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-2654_7/ukb-d-2654_7.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-2654_7/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 17:07:50 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-2654_7/ukb-d-2654_7.vcf.gz ...
Read summary statistics for 13579654 SNPs.
Dropped 12707 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: 0.0063 (0.0026)
Lambda GC: 1.0183
Mean Chi^2: 1.0241
Intercept: 1.0005 (0.006)
Ratio: 0.0208 (0.248)
Analysis finished at Mon Nov 25 17:10:10 2019
Total time elapsed: 2.0m:20.22s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9521,
    "inflation_factor": 1.0145,
    "mean_EFFECT": -0,
    "n": 190094,
    "n_snps": 13579654,
    "n_clumped_hits": 0,
    "n_p_sig": 0,
    "n_mono": 0,
    "n_ns": 1251573,
    "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": 567533,
    "n_est": 191010.5189,
    "ratio_se_n": 1.0024,
    "mean_diff": -0,
    "ratio_diff": 12.727,
    "sd_y_est1": 0.4744,
    "sd_y_est2": 0.4755,
    "r2_sum1": 0,
    "r2_sum2": 0,
    "r2_sum3": 0,
    "r2_sum4": 0,
    "ldsc_nsnp_merge_refpanel_ld": 1283503,
    "ldsc_nsnp_merge_regression_ld": 1283503,
    "ldsc_observed_scale_h2_beta": 0.0063,
    "ldsc_observed_scale_h2_se": 0.0026,
    "ldsc_intercept_beta": 1.0005,
    "ldsc_intercept_se": 0.006,
    "ldsc_lambda_gc": 1.0183,
    "ldsc_mean_chisq": 1.0241,
    "ldsc_ratio": 0.0207
}

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 13567611 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 100 0 57067 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 342 0 33337 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 9.051424e+00 6.185000e+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 7.902585e+07 5.591487e+07 3.02000e+02 3.293138e+07 7.013970e+07 1.148611e+08 2.492309e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA -1.520000e-05 8.974500e-03 -1.06435e-01 -2.415800e-03 -1.400000e-05 2.378200e-03 1.277030e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 6.455700e-03 6.225100e-03 1.24780e-03 1.840600e-03 3.389000e-03 9.272200e-03 3.190510e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.974898e-01 2.894948e-01 1.00000e-07 2.460433e-01 4.969034e-01 7.480971e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.974890e-01 2.894953e-01 1.00000e-07 2.460416e-01 4.969030e-01 7.480975e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA 1.908921e-01 2.544211e-01 1.00000e-03 7.452100e-03 6.013660e-02 2.954960e-01 9.990000e-01 ▇▂▁▁▁
numeric AF_reference 567533 0.9582071 NA NA NA NA NA 1.963986e-01 2.471123e-01 0.00000e+00 5.591000e-03 8.426520e-02 3.083070e-01 1.000000e+00 ▇▂▁▁▁
numeric N 0 1.0000000 NA NA NA NA NA 1.900940e+05 0.000000e+00 1.90094e+05 1.900940e+05 1.900940e+05 1.900940e+05 1.900940e+05 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 692794 rs530212009 CA C -0.0018047 0.0026976 0.5034913 0.5034918 0.1105380 0.1894970 190094
1 693731 rs12238997 A G -0.0002983 0.0025453 0.9066889 0.9066889 0.1158410 0.1417730 190094
1 707522 rs371890604 G C 0.0004700 0.0028627 0.8695849 0.8695849 0.0972978 0.1293930 190094
1 717587 rs144155419 G A -0.0061952 0.0068754 0.3675522 0.3675514 0.0155305 0.0045926 190094
1 723329 rs189787166 A T 0.0144257 0.0199456 0.4695242 0.4695249 0.0017702 0.0003994 190094
1 730087 rs148120343 T C -0.0022779 0.0035452 0.5205219 0.5205209 0.0565146 0.0127796 190094
1 731718 rs142557973 T C -0.0004605 0.0024151 0.8487991 0.8487983 0.1216850 0.1543530 190094
1 732032 rs61770163 A C -0.0008206 0.0025780 0.7502379 0.7502370 0.1210600 0.1555510 190094
1 734349 rs141242758 T C -0.0004815 0.0024166 0.8420780 0.8420787 0.1209220 0.1525560 190094
1 740284 rs61770167 C T 0.0115580 0.0110412 0.2951882 0.2951888 0.0058053 0.0023962 190094
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T 0.0010525 0.0027573 0.7026645 0.7026640 0.0559986 0.0309934 190094
23 154923374 rs111332691 T A -0.0020876 0.0030380 0.4919886 0.4919878 0.0444754 0.0116556 190094
23 154925045 rs509981 C T 0.0016478 0.0014580 0.2583991 0.2583982 0.2448880 0.3634440 190094
23 154925895 rs538470 C T 0.0016047 0.0014909 0.2817812 0.2817789 0.2413400 0.3634440 190094
23 154927581 rs644138 G A 0.0017093 0.0013704 0.2122971 0.2122942 0.3012450 0.4635760 190094
23 154929412 rs557132 C T 0.0017265 0.0014584 0.2364869 0.2364874 0.2447450 0.3568210 190094
23 154929637 rs35185538 CT C 0.0021986 0.0015221 0.1486168 0.1486134 0.2287730 0.3011920 190094
23 154929952 rs4012982 CAA C 0.0015106 0.0015329 0.3244292 0.3244273 0.2390160 0.3165560 190094
23 154930230 rs781880 A G 0.0016973 0.0014580 0.2443701 0.2443685 0.2451860 0.3618540 190094
23 154930487 rs781879 T A 0.0000159 0.0049810 0.9974490 0.9974487 0.0196401 0.1263580 190094

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.110538 ES:SE:LP:AF:SS:ID   -0.00180473:0.00269763:0.298008:0.110538:190094:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.115841 ES:SE:LP:AF:SS:ID   -0.000298344:0.00254525:0.0425417:0.115841:190094:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0972978    ES:SE:LP:AF:SS:ID   0.000470013:0.00286269:0.060688:0.0972978:190094:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.0155305    ES:SE:LP:AF:SS:ID   -0.00619524:0.00687543:0.434681:0.0155305:190094:rs144155419
1   723329  rs189787166 A   T   .   PASS    AF=0.00177021   ES:SE:LP:AF:SS:ID   0.0144257:0.0199456:0.328342:0.00177021:190094:rs189787166
1   730087  rs148120343 T   C   .   PASS    AF=0.0565146    ES:SE:LP:AF:SS:ID   -0.00227791:0.00354515:0.283561:0.0565146:190094:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.121685 ES:SE:LP:AF:SS:ID   -0.000460451:0.00241514:0.0711951:0.121685:190094:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.12106  ES:SE:LP:AF:SS:ID   -0.000820634:0.00257796:0.124801:0.12106:190094:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.120922 ES:SE:LP:AF:SS:ID   -0.000481464:0.00241656:0.0746477:0.120922:190094:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.00580529   ES:SE:LP:AF:SS:ID   0.011558:0.0110412:0.529901:0.00580529:190094:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.00196152   ES:SE:LP:AF:SS:ID   -0.00656748:0.0204033:0.126364:0.00196152:190094:rs112573343
1   749963  rs529266287 T   TAA .   PASS    AF=0.869749 ES:SE:LP:AF:SS:ID   0.00135632:0.00238094:0.244955:0.869749:190094:rs529266287
1   750230  rs190826124 G   C   .   PASS    AF=0.00154354   ES:SE:LP:AF:SS:ID   0.0140749:0.0213961:0.291877:0.00154354:190094:rs190826124
1   751343  rs28544273  T   A   .   PASS    AF=0.122867 ES:SE:LP:AF:SS:ID   -0.00131811:0.00235934:0.23929:0.122867:190094:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.142766 ES:SE:LP:AF:SS:ID   -0.00258327:0.00233029:0.57248:0.142766:190094:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.122958 ES:SE:LP:AF:SS:ID   -0.00132768:0.00235627:0.241757:0.122958:190094:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.870967 ES:SE:LP:AF:SS:ID   0.000640553:0.00229608:0.107759:0.870967:190094:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.875573 ES:SE:LP:AF:SS:ID   0.00100874:0.00233446:0.176744:0.875573:190094:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.128534 ES:SE:LP:AF:SS:ID   -0.000625531:0.00230201:0.104672:0.128534:190094:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0363057    ES:SE:LP:AF:SS:ID   0.00295777:0.00417915:0.319569:0.0363057:190094:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.870568 ES:SE:LP:AF:SS:ID   0.00055551:0.00229371:0.092248:0.870568:190094:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.870672 ES:SE:LP:AF:SS:ID   0.000510475:0.00229484:0.0840897:0.870672:190094:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.870565 ES:SE:LP:AF:SS:ID   0.000585431:0.00229365:0.0977044:0.870565:190094:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00503531   ES:SE:LP:AF:SS:ID   -0.00610326:0.0117749:0.218798:0.00503531:190094:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00500306   ES:SE:LP:AF:SS:ID   -0.0059482:0.0118067:0.211547:0.00500306:190094:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.00567154   ES:SE:LP:AF:SS:ID   0.00980909:0.0112927:0.414475:0.00567154:190094:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.870656 ES:SE:LP:AF:SS:ID   0.000578074:0.00228949:0.0965502:0.870656:190094:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.125912 ES:SE:LP:AF:SS:ID   -0.00073692:0.00233631:0.123527:0.125912:190094:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.870227 ES:SE:LP:AF:SS:ID   0.000473748:0.00228389:0.0779631:0.870227:190094:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.869481 ES:SE:LP:AF:SS:ID   0.00049095:0.00228178:0.0811093:0.869481:190094:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.870368 ES:SE:LP:AF:SS:ID   0.000594357:0.00228585:0.0997137:0.870368:190094:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.870374 ES:SE:LP:AF:SS:ID   0.000591659:0.00228602:0.0992086:0.870374:190094:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.870381 ES:SE:LP:AF:SS:ID   0.000587365:0.00228608:0.0984153:0.870381:190094:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.870831 ES:SE:LP:AF:SS:ID   0.000631263:0.00229217:0.106233:0.870831:190094:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.0989106    ES:SE:LP:AF:SS:ID   0.000314781:0.00266184:0.042937:0.0989106:190094:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.00651227   ES:SE:LP:AF:SS:ID   0.0077023:0.0102739:0.34348:0.00651227:190094:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.87474  ES:SE:LP:AF:SS:ID   0.000512542:0.00232648:0.0832135:0.87474:190094:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.864063 ES:SE:LP:AF:SS:ID   0.000543481:0.00228077:0.0906275:0.864063:190094:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.869532 ES:SE:LP:AF:SS:ID   0.000270596:0.00230364:0.042636:0.869532:190094:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.866506 ES:SE:LP:AF:SS:ID   4.75424e-06:0.00230332:0.000715873:0.866506:190094:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0985314    ES:SE:LP:AF:SS:ID   0.000250803:0.00276256:0.0326102:0.0985314:190094:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.871615 ES:SE:LP:AF:SS:ID   0.000143082:0.0023293:0.0218108:0.871615:190094:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.871615 ES:SE:LP:AF:SS:ID   0.000142812:0.0023293:0.0217688:0.871615:190094:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.871616 ES:SE:LP:AF:SS:ID   0.000131752:0.00232938:0.0200444:0.871616:190094:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.872007 ES:SE:LP:AF:SS:ID   -1.55385e-05:0.00233099:0.00231618:0.872007:190094:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.125446 ES:SE:LP:AF:SS:ID   -0.000606846:0.00233389:0.0997132:0.125446:190094:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.10548  ES:SE:LP:AF:SS:ID   0.000565762:0.00253784:0.0842889:0.10548:190094:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.855514 ES:SE:LP:AF:SS:ID   3.9033e-05:0.00228138:0.00596939:0.855514:190094:rs2519015
1   767393  rs538667473 A   C   .   PASS    AF=0.00169612   ES:SE:LP:AF:SS:ID   0.0135897:0.0199311:0.305092:0.00169612:190094:rs538667473
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.838708 ES:SE:LP:AF:SS:ID   0.00459657:0.00224926:1.38727:0.838708:190094:rs376645387