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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    "FORMAT.1": "<ID=SE,Number=A,Type=Float,Description=\"Standard error of effect size estimate\">",
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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_7/ukb-d-20548_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-20548_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 16:41:10 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-20548_7/ukb-d-20548_7.vcf.gz ...
Read summary statistics for 13113619 SNPs.
Dropped 12046 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, 1283411 SNPs remain.
After merging with regression SNP LD, 1283411 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0207 (0.0045)
Lambda GC: 1.0495
Mean Chi^2: 1.0451
Intercept: 0.9988 (0.0057)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Mon Nov 25 16:43:15 2019
Total time elapsed: 2.0m:4.38s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9513,
    "inflation_factor": 1.022,
    "mean_EFFECT": 4.248e-06,
    "n": 114422,
    "n_snps": 13113619,
    "n_clumped_hits": 0,
    "n_p_sig": 1,
    "n_mono": 0,
    "n_ns": 1233944,
    "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": 454000,
    "n_est": 114796.6972,
    "ratio_se_n": 1.0016,
    "mean_diff": 0,
    "ratio_diff": 7.9029,
    "sd_y_est1": 0.2695,
    "sd_y_est2": 0.2699,
    "r2_sum1": 0,
    "r2_sum2": 0,
    "r2_sum3": 0,
    "r2_sum4": 0,
    "ldsc_nsnp_merge_refpanel_ld": 1283411,
    "ldsc_nsnp_merge_regression_ld": 1283411,
    "ldsc_observed_scale_h2_beta": 0.0207,
    "ldsc_observed_scale_h2_se": 0.0045,
    "ldsc_intercept_beta": 0.9988,
    "ldsc_intercept_se": 0.0057,
    "ldsc_lambda_gc": 1.0495,
    "ldsc_mean_chisq": 1.0451,
    "ldsc_ratio": -0.0266
}

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 13102228 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 100 0 56084 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 342 0 33042 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 9.057072e+00 6.185479e+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 7.898272e+07 5.593138e+07 3.02000e+02 3.285327e+07 7.006688e+07 1.148147e+08 2.492309e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA 4.200000e-06 5.855600e-03 -6.03836e-02 -1.696800e-03 -9.100000e-06 1.666300e-03 7.797540e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 4.285500e-03 3.970800e-03 9.32500e-04 1.331600e-03 2.341200e-03 6.095800e-03 2.123340e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.964269e-01 2.898010e-01 0.00000e+00 2.444308e-01 4.953122e-01 7.474170e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.964255e-01 2.898018e-01 0.00000e+00 2.444289e-01 4.953104e-01 7.474166e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA 1.973105e-01 2.559550e-01 1.36430e-03 9.210600e-03 6.837650e-02 3.082230e-01 9.986360e-01 ▇▂▁▁▁
numeric AF_reference 454000 0.9653795 NA NA NA NA NA 2.012899e-01 2.481843e-01 0.00000e+00 6.789100e-03 9.105430e-02 3.174920e-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 -0.0000739 0.0019623 0.9699789 0.9699794 0.1117110 0.1894970 114422
1 693731 rs12238997 A G 0.0007598 0.0018592 0.6827803 0.6827802 0.1166780 0.1417730 114422
1 707522 rs371890604 G C 0.0009251 0.0020856 0.6573747 0.6573740 0.0983298 0.1293930 114422
1 717587 rs144155419 G A -0.0013728 0.0049972 0.7835361 0.7835351 0.0157526 0.0045926 114422
1 723329 rs189787166 A T 0.0006290 0.0145104 0.9654229 0.9654225 0.0017896 0.0003994 114422
1 730087 rs148120343 T C -0.0006562 0.0025864 0.7997260 0.7997255 0.0568713 0.0127796 114422
1 731718 rs142557973 T C 0.0007196 0.0017640 0.6833355 0.6833342 0.1226010 0.1543530 114422
1 732032 rs61770163 A C 0.0005094 0.0018800 0.7864244 0.7864245 0.1222220 0.1555510 114422
1 734349 rs141242758 T C 0.0007353 0.0017650 0.6769849 0.6769850 0.1218520 0.1525560 114422
1 740284 rs61770167 C T -0.0038532 0.0080489 0.6321381 0.6321377 0.0058609 0.0023962 114422
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T -0.0012461 0.0020741 0.5479766 0.5479755 0.0553623 0.0309934 114422
23 154923374 rs111332691 T A -0.0019684 0.0022577 0.3832763 0.3832735 0.0448384 0.0116556 114422
23 154925045 rs509981 C T 0.0007317 0.0010880 0.5012322 0.5012293 0.2446210 0.3634440 114422
23 154925895 rs538470 C T 0.0008328 0.0011127 0.4541999 0.4541973 0.2409890 0.3634440 114422
23 154927581 rs644138 G A 0.0003399 0.0010238 0.7399170 0.7399163 0.3003390 0.4635760 114422
23 154929412 rs557132 C T 0.0007462 0.0010883 0.4929286 0.4929282 0.2444980 0.3568210 114422
23 154929637 rs35185538 CT C 0.0011484 0.0011367 0.3123669 0.3123633 0.2287980 0.3011920 114422
23 154929952 rs4012982 CAA C 0.0007627 0.0011454 0.5055021 0.5055008 0.2385330 0.3165560 114422
23 154930230 rs781880 A G 0.0007411 0.0010880 0.4957674 0.4957647 0.2449060 0.3618540 114422
23 154930487 rs781879 T A -0.0002512 0.0036923 0.9457501 0.9457501 0.0197245 0.1263580 114422

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.111711 ES:SE:LP:AF:SS:ID   -7.38503e-05:0.00196232:0.0132377:0.111711:114422:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.116678 ES:SE:LP:AF:SS:ID   0.000759787:0.00185916:0.165719:0.116678:114422:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0983298    ES:SE:LP:AF:SS:ID   0.000925055:0.00208561:0.182187:0.0983298:114422:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.0157526    ES:SE:LP:AF:SS:ID   -0.00137282:0.00499725:0.105941:0.0157526:114422:rs144155419
1   723329  rs189787166 A   T   .   PASS    AF=0.00178963   ES:SE:LP:AF:SS:ID   0.000629026:0.0145104:0.0152824:0.00178963:114422:rs189787166
1   730087  rs148120343 T   C   .   PASS    AF=0.0568713    ES:SE:LP:AF:SS:ID   -0.000656181:0.00258642:0.0970588:0.0568713:114422:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122601 ES:SE:LP:AF:SS:ID   0.000719587:0.00176405:0.165366:0.122601:114422:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.122222 ES:SE:LP:AF:SS:ID   0.000509401:0.00188001:0.104343:0.122222:114422:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.121852 ES:SE:LP:AF:SS:ID   0.000735257:0.00176498:0.169421:0.121852:114422:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.0058609    ES:SE:LP:AF:SS:ID   -0.00385318:0.00804892:0.199188:0.0058609:114422:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.0017873    ES:SE:LP:AF:SS:ID   0.0233067:0.0157294:0.85882:0.0017873:114422:rs112573343
1   749963  rs529266287 T   TAA .   PASS    AF=0.869045 ES:SE:LP:AF:SS:ID   0.000205366:0.0017387:0.0428828:0.869045:114422:rs529266287
1   750230  rs190826124 G   C   .   PASS    AF=0.00160053   ES:SE:LP:AF:SS:ID   -0.00834644:0.0153789:0.231123:0.00160053:114422:rs190826124
1   751343  rs28544273  T   A   .   PASS    AF=0.123663 ES:SE:LP:AF:SS:ID   0.000144784:0.00172279:0.0301067:0.123663:114422:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.143266 ES:SE:LP:AF:SS:ID   0.000541707:0.001704:0.124616:0.143266:114422:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.123787 ES:SE:LP:AF:SS:ID   0.000134887:0.0017204:0.0280259:0.123787:114422:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.86984  ES:SE:LP:AF:SS:ID   -0.000351429:0.00167577:0.0788897:0.86984:114422:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.874607 ES:SE:LP:AF:SS:ID   -0.000401805:0.00170367:0.0896147:0.874607:114422:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.129637 ES:SE:LP:AF:SS:ID   0.000262108:0.00168032:0.0574746:0.129637:114422:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0366494    ES:SE:LP:AF:SS:ID   -0.000720374:0.0030456:0.0898977:0.0366494:114422:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869441 ES:SE:LP:AF:SS:ID   -0.000457711:0.00167417:0.105379:0.869441:114422:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869515 ES:SE:LP:AF:SS:ID   -0.000391298:0.00167481:0.0887001:0.869515:114422:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.86944  ES:SE:LP:AF:SS:ID   -0.00045508:0.00167413:0.104714:0.86944:114422:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00515222   ES:SE:LP:AF:SS:ID   -0.0120117:0.00852451:0.799111:0.00515222:114422:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00512096   ES:SE:LP:AF:SS:ID   -0.0122287:0.0085433:0.817229:0.00512096:114422:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.00569216   ES:SE:LP:AF:SS:ID   0.012898:0.00824634:0.928851:0.00569216:114422:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.869508 ES:SE:LP:AF:SS:ID   -0.000346306:0.00167107:0.0778841:0.869508:114422:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.126917 ES:SE:LP:AF:SS:ID   -1.67972e-05:0.00170559:0.00342617:0.126917:114422:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.869029 ES:SE:LP:AF:SS:ID   -0.000291478:0.00166667:0.0649116:0.869029:114422:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.868436 ES:SE:LP:AF:SS:ID   -0.000390206:0.00166611:0.0889335:0.868436:114422:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869215 ES:SE:LP:AF:SS:ID   -0.000318603:0.00166832:0.0713236:0.869215:114422:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869222 ES:SE:LP:AF:SS:ID   -0.00031996:0.00166844:0.0716446:0.869222:114422:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869224 ES:SE:LP:AF:SS:ID   -0.000317239:0.00166845:0.07099:0.869224:114422:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869663 ES:SE:LP:AF:SS:ID   -0.000363637:0.00167275:0.082019:0.869663:114422:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.099489 ES:SE:LP:AF:SS:ID   0.00104879:0.00194458:0.229403:0.099489:114422:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.00655187   ES:SE:LP:AF:SS:ID   0.0123959:0.00748405:1.01027:0.00655187:114422:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.873708 ES:SE:LP:AF:SS:ID   -0.000206406:0.00169813:0.0441887:0.873708:114422:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.863017 ES:SE:LP:AF:SS:ID   -0.000505169:0.00166576:0.118223:0.863017:114422:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.868401 ES:SE:LP:AF:SS:ID   -0.000225171:0.00168155:0.0489171:0.868401:114422:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.865415 ES:SE:LP:AF:SS:ID   8.30723e-05:0.00168173:0.0174561:0.865415:114422:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0997473    ES:SE:LP:AF:SS:ID   6.28882e-05:0.00200931:0.0109811:0.0997473:114422:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.870695 ES:SE:LP:AF:SS:ID   -6.36961e-05:0.00170149:0.0131665:0.870695:114422:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.870695 ES:SE:LP:AF:SS:ID   -6.33626e-05:0.00170149:0.0130967:0.870695:114422:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.870693 ES:SE:LP:AF:SS:ID   -6.57155e-05:0.00170149:0.0135902:0.870693:114422:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.871018 ES:SE:LP:AF:SS:ID   -5.22524e-05:0.00170224:0.0107674:0.871018:114422:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.126512 ES:SE:LP:AF:SS:ID   -6.7084e-07:0.00170367:0.00013639:0.126512:114422:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.106279 ES:SE:LP:AF:SS:ID   0.000914998:0.00185132:0.206813:0.106279:114422:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.854464 ES:SE:LP:AF:SS:ID   -0.00102722:0.00166524:0.269761:0.854464:114422:rs2519015
1   767393  rs538667473 A   C   .   PASS    AF=0.00170509   ES:SE:LP:AF:SS:ID   0.00174197:0.0145544:0.04348:0.00170509:114422:rs538667473
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.839076 ES:SE:LP:AF:SS:ID   -0.00216885:0.00164868:0.725053:0.839076:114422:rs376645387