.389524:0.215048:1.15436:0.0110828:37954:rs114397781
1   871800  rs7520955   G   A   .   PASS    AF=0.0172606    ES:SE:LP:AF:SS:ID   -0.020393:0.18604:0.0396653:0.0172606:37954:rs7520955
1   879911  rs143853699 G   A   .   PASS    AF=0.0200655    ES:SE:LP:AF:SS:ID   0.198557:0.177952:0.577553:0.0200655:37954:rs143853699
1   881918  rs35471880  G   A   .   PASS    AF=0.0494751    ES:SE:LP:AF:SS:ID   -0.0236378:0.105699:0.0845775:0.0494751:37954:rs35471880
1   885765  rs114631528 G   A   .   PASS    AF=0.0141153    ES:SE:LP:AF:SS:ID   0.076586:0.213384:0.142871:0.0141153:37954:rs114631528
1   886409  rs77573237  G   C   .   PASS    AF=0.0343608    ES:SE:LP:AF:SS:ID   -0.0963357:0.132806:0.329555:0.0343608:37954:rs77573237
1   892569  rs41285806  C   T   .   PASS    AF=0.0110951    ES:SE:LP:AF:SS:ID   -0.383827:0.213996:1.13743:0.0110951:37954:rs41285806
1   894519  rs115438739 G   A   .   PASS    AF=0.0242215    ES:SE:LP:AF:SS:ID   -0.324493:0.157627:1.40307:0.0242215:37954:rs115438739
1   903245  rs28690976  A   G   .   PASS    AF=0.530827 ES:SE:LP:AF:SS:ID   0.00822474:0.0479634:0.0635632:0.530827:37954:rs28690976
1   909221  rs62639985  T   C   .   PASS    AF=0.0375081    ES:SE:LP:AF:SS:ID   0.040427:0.129227:0.122395:0.0375081:37954:rs62639985
1   913889  rs2340596   G   A   .   PASS    AF=0.577237 ES:SE:LP:AF:SS:ID   -0.0085314:0.0430211:0.0742729:0.577237:37954:rs2340596
1   914333  rs13302979  C   G   .   PASS    AF=0.590325 ES:SE:LP:AF:SS:ID   -0.000334128:0.0432814:0.00268353:0.590325:37954:rs13302979
1   914852  rs13303368  G   C   .   PASS    AF=0.575864 ES:SE:LP:AF:SS:ID   -0.0102257:0.0430049:0.0904167:0.575864:37954:rs13303368
1   914940  rs13303033  T   C   .   PASS    AF=0.562375 ES:SE:LP:AF:SS:ID   -0.00624867:0.0426061:0.0538431:0.562375:37954:rs13303033
1   916549  rs6660139   A   G   .   PASS    AF=0.741249 ES:SE:LP:AF:SS:ID   0.0299958:0.0508501:0.255498:0.741249:37954:rs6660139
1   916834  rs6694632   G   A   .   PASS    AF=0.590205 ES:SE:LP:AF:SS:ID   0.0130509:0.0428171:0.118893:0.590205:37954:rs6694632
1   917640  rs41285816  G   A   .   PASS    AF=0.215578 ES:SE:LP:AF:SS:ID   -0.0646032:0.0548277:0.622186:0.215578:37954:rs41285816
1   918384  rs13303118  G   T   .   PASS    AF=0.567263 ES:SE:LP:AF:SS:ID   -0.00635294:0.0421031:0.0554867:0.567263:37954:rs13303118
1   918573  rs2341354   A   G   .   PASS    AF=0.586668 ES:SE:LP:AF:SS:ID   0.00856989:0.0423314:0.0759446:0.586668:37954:rs2341354
1   919501  rs4970414   G   T   .   PASS    AF=0.584508 ES:SE:LP:AF:SS:ID   0.0455855:0.0441681:0.51995:0.584508:37954:rs4970414
1   920503  rs28896913  G   A   .   PASS    AF=0.14908  ES:SE:LP:AF:SS:ID   -0.040613:0.0639485:0.279534:0.14908:37954:rs28896913
1   920640  rs28869591  C   T   .   PASS    AF=0.164874 ES:SE:LP:AF:SS:ID   -0.0361127:0.0618686:0.25226:0.164874:37954:rs28869591
1   920977  rs55692389  T   C   .   PASS    AF=0.160967 ES:SE:LP:AF:SS:ID   -0.0269847:0.0621819:0.177626:0.160967:37954:rs55692389
1   921245  rs148047438 C   T   .   PASS    AF=0.0102785    ES:SE:LP:AF:SS:ID   0.499254:0.216671:1.67344:0.0102785:37954:rs148047438
1   921660  rs6703008   C   T   .   PASS    AF=0.207307 ES:SE:LP:AF:SS:ID   -0.0758499:0.0532648:0.811235:0.207307:37954:rs6703008
1   921716  rs13303278  C   A   .   PASS    AF=0.519747 ES:SE:LP:AF:SS:ID   0.0822617:0.0411926:1.3389:0.519747:37954:rs13303278
1   922009  rs6693747   G   A   .   PASS    AF=0.204478 ES:SE:LP:AF:SS:ID   -0.0867827:0.0536476:0.975765:0.204478:37954:rs6693747
1   922011  rs185011518 G   A   .   PASS    AF=0.0125065    ES:SE:LP:AF:SS:ID   0.152897:0.213477:0.324356:0.0125065:37954:rs185011518
1   922483  rs114583033 T   C   .   PASS    AF=0.16108  ES:SE:LP:AF:SS:ID   -0.0292903:0.0621301:0.195636:0.16108:37954:rs114583033
1   924111  rs28447224  T   A   .   PASS    AF=0.225241 ES:SE:LP:AF:SS:ID   -0.113916:0.0525018:1.52251:0.225241:37954:rs28447224
1   924528  rs34712273  C   A   .   PASS    AF=0.560968 ES:SE:LP:AF:SS:ID   0.0904485:0.0399761:1.62594:0.560968:37954:rs34712273
1   926968  rs140144962 C   T   .   PASS    AF=0.15869  ES:SE:LP:AF:SS:ID   -0.0289765:0.0627218:0.191052:0.15869:37954:rs140144962
1   927741  rs6665587   G   A   .   PASS    AF=0.210851 ES:SE:LP:AF:SS:ID   -0.097363:0.0530517:1.17738:0.210851:37954:rs6665587
1   928416  rs111754459 G   A   .   PASS    AF=0.222596 ES:SE:LP:AF:SS:ID   -0.117646:0.0523231:1.61:0.222596:37954:rs111754459
1   929550  rs138560419 A   C   .   PASS    AF=0.0118131    ES:SE:LP:AF:SS:ID   0.383197:0.17835:1.49936:0.0118131:37954:rs138560419
1   930329  rs3128108   C   T   .   PASS    AF=0.220907 ES:SE:LP:AF:SS:ID   -0.114977:0.0523847:1.55016:0.220907:37954:rs3128108
1   930377  rs3128109   C   T   .   PASS    AF=0.220907 ES:SE:LP:AF:SS:ID   -0.114981:0.0523841:1.55027:0.220907:37954:rs3128109
1   930533  rs3128110   C   G   .   PASS    AF=0.385204 ES:SE:LP:AF:SS:ID   -0.0950173:0.0425275:1.59403:0.385204:37954:rs3128110
1   930567  rs3121574   A   G   .   PASS    AF=0.385584 ES:SE:LP:AF:SS:ID   -0.0937976:0.0424931:1.56402:0.385584:37954:rs3121574
1   930751  rs3128111   C   G   .   PASS    AF=0.372268 ES:SE:LP:AF:SS:ID   -0.0777114:0.0427356:1.16115:0.372268:37954:rs3128111
1   930923  rs2710882   A   G   .   PASS    AF=0.794494 ES:SE:LP:AF:SS:ID   0.0250457:0.0556662:0.185244:0.794494:37954:rs2710882
1   931166  rs2710880   A   G   .   PASS    AF=0.382544 ES:SE:LP:AF:SS:ID   -0.0913526:0.0427241:1.48811:0.382544:37954:rs2710880