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_6115/ukb-d-22617_6115.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_6115/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 15:46:38 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-22617_6115/ukb-d-22617_6115.vcf.gz ...
Read summary statistics for 9557573 SNPs.
Dropped 7136 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, 1273916 SNPs remain.
After merging with regression SNP LD, 1273916 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0098 (0.0046)
Lambda GC: 1.0118
Mean Chi^2: 1.0087
Intercept: 0.9909 (0.0061)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Mon Nov 25 15:49:00 2019
Total time elapsed: 2.0m:22.31s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9392,
    "inflation_factor": 1.0077,
    "mean_EFFECT": 5.483e-07,
    "n": 91149,
    "n_snps": 9557573,
    "n_clumped_hits": 0,
    "n_p_sig": 0,
    "n_mono": 0,
    "n_ns": 1054595,
    "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": 190983,
    "n_est": 91146.2885,
    "ratio_se_n": 1,
    "mean_diff": 3.9254e-06,
    "ratio_diff": 10.1556,
    "sd_y_est1": 0.1128,
    "sd_y_est2": 0.1128,
    "r2_sum1": 0,
    "r2_sum2": 0,
    "r2_sum3": 0,
    "r2_sum4": 0,
    "ldsc_nsnp_merge_refpanel_ld": 1273916,
    "ldsc_nsnp_merge_regression_ld": 1273916,
    "ldsc_observed_scale_h2_beta": 0.0098,
    "ldsc_observed_scale_h2_se": 0.0046,
    "ldsc_intercept_beta": 0.9909,
    "ldsc_intercept_se": 0.0061,
    "ldsc_lambda_gc": 1.0118,
    "ldsc_mean_chisq": 1.0087,
    "ldsc_ratio": -1.046
}

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 9550978 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 98 0 46608 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 342 0 29502 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 9.080139e+00 6.174377e+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 7.871619e+07 5.611323e+07 3.02000e+02 3.241931e+07 6.959225e+07 1.147325e+08 2.492297e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA 5.000000e-07 1.156200e-03 -1.19032e-02 -5.713000e-04 -4.200000e-06 5.591000e-04 1.294450e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 1.010300e-03 5.599000e-04 4.36600e-04 5.772000e-04 7.635000e-04 1.286600e-03 3.119200e-03 ▇▂▂▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.991904e-01 2.890340e-01 1.00000e-07 2.485370e-01 4.983575e-01 7.497354e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.991886e-01 2.890351e-01 1.00000e-07 2.485342e-01 4.983553e-01 7.497340e-01 9.999996e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA 2.651229e-01 2.628038e-01 1.06655e-02 4.692320e-02 1.644280e-01 4.214930e-01 9.893340e-01 ▇▂▂▁▁
numeric AF_reference 190983 0.9800176 NA NA NA NA NA 2.645275e-01 2.536010e-01 0.00000e+00 5.351440e-02 1.793130e-01 4.165340e-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 0.0005586 0.0009236 0.5453069 0.5453053 0.1114870 0.1894970 91149
1 693731 rs12238997 A G 0.0006038 0.0008747 0.4900157 0.4900145 0.1164590 0.1417730 91149
1 707522 rs371890604 G C -0.0004808 0.0009830 0.6247611 0.6247599 0.0978765 0.1293930 91149
1 717587 rs144155419 G A 0.0018293 0.0023433 0.4350037 0.4350012 0.0157837 0.0045926 91149
1 730087 rs148120343 T C 0.0007567 0.0012124 0.5325385 0.5325365 0.0569973 0.0127796 91149
1 731718 rs142557973 T C 0.0005824 0.0008301 0.4828798 0.4828785 0.1223590 0.1543530 91149
1 732032 rs61770163 A C 0.0002484 0.0008851 0.7790099 0.7790094 0.1220470 0.1555510 91149
1 734349 rs141242758 T C 0.0006052 0.0008307 0.4662792 0.4662771 0.1215880 0.1525560 91149
1 749963 rs529266287 T TAA -0.0006748 0.0008171 0.4089263 0.4089237 0.8689170 0.7641770 91149
1 751343 rs28544273 T A 0.0006559 0.0008101 0.4181547 0.4181527 0.1236990 0.2426120 91149
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T 0.0003900 0.0009717 0.6881340 0.6881332 0.0552869 0.0309934 91149
23 154923374 rs111332691 T A -0.0004993 0.0010575 0.6368263 0.6368241 0.0448387 0.0116556 91149
23 154925045 rs509981 C T 0.0000755 0.0005096 0.8821540 0.8821540 0.2444670 0.3634440 91149
23 154925895 rs538470 C T 0.0001475 0.0005210 0.7770751 0.7770747 0.2409490 0.3634440 91149
23 154927581 rs644138 G A 0.0001639 0.0004793 0.7323725 0.7323710 0.3001640 0.4635760 91149
23 154929412 rs557132 C T 0.0000821 0.0005098 0.8720530 0.8720527 0.2443160 0.3568210 91149
23 154929637 rs35185538 CT C 0.0002402 0.0005323 0.6517965 0.6517960 0.2286640 0.3011920 91149
23 154929952 rs4012982 CAA C 0.0002214 0.0005365 0.6798185 0.6798184 0.2382530 0.3165560 91149
23 154930230 rs781880 A G 0.0000968 0.0005096 0.8493201 0.8493194 0.2447660 0.3618540 91149
23 154930487 rs781879 T A -0.0013495 0.0017283 0.4349026 0.4348993 0.0196632 0.1263580 91149

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.111487 ES:SE:LP:AF:SS:ID   0.0005586:0.000923596:0.263359:0.111487:91149:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.116459 ES:SE:LP:AF:SS:ID   0.000603822:0.000874742:0.30979:0.116459:91149:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0978765    ES:SE:LP:AF:SS:ID   -0.000480805:0.000983009:0.204286:0.0978765:91149:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.0157837    ES:SE:LP:AF:SS:ID   0.00182931:0.00234328:0.361507:0.0157837:91149:rs144155419
1   730087  rs148120343 T   C   .   PASS    AF=0.0569973    ES:SE:LP:AF:SS:ID   0.000756737:0.00121245:0.273649:0.0569973:91149:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122359 ES:SE:LP:AF:SS:ID   0.000582448:0.000830076:0.316161:0.122359:91149:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.122047 ES:SE:LP:AF:SS:ID   0.000248356:0.000885057:0.108457:0.122047:91149:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.121588 ES:SE:LP:AF:SS:ID   0.00060521:0.000830705:0.331354:0.121588:91149:rs141242758
1   749963  rs529266287 T   TAA .   PASS    AF=0.868917 ES:SE:LP:AF:SS:ID   -0.000674782:0.000817136:0.388355:0.868917:91149:rs529266287
1   751343  rs28544273  T   A   .   PASS    AF=0.123699 ES:SE:LP:AF:SS:ID   0.000655905:0.000810129:0.378663:0.123699:91149:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.143202 ES:SE:LP:AF:SS:ID   0.000824157:0.000802032:0.516915:0.143202:91149:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.123834 ES:SE:LP:AF:SS:ID   0.000635956:0.000808957:0.364731:0.123834:91149:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.869892 ES:SE:LP:AF:SS:ID   -0.000843954:0.000788248:0.546191:0.869892:91149:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.874553 ES:SE:LP:AF:SS:ID   -0.000521831:0.000801035:0.288395:0.874553:91149:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.129529 ES:SE:LP:AF:SS:ID   0.000881179:0.000790721:0.576572:0.129529:91149:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0358205    ES:SE:LP:AF:SS:ID   0.000448083:0.00144457:0.121236:0.0358205:91149:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869539 ES:SE:LP:AF:SS:ID   -0.000861874:0.000787589:0.562538:0.869539:91149:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869616 ES:SE:LP:AF:SS:ID   -0.000869672:0.000787933:0.569103:0.869616:91149:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.869535 ES:SE:LP:AF:SS:ID   -0.000860461:0.000787557:0.561323:0.869535:91149:rs3131967
1   755890  rs3115858   A   T   .   PASS    AF=0.86952  ES:SE:LP:AF:SS:ID   -0.000911639:0.000785933:0.608932:0.86952:91149:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.126911 ES:SE:LP:AF:SS:ID   0.000901888:0.0008023:0.583423:0.126911:91149:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.86905  ES:SE:LP:AF:SS:ID   -0.000878558:0.000783899:0.581044:0.86905:91149:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.868436 ES:SE:LP:AF:SS:ID   -0.000939866:0.000783401:0.6378:0.868436:91149:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869199 ES:SE:LP:AF:SS:ID   -0.000896716:0.000784609:0.596723:0.869199:91149:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869205 ES:SE:LP:AF:SS:ID   -0.000896149:0.00078466:0.596156:0.869205:91149:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869207 ES:SE:LP:AF:SS:ID   -0.000897261:0.000784667:0.597159:0.869207:91149:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869687 ES:SE:LP:AF:SS:ID   -0.000872995:0.000786767:0.573203:0.869687:91149:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.0991787    ES:SE:LP:AF:SS:ID   0.000234326:0.000915611:0.0979911:0.0991787:91149:rs10157329
1   759837  rs3115851   T   A   .   PASS    AF=0.873682 ES:SE:LP:AF:SS:ID   -0.000729348:0.000798421:0.442507:0.873682:91149:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.863058 ES:SE:LP:AF:SS:ID   -0.000872846:0.000783185:0.576631:0.863058:91149:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.868472 ES:SE:LP:AF:SS:ID   -0.00085083:0.000790956:0.549651:0.868472:91149:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.865369 ES:SE:LP:AF:SS:ID   -0.000667886:0.000791107:0.399532:0.865369:91149:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0995228    ES:SE:LP:AF:SS:ID   0.000768455:0.000946806:0.379858:0.0995228:91149:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.870657 ES:SE:LP:AF:SS:ID   -0.000710368:0.000800118:0.426392:0.870657:91149:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.870657 ES:SE:LP:AF:SS:ID   -0.000710363:0.000800118:0.426388:0.870657:91149:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.870654 ES:SE:LP:AF:SS:ID   -0.000710409:0.000800121:0.426422:0.870654:91149:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.871019 ES:SE:LP:AF:SS:ID   -0.000748485:0.000800492:0.456211:0.871019:91149:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.126515 ES:SE:LP:AF:SS:ID   0.0008593:0.000801381:0.547294:0.126515:91149:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.106037 ES:SE:LP:AF:SS:ID   0.000461176:0.000871111:0.224374:0.106037:91149:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.85438  ES:SE:LP:AF:SS:ID   -0.000612107:0.000782889:0.362209:0.85438:91149:rs2519015
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.839153 ES:SE:LP:AF:SS:ID   -0.000317774:0.00077353:0.166717:0.839153:91149:rs376645387
1   768253  rs2977608   A   C   .   PASS    AF=0.763195 ES:SE:LP:AF:SS:ID   -0.00038487:0.000621958:0.270797:0.763195:91149:rs2977608
1   768448  rs12562034  G   A   .   PASS    AF=0.105092 ES:SE:LP:AF:SS:ID   -0.000264383:0.000857065:0.120491:0.105092:91149:rs12562034
1   769138  rs59306077  CAT C   .   PASS    AF=0.129904 ES:SE:LP:AF:SS:ID   0.000896201:0.000790187:0.590525:0.129904:91149:rs762168062
1   769223  rs60320384  C   G   .   PASS    AF=0.129636 ES:SE:LP:AF:SS:ID   0.000885847:0.000789148:0.582299:0.129636:91149:rs60320384
1   769963  rs7518545   G   A   .   PASS    AF=0.104365 ES:SE:LP:AF:SS:ID   -0.000271884:0.000863719:0.123247:0.104365:91149:rs7518545
1   770886  rs371458725 G   A   .   PASS    AF=0.103379 ES:SE:LP:AF:SS:ID   -0.000294425:0.000871189:0.133479:0.103379:91149:rs371458725
1   771410  rs2519006   C   T   .   PASS    AF=0.829291 ES:SE:LP:AF:SS:ID   -0.000887707:0.000769912:0.603951:0.829291:91149:rs2519006
1   771823  rs2977605   T   C   .   PASS    AF=0.869067 ES:SE:LP:AF:SS:ID   -0.000886966:0.000785344:0.58715:0.869067:91149:rs2977605
1   771967  rs59066358  G   A   .   PASS    AF=0.129741 ES:SE:LP:AF:SS:ID   0.000927187:0.000788716:0.620202:0.129741:91149:rs59066358