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-20548_8/ukb-d-20548_8.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-20548_8/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 16:48:36 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-20548_8/ukb-d-20548_8.vcf.gz ...
Read summary statistics for 11306769 SNPs.
Dropped 9500 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, 1282379 SNPs remain.
After merging with regression SNP LD, 1282379 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0038 (0.004)
Lambda GC: 1.0202
Mean Chi^2: 1.0207
Intercept: 1.0121 (0.0068)
Ratio: 0.5881 (0.3304)
Analysis finished at Mon Nov 25 16:50:29 2019
Total time elapsed: 1.0m:52.72s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.947,
    "inflation_factor": 1.0196,
    "mean_EFFECT": 3.5234e-06,
    "n": 114422,
    "n_snps": 11306769,
    "n_clumped_hits": 0,
    "n_p_sig": 6,
    "n_mono": 0,
    "n_ns": 1152844,
    "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": 233118,
    "n_est": 114613.5983,
    "ratio_se_n": 1.0008,
    "mean_diff": 0,
    "ratio_diff": 8.9744,
    "sd_y_est1": 0.1671,
    "sd_y_est2": 0.1672,
    "r2_sum1": 0,
    "r2_sum2": 0,
    "r2_sum3": 0,
    "r2_sum4": 0,
    "ldsc_nsnp_merge_refpanel_ld": 1282379,
    "ldsc_nsnp_merge_regression_ld": 1282379,
    "ldsc_observed_scale_h2_beta": 0.0038,
    "ldsc_observed_scale_h2_se": 0.004,
    "ldsc_intercept_beta": 1.0121,
    "ldsc_intercept_se": 0.0068,
    "ldsc_lambda_gc": 1.0202,
    "ldsc_mean_chisq": 1.0207,
    "ldsc_ratio": 0.5845
}

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 TRUE
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 11297865 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 100 0 51821 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 342 0 31570 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 9.073732e+00 6.182369e+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 7.883518e+07 5.600298e+07 3.02000e+02 3.262221e+07 6.983882e+07 1.147333e+08 2.492309e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA 3.500000e-06 2.314600e-03 -2.29146e-02 -8.887000e-04 -8.600000e-06 8.712000e-04 3.102520e-02 ▁▂▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 1.840400e-03 1.391700e-03 5.78600e-04 7.907000e-04 1.183800e-03 2.487700e-03 8.260600e-03 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.966851e-01 2.895667e-01 0.00000e+00 2.448432e-01 4.958291e-01 7.473757e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.966837e-01 2.895675e-01 0.00000e+00 2.448410e-01 4.958273e-01 7.473742e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA 2.271080e-01 2.611554e-01 3.80520e-03 2.113800e-02 1.101060e-01 3.631210e-01 9.961940e-01 ▇▂▁▁▁
numeric AF_reference 233118 0.9793824 NA NA NA NA NA 2.275940e-01 2.523438e-01 0.00000e+00 1.896960e-02 1.281950e-01 3.624200e-01 1.000000e+00 ▇▂▂▁▁
numeric N 0 1.0000000 NA NA NA NA NA 1.144220e+05 0.000000e+00 1.14422e+05 1.144220e+05 1.144220e+05 1.144220e+05 1.144220e+05 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 692794 rs530212009 CA C -0.0012858 0.0012176 0.2909853 0.2909857 0.1117110 0.1894970 114422
1 693731 rs12238997 A G -0.0003847 0.0011536 0.7387526 0.7387516 0.1166780 0.1417730 114422
1 707522 rs371890604 G C -0.0001033 0.0012941 0.9363950 0.9363943 0.0983298 0.1293930 114422
1 717587 rs144155419 G A -0.0026425 0.0031008 0.3941114 0.3941090 0.0157526 0.0045926 114422
1 730087 rs148120343 T C -0.0022248 0.0016049 0.1656651 0.1656632 0.0568713 0.0127796 114422
1 731718 rs142557973 T C -0.0001356 0.0010946 0.9013800 0.9013799 0.1226010 0.1543530 114422
1 732032 rs61770163 A C -0.0008990 0.0011666 0.4409253 0.4409255 0.1222220 0.1555510 114422
1 734349 rs141242758 T C -0.0001453 0.0010952 0.8944400 0.8944394 0.1218520 0.1525560 114422
1 740284 rs61770167 C T -0.0031387 0.0049944 0.5297122 0.5297112 0.0058609 0.0023962 114422
1 749963 rs529266287 T TAA 0.0003998 0.0010789 0.7109810 0.7109812 0.8690450 0.7641770 114422
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T 0.0005512 0.0012870 0.6684578 0.6684572 0.0553623 0.0309934 114422
23 154923374 rs111332691 T A -0.0007059 0.0014009 0.6143332 0.6143309 0.0448384 0.0116556 114422
23 154925045 rs509981 C T -0.0002353 0.0006751 0.7274715 0.7274707 0.2446210 0.3634440 114422
23 154925895 rs538470 C T -0.0001955 0.0006904 0.7770304 0.7770290 0.2409890 0.3634440 114422
23 154927581 rs644138 G A -0.0000712 0.0006353 0.9108140 0.9108138 0.3003390 0.4635760 114422
23 154929412 rs557132 C T -0.0002638 0.0006753 0.6960124 0.6960122 0.2444980 0.3568210 114422
23 154929637 rs35185538 CT C -0.0007369 0.0007053 0.2961202 0.2961175 0.2287980 0.3011920 114422
23 154929952 rs4012982 CAA C -0.0004528 0.0007108 0.5240817 0.5240809 0.2385330 0.3165560 114422
23 154930230 rs781880 A G -0.0002315 0.0006751 0.7316915 0.7316912 0.2449060 0.3618540 114422
23 154930487 rs781879 T A 0.0003828 0.0022911 0.8672999 0.8672994 0.0197245 0.1263580 114422

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.111711 ES:SE:LP:AF:SS:ID   -0.00128576:0.00121762:0.536129:0.111711:114422:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.116678 ES:SE:LP:AF:SS:ID   -0.000384737:0.00115361:0.131501:0.116678:114422:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0983298    ES:SE:LP:AF:SS:ID   -0.000103274:0.00129412:0.0285409:0.0983298:114422:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.0157526    ES:SE:LP:AF:SS:ID   -0.00264246:0.00310079:0.404381:0.0157526:114422:rs144155419
1   730087  rs148120343 T   C   .   PASS    AF=0.0568713    ES:SE:LP:AF:SS:ID   -0.00222479:0.00160487:0.780769:0.0568713:114422:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122601 ES:SE:LP:AF:SS:ID   -0.000135641:0.0010946:0.0450921:0.122601:114422:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.122222 ES:SE:LP:AF:SS:ID   -0.00089898:0.00116655:0.355635:0.122222:114422:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.121852 ES:SE:LP:AF:SS:ID   -0.000145317:0.00109517:0.0484488:0.121852:114422:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.0058609    ES:SE:LP:AF:SS:ID   -0.00313869:0.00499436:0.27596:0.0058609:114422:rs61770167
1   749963  rs529266287 T   TAA .   PASS    AF=0.869045 ES:SE:LP:AF:SS:ID   0.000399763:0.00107887:0.148142:0.869045:114422:rs529266287
1   751343  rs28544273  T   A   .   PASS    AF=0.123663 ES:SE:LP:AF:SS:ID   -0.000694299:0.00106899:0.287331:0.123663:114422:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.143266 ES:SE:LP:AF:SS:ID   -0.000763114:0.00105733:0.32748:0.143266:114422:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.123787 ES:SE:LP:AF:SS:ID   -0.000671558:0.00106751:0.276305:0.123787:114422:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.86984  ES:SE:LP:AF:SS:ID   0.000329365:0.00103981:0.124111:0.86984:114422:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.874607 ES:SE:LP:AF:SS:ID   0.00050455:0.00105713:0.198485:0.874607:114422:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.129637 ES:SE:LP:AF:SS:ID   -0.000339527:0.00104264:0.128021:0.129637:114422:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0366494    ES:SE:LP:AF:SS:ID   0.000725719:0.0018898:0.154304:0.0366494:114422:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869441 ES:SE:LP:AF:SS:ID   0.000293459:0.00103882:0.109262:0.869441:114422:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869515 ES:SE:LP:AF:SS:ID   0.000319181:0.00103922:0.119906:0.869515:114422:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.86944  ES:SE:LP:AF:SS:ID   0.000291343:0.0010388:0.108393:0.86944:114422:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00515222   ES:SE:LP:AF:SS:ID   -0.00032992:0.00528952:0.0221548:0.00515222:114422:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00512096   ES:SE:LP:AF:SS:ID   -0.000224649:0.00530118:0.0149339:0.00512096:114422:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.00569216   ES:SE:LP:AF:SS:ID   0.00337638:0.00511691:0.292981:0.00569216:114422:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.869508 ES:SE:LP:AF:SS:ID   0.000300596:0.0010369:0.112442:0.869508:114422:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.126917 ES:SE:LP:AF:SS:ID   -0.000509682:0.00105832:0.200594:0.126917:114422:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.869029 ES:SE:LP:AF:SS:ID   0.000236959:0.00103417:0.0868386:0.869029:114422:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.868436 ES:SE:LP:AF:SS:ID   0.000154382:0.00103383:0.0548797:0.868436:114422:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869215 ES:SE:LP:AF:SS:ID   0.000194358:0.0010352:0.0700332:0.869215:114422:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869222 ES:SE:LP:AF:SS:ID   0.000194145:0.00103527:0.0699454:0.869222:114422:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869224 ES:SE:LP:AF:SS:ID   0.00019406:0.00103527:0.0699123:0.869224:114422:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869663 ES:SE:LP:AF:SS:ID   0.000278544:0.00103794:0.103242:0.869663:114422:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.099489 ES:SE:LP:AF:SS:ID   0.000422619:0.00120662:0.138974:0.099489:114422:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.00655187   ES:SE:LP:AF:SS:ID   0.00481765:0.0046439:0.523538:0.00655187:114422:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.873708 ES:SE:LP:AF:SS:ID   0.000287953:0.00105369:0.10533:0.873708:114422:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.863017 ES:SE:LP:AF:SS:ID   0.000243119:0.00103361:0.0893527:0.863017:114422:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.868401 ES:SE:LP:AF:SS:ID   0.000189818:0.0010434:0.0677069:0.868401:114422:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.865415 ES:SE:LP:AF:SS:ID   0.000119879:0.00104351:0.0416555:0.865415:114422:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0997473    ES:SE:LP:AF:SS:ID   -2.20463e-05:0.00124678:0.00617066:0.0997473:114422:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.870695 ES:SE:LP:AF:SS:ID   0.000313025:0.00105577:0.115286:0.870695:114422:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.870695 ES:SE:LP:AF:SS:ID   0.000313036:0.00105577:0.11529:0.870695:114422:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.870693 ES:SE:LP:AF:SS:ID   0.00031254:0.00105578:0.115086:0.870693:114422:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.871018 ES:SE:LP:AF:SS:ID   0.000393295:0.00105624:0.148968:0.871018:114422:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.126512 ES:SE:LP:AF:SS:ID   -0.000419671:0.00105713:0.160287:0.126512:114422:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.106279 ES:SE:LP:AF:SS:ID   -9.68672e-05:0.00114875:0.0302119:0.106279:114422:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.854464 ES:SE:LP:AF:SS:ID   0.000385011:0.00103329:0.149084:0.854464:114422:rs2519015
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.839076 ES:SE:LP:AF:SS:ID   -6.48203e-05:0.00102301:0.0225151:0.839076:114422:rs376645387
1   768253  rs2977608   A   C   .   PASS    AF=0.763122 ES:SE:LP:AF:SS:ID   -0.000196543:0.000820192:0.0911843:0.763122:114422:rs2977608
1   768448  rs12562034  G   A   .   PASS    AF=0.105233 ES:SE:LP:AF:SS:ID   0.000599915:0.00113411:0.224154:0.105233:114422:rs12562034
1   768819  rs12562811  C   T   .   PASS    AF=0.00774894   ES:SE:LP:AF:SS:ID   0.00231439:0.00418475:0.236402:0.00774894:114422:rs12562811
1   769138  rs59306077  CAT C   .   PASS    AF=0.12991  ES:SE:LP:AF:SS:ID   -0.000381821:0.00104212:0.146256:0.12991:114422:rs762168062