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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    "FORMAT.1": "<ID=SE,Number=A,Type=Float,Description=\"Standard error of effect size estimate\">",
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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-22506_112/ukb-d-22506_112.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-22506_112/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 15:00:09 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-22506_112/ukb-d-22506_112.vcf.gz ...
Read summary statistics for 9821990 SNPs.
Dropped 7490 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, 1276691 SNPs remain.
After merging with regression SNP LD, 1276691 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0104 (0.0045)
Lambda GC: 1.0274
Mean Chi^2: 1.0161
Intercept: 0.9974 (0.0061)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Mon Nov 25 15:01:47 2019
Total time elapsed: 1.0m:38.74s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9407,
    "inflation_factor": 1.026,
    "mean_EFFECT": 1.4072e-06,
    "n": 91353,
    "n_snps": 9821990,
    "n_clumped_hits": 1,
    "n_p_sig": 1,
    "n_mono": 0,
    "n_ns": 1071772,
    "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": 194996,
    "n_est": 91358.3157,
    "ratio_se_n": 1,
    "mean_diff": 0,
    "ratio_diff": 7.8435,
    "sd_y_est1": 0.122,
    "sd_y_est2": 0.122,
    "r2_sum1": 5.1014e-06,
    "r2_sum2": 0.0003,
    "r2_sum3": 0.0003,
    "r2_sum4": 0.0003,
    "ldsc_nsnp_merge_refpanel_ld": 1276691,
    "ldsc_nsnp_merge_regression_ld": 1276691,
    "ldsc_observed_scale_h2_beta": 0.0104,
    "ldsc_observed_scale_h2_se": 0.0045,
    "ldsc_intercept_beta": 0.9974,
    "ldsc_intercept_se": 0.0061,
    "ldsc_lambda_gc": 1.0274,
    "ldsc_mean_chisq": 1.0161,
    "ldsc_ratio": -0.1615
}
 

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 1.000000 3 94 0 9815049 0 NA NA NA NA NA NA NA NA
character REF 0 1.000000 1 98 0 47568 0 NA NA NA NA NA NA NA NA
character ALT 0 1.000000 1 342 0 29883 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.000000 NA NA NA NA NA 9.079761e+00 6.176204e+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 7.874765e+07 5.610257e+07 3.02000e+02 3.244938e+07 6.964078e+07 1.147443e+08 2.492297e+08 ▇▆▅▂▁
numeric EFFECT 0 1.000000 NA NA NA NA NA 1.400000e-06 1.327600e-03 -1.19413e-02 -6.353000e-04 -5.200000e-06 6.240000e-04 1.385900e-02 ▁▁▇▁▁
numeric SE 0 1.000000 NA NA NA NA NA 1.141300e-03 6.690000e-04 4.71400e-04 6.264000e-04 8.430000e-04 1.467800e-03 3.787500e-03 ▇▂▁▁▁
numeric PVAL 0 1.000000 NA NA NA NA NA 4.963415e-01 2.890677e-01 0.00000e+00 2.451320e-01 4.944849e-01 7.467290e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.000000 NA NA NA NA NA 4.963397e-01 2.890687e-01 0.00000e+00 2.451285e-01 4.944832e-01 7.467283e-01 9.999998e-01 ▇▇▇▇▇
numeric AF 0 1.000000 NA NA NA NA NA 2.587007e-01 2.629273e-01 9.09750e-03 4.166620e-02 1.553950e-01 4.120670e-01 9.909020e-01 ▇▂▂▁▁
numeric AF_reference 194996 0.980147 NA NA NA NA NA 2.582676e-01 2.537404e-01 0.00000e+00 4.692490e-02 1.707270e-01 4.079470e-01 1.000000e+00 ▇▃▂▁▁
numeric N 0 1.000000 NA NA NA NA NA 9.135300e+04 0.000000e+00 9.13530e+04 9.135300e+04 9.135300e+04 9.135300e+04 9.135300e+04 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 692794 rs530212009 CA C 0.0004534 0.0009977 0.6495087 0.6495076 0.1114110 0.1894970 91353
1 693731 rs12238997 A G 0.0008529 0.0009450 0.3667322 0.3667296 0.1163870 0.1417730 91353
1 707522 rs371890604 G C 0.0003752 0.0010623 0.7239374 0.7239370 0.0978018 0.1293930 91353
1 717587 rs144155419 G A -0.0015418 0.0025345 0.5429615 0.5429608 0.0157459 0.0045926 91353
1 730087 rs148120343 T C 0.0016580 0.0013103 0.2057420 0.2057385 0.0569530 0.0127796 91353
1 731718 rs142557973 T C 0.0012276 0.0008968 0.1710319 0.1710294 0.1222870 0.1543530 91353
1 732032 rs61770163 A C 0.0006410 0.0009563 0.5026724 0.5026704 0.1219550 0.1555510 91353
1 734349 rs141242758 T C 0.0011698 0.0008975 0.1924332 0.1924294 0.1215170 0.1525560 91353
1 749963 rs529266287 T TAA -0.0006075 0.0008828 0.4913885 0.4913868 0.8690100 0.7641770 91353
1 751343 rs28544273 T A 0.0006345 0.0008753 0.4684972 0.4684956 0.1236040 0.2426120 91353
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T 0.0004848 0.0010490 0.6439972 0.6439939 0.0553192 0.0309934 91353
23 154923374 rs111332691 T A -0.0019074 0.0011414 0.0947153 0.0947106 0.0448863 0.0116556 91353
23 154925045 rs509981 C T 0.0004093 0.0005504 0.4570566 0.4570545 0.2442380 0.3634440 91353
23 154925895 rs538470 C T 0.0004181 0.0005627 0.4574504 0.4574480 0.2407140 0.3634440 91353
23 154927581 rs644138 G A 0.0004937 0.0005177 0.3402468 0.3402448 0.2999670 0.4635760 91353
23 154929412 rs557132 C T 0.0004139 0.0005506 0.4521578 0.4521555 0.2440870 0.3568210 91353
23 154929637 rs35185538 CT C 0.0004806 0.0005749 0.4031958 0.4031936 0.2285200 0.3011920 91353
23 154929952 rs4012982 CAA C 0.0004464 0.0005794 0.4410380 0.4410355 0.2380650 0.3165560 91353
23 154930230 rs781880 A G 0.0004544 0.0005504 0.4090120 0.4090102 0.2445480 0.3618540 91353
23 154930487 rs781879 T A 0.0036504 0.0018666 0.0505126 0.0505093 0.0196444 0.1263580 91353

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.111411 ES:SE:LP:AF:SS:ID   0.000453402:0.000997702:0.187415:0.111411:91353:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.116387 ES:SE:LP:AF:SS:ID   0.000852929:0.000944952:0.435651:0.116387:91353:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0978018    ES:SE:LP:AF:SS:ID   0.000375203:0.00106229:0.140299:0.0978018:91353:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.0157459    ES:SE:LP:AF:SS:ID   -0.00154183:0.00253448:0.265231:0.0157459:91353:rs144155419
1   730087  rs148120343 T   C   .   PASS    AF=0.056953 ES:SE:LP:AF:SS:ID   0.00165799:0.00131028:0.686677:0.056953:91353:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122287 ES:SE:LP:AF:SS:ID   0.00122763:0.000896798:0.766923:0.122287:91353:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.121955 ES:SE:LP:AF:SS:ID   0.000641031:0.000956343:0.298715:0.121955:91353:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.121517 ES:SE:LP:AF:SS:ID   0.0011698:0.000897481:0.71572:0.121517:91353:rs141242758
1   749963  rs529266287 T   TAA .   PASS    AF=0.86901  ES:SE:LP:AF:SS:ID   -0.000607485:0.000882838:0.308575:0.86901:91353:rs529266287
1   751343  rs28544273  T   A   .   PASS    AF=0.123604 ES:SE:LP:AF:SS:ID   0.00063451:0.000875272:0.329293:0.123604:91353:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.143109 ES:SE:LP:AF:SS:ID   0.00129876:0.000866406:0.873307:0.143109:91353:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.12374  ES:SE:LP:AF:SS:ID   0.000668937:0.000874003:0.352565:0.12374:91353:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.869977 ES:SE:LP:AF:SS:ID   -0.000652052:0.000851604:0.352743:0.869977:91353:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.874656 ES:SE:LP:AF:SS:ID   -0.00051801:0.000865484:0.260037:0.874656:91353:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.129446 ES:SE:LP:AF:SS:ID   0.000660596:0.00085423:0.357206:0.129446:91353:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0358609    ES:SE:LP:AF:SS:ID   0.000653341:0.00155933:0.170551:0.0358609:91353:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869624 ES:SE:LP:AF:SS:ID   -0.000639076:0.000850851:0.344292:0.869624:91353:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869701 ES:SE:LP:AF:SS:ID   -0.000648144:0.000851223:0.350272:0.869701:91353:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.86962  ES:SE:LP:AF:SS:ID   -0.000638319:0.000850815:0.343796:0.86962:91353:rs3131967
1   755890  rs3115858   A   T   .   PASS    AF=0.8696   ES:SE:LP:AF:SS:ID   -0.000565322:0.000849092:0.296242:0.8696:91353:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.126828 ES:SE:LP:AF:SS:ID   0.000452895:0.000866821:0.220881:0.126828:91353:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.869129 ES:SE:LP:AF:SS:ID   -0.000532119:0.000846888:0.275893:0.869129:91353:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.868516 ES:SE:LP:AF:SS:ID   -0.000708336:0.00084631:0.395114:0.868516:91353:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869278 ES:SE:LP:AF:SS:ID   -0.000558278:0.000847657:0.292306:0.869278:91353:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869283 ES:SE:LP:AF:SS:ID   -0.000558002:0.000847713:0.292104:0.869283:91353:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869286 ES:SE:LP:AF:SS:ID   -0.000559962:0.00084772:0.293365:0.869286:91353:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869767 ES:SE:LP:AF:SS:ID   -0.000537254:0.000849996:0.277905:0.869767:91353:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.0991021    ES:SE:LP:AF:SS:ID   0.000414666:0.000989467:0.170595:0.0991021:91353:rs10157329
1   759837  rs3115851   T   A   .   PASS    AF=0.873778 ES:SE:LP:AF:SS:ID   -0.00042425:0.000862622:0.205615:0.873778:91353:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.86314  ES:SE:LP:AF:SS:ID   -0.000656027:0.000846083:0.358404:0.86314:91353:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.868548 ES:SE:LP:AF:SS:ID   -0.000489052:0.000854524:0.24633:0.868548:91353:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.865453 ES:SE:LP:AF:SS:ID   -0.000765735:0.000854679:0.431457:0.865453:91353:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0994287    ES:SE:LP:AF:SS:ID   0.000141793:0.00102315:0.0507183:0.0994287:91353:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.870755 ES:SE:LP:AF:SS:ID   -0.000572371:0.00086445:0.294226:0.870755:91353:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.870755 ES:SE:LP:AF:SS:ID   -0.000572368:0.00086445:0.294224:0.870755:91353:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.870752 ES:SE:LP:AF:SS:ID   -0.000572363:0.000864452:0.29422:0.870752:91353:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.871109 ES:SE:LP:AF:SS:ID   -0.000553194:0.00086484:0.281994:0.871109:91353:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.126424 ES:SE:LP:AF:SS:ID   0.000475018:0.000865814:0.234142:0.126424:91353:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.105999 ES:SE:LP:AF:SS:ID   0.000800928:0.000941087:0.403694:0.105999:91353:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.854464 ES:SE:LP:AF:SS:ID   -0.000452995:0.000845806:0.227493:0.854464:91353:rs2519015
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.83913  ES:SE:LP:AF:SS:ID   -0.000972564:0.000835385:0.612002:0.83913:91353:rs376645387
1   768253  rs2977608   A   C   .   PASS    AF=0.763198 ES:SE:LP:AF:SS:ID   -0.00111059:0.000671524:1.00805:0.763198:91353:rs2977608
1   768448  rs12562034  G   A   .   PASS    AF=0.105174 ES:SE:LP:AF:SS:ID   0.00124212:0.000925296:0.746008:0.105174:91353:rs12562034
1   769138  rs59306077  CAT C   .   PASS    AF=0.129817 ES:SE:LP:AF:SS:ID   0.000455623:0.000853721:0.226538:0.129817:91353:rs762168062
1   769223  rs60320384  C   G   .   PASS    AF=0.129553 ES:SE:LP:AF:SS:ID   0.00043792:0.000852569:0.216454:0.129553:91353:rs60320384
1   769963  rs7518545   G   A   .   PASS    AF=0.104452 ES:SE:LP:AF:SS:ID   0.00129754:0.000932459:0.784971:0.104452:91353:rs7518545
1   770886  rs371458725 G   A   .   PASS    AF=0.103466 ES:SE:LP:AF:SS:ID   0.0014069:0.00094049:0.870707:0.103466:91353:rs371458725
1   771410  rs2519006   C   T   .   PASS    AF=0.829379 ES:SE:LP:AF:SS:ID   -0.000956082:0.000831706:0.601478:0.829379:91353:rs2519006
1   771823  rs2977605   T   C   .   PASS    AF=0.869153 ES:SE:LP:AF:SS:ID   -0.000472842:0.000848487:0.238568:0.869153:91353:rs2977605
1   771967  rs59066358  G   A   .   PASS    AF=0.129658 ES:SE:LP:AF:SS:ID   0.000484335:0.000852126:0.244296:0.129658:91353:rs59066358