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/ubm-a-import/processed/ubm-a-2903/ubm-a-2903.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/ubm-a-import/processed/ubm-a-2903/ldsc.txt \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ 

Beginning analysis at Sat Feb  8 02:23:49 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ubm-a-import/processed/ubm-a-2903/ubm-a-2903.vcf.gz ...
Read summary statistics for 11734353 SNPs.
Dropped 14445 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, 1288946 SNPs remain.
After merging with regression SNP LD, 1288946 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0621 (0.0531)
Lambda GC: 1.0125
Mean Chi^2: 1.0154
Intercept: 1.006 (0.0055)
Ratio: 0.3883 (0.36)
Analysis finished at Sat Feb  8 02:25:37 2020
Total time elapsed: 1.0m:47.35s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9541,
    "inflation_factor": 0.9945,
    "mean_EFFECT": 0,
    "n": "-Inf",
    "n_snps": 11734353,
    "n_clumped_hits": 0,
    "n_p_sig": 0,
    "n_mono": 0,
    "n_ns": 0,
    "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": 433877,
    "n_est": "NA",
    "ratio_se_n": "NA",
    "mean_diff": "NaN",
    "ratio_diff": "NaN",
    "sd_y_est1": "NaN",
    "sd_y_est2": "NA",
    "r2_sum1": 0,
    "r2_sum2": 0,
    "r2_sum3": 0,
    "r2_sum4": 0,
    "ldsc_nsnp_merge_refpanel_ld": 1288946,
    "ldsc_nsnp_merge_regression_ld": 1288946,
    "ldsc_observed_scale_h2_beta": 0.0621,
    "ldsc_observed_scale_h2_se": 0.0531,
    "ldsc_intercept_beta": 1.006,
    "ldsc_intercept_se": 0.0055,
    "ldsc_lambda_gc": 1.0125,
    "ldsc_mean_chisq": 1.0154,
    "ldsc_ratio": 0.3896
}

Flags

name value
af_correlation FALSE
inflation_factor FALSE
n TRUE
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 logical.mean logical.count numeric.mean numeric.sd numeric.p0 numeric.p25 numeric.p50 numeric.p75 numeric.p100 numeric.hist
character ID 0 1.0000000 3 64 0 11719943 0 NA NA NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 1 0 4 0 NA NA NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 1 0 4 0 NA NA NA NA NA NA NA NA NA NA
logical N 11734353 0.0000000 NA NA NA NA NA NaN : NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA NA NA 8.650690e+00 5.767834e+00 1.0000000 4.000000e+00 8.000000e+00 1.300000e+01 2.200000e+01 ▇▅▃▂▂
numeric POS 0 1.0000000 NA NA NA NA NA NA NA 7.882237e+07 5.627455e+07 173.0000000 3.258219e+07 6.943117e+07 1.145037e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 4.980000e-05 9.793990e-02 -1.5258000 -2.530000e-02 1.000000e-04 2.550000e-02 1.454900e+00 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 7.132410e-02 6.747980e-02 0.0142000 1.850000e-02 3.910000e-02 1.090000e-01 8.936000e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.990409e-01 2.892534e-01 0.0000002 2.454709e-01 5.011872e-01 7.413102e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.989874e-01 2.891953e-01 0.0000002 2.478276e-01 4.991282e-01 7.495164e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 1.704462e-01 2.461625e-01 0.0010000 5.052400e-03 3.933600e-02 2.479800e-01 9.990000e-01 ▇▁▁▁▁
numeric AF_reference 433877 0.9630251 NA NA NA NA NA NA NA 1.762844e-01 2.405031e-01 0.0000000 3.394600e-03 5.890580e-02 2.647760e-01 1.000000e+00 ▇▂▁▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 54676 rs2462492 C T -0.0428 0.0256 0.0954993 0.0945490 0.3996650 NA NA
1 55326 rs3107975 T C -0.0952 0.1300 0.4677351 0.4639808 0.0089751 0.0459265 NA
1 57033 rs2691311 T C 0.5444 0.2999 0.0691831 0.0694822 0.0018298 NA NA
1 86028 rs114608975 T C -0.0003 0.0411 1.0000000 0.9941761 0.1044800 0.0277556 NA
1 91536 rs6702460 G T -0.0265 0.0250 0.2884032 0.2891446 0.4542200 0.4207270 NA
1 234313 rs8179466 C T -0.0034 0.0499 0.9549930 0.9456772 0.0749740 NA NA
1 526736 rs28863004 C G 0.1971 0.1779 0.2691529 0.2678939 0.0058798 0.1317890 NA
1 533198 rs78497331 C T 0.5891 0.4408 0.1819701 0.1814075 0.0010150 0.0814696 NA
1 534192 rs6680723 C T 0.0072 0.0290 0.7943282 0.8039210 0.2387500 NA NA
1 534583 rs6683466 C G 0.0173 0.1500 0.9120110 0.9081809 0.0068656 NA NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51221731 rs115055839 T C -0.0038 0.0273 0.8912509 0.8892967 0.0764300 0.0625000 NA
22 51222100 rs114553188 G T 0.0012 0.0329 0.9772370 0.9709043 0.0538360 0.0880591 NA
22 51226692 rs150189434 G A -0.1290 0.1713 0.4466836 0.4514110 0.0020807 0.0155751 NA
22 51227766 rs186062720 T C -0.3300 0.1970 0.0933254 0.0939093 0.0021656 0.0005990 NA
22 51229805 rs9616985 T C -0.0063 0.0273 0.8128311 0.8174941 0.0763670 0.0730831 NA
22 51230673 rs555680442 G C -0.0781 0.1690 0.6456542 0.6439880 0.0027488 0.0017971 NA
22 51231424 rs539541647 A G -0.5794 0.2394 0.0154882 0.0155112 0.0011219 0.0011981 NA
22 51232488 rs376461333 A G -0.0441 0.0669 0.5128614 0.5097720 0.0194930 NA NA
22 51237063 rs3896457 T C 0.0426 0.0172 0.0131826 0.0132587 0.2945100 0.2050720 NA
22 51239586 rs535432390 T G -0.0635 0.1659 0.7079458 0.7018972 0.0025620 0.0001997 NA

bcf preview

1   54676   rs2462492   C   T   .   PASS    AF=0.399665 ES:SE:LP:AF:ID  -0.0428:0.0256:1.02:0.399665:rs2462492
1   55326   rs3107975   T   C   .   PASS    AF=0.0089751    ES:SE:LP:AF:ID  -0.0952:0.13:0.33:0.0089751:rs3107975
1   57033   rs2691311   T   C   .   PASS    AF=0.0018298    ES:SE:LP:AF:ID  0.5444:0.2999:1.16:0.0018298:rs74447903
1   86028   rs114608975 T   C   .   PASS    AF=0.10448  ES:SE:LP:AF:ID  -0.0003:0.0411:-0:0.10448:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.45422  ES:SE:LP:AF:ID  -0.0265:0.025:0.54:0.45422:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074974 ES:SE:LP:AF:ID  -0.0034:0.0499:0.0199998:0.074974:rs8179466
1   526736  rs28863004  C   G   .   PASS    AF=0.0058798    ES:SE:LP:AF:ID  0.1971:0.1779:0.570001:0.0058798:rs28863004
1   533198  rs78497331  C   T   .   PASS    AF=0.001015 ES:SE:LP:AF:ID  0.5891:0.4408:0.74:0.001015:rs78497331
1   534192  rs6680723   C   T   .   PASS    AF=0.23875  ES:SE:LP:AF:ID  0.0072:0.029:0.1:0.23875:rs6680723
1   534583  rs6683466   C   G   .   PASS    AF=0.0068656    ES:SE:LP:AF:ID  0.0173:0.15:0.0399999:0.0068656:rs6683466
1   544584  rs576404767 C   T   .   PASS    AF=0.0016212    ES:SE:LP:AF:ID  0.5774:0.2533:1.65:0.0016212:rs576404767
1   546697  rs12025928  A   G   .   PASS    AF=0.91442  ES:SE:LP:AF:ID  0.0256:0.0363:0.32:0.91442:rs12025928
1   564862  rs1988726   T   C   .   PASS    AF=0.0012083    ES:SE:LP:AF:ID  0.2861:0.3581:0.369999:0.0012083:rs1988726
1   565111  rs573042692 T   C   .   PASS    AF=0.0015048    ES:SE:LP:AF:ID  0.2092:0.3132:0.3:0.0015048:rs573042692
1   565130  rs371431021 G   A   .   PASS    AF=0.0043852    ES:SE:LP:AF:ID  -0.0363:0.192:0.07:0.0043852:rs371431021
1   565196  rs538567606 T   C   .   PASS    AF=0.0021264    ES:SE:LP:AF:ID  -0.0708:0.2745:0.1:0.0021264:rs538567606
1   565469  rs554127336 C   T   .   PASS    AF=0.0016408    ES:SE:LP:AF:ID  -0.1418:0.2907:0.2:0.0016408:rs554127336
1   565490  rs7349153   T   C   .   PASS    AF=0.001359 ES:SE:LP:AF:ID  0.3206:0.3391:0.46:0.001359:rs7349153
1   566024  rs6421779   G   A   .   PASS    AF=0.0012223    ES:SE:LP:AF:ID  0.2308:0.3407:0.3:0.0012223:rs6421779
1   566792  rs9283152   T   C   .   PASS    AF=0.002176 ES:SE:LP:AF:ID  0.2319:0.2815:0.39:0.002176:rs9283152
1   566933  rs113120793 A   G   .   PASS    AF=0.0012255    ES:SE:LP:AF:ID  0.3076:0.3579:0.41:0.0012255:rs113120793
1   566960  rs2185540   T   C   .   PASS    AF=0.0013642    ES:SE:LP:AF:ID  0.3451:0.3419:0.5:0.0013642:rs2185540
1   567006  rs565235853 G   T   .   PASS    AF=0.002852 ES:SE:LP:AF:ID  -0.0689:0.1654:0.17:0.002852:rs565235853
1   567092  rs9326622   T   C   .   PASS    AF=0.0013225    ES:SE:LP:AF:ID  0.3182:0.3425:0.45:0.0013225:rs9326622
1   567119  rs9283153   A   C   .   PASS    AF=0.0014721    ES:SE:LP:AF:ID  0.2206:0.3297:0.3:0.0014721:rs9283153
1   567867  rs2000096   A   G   .   PASS    AF=0.0028155    ES:SE:LP:AF:ID  0.3423:0.2476:0.779999:0.0028155:rs2000096
1   568072  rs2853820   A   G   .   PASS    AF=0.0014994    ES:SE:LP:AF:ID  0.3381:0.3133:0.55:0.0014994:rs2853820
1   568463  rs2153587   A   G   .   PASS    AF=0.0011247    ES:SE:LP:AF:ID  0.2299:0.3636:0.28:0.0011247:rs2153587
1   568800  rs375217967 G   A   .   PASS    AF=0.016753 ES:SE:LP:AF:ID  -0.058:0.0998:0.25:0.016753:rs375217967
1   569004  rs9285835   T   C   .   PASS    AF=0.015599 ES:SE:LP:AF:ID  0.0628:0.0939:0.3:0.015599:rs9285835
1   569543  rs538153094 G   A   .   PASS    AF=0.0015978    ES:SE:LP:AF:ID  -0.0726:0.3428:0.0799999:0.0015978:rs538153094
1   569604  rs9645429   G   A   .   PASS    AF=0.001787 ES:SE:LP:AF:ID  0.1524:0.2818:0.23:0.001787:rs9645429
1   601550  rs2491328   G   A   .   PASS    AF=0.0035313    ES:SE:LP:AF:ID  -0.4004:0.2105:1.24:0.0035313:rs78266750
1   693731  rs12238997  A   G   .   PASS    AF=0.12069  ES:SE:LP:AF:ID  0.028:0.0239:0.620001:0.12069:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.064537 ES:SE:LP:AF:ID  0.0068:0.0362:0.07:0.064537:rs72631875
1   705942  rs544671234 A   T   .   PASS    AF=0.0034158    ES:SE:LP:AF:ID  -0.0428:0.1594:0.1:0.0034158:rs544671234
1   713092  rs4565649   G   A   .   PASS    AF=0.0019147    ES:SE:LP:AF:ID  -0.0566:0.2016:0.11:0.0019147:rs4565649
1   714277  rs138660747 C   A   .   PASS    AF=0.0072919    ES:SE:LP:AF:ID  0.0326:0.1168:0.11:0.0072919:rs138660747
1   714596  rs149887893 T   C   .   PASS    AF=0.028642 ES:SE:LP:AF:ID  -0.02:0.0482:0.17:0.028642:rs149887893
1   715205  rs141090730 C   G   .   PASS    AF=0.0018524    ES:SE:LP:AF:ID  -0.0756:0.2079:0.14:0.0018524:rs141090730
1   715265  rs12184267  C   T   .   PASS    AF=0.03198  ES:SE:LP:AF:ID  0.0033:0.0436:0.0300001:0.03198:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.032112 ES:SE:LP:AF:ID  -0.0013:0.0435:0.0100001:0.032112:rs12184277
1   717474  rs141784362 C   T   .   PASS    AF=0.0016827    ES:SE:LP:AF:ID  -0.1441:0.22:0.29:0.0016827:rs141784362
1   717485  rs12184279  C   A   .   PASS    AF=0.031853 ES:SE:LP:AF:ID  0.0094:0.0438:0.0799999:0.031853:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.01666  ES:SE:LP:AF:ID  0.1237:0.0627:1.32:0.01666:rs144155419
1   718336  rs188996809 T   C   .   PASS    AF=0.0018748    ES:SE:LP:AF:ID  0.0236:0.2464:0.0300001:0.0018748:rs188996809
1   720381  rs116801199 G   T   .   PASS    AF=0.032321 ES:SE:LP:AF:ID  0.0027:0.0433:0.0199998:0.032321:rs116801199
1   720583  rs551231909 G   A   .   PASS    AF=0.001407 ES:SE:LP:AF:ID  -0.2381:0.2532:0.46:0.001407:rs551231909
1   720984  rs564367954 T   G   .   PASS    AF=0.0016131    ES:SE:LP:AF:ID  0.1473:0.2286:0.28:0.0016131:rs564367954
1   721290  rs12565286  G   C   .   PASS    AF=0.032465 ES:SE:LP:AF:ID  0.0017:0.0431:0.0100001:0.032465:rs12565286