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_7212/ukb-d-22617_7212.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_7212/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 14:59:03 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-22617_7212/ukb-d-22617_7212.vcf.gz ...
Read summary statistics for 10159406 SNPs.
Dropped 7917 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, 1279198 SNPs remain.
After merging with regression SNP LD, 1279198 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0085 (0.0046)
Lambda GC: 1.0039
Mean Chi^2: 1.0072
Intercept: 0.9916 (0.006)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Mon Nov 25 15:00:37 2019
Total time elapsed: 1.0m:33.54s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9424,
    "inflation_factor": 0.9979,
    "mean_EFFECT": 5.2764e-06,
    "n": 91149,
    "n_snps": 10159406,
    "n_clumped_hits": 0,
    "n_p_sig": 1,
    "n_mono": 0,
    "n_ns": 1092534,
    "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": 200283,
    "n_est": 91191.5121,
    "ratio_se_n": 1.0002,
    "mean_diff": 0,
    "ratio_diff": 123.5031,
    "sd_y_est1": 0.1347,
    "sd_y_est2": 0.1347,
    "r2_sum1": 0,
    "r2_sum2": 0,
    "r2_sum3": 0,
    "r2_sum4": 0,
    "ldsc_nsnp_merge_refpanel_ld": 1279198,
    "ldsc_nsnp_merge_regression_ld": 1279198,
    "ldsc_observed_scale_h2_beta": 0.0085,
    "ldsc_observed_scale_h2_se": 0.0046,
    "ldsc_intercept_beta": 0.9916,
    "ldsc_intercept_se": 0.006,
    "ldsc_lambda_gc": 1.0039,
    "ldsc_mean_chisq": 1.0072,
    "ldsc_ratio": -1.1667
}

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 10152046 0 NA NA NA NA NA NA NA NA
character REF 0 1.000000 1 100 0 48688 0 NA NA NA NA NA NA NA NA
character ALT 0 1.000000 1 342 0 30348 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.000000 NA NA NA NA NA 9.079709e+00 6.178031e+00 1.0000e+00 4.000000e+00 8.000000e+00 1.300000e+01 2.300000e+01 ▇▅▅▂▂
numeric POS 0 1.000000 NA NA NA NA NA 7.876473e+07 5.606686e+07 3.0200e+02 3.251054e+07 6.968279e+07 1.147219e+08 2.492297e+08 ▇▆▅▂▁
numeric EFFECT 0 1.000000 NA NA NA NA NA 5.300000e-06 1.578000e-03 -1.4676e-02 -7.138000e-04 -3.400000e-06 7.050000e-04 1.960270e-02 ▁▂▇▁▁
numeric SE 0 1.000000 NA NA NA NA NA 1.339100e-03 8.390000e-04 5.2130e-04 6.971000e-04 9.589000e-04 1.740800e-03 4.752100e-03 ▇▂▁▁▁
numeric PVAL 0 1.000000 NA NA NA NA NA 4.999935e-01 2.887982e-01 0.0000e+00 2.499488e-01 5.004561e-01 7.500906e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.000000 NA NA NA NA NA 4.999918e-01 2.887992e-01 0.0000e+00 2.499466e-01 5.004545e-01 7.500896e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.000000 NA NA NA NA NA 2.509516e-01 2.629315e-01 7.4272e-03 3.581700e-02 1.440480e-01 4.008220e-01 9.925730e-01 ▇▂▂▁▁
numeric AF_reference 200283 0.980286 NA NA NA NA NA 2.506792e-01 2.537891e-01 0.0000e+00 3.913740e-02 1.603430e-01 3.973640e-01 1.000000e+00 ▇▃▂▁▁
numeric N 0 1.000000 NA NA NA NA NA 9.114900e+04 0.000000e+00 9.1149e+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 -0.0004528 0.0011028 0.6813998 0.6813996 0.1114870 0.1894970 91149
1 693731 rs12238997 A G -0.0012289 0.0010445 0.2393492 0.2393482 0.1164590 0.1417730 91149
1 707522 rs371890604 G C -0.0003881 0.0011737 0.7409297 0.7409289 0.0978765 0.1293930 91149
1 717587 rs144155419 G A -0.0011349 0.0027979 0.6850228 0.6850219 0.0157837 0.0045926 91149
1 730087 rs148120343 T C -0.0000154 0.0014477 0.9914920 0.9914918 0.0569973 0.0127796 91149
1 731718 rs142557973 T C -0.0010531 0.0009911 0.2879791 0.2879778 0.1223590 0.1543530 91149
1 732032 rs61770163 A C -0.0006960 0.0010568 0.5101372 0.5101358 0.1220470 0.1555510 91149
1 734349 rs141242758 T C -0.0011901 0.0009919 0.2302189 0.2302147 0.1215880 0.1525560 91149
1 749963 rs529266287 T TAA 0.0008892 0.0009757 0.3621262 0.3621234 0.8689170 0.7641770 91149
1 751343 rs28544273 T A -0.0008103 0.0009673 0.4021944 0.4021917 0.1236990 0.2426120 91149
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T -0.0003799 0.0011602 0.7433186 0.7433193 0.0552869 0.0309934 91149
23 154923374 rs111332691 T A 0.0017328 0.0012626 0.1699499 0.1699481 0.0448387 0.0116556 91149
23 154925045 rs509981 C T 0.0006686 0.0006085 0.2718323 0.2718290 0.2444670 0.3634440 91149
23 154925895 rs538470 C T 0.0007382 0.0006221 0.2353412 0.2353382 0.2409490 0.3634440 91149
23 154927581 rs644138 G A 0.0005182 0.0005723 0.3652179 0.3652150 0.3001640 0.4635760 91149
23 154929412 rs557132 C T 0.0006881 0.0006087 0.2582778 0.2582747 0.2443160 0.3568210 91149
23 154929637 rs35185538 CT C 0.0009806 0.0006355 0.1228429 0.1228393 0.2286640 0.3011920 91149
23 154929952 rs4012982 CAA C 0.0007879 0.0006406 0.2187268 0.2187234 0.2382530 0.3165560 91149
23 154930230 rs781880 A G 0.0006651 0.0006085 0.2743589 0.2743561 0.2447660 0.3618540 91149
23 154930487 rs781879 T A 0.0000892 0.0020636 0.9655321 0.9655317 0.0196632 0.1263580 91149

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.111487 ES:SE:LP:AF:SS:ID   -0.000452759:0.0011028:0.166598:0.111487:91149:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.116459 ES:SE:LP:AF:SS:ID   -0.00122893:0.00104446:0.620968:0.116459:91149:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0978765    ES:SE:LP:AF:SS:ID   -0.000388066:0.00117374:0.130223:0.0978765:91149:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.0157837    ES:SE:LP:AF:SS:ID   -0.0011349:0.00279794:0.164295:0.0157837:91149:rs144155419
1   730087  rs148120343 T   C   .   PASS    AF=0.0569973    ES:SE:LP:AF:SS:ID   -1.54377e-05:0.0014477:0.00371079:0.0569973:91149:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122359 ES:SE:LP:AF:SS:ID   -0.00105314:0.000991127:0.540639:0.122359:91149:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.122047 ES:SE:LP:AF:SS:ID   -0.000696023:0.00105678:0.292313:0.122047:91149:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.121588 ES:SE:LP:AF:SS:ID   -0.00119006:0.000991877:0.637859:0.121588:91149:rs141242758
1   749963  rs529266287 T   TAA .   PASS    AF=0.868917 ES:SE:LP:AF:SS:ID   0.000889162:0.000975679:0.44114:0.868917:91149:rs529266287
1   751343  rs28544273  T   A   .   PASS    AF=0.123699 ES:SE:LP:AF:SS:ID   -0.000810331:0.000967313:0.395564:0.123699:91149:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.143202 ES:SE:LP:AF:SS:ID   -0.000952788:0.000957647:0.495155:0.143202:91149:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.123834 ES:SE:LP:AF:SS:ID   -0.000817864:0.000965914:0.401045:0.123834:91149:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.869892 ES:SE:LP:AF:SS:ID   0.000652813:0.000941191:0.31164:0.869892:91149:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.874553 ES:SE:LP:AF:SS:ID   0.00084259:0.000956453:0.422111:0.874553:91149:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.129529 ES:SE:LP:AF:SS:ID   -0.000595825:0.000944145:0.277371:0.129529:91149:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0358205    ES:SE:LP:AF:SS:ID   -0.000221577:0.00172486:0.0468281:0.0358205:91149:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869539 ES:SE:LP:AF:SS:ID   0.000617935:0.000940405:0.291476:0.869539:91149:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869616 ES:SE:LP:AF:SS:ID   0.000606965:0.000940815:0.284972:0.869616:91149:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.869535 ES:SE:LP:AF:SS:ID   0.000614552:0.000940366:0.289527:0.869535:91149:rs3131967
1   755890  rs3115858   A   T   .   PASS    AF=0.86952  ES:SE:LP:AF:SS:ID   0.000589124:0.000938428:0.2756:0.86952:91149:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.126911 ES:SE:LP:AF:SS:ID   -0.000568573:0.000957971:0.257404:0.126911:91149:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.86905  ES:SE:LP:AF:SS:ID   0.000529196:0.000935999:0.242744:0.86905:91149:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.868436 ES:SE:LP:AF:SS:ID   0.000457439:0.000935407:0.204242:0.868436:91149:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869199 ES:SE:LP:AF:SS:ID   0.000520696:0.000936848:0.237808:0.869199:91149:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869205 ES:SE:LP:AF:SS:ID   0.000521367:0.000936909:0.238157:0.869205:91149:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869207 ES:SE:LP:AF:SS:ID   0.000520041:0.000936917:0.237428:0.869207:91149:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869687 ES:SE:LP:AF:SS:ID   0.000569969:0.000939424:0.264372:0.869687:91149:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.0991787    ES:SE:LP:AF:SS:ID   -0.000556548:0.00109326:0.21417:0.0991787:91149:rs10157329
1   759837  rs3115851   T   A   .   PASS    AF=0.873682 ES:SE:LP:AF:SS:ID   0.00076619:0.000953335:0.375124:0.873682:91149:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.863058 ES:SE:LP:AF:SS:ID   0.000572891:0.000935146:0.267503:0.863058:91149:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.868472 ES:SE:LP:AF:SS:ID   0.000646281:0.000944424:0.306467:0.868472:91149:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.865369 ES:SE:LP:AF:SS:ID   0.000643283:0.000944602:0.304635:0.865369:91149:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0995228    ES:SE:LP:AF:SS:ID   -0.00130154:0.0011305:0.602731:0.0995228:91149:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.870657 ES:SE:LP:AF:SS:ID   0.000809278:0.000955361:0.401269:0.870657:91149:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.870657 ES:SE:LP:AF:SS:ID   0.000809289:0.000955361:0.401275:0.870657:91149:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.870654 ES:SE:LP:AF:SS:ID   0.000809588:0.000955364:0.401464:0.870654:91149:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.871019 ES:SE:LP:AF:SS:ID   0.00077479:0.000955808:0.37925:0.871019:91149:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.126515 ES:SE:LP:AF:SS:ID   -0.000657733:0.000956872:0.308171:0.126515:91149:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.106037 ES:SE:LP:AF:SS:ID   -0.000765869:0.00104013:0.335794:0.106037:91149:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.85438  ES:SE:LP:AF:SS:ID   0.000307794:0.000934791:0.129622:0.85438:91149:rs2519015
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.839153 ES:SE:LP:AF:SS:ID   0.00140154:0.000923602:0.888906:0.839153:91149:rs376645387
1   768253  rs2977608   A   C   .   PASS    AF=0.763195 ES:SE:LP:AF:SS:ID   0.000964379:0.000742627:0.712013:0.763195:91149:rs2977608
1   768448  rs12562034  G   A   .   PASS    AF=0.105092 ES:SE:LP:AF:SS:ID   -0.00134592:0.00102335:0.72482:0.105092:91149:rs12562034
1   768819  rs12562811  C   T   .   PASS    AF=0.00772317   ES:SE:LP:AF:SS:ID   0.00301166:0.00379187:0.369513:0.00772317:91149:rs12562811
1   769138  rs59306077  CAT C   .   PASS    AF=0.129904 ES:SE:LP:AF:SS:ID   -0.000467612:0.000943508:0.207489:0.129904:91149:rs762168062
1   769223  rs60320384  C   G   .   PASS    AF=0.129636 ES:SE:LP:AF:SS:ID   -0.000465398:0.000942267:0.20665:0.129636:91149:rs60320384
1   769963  rs7518545   G   A   .   PASS    AF=0.104365 ES:SE:LP:AF:SS:ID   -0.00128402:0.00103129:0.671388:0.104365:91149:rs7518545
1   770181  rs146076599 A   G   .   PASS    AF=0.00906976   ES:SE:LP:AF:SS:ID   0.00401471:0.00362581:0.571566:0.00906976:91149:rs146076599
1   770377  rs112563271 A   T   .   PASS    AF=0.00773917   ES:SE:LP:AF:SS:ID   0.00300011:0.0038077:0.365772:0.00773917:91149:rs112563271
1   770886  rs371458725 G   A   .   PASS    AF=0.103379 ES:SE:LP:AF:SS:ID   -0.00133172:0.00104021:0.697966:0.103379:91149:rs371458725