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_4216/ukb-d-22617_4216.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_4216/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 15:00:05 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-22617_4216/ukb-d-22617_4216.vcf.gz ...
Read summary statistics for 9861188 SNPs.
Dropped 7547 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, 1277042 SNPs remain.
After merging with regression SNP LD, 1277042 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0102 (0.0049)
Lambda GC: 1.0245
Mean Chi^2: 1.0164
Intercept: 0.9979 (0.0063)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Mon Nov 25 15:01:43 2019
Total time elapsed: 1.0m:38.38s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9409,
    "inflation_factor": 1.0081,
    "mean_EFFECT": 2.0821e-06,
    "n": 91149,
    "n_snps": 9861188,
    "n_clumped_hits": 1,
    "n_p_sig": 2,
    "n_mono": 0,
    "n_ns": 1074205,
    "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": 195599,
    "n_est": 91145.1205,
    "ratio_se_n": 1,
    "mean_diff": 0,
    "ratio_diff": 75.8243,
    "sd_y_est1": 0.123,
    "sd_y_est2": 0.123,
    "r2_sum1": 5.213e-06,
    "r2_sum2": 0.0003,
    "r2_sum3": 0.0003,
    "r2_sum4": 0.0003,
    "ldsc_nsnp_merge_refpanel_ld": 1277042,
    "ldsc_nsnp_merge_regression_ld": 1277042,
    "ldsc_observed_scale_h2_beta": 0.0102,
    "ldsc_observed_scale_h2_se": 0.0049,
    "ldsc_intercept_beta": 0.9979,
    "ldsc_intercept_se": 0.0063,
    "ldsc_lambda_gc": 1.0245,
    "ldsc_mean_chisq": 1.0164,
    "ldsc_ratio": -0.128
}

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 1.0000000 3 94 0 9854189 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 98 0 47697 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 342 0 29941 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 9.080388e+00 6.176741e+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 7.874998e+07 5.609854e+07 3.02000e+02 3.245570e+07 6.964758e+07 1.147400e+08 2.492297e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA 2.100000e-06 1.347600e-03 -1.32098e-02 -6.410000e-04 -7.800000e-06 6.216000e-04 1.635840e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 1.159700e-03 6.852000e-04 4.75900e-04 6.326000e-04 8.537000e-04 1.493600e-03 3.812400e-03 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.990853e-01 2.887108e-01 0.00000e+00 2.489729e-01 4.982760e-01 7.493712e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.990835e-01 2.887119e-01 0.00000e+00 2.489699e-01 4.982744e-01 7.493712e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA 2.577751e-01 2.629357e-01 8.88420e-03 4.094640e-02 1.540730e-01 4.107090e-01 9.911160e-01 ▇▂▂▁▁
numeric AF_reference 195599 0.9801648 NA NA NA NA NA 2.573618e-01 2.537560e-01 0.00000e+00 4.612620e-02 1.695290e-01 4.067490e-01 1.000000e+00 ▇▃▂▁▁
numeric N 0 1.0000000 NA NA NA NA NA 9.114900e+04 0.000000e+00 9.11490e+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 0.0002630 0.0010067 0.7938949 0.7938952 0.1114870 0.1894970 91149
1 693731 rs12238997 A G 0.0003170 0.0009534 0.7395593 0.7395575 0.1164590 0.1417730 91149
1 707522 rs371890604 G C 0.0003709 0.0010714 0.7292224 0.7292205 0.0978765 0.1293930 91149
1 717587 rs144155419 G A 0.0018752 0.0025541 0.4628263 0.4628223 0.0157837 0.0045926 91149
1 730087 rs148120343 T C 0.0004635 0.0013215 0.7257984 0.7257976 0.0569973 0.0127796 91149
1 731718 rs142557973 T C 0.0006044 0.0009047 0.5040853 0.5040826 0.1223590 0.1543530 91149
1 732032 rs61770163 A C 0.0006439 0.0009647 0.5044789 0.5044772 0.1220470 0.1555510 91149
1 734349 rs141242758 T C 0.0006599 0.0009054 0.4661181 0.4661159 0.1215880 0.1525560 91149
1 749963 rs529266287 T TAA -0.0005017 0.0008906 0.5732301 0.5732287 0.8689170 0.7641770 91149
1 751343 rs28544273 T A 0.0003627 0.0008830 0.6812461 0.6812450 0.1236990 0.2426120 91149
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T 0.0002501 0.0010591 0.8133001 0.8132998 0.0552869 0.0309934 91149
23 154923374 rs111332691 T A -0.0017056 0.0011526 0.1389182 0.1389146 0.0448387 0.0116556 91149
23 154925045 rs509981 C T -0.0001430 0.0005555 0.7968600 0.7968594 0.2444670 0.3634440 91149
23 154925895 rs538470 C T -0.0001377 0.0005679 0.8083831 0.8083828 0.2409490 0.3634440 91149
23 154927581 rs644138 G A -0.0000538 0.0005224 0.9179609 0.9179609 0.3001640 0.4635760 91149
23 154929412 rs557132 C T -0.0001339 0.0005556 0.8095020 0.8095019 0.2443160 0.3568210 91149
23 154929637 rs35185538 CT C -0.0001143 0.0005802 0.8438670 0.8438660 0.2286640 0.3011920 91149
23 154929952 rs4012982 CAA C -0.0001441 0.0005848 0.8053949 0.8053938 0.2382530 0.3165560 91149
23 154930230 rs781880 A G -0.0001178 0.0005554 0.8320639 0.8320639 0.2447660 0.3618540 91149
23 154930487 rs781879 T A 0.0004953 0.0018838 0.7926181 0.7926180 0.0196632 0.1263580 91149

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.111487 ES:SE:LP:AF:SS:ID   0.000263001:0.00100668:0.100237:0.111487:91149:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.116459 ES:SE:LP:AF:SS:ID   0.000316957:0.000953428:0.131027:0.116459:91149:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0978765    ES:SE:LP:AF:SS:ID   0.000370889:0.00107143:0.13714:0.0978765:91149:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.0157837    ES:SE:LP:AF:SS:ID   0.00187521:0.00255406:0.334582:0.0157837:91149:rs144155419
1   730087  rs148120343 T   C   .   PASS    AF=0.0569973    ES:SE:LP:AF:SS:ID   0.000463485:0.00132152:0.139184:0.0569973:91149:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122359 ES:SE:LP:AF:SS:ID   0.00060444:0.000904742:0.297496:0.122359:91149:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.122047 ES:SE:LP:AF:SS:ID   0.000643879:0.000964668:0.297157:0.122047:91149:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.121588 ES:SE:LP:AF:SS:ID   0.000659888:0.000905428:0.331504:0.121588:91149:rs141242758
1   749963  rs529266287 T   TAA .   PASS    AF=0.868917 ES:SE:LP:AF:SS:ID   -0.0005017:0.00089064:0.241671:0.868917:91149:rs529266287
1   751343  rs28544273  T   A   .   PASS    AF=0.123699 ES:SE:LP:AF:SS:ID   0.000362707:0.000883004:0.166696:0.123699:91149:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.143202 ES:SE:LP:AF:SS:ID   0.00039175:0.000874181:0.184384:0.143202:91149:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.123834 ES:SE:LP:AF:SS:ID   0.000346642:0.000881726:0.158505:0.123834:91149:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.869892 ES:SE:LP:AF:SS:ID   -0.000588492:0.000859155:0.30683:0.869892:91149:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.874553 ES:SE:LP:AF:SS:ID   -0.000365252:0.00087309:0.170248:0.874553:91149:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.129529 ES:SE:LP:AF:SS:ID   0.000463887:0.000861852:0.228846:0.129529:91149:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0358205    ES:SE:LP:AF:SS:ID   -0.00248105:0.0015745:0.938996:0.0358205:91149:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869539 ES:SE:LP:AF:SS:ID   -0.000560038:0.000858438:0.28891:0.869539:91149:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869616 ES:SE:LP:AF:SS:ID   -0.000569983:0.000858812:0.295085:0.869616:91149:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.869535 ES:SE:LP:AF:SS:ID   -0.000547536:0.000858402:0.281026:0.869535:91149:rs3131967
1   755890  rs3115858   A   T   .   PASS    AF=0.86952  ES:SE:LP:AF:SS:ID   -0.000531188:0.000856634:0.271483:0.86952:91149:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.126911 ES:SE:LP:AF:SS:ID   0.000446191:0.000874473:0.214753:0.126911:91149:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.86905  ES:SE:LP:AF:SS:ID   -0.00047955:0.000854416:0.240618:0.86905:91149:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.868436 ES:SE:LP:AF:SS:ID   -0.000568038:0.000853874:0.29594:0.868436:91149:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869199 ES:SE:LP:AF:SS:ID   -0.000493719:0.000855191:0.248933:0.869199:91149:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869205 ES:SE:LP:AF:SS:ID   -0.000493187:0.000855246:0.24859:0.869205:91149:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869207 ES:SE:LP:AF:SS:ID   -0.000492987:0.000855254:0.248465:0.869207:91149:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869687 ES:SE:LP:AF:SS:ID   -0.000537375:0.000857542:0.274992:0.869687:91149:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.0991787    ES:SE:LP:AF:SS:ID   -5.14811e-05:0.000997972:0.0182453:0.0991787:91149:rs10157329
1   759837  rs3115851   T   A   .   PASS    AF=0.873682 ES:SE:LP:AF:SS:ID   -0.000392055:0.000870243:0.185525:0.873682:91149:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.863058 ES:SE:LP:AF:SS:ID   -0.000297554:0.000853639:0.13822:0.863058:91149:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.868472 ES:SE:LP:AF:SS:ID   -0.000467577:0.000862107:0.230941:0.868472:91149:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.865369 ES:SE:LP:AF:SS:ID   -0.000273735:0.000862271:0.12442:0.865369:91149:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0995228    ES:SE:LP:AF:SS:ID   0.000149838:0.00103198:0.0532742:0.0995228:91149:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.870657 ES:SE:LP:AF:SS:ID   -0.000225943:0.000872093:0.09932:0.870657:91149:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.870657 ES:SE:LP:AF:SS:ID   -0.000225924:0.000872093:0.0993107:0.870657:91149:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.870654 ES:SE:LP:AF:SS:ID   -0.000225314:0.000872096:0.099016:0.870654:91149:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.871019 ES:SE:LP:AF:SS:ID   -0.00025916:0.000872501:0.11552:0.871019:91149:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.126515 ES:SE:LP:AF:SS:ID   0.000330908:0.000873471:0.15193:0.126515:91149:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.106037 ES:SE:LP:AF:SS:ID   0.000306796:0.00094947:0.12691:0.106037:91149:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.85438  ES:SE:LP:AF:SS:ID   -0.000179193:0.000853313:0.0790058:0.85438:91149:rs2519015
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.839153 ES:SE:LP:AF:SS:ID   0.000119829:0.000843111:0.0520862:0.839153:91149:rs376645387
1   768253  rs2977608   A   C   .   PASS    AF=0.763195 ES:SE:LP:AF:SS:ID   -0.000218055:0.000677905:0.126266:0.763195:91149:rs2977608
1   768448  rs12562034  G   A   .   PASS    AF=0.105092 ES:SE:LP:AF:SS:ID   -0.000111741:0.00093416:0.0434536:0.105092:91149:rs12562034
1   769138  rs59306077  CAT C   .   PASS    AF=0.129904 ES:SE:LP:AF:SS:ID   0.000264017:0.000861271:0.119649:0.129904:91149:rs762168062
1   769223  rs60320384  C   G   .   PASS    AF=0.129636 ES:SE:LP:AF:SS:ID   0.000293555:0.000860138:0.134963:0.129636:91149:rs60320384
1   769963  rs7518545   G   A   .   PASS    AF=0.104365 ES:SE:LP:AF:SS:ID   -9.59697e-05:0.000941412:0.0367776:0.104365:91149:rs7518545
1   770181  rs146076599 A   G   .   PASS    AF=0.00906976   ES:SE:LP:AF:SS:ID   -0.000574564:0.0033098:0.0644:0.00906976:91149:rs146076599
1   770886  rs371458725 G   A   .   PASS    AF=0.103379 ES:SE:LP:AF:SS:ID   -9.0884e-05:0.000949554:0.034446:0.103379:91149:rs371458725
1   771410  rs2519006   C   T   .   PASS    AF=0.829291 ES:SE:LP:AF:SS:ID   -0.000231233:0.000839172:0.106296:0.829291:91149:rs2519006
1   771823  rs2977605   T   C   .   PASS    AF=0.869067 ES:SE:LP:AF:SS:ID   -0.000412428:0.000855991:0.200701:0.869067:91149:rs2977605