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/ieu-a-import/processed/ieu-a-1092/ieu-a-1092.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/ieu-a-import/processed/ieu-a-1092/ldsc.txt \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ 

Beginning analysis at Wed Feb  5 08:42:22 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-1092/ieu-a-1092.vcf.gz ...
Read summary statistics for 8973300 SNPs.
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, 1258582 SNPs remain.
After merging with regression SNP LD, 1258582 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0398 (0.0068)
Lambda GC: 1.0781
Mean Chi^2: 1.0775
Intercept: 1.0181 (0.0067)
Ratio: 0.2329 (0.0867)
Analysis finished at Wed Feb  5 08:43:52 2020
Total time elapsed: 1.0m:29.82s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9438,
    "inflation_factor": 1.0475,
    "mean_EFFECT": -0,
    "n": 75244,
    "n_snps": 8973300,
    "n_clumped_hits": 1,
    "n_p_sig": 33,
    "n_mono": 0,
    "n_ns": 615334,
    "n_mac": 0,
    "is_snpid_unique": true,
    "n_miss_EFFECT": 0,
    "n_miss_SE": 0,
    "n_miss_PVAL": 0,
    "n_miss_AF": 0,
    "n_miss_AF_reference": 166203,
    "n_est": 74393.6444,
    "ratio_se_n": 0.9943,
    "mean_diff": -2.8035e-06,
    "ratio_diff": 1.5402,
    "sd_y_est1": 0.899,
    "sd_y_est2": 0.8939,
    "r2_sum1": 0.0003,
    "r2_sum2": 0.0004,
    "r2_sum3": 0.0004,
    "r2_sum4": 0.0004,
    "ldsc_nsnp_merge_refpanel_ld": 1258582,
    "ldsc_nsnp_merge_regression_ld": 1258582,
    "ldsc_observed_scale_h2_beta": 0.0398,
    "ldsc_observed_scale_h2_se": 0.0068,
    "ldsc_intercept_beta": 1.0181,
    "ldsc_intercept_se": 0.0067,
    "ldsc_lambda_gc": 1.0781,
    "ldsc_mean_chisq": 1.0775,
    "ldsc_ratio": 0.2335
}

Flags

name value
af_correlation FALSE
inflation_factor FALSE
n FALSE
is_snpid_non_unique FALSE
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 58 0 8973299 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 45 0 34759 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 45 0 18428 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 8.670575e+00 5.777711e+00 1.00000e+00 4.000000e+00 8.000000e+00 1.300000e+01 2.200000e+01 ▇▅▅▂▂
numeric POS 0 1.0000000 NA NA NA NA NA 7.865655e+07 5.656167e+07 3.02000e+02 3.199260e+07 6.913289e+07 1.145153e+08 2.492393e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA -3.900000e-05 1.086940e-02 -1.26531e-01 -5.286900e-03 -4.790000e-05 5.207100e-03 1.370990e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 9.234000e-03 5.312700e-03 4.53450e-03 5.089000e-03 6.899800e-03 1.196590e-02 3.615750e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.891061e-01 2.914947e-01 0.00000e+00 2.300001e-01 4.899999e-01 7.400005e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.891050e-01 2.914696e-01 0.00000e+00 2.342568e-01 4.851529e-01 7.412875e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA 2.586996e-01 2.637445e-01 1.00010e-02 4.224200e-02 1.529370e-01 4.112290e-01 9.899990e-01 ▇▂▂▁▁
numeric AF_reference 166203 0.9814781 NA NA NA NA NA 2.549491e-01 2.527002e-01 0.00000e+00 4.612620e-02 1.661340e-01 4.007590e-01 1.000000e+00 ▇▃▂▁▁
numeric N 0 1.0000000 NA NA NA NA NA 7.524400e+04 0.000000e+00 7.52440e+04 7.524400e+04 7.524400e+04 7.524400e+04 7.524400e+04 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 10177 rs367896724 A AC -0.0045508 0.0066609 0.4899999 0.4944774 0.402314 0.4253190 75244
1 10352 rs555500075 T TA -0.0081098 0.0069301 0.2399999 0.2419115 0.390095 0.4375000 75244
1 11008 rs575272151 C G -0.0018841 0.0115483 0.8700001 0.8703986 0.086367 0.0880591 75244
1 11012 rs544419019 C G -0.0018841 0.0115483 0.8700001 0.8703986 0.086367 0.0880591 75244
1 13110 rs540538026 G A 0.0033456 0.0147842 0.8200001 0.8209694 0.059597 0.0267572 75244
1 13116 rs62635286 T G 0.0007276 0.0090807 0.9400001 0.9361373 0.188535 0.0970447 75244
1 13273 rs531730856 G C -0.0104917 0.0104064 0.3100002 0.3133600 0.133959 0.0950479 75244
1 14464 rs546169444 A T -0.0057434 0.0094689 0.5400003 0.5441486 0.155098 0.0958466 75244
1 14604 rs541940975 A G 0.0036009 0.0086883 0.6800001 0.6785437 0.193476 0.1475640 75244
1 14933 rs199856693 G A 0.0015827 0.0161103 0.9199999 0.9217386 0.047152 0.0283546 75244
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51233347 rs62240044 T C -0.0074253 0.0058113 0.2000000 0.2013427 0.275895 0.2134580 75244
22 51233666 rs11091014 C T 0.0207188 0.0208594 0.3200000 0.3205834 0.020872 0.0970447 75244
22 51234343 rs374867791 G T 0.0207182 0.0208482 0.3200000 0.3203376 0.021048 0.1002400 75244
22 51234799 rs191117135 G A 0.0265410 0.0210541 0.2099999 0.2074495 0.015260 0.0059904 75244
22 51235959 rs200189535 T C 0.0035506 0.0068102 0.5999997 0.6021100 0.192540 0.1996810 75244
22 51236013 rs200507571 A AT 0.0043461 0.0057942 0.4500005 0.4532113 0.252329 0.1487620 75244
22 51237063 rs3896457 T C -0.0061560 0.0055897 0.2700001 0.2707685 0.294328 0.2050720 75244
22 51237364 rs200607599 A G -0.0377196 0.0229992 0.1000000 0.1009969 0.015952 0.0187700 75244
22 51237712 rs370652263 G A -0.0139551 0.0106905 0.1900002 0.1917656 0.055591 0.0690895 75244
22 51244237 rs575160859 C T -0.0089389 0.0283167 0.7499995 0.7522493 0.013418 0.0037939 75244

bcf preview

1   10177   rs367896724 A   AC  .   PASS    AF=0.402314 ES:SE:LP:AF:SS:ID   -0.00455078:0.00666093:0.309804:0.402314:75244:rs367896724
1   10352   rs555500075 T   TA  .   PASS    AF=0.390095 ES:SE:LP:AF:SS:ID   -0.00810977:0.00693011:0.619789:0.390095:75244:rs555500075
1   11008   rs575272151 C   G   .   PASS    AF=0.086367 ES:SE:LP:AF:SS:ID   -0.00188413:0.0115483:0.0604807:0.086367:75244:rs575272151
1   11012   rs544419019 C   G   .   PASS    AF=0.086367 ES:SE:LP:AF:SS:ID   -0.00188413:0.0115483:0.0604807:0.086367:75244:rs544419019
1   13110   rs540538026 G   A   .   PASS    AF=0.059597 ES:SE:LP:AF:SS:ID   0.00334564:0.0147842:0.0861861:0.059597:75244:rs540538026
1   13116   rs62635286  T   G   .   PASS    AF=0.188535 ES:SE:LP:AF:SS:ID   0.000727598:0.00908071:0.0268721:0.188535:75244:rs62635286
1   13273   rs531730856 G   C   .   PASS    AF=0.133959 ES:SE:LP:AF:SS:ID   -0.0104917:0.0104064:0.508638:0.133959:75244:rs531730856
1   14464   rs546169444 A   T   .   PASS    AF=0.155098 ES:SE:LP:AF:SS:ID   -0.0057434:0.00946894:0.267606:0.155098:75244:rs546169444
1   14604   rs541940975 A   G   .   PASS    AF=0.193476 ES:SE:LP:AF:SS:ID   0.00360086:0.00868826:0.167491:0.193476:75244:rs541940975
1   14933   rs199856693 G   A   .   PASS    AF=0.047152 ES:SE:LP:AF:SS:ID   0.00158274:0.0161103:0.0362122:0.047152:75244:rs199856693
1   15820   rs2691315   G   T   .   PASS    AF=0.27174  ES:SE:LP:AF:SS:ID   0.0101415:0.00795189:0.69897:0.27174:75244:rs2691315
1   15903   rs557514207 G   GC  .   PASS    AF=0.408683 ES:SE:LP:AF:SS:ID   -0.00198132:0.00662145:0.119186:0.408683:75244:rs557514207
1   16949   rs199745162 A   C   .   PASS    AF=0.020279 ES:SE:LP:AF:SS:ID   0.0172853:0.0238933:0.327902:0.020279:75244:rs199745162
1   18849   rs533090414 C   G   .   PASS    AF=0.974295 ES:SE:LP:AF:SS:ID   -0.000333152:0.0201051:0.00436481:0.974295:75244:rs533090414
1   30923   rs806731    G   T   .   PASS    AF=0.903876 ES:SE:LP:AF:SS:ID   0.000200262:0.0119506:0.00436481:0.903876:75244:rs806731
1   47159   rs540662756 T   C   .   PASS    AF=0.065472 ES:SE:LP:AF:SS:ID   0.000516338:0.0141865:0.0132283:0.065472:75244:rs540662756
1   49554   rs539322794 A   G   .   PASS    AF=0.097473 ES:SE:LP:AF:SS:ID   -0.00771457:0.0117225:0.29243:0.097473:75244:rs539322794
1   51479   rs116400033 T   A   .   PASS    AF=0.209986 ES:SE:LP:AF:SS:ID   0.00160209:0.00835118:0.0705811:0.209986:75244:rs116400033
1   52238   rs2691277   T   G   .   PASS    AF=0.976797 ES:SE:LP:AF:SS:ID   0.0105561:0.0245501:0.173925:0.976797:75244:rs2691277
1   54490   rs141149254 G   A   .   PASS    AF=0.151422 ES:SE:LP:AF:SS:ID   -0.00167812:0.00936957:0.0655015:0.151422:75244:rs141149254
1   54716   rs569128616 C   T   .   PASS    AF=0.42454  ES:SE:LP:AF:SS:ID   -0.0137921:0.00706044:1.29243:0.42454:75244:rs569128616
1   55164   rs3091274   C   A   .   PASS    AF=0.981509 ES:SE:LP:AF:SS:ID   0.00537332:0.026904:0.0757207:0.981509:75244:rs3091274
1   55326   rs3107975   T   C   .   PASS    AF=0.01603  ES:SE:LP:AF:SS:ID   0.0248986:0.0289904:0.408935:0.01603:75244:rs3107975
1   55545   rs28396308  C   T   .   PASS    AF=0.261517 ES:SE:LP:AF:SS:ID   -0.00275161:0.00790048:0.136677:0.261517:75244:rs28396308
1   57292   rs201418760 C   T   .   PASS    AF=0.02082  ES:SE:LP:AF:SS:ID   0.0288052:0.0251952:0.60206:0.02082:75244:rs201418760
1   58814   rs114420996 G   A   .   PASS    AF=0.094084 ES:SE:LP:AF:SS:ID   -0.00703133:0.0119751:0.251812:0.094084:75244:rs114420996
1   59040   rs62637815  T   C   .   PASS    AF=0.091395 ES:SE:LP:AF:SS:ID   -0.00176896:0.012043:0.0555173:0.091395:75244:rs62637815
1   60249   rs547227933 C   T   .   PASS    AF=0.01879  ES:SE:LP:AF:SS:ID   0.0409042:0.0244997:1.02228:0.01879:75244:rs547227933
1   60351   rs62637817  A   G   .   PASS    AF=0.084746 ES:SE:LP:AF:SS:ID   -0.00399361:0.0124292:0.124939:0.084746:75244:rs62637817
1   61920   rs62637820  G   A   .   PASS    AF=0.030102 ES:SE:LP:AF:SS:ID   -0.0149515:0.019922:0.346787:0.030102:75244:rs62637820
1   63671   rs80011619  G   A   .   PASS    AF=0.160843 ES:SE:LP:AF:SS:ID   -0.0013578:0.00915964:0.0555173:0.160843:75244:rs80011619
1   64649   rs181431124 A   C   .   PASS    AF=0.027235 ES:SE:LP:AF:SS:ID   -0.0135569:0.0207958:0.29243:0.027235:75244:rs181431124
1   64931   rs62639104  G   A   .   PASS    AF=0.083092 ES:SE:LP:AF:SS:ID   -0.0054557:0.0125756:0.180456:0.083092:75244:rs62639104
1   66219   rs181028663 A   T   .   PASS    AF=0.018879 ES:SE:LP:AF:SS:ID   -0.0110774:0.0234231:0.19382:0.018879:75244:rs181028663
1   68082   rs367789441 T   C   .   PASS    AF=0.072252 ES:SE:LP:AF:SS:ID   -0.00266841:0.0133316:0.0757207:0.072252:75244:rs367789441
1   69428   rs140739101 T   G   .   PASS    AF=0.036251 ES:SE:LP:AF:SS:ID   -0.017649:0.0189251:0.455932:0.036251:75244:rs140739101
1   69761   rs200505207 A   T   .   PASS    AF=0.075779 ES:SE:LP:AF:SS:ID   0.00532292:0.0130562:0.167491:0.075779:75244:rs200505207
1   69897   rs200676709 T   C   .   PASS    AF=0.742291 ES:SE:LP:AF:SS:ID   0.0160314:0.00801374:1.34679:0.742291:75244:rs200676709
1   72526   rs547237130 A   G   .   PASS    AF=0.041676 ES:SE:LP:AF:SS:ID   -0.0107322:0.0177575:0.259637:0.041676:75244:rs547237130
1   73490   rs558384541 T   C   .   PASS    AF=0.017948 ES:SE:LP:AF:SS:ID   0.00270295:0.0264925:0.0362122:0.017948:75244:rs558384541
1   74790   rs13328700  C   G   .   PASS    AF=0.035774 ES:SE:LP:AF:SS:ID   -0.000207253:0.018291:0.00436481:0.035774:75244:rs13328700
1   74792   rs13328684  G   A   .   PASS    AF=0.035774 ES:SE:LP:AF:SS:ID   -0.000207253:0.018291:0.00436481:0.035774:75244:rs13328684
1   76838   rs563953605 T   G   .   PASS    AF=0.078788 ES:SE:LP:AF:SS:ID   -0.00997778:0.0127591:0.366532:0.078788:75244:rs563953605
1   76854   rs367666799 A   G   .   PASS    AF=0.081423 ES:SE:LP:AF:SS:ID   -0.00800837:0.0125755:0.283997:0.081423:75244:rs367666799
1   77866   rs563593912 C   T   .   PASS    AF=0.078671 ES:SE:LP:AF:SS:ID   -0.00996991:0.0127614:0.366532:0.078671:75244:rs563593912
1   77874   rs62641297  G   A   .   PASS    AF=0.078671 ES:SE:LP:AF:SS:ID   -0.00996991:0.0127614:0.366532:0.078671:75244:rs62641297
1   78061   rs550898066 C   G   .   PASS    AF=0.010458 ES:SE:LP:AF:SS:ID   0.0647983:0.0336223:1.26761:0.010458:75244:rs550898066
1   81587   rs536406113 C   T   .   PASS    AF=0.062285 ES:SE:LP:AF:SS:ID   -0.0011756:0.0141542:0.0315171:0.062285:75244:rs536406113
1   82163   rs139113303 G   A   .   PASS    AF=0.079566 ES:SE:LP:AF:SS:ID   -0.00792695:0.0127054:0.275724:0.079566:75244:rs139113303
1   82609   rs149189449 C   G   .   PASS    AF=0.079597 ES:SE:LP:AF:SS:ID   -0.00794296:0.0127046:0.275724:0.079597:75244:rs149189449