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-22601_11123211/ukb-d-22601_11123211.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-22601_11123211/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 14:57:18 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-22601_11123211/ukb-d-22601_11123211.vcf.gz ...
Read summary statistics for 10972748 SNPs.
Dropped 9027 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, 1281861 SNPs remain.
After merging with regression SNP LD, 1281861 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0085 (0.006)
Lambda GC: 1.0121
Mean Chi^2: 1.0121
Intercept: 0.9966 (0.0073)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Mon Nov 25 14:58:56 2019
Total time elapsed: 1.0m:38.26s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9458,
    "inflation_factor": 1.0109,
    "mean_EFFECT": 4.4406e-06,
    "n": 89866,
    "n_snps": 10972748,
    "n_clumped_hits": 1,
    "n_p_sig": 1,
    "n_mono": 0,
    "n_ns": 1136715,
    "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": 219284,
    "n_est": 90034.5756,
    "ratio_se_n": 1.0009,
    "mean_diff": 0,
    "ratio_diff": 18.2536,
    "sd_y_est1": 0.1711,
    "sd_y_est2": 0.1712,
    "r2_sum1": 0,
    "r2_sum2": 0.0004,
    "r2_sum3": 0.0004,
    "r2_sum4": 0.0004,
    "ldsc_nsnp_merge_refpanel_ld": 1281861,
    "ldsc_nsnp_merge_regression_ld": 1281861,
    "ldsc_observed_scale_h2_beta": 0.0085,
    "ldsc_observed_scale_h2_se": 0.006,
    "ldsc_intercept_beta": 0.9966,
    "ldsc_intercept_se": 0.0073,
    "ldsc_lambda_gc": 1.0121,
    "ldsc_mean_chisq": 1.0121,
    "ldsc_ratio": -0.281
}

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 10964308 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 100 0 51000 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 342 0 31287 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 9.073846e+00 6.180176e+00 1.0000e+00 4.000000e+00 8.000000e+00 1.300000e+01 2.300000e+01 ▇▅▅▂▂
numeric POS 0 1.0000000 NA NA NA NA NA 7.881366e+07 5.602448e+07 3.0200e+02 3.259283e+07 6.979701e+07 1.147358e+08 2.492309e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA 4.400000e-06 2.468700e-03 -2.2874e-02 -9.862000e-04 -4.200000e-06 9.714000e-04 2.963380e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 1.993000e-03 1.435400e-03 6.6740e-04 9.069000e-04 1.322200e-03 2.662800e-03 8.360900e-03 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.986247e-01 2.891539e-01 0.0000e+00 2.485290e-01 4.976775e-01 7.491366e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.986230e-01 2.891549e-01 0.0000e+00 2.485254e-01 4.976758e-01 7.491362e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA 2.336185e-01 2.618655e-01 4.6108e-03 2.469680e-02 1.193930e-01 3.737840e-01 9.953890e-01 ▇▂▂▁▁
numeric AF_reference 219284 0.9800156 NA NA NA NA NA 2.338020e-01 2.529480e-01 0.0000e+00 2.356230e-02 1.367810e-01 3.722040e-01 1.000000e+00 ▇▂▂▁▁
numeric N 0 1.0000000 NA NA NA NA NA 8.986600e+04 0.000000e+00 8.9866e+04 8.986600e+04 8.986600e+04 8.986600e+04 8.986600e+04 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 692794 rs530212009 CA C 0.0000957 0.0014100 0.9458969 0.9458969 0.1114870 0.1894970 89866
1 693731 rs12238997 A G -0.0000915 0.0013357 0.9453741 0.9453737 0.1164980 0.1417730 89866
1 707522 rs371890604 G C 0.0002821 0.0015016 0.8509789 0.8509789 0.0978285 0.1293930 89866
1 717587 rs144155419 G A 0.0001126 0.0035767 0.9748960 0.9748958 0.0158090 0.0045926 89866
1 730087 rs148120343 T C 0.0001667 0.0018521 0.9283010 0.9283005 0.0569661 0.0127796 89866
1 731718 rs142557973 T C 0.0000898 0.0012675 0.9435500 0.9435496 0.1223780 0.1543530 89866
1 732032 rs61770163 A C -0.0004062 0.0013513 0.7637443 0.7637420 0.1220530 0.1555510 89866
1 734349 rs141242758 T C 0.0000740 0.0012684 0.9534671 0.9534672 0.1216170 0.1525560 89866
1 740284 rs61770167 C T 0.0042387 0.0057964 0.4646212 0.4646188 0.0057975 0.0023962 89866
1 749963 rs529266287 T TAA 0.0006422 0.0012479 0.6068216 0.6068185 0.8689590 0.7641770 89866
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T 0.0009931 0.0014836 0.5032248 0.5032222 0.0553576 0.0309934 89866
23 154923374 rs111332691 T A 0.0013458 0.0016151 0.4047101 0.4047080 0.0449113 0.0116556 89866
23 154925045 rs509981 C T -0.0003404 0.0007789 0.6620762 0.6620749 0.2444280 0.3634440 89866
23 154925895 rs538470 C T -0.0003966 0.0007965 0.6185090 0.6185076 0.2408760 0.3634440 89866
23 154927581 rs644138 G A -0.0001138 0.0007327 0.8766011 0.8766005 0.3001910 0.4635760 89866
23 154929412 rs557132 C T -0.0003258 0.0007792 0.6758464 0.6758443 0.2442750 0.3568210 89866
23 154929637 rs35185538 CT C -0.0005287 0.0008136 0.5158364 0.5158342 0.2286930 0.3011920 89866
23 154929952 rs4012982 CAA C -0.0001650 0.0008200 0.8405000 0.8404998 0.2381980 0.3165560 89866
23 154930230 rs781880 A G -0.0003278 0.0007789 0.6738403 0.6738398 0.2447120 0.3618540 89866
23 154930487 rs781879 T A 0.0000822 0.0026422 0.9751861 0.9751862 0.0196791 0.1263580 89866

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.111487 ES:SE:LP:AF:SS:ID   9.56842e-05:0.00141002:0.0241562:0.111487:89866:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.116498 ES:SE:LP:AF:SS:ID   -9.15168e-05:0.00133567:0.0243963:0.116498:89866:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0978285    ES:SE:LP:AF:SS:ID   0.000282103:0.00150159:0.0700812:0.0978285:89866:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.015809 ES:SE:LP:AF:SS:ID   0.000112554:0.0035767:0.0110417:0.015809:89866:rs144155419
1   730087  rs148120343 T   C   .   PASS    AF=0.0569661    ES:SE:LP:AF:SS:ID   0.000166659:0.00185211:0.0323112:0.0569661:89866:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122378 ES:SE:LP:AF:SS:ID   8.975e-05:0.00126749:0.0252351:0.122378:89866:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.122053 ES:SE:LP:AF:SS:ID   -0.000406155:0.00135128:0.117052:0.122053:89866:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.121617 ES:SE:LP:AF:SS:ID   7.40171e-05:0.00126843:0.0206943:0.121617:89866:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.00579754   ES:SE:LP:AF:SS:ID   0.00423869:0.0057964:0.332901:0.00579754:89866:rs61770167
1   749963  rs529266287 T   TAA .   PASS    AF=0.868959 ES:SE:LP:AF:SS:ID   0.000642215:0.00124794:0.216939:0.868959:89866:rs529266287
1   751343  rs28544273  T   A   .   PASS    AF=0.123678 ES:SE:LP:AF:SS:ID   -0.000725179:0.00123716:0.253548:0.123678:89866:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.143191 ES:SE:LP:AF:SS:ID   -0.000308521:0.00122486:0.0962959:0.143191:89866:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.123813 ES:SE:LP:AF:SS:ID   -0.000715882:0.00123537:0.250061:0.123813:89866:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.869927 ES:SE:LP:AF:SS:ID   0.000538313:0.00120369:0.183945:0.869927:89866:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.87461  ES:SE:LP:AF:SS:ID   0.000749877:0.00122332:0.267697:0.87461:89866:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.12951  ES:SE:LP:AF:SS:ID   -0.00041696:0.00120737:0.136774:0.12951:89866:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0357365    ES:SE:LP:AF:SS:ID   -0.00037073:0.0022089:0.0621242:0.0357365:89866:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869574 ES:SE:LP:AF:SS:ID   0.000551389:0.00120263:0.18936:0.869574:89866:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869652 ES:SE:LP:AF:SS:ID   0.000528342:0.00120317:0.18008:0.869652:89866:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.86957  ES:SE:LP:AF:SS:ID   0.000552784:0.00120259:0.18993:0.86957:89866:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00528163   ES:SE:LP:AF:SS:ID   -0.00679669:0.00603413:0.58501:0.00528163:89866:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00524722   ES:SE:LP:AF:SS:ID   -0.00688432:0.00604917:0.593291:0.00524722:89866:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.0056565    ES:SE:LP:AF:SS:ID   -0.00283535:0.0059239:0.199143:0.0056565:89866:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.869566 ES:SE:LP:AF:SS:ID   0.000602915:0.0012002:0.210824:0.869566:89866:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.126879 ES:SE:LP:AF:SS:ID   -0.000755887:0.00122522:0.269803:0.126879:89866:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.869094 ES:SE:LP:AF:SS:ID   0.000700425:0.00119709:0.252994:0.869094:89866:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.868498 ES:SE:LP:AF:SS:ID   0.000638164:0.00119636:0.226401:0.868498:89866:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869245 ES:SE:LP:AF:SS:ID   0.000644864:0.00119818:0.228825:0.869245:89866:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869251 ES:SE:LP:AF:SS:ID   0.000643987:0.00119826:0.228435:0.869251:89866:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869252 ES:SE:LP:AF:SS:ID   0.00064327:0.00119827:0.228129:0.869252:89866:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869724 ES:SE:LP:AF:SS:ID   0.000653477:0.00120146:0.231725:0.869724:89866:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.0991203    ES:SE:LP:AF:SS:ID   -0.000205338:0.0013987:0.0538991:0.0991203:89866:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.00649047   ES:SE:LP:AF:SS:ID   -0.000116043:0.00539815:0.0075132:0.00649047:89866:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.873732 ES:SE:LP:AF:SS:ID   0.000890641:0.00121931:0.332438:0.873732:89866:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.863116 ES:SE:LP:AF:SS:ID   0.000625612:0.00119595:0.221197:0.863116:89866:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.868511 ES:SE:LP:AF:SS:ID   0.000731302:0.0012079:0.263689:0.868511:89866:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.86539  ES:SE:LP:AF:SS:ID   0.00101565:0.00120808:0.397389:0.86539:89866:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0994301    ES:SE:LP:AF:SS:ID   -0.00176946:0.00144667:0.655048:0.0994301:89866:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.870696 ES:SE:LP:AF:SS:ID   0.00104065:0.00122197:0.40403:0.870696:89866:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.870695 ES:SE:LP:AF:SS:ID   0.00104066:0.00122197:0.404032:0.870695:89866:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.870693 ES:SE:LP:AF:SS:ID   0.00104124:0.00122198:0.404323:0.870693:89866:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.871061 ES:SE:LP:AF:SS:ID   0.00105055:0.00122255:0.408744:0.871061:89866:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.126484 ES:SE:LP:AF:SS:ID   -0.000796521:0.00122379:0.288078:0.126484:89866:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.106022 ES:SE:LP:AF:SS:ID   0.000134238:0.00133036:0.0363878:0.106022:89866:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.854451 ES:SE:LP:AF:SS:ID   0.00086779:0.00119567:0.329776:0.854451:89866:rs2519015
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.839008 ES:SE:LP:AF:SS:ID   -0.000109863:0.0011812:0.0334378:0.839008:89866:rs376645387
1   768253  rs2977608   A   C   .   PASS    AF=0.76317  ES:SE:LP:AF:SS:ID   0.000377806:0.000949675:0.160673:0.76317:89866:rs2977608
1   768448  rs12562034  G   A   .   PASS    AF=0.10519  ES:SE:LP:AF:SS:ID   8.44431e-05:0.0013088:0.0229366:0.10519:89866:rs12562034
1   768819  rs12562811  C   T   .   PASS    AF=0.00773441   ES:SE:LP:AF:SS:ID   0.00765315:0.00484879:0.941248:0.00773441:89866:rs12562811
1   769138  rs59306077  CAT C   .   PASS    AF=0.129876 ES:SE:LP:AF:SS:ID   -0.00075401:0.00120665:0.274046:0.129876:89866:rs762168062