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-20126_0/ukb-d-20126_0.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-20126_0/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 16:40:39 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-20126_0/ukb-d-20126_0.vcf.gz ...
Read summary statistics for 13562961 SNPs.
Dropped 12680 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, 1283503 SNPs remain.
After merging with regression SNP LD, 1283503 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0626 (0.0057)
Lambda GC: 1.1044
Mean Chi^2: 1.1091
Intercept: 1.0027 (0.0059)
Ratio: 0.025 (0.0545)
Analysis finished at Mon Nov 25 16:42:58 2019
Total time elapsed: 2.0m:18.8s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9521,
    "inflation_factor": 1.0612,
    "mean_EFFECT": 0,
    "n": 86895,
    "n_snps": 13562961,
    "n_clumped_hits": 1,
    "n_p_sig": 13,
    "n_mono": 0,
    "n_ns": 1251020,
    "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": 562079,
    "n_est": 87270.0968,
    "ratio_se_n": 1.0022,
    "mean_diff": 0,
    "ratio_diff": 10.5175,
    "sd_y_est1": 0.4432,
    "sd_y_est2": 0.4442,
    "r2_sum1": 0.0001,
    "r2_sum2": 0.0004,
    "r2_sum3": 0.0004,
    "r2_sum4": 0.0004,
    "ldsc_nsnp_merge_refpanel_ld": 1283503,
    "ldsc_nsnp_merge_regression_ld": 1283503,
    "ldsc_observed_scale_h2_beta": 0.0626,
    "ldsc_observed_scale_h2_se": 0.0057,
    "ldsc_intercept_beta": 1.0027,
    "ldsc_intercept_se": 0.0059,
    "ldsc_lambda_gc": 1.1044,
    "ldsc_mean_chisq": 1.1091,
    "ldsc_ratio": 0.0247
}

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 13550945 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 100 0 57040 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 342 0 33330 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 9.051482e+00 6.184990e+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 7.902551e+07 5.591660e+07 3.02000e+02 3.292876e+07 7.013957e+07 1.148576e+08 2.492309e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA 1.790000e-05 1.239910e-02 -1.56302e-01 -3.365800e-03 2.250000e-05 3.414500e-03 1.448620e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 8.891300e-03 8.566800e-03 1.74030e-03 2.541800e-03 4.672800e-03 1.276580e-02 4.459740e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.903192e-01 2.916944e-01 0.00000e+00 2.350461e-01 4.871739e-01 7.433854e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.903173e-01 2.916955e-01 0.00000e+00 2.350427e-01 4.871713e-01 7.433855e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA 1.911183e-01 2.544724e-01 1.00000e-03 7.522600e-03 6.037660e-02 2.959460e-01 9.990000e-01 ▇▂▁▁▁
numeric AF_reference 562079 0.9585578 NA NA NA NA NA 1.965576e-01 2.471489e-01 0.00000e+00 5.790700e-03 8.446490e-02 3.085060e-01 1.000000e+00 ▇▂▁▁▁
numeric N 0 1.0000000 NA NA NA NA NA 8.689500e+04 0.000000e+00 8.68950e+04 8.689500e+04 8.689500e+04 8.689500e+04 8.689500e+04 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 692794 rs530212009 CA C -0.0004013 0.0037176 0.9140381 0.9140377 0.1116050 0.1894970 86895
1 693731 rs12238997 A G 0.0022158 0.0035167 0.5286412 0.5286399 0.1167060 0.1417730 86895
1 707522 rs371890604 G C 0.0003453 0.0039552 0.9304240 0.9304237 0.0977795 0.1293930 86895
1 717587 rs144155419 G A -0.0017624 0.0095383 0.8534099 0.8534099 0.0156039 0.0045926 86895
1 723329 rs189787166 A T 0.0087802 0.0281228 0.7548801 0.7548806 0.0017005 0.0003994 86895
1 730087 rs148120343 T C -0.0011829 0.0049103 0.8096339 0.8096336 0.0567934 0.0127796 86895
1 731718 rs142557973 T C -0.0000841 0.0033394 0.9799180 0.9799181 0.1225050 0.1543530 86895
1 732032 rs61770163 A C 0.0005143 0.0035648 0.8852901 0.8852892 0.1217780 0.1555510 86895
1 734349 rs141242758 T C 0.0000653 0.0033414 0.9844180 0.9844184 0.1217300 0.1525560 86895
1 740284 rs61770167 C T 0.0194797 0.0149348 0.1921308 0.1921258 0.0060074 0.0023962 86895
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T 0.0047520 0.0039102 0.2242570 0.2242532 0.0545474 0.0309934 86895
23 154923374 rs111332691 T A 0.0003113 0.0042458 0.9415459 0.9415458 0.0440819 0.0116556 86895
23 154925045 rs509981 C T 0.0001355 0.0020350 0.9468991 0.9468994 0.2440460 0.3634440 86895
23 154925895 rs538470 C T 0.0005300 0.0020811 0.7989640 0.7989632 0.2404030 0.3634440 86895
23 154927581 rs644138 G A 0.0012451 0.0019166 0.5159101 0.5159081 0.2989350 0.4635760 86895
23 154929412 rs557132 C T 0.0001483 0.0020356 0.9419219 0.9419220 0.2439040 0.3568210 86895
23 154929637 rs35185538 CT C 0.0012147 0.0021230 0.5672297 0.5672273 0.2276490 0.3011920 86895
23 154929952 rs4012982 CAA C 0.0003211 0.0021429 0.8808730 0.8808729 0.2379000 0.3165560 86895
23 154930230 rs781880 A G 0.0002928 0.0020351 0.8856000 0.8855991 0.2443990 0.3618540 86895
23 154930487 rs781879 T A 0.0107108 0.0070935 0.1310581 0.1310556 0.0190795 0.1263580 86895

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.111605 ES:SE:LP:AF:SS:ID   -0.000401306:0.00371762:0.0390357:0.111605:86895:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.116706 ES:SE:LP:AF:SS:ID   0.00221579:0.00351666:0.276839:0.116706:86895:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0977795    ES:SE:LP:AF:SS:ID   0.000345339:0.00395524:0.0313191:0.0977795:86895:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.0156039    ES:SE:LP:AF:SS:ID   -0.00176238:0.00953827:0.0688423:0.0156039:86895:rs144155419
1   723329  rs189787166 A   T   .   PASS    AF=0.00170052   ES:SE:LP:AF:SS:ID   0.00878023:0.0281228:0.122122:0.00170052:86895:rs189787166
1   730087  rs148120343 T   C   .   PASS    AF=0.0567934    ES:SE:LP:AF:SS:ID   -0.00118288:0.00491028:0.0917113:0.0567934:86895:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122505 ES:SE:LP:AF:SS:ID   -8.40582e-05:0.0033394:0.00881026:0.122505:86895:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.121778 ES:SE:LP:AF:SS:ID   0.000514292:0.00356485:0.0529144:0.121778:86895:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.12173  ES:SE:LP:AF:SS:ID   6.52567e-05:0.00334138:0.00682045:0.12173:86895:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.00600736   ES:SE:LP:AF:SS:ID   0.0194797:0.0149348:0.716403:0.00600736:86895:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.00181682   ES:SE:LP:AF:SS:ID   -0.0201727:0.0298081:0.302278:0.00181682:86895:rs112573343
1   749963  rs529266287 T   TAA .   PASS    AF=0.869023 ES:SE:LP:AF:SS:ID   0.000297071:0.00329511:0.0323753:0.869023:86895:rs529266287
1   750230  rs190826124 G   C   .   PASS    AF=0.0016185    ES:SE:LP:AF:SS:ID   -0.0211247:0.02889:0.332874:0.0016185:86895:rs190826124
1   751343  rs28544273  T   A   .   PASS    AF=0.123453 ES:SE:LP:AF:SS:ID   -0.000836308:0.00326548:0.0980679:0.123453:86895:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.143082 ES:SE:LP:AF:SS:ID   -0.00194444:0.0032293:0.261939:0.143082:86895:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.123565 ES:SE:LP:AF:SS:ID   -0.000825605:0.00326087:0.0968422:0.123565:86895:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.870217 ES:SE:LP:AF:SS:ID   0.000612053:0.0031741:0.0720679:0.870217:86895:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.874964 ES:SE:LP:AF:SS:ID   0.000949113:0.00322823:0.114212:0.874964:86895:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.129377 ES:SE:LP:AF:SS:ID   -0.000701576:0.00318079:0.0833198:0.129377:86895:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.037062 ES:SE:LP:AF:SS:ID   -0.00373424:0.00571908:0.289212:0.037062:86895:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869818 ES:SE:LP:AF:SS:ID   0.000713407:0.0031709:0.0851329:0.869818:86895:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869943 ES:SE:LP:AF:SS:ID   0.000764492:0.00317258:0.0917402:0.869943:86895:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.869807 ES:SE:LP:AF:SS:ID   0.000766035:0.00317069:0.0920021:0.869807:86895:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00505171   ES:SE:LP:AF:SS:ID   0.00446229:0.0163128:0.105444:0.00505171:86895:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00501974   ES:SE:LP:AF:SS:ID   0.00456966:0.0163545:0.107945:0.00501974:86895:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.00545651   ES:SE:LP:AF:SS:ID   -0.00124314:0.0159073:0.0279315:0.00545651:86895:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.869938 ES:SE:LP:AF:SS:ID   0.000499269:0.00316562:0.0581503:0.869938:86895:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.126618 ES:SE:LP:AF:SS:ID   -0.000472472:0.00322983:0.0536961:0.126618:86895:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.869503 ES:SE:LP:AF:SS:ID   0.00052005:0.00315803:0.0608803:0.869503:86895:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.868702 ES:SE:LP:AF:SS:ID   0.000410399:0.00315449:0.047456:0.868702:86895:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869653 ES:SE:LP:AF:SS:ID   0.000620782:0.00316061:0.0735094:0.869653:86895:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869661 ES:SE:LP:AF:SS:ID   0.000618344:0.00316082:0.0731937:0.869661:86895:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869671 ES:SE:LP:AF:SS:ID   0.00064179:0.00316097:0.0761821:0.869671:86895:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.870111 ES:SE:LP:AF:SS:ID   0.000590604:0.00316912:0.0694783:0.870111:86895:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.0995027    ES:SE:LP:AF:SS:ID   0.000426017:0.00367908:0.0420022:0.0995027:86895:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.00626612   ES:SE:LP:AF:SS:ID   -0.00513981:0.014429:0.141655:0.00626612:86895:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.87409  ES:SE:LP:AF:SS:ID   0.000395249:0.00321772:0.0446789:0.87409:86895:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.863341 ES:SE:LP:AF:SS:ID   0.000351974:0.00315558:0.040392:0.863341:86895:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.868865 ES:SE:LP:AF:SS:ID   0.000512872:0.00318499:0.0594477:0.868865:86895:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.865652 ES:SE:LP:AF:SS:ID   0.00109478:0.00318383:0.13611:0.865652:86895:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0991335    ES:SE:LP:AF:SS:ID   0.001297:0.003819:0.134219:0.0991335:86895:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.870946 ES:SE:LP:AF:SS:ID   0.000437876:0.00322171:0.0496887:0.870946:86895:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.870946 ES:SE:LP:AF:SS:ID   0.000437938:0.00322171:0.0496965:0.870946:86895:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.870944 ES:SE:LP:AF:SS:ID   0.000438537:0.00322173:0.0497676:0.870944:86895:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.871297 ES:SE:LP:AF:SS:ID   0.000382733:0.00322326:0.0431207:0.871297:86895:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.126178 ES:SE:LP:AF:SS:ID   -0.000387688:0.00322603:0.0436659:0.126178:86895:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.106184 ES:SE:LP:AF:SS:ID   0.000679353:0.00350818:0.0723977:0.106184:86895:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.854792 ES:SE:LP:AF:SS:ID   0.0015269:0.00315508:0.201748:0.854792:86895:rs2519015
1   767393  rs538667473 A   C   .   PASS    AF=0.00161078   ES:SE:LP:AF:SS:ID   0.0112348:0.0281732:0.161114:0.00161078:86895:rs538667473
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.838495 ES:SE:LP:AF:SS:ID   0.00183745:0.00310881:0.256106:0.838495:86895:rs376645387