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-977/ieu-a-977.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-977/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 09:21:12 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-977/ieu-a-977.vcf.gz ...
Read summary statistics for 9095211 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, 1221346 SNPs remain.
After merging with regression SNP LD, 1221346 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0807 (0.0435)
Lambda GC: 1.0684
Mean Chi^2: 1.0715
Intercept: 1.0547 (0.0066)
Ratio: 0.7644 (0.0924)
Analysis finished at Wed Feb  5 09:22:42 2020
Total time elapsed: 1.0m:30.26s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9807,
    "inflation_factor": 1.0156,
    "mean_EFFECT": -0.0001,
    "n": 10805,
    "n_snps": 9095211,
    "n_clumped_hits": 3,
    "n_p_sig": 152,
    "n_mono": 0,
    "n_ns": 0,
    "n_mac": 9940,
    "is_snpid_unique": true,
    "n_miss_EFFECT": 0,
    "n_miss_SE": 0,
    "n_miss_PVAL": 0,
    "n_miss_AF": 230495,
    "n_miss_AF_reference": 288011,
    "n_est": 9435.3589,
    "ratio_se_n": 0.9345,
    "mean_diff": 0,
    "ratio_diff": 5.3956,
    "sd_y_est1": 1.2277,
    "sd_y_est2": 1.1473,
    "r2_sum1": 0.0212,
    "r2_sum2": 0.0141,
    "r2_sum3": 0.0161,
    "r2_sum4": 0.0164,
    "ldsc_nsnp_merge_refpanel_ld": 1221346,
    "ldsc_nsnp_merge_regression_ld": 1221346,
    "ldsc_observed_scale_h2_beta": 0.0807,
    "ldsc_observed_scale_h2_se": 0.0435,
    "ldsc_intercept_beta": 1.0547,
    "ldsc_intercept_se": 0.0066,
    "ldsc_lambda_gc": 1.0684,
    "ldsc_mean_chisq": 1.0715,
    "ldsc_ratio": 0.765
}

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 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 57 0 9095210 0 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
character ALT 0 1.0000000 1 1 0 4 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 8.675276e+00 5.775939e+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 7.851127e+07 5.641583e+07 8.28000e+02 3.214582e+07 6.892424e+07 1.142526e+08 2.492391e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA -1.274000e-04 5.221350e-02 -1.11887e+00 -2.037200e-02 -1.880000e-04 1.991200e-02 1.357500e+00 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 4.060520e-02 3.074820e-02 1.47830e-02 1.800400e-02 2.719800e-02 5.416000e-02 7.103750e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.974084e-01 2.897463e-01 0.00000e+00 2.451670e-01 4.966735e-01 7.485624e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.911540e-01 2.913648e-01 0.00000e+00 2.359360e-01 4.881451e-01 7.437316e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 230495 0.9746575 NA NA NA NA NA 2.334685e-01 2.579183e-01 7.96000e-05 2.776450e-02 1.250600e-01 3.695310e-01 9.999200e-01 ▇▂▂▁▁
numeric AF_reference 288011 0.9683338 NA NA NA NA NA 2.338291e-01 2.561669e-01 0.00000e+00 2.276360e-02 1.339860e-01 3.702080e-01 1.000000e+00 ▇▂▂▁▁
numeric N 0 1.0000000 NA NA NA NA NA 1.080500e+04 0.000000e+00 1.08050e+04 1.080500e+04 1.080500e+04 1.080500e+04 1.080500e+04 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 51479 rs116400033 T A 0.023757 0.029034 0.4223845 0.4132158 0.175736 0.1281950 10805
1 52058 rs62637813 G C 0.009917 0.085901 0.9098561 0.9080909 NA NA 10805
1 54421 rs146477069 A G 0.042550 0.073059 0.5679786 0.5602932 NA NA 10805
1 54490 rs141149254 G A 0.018821 0.030183 0.5409386 0.5329147 0.125457 0.0960463 10805
1 55164 rs3091274 C A -0.027291 0.036487 0.4633274 0.4544813 0.960700 0.9233230 10805
1 55299 rs10399749 C T 0.004449 0.026041 0.8669561 0.8643449 NA NA 10805
1 55326 rs3107975 T C 0.019239 0.113058 0.8674671 0.8648770 0.028401 0.0459265 10805
1 58814 rs114420996 G A -0.045841 0.051696 0.3845838 0.3752180 0.101034 0.1090260 10805
1 61987 rs76735897 A G 0.007117 0.025185 0.7817232 0.7774920 NA NA 10805
1 61989 rs77573425 G C 0.006203 0.025202 0.8092830 0.8055806 NA NA 10805
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51224600 rs187908482 G A -0.084500 0.089052 0.3521584 0.3426797 0.0765314 0.0387380 10805
22 51224635 rs143053851 G A 0.023716 0.083885 0.7816170 0.7773912 NA NA 10805
22 51227891 rs6010091 G A -0.048122 0.028664 0.0997424 0.0931851 0.4236280 0.4039540 10805
22 51229805 rs9616985 T C -0.041149 0.037714 0.2846820 0.2752376 0.0715195 0.0730831 10805
22 51229855 rs144549712 G A -0.014116 0.093745 0.8826050 0.8803081 0.1306280 0.1160140 10805
22 51234677 rs189571549 A C 0.088928 0.090908 0.3374699 0.3279657 0.0098648 0.0227636 10805
23 35747397 rs74987454 T C 0.031428 0.117875 0.7937633 0.7897607 NA NA 10805
23 50040891 rs192267219 C T -0.000103 0.178113 0.9995470 0.9995386 NA 0.0222517 10805
23 75822996 rs147134110 C T -0.094912 0.046552 0.0456152 0.0414662 0.0243437 0.0773510 10805
23 100784211 rs188350543 C A 0.002259 0.020791 0.9151239 0.9134779 0.7770880 0.6498010 10805

bcf preview

1   51479   rs116400033 T   A   .   PASS    AF=0.175736 ES:SE:LP:AF:SS:ID   0.023757:0.029034:0.374292:0.175736:10805:rs116400033
1   52058   rs62637813  G   C   .   PASS    .   ES:SE:LP:SS:ID  0.009917:0.085901:0.0410273:10805:rs62637813
1   54421   rs146477069 A   G   .   PASS    .   ES:SE:LP:SS:ID  0.04255:0.073059:0.245668:10805:rs146477069
1   54490   rs141149254 G   A   .   PASS    AF=0.125457 ES:SE:LP:AF:SS:ID   0.018821:0.030183:0.266852:0.125457:10805:rs141149254
1   55164   rs3091274   C   A   .   PASS    AF=0.9607   ES:SE:LP:AF:SS:ID   -0.027291:0.036487:0.334112:0.9607:10805:rs3091274
1   55299   rs10399749  C   T   .   PASS    .   ES:SE:LP:SS:ID  0.004449:0.026041:0.0620029:10805:rs10399749
1   55326   rs3107975   T   C   .   PASS    AF=0.028401 ES:SE:LP:AF:SS:ID   0.019239:0.113058:0.061747:0.028401:10805:rs3107975
1   58814   rs114420996 G   A   .   PASS    AF=0.101034 ES:SE:LP:AF:SS:ID   -0.045841:0.051696:0.415009:0.101034:10805:rs114420996
1   61987   rs76735897  A   G   .   PASS    .   ES:SE:LP:SS:ID  0.007117:0.025185:0.106947:10805:rs76735897
1   61989   rs77573425  G   C   .   PASS    .   ES:SE:LP:SS:ID  0.006203:0.025202:0.0918996:10805:rs77573425
1   66162   rs62639105  A   T   .   PASS    .   ES:SE:LP:SS:ID  0.007855:0.025558:0.117392:10805:rs62639105
1   66507   rs12401368  T   A   .   PASS    .   ES:SE:LP:SS:ID  -0.021882:0.037954:0.242687:10805:rs12401368
1   73841   rs143773730 C   T   .   PASS    .   ES:SE:LP:SS:ID  0.034018:0.058455:0.24543:10805:rs143773730
1   82163   rs139113303 G   A   .   PASS    AF=0.0603819    ES:SE:LP:AF:SS:ID   -0.148278:0.149722:0.479507:0.0603819:10805:rs139113303
1   82249   rs1851945   A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.039195:0.041107:0.456144:10805:rs1851945
1   82303   rs3871776   T   C   .   PASS    .   ES:SE:LP:SS:ID  0.017926:0.036743:0.199:10805:rs3871776
1   82316   rs4030305   A   C   .   PASS    .   ES:SE:LP:SS:ID  0.030712:0.072284:0.169416:10805:rs4030305
1   82609   rs149189449 C   G   .   PASS    AF=0.0604614    ES:SE:LP:AF:SS:ID   -0.074844:0.053809:0.762934:0.0604614:10805:rs149189449
1   82676   rs185237834 T   G   .   PASS    .   ES:SE:LP:SS:ID  0.041392:0.040048:0.507455:10805:rs185237834
1   82734   rs4030331   T   C   .   PASS    .   ES:SE:LP:SS:ID  -0.018023:0.032782:0.229248:10805:rs4030331
1   84002   rs28850140  G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.009873:0.07175:0.0493149:10805:rs28850140
1   84010   rs186443818 G   A   .   PASS    .   ES:SE:LP:SS:ID  0.161584:0.142136:0.5768:10805:rs186443818
1   84244   rs191297051 A   C   .   PASS    .   ES:SE:LP:SS:ID  0.025486:0.040439:0.270337:10805:rs191297051
1   85597   rs192472955 A   C   .   PASS    .   ES:SE:LP:SS:ID  0.008175:0.026719:0.116811:10805:rs192472955
1   86018   rs142878000 C   G   .   PASS    .   ES:SE:LP:SS:ID  0.0276:0.038321:0.318679:10805:rs142878000
1   86028   rs114608975 T   C   .   PASS    AF=0.0540971    ES:SE:LP:AF:SS:ID   -0.056647:0.042448:0.71969:0.0540971:10805:rs114608975
1   86331   rs115209712 A   G   .   PASS    AF=0.110183 ES:SE:LP:AF:SS:ID   0.039454:0.038278:0.505589:0.110183:10805:rs115209712
1   87021   rs188486692 T   C   .   PASS    AF=0.00906921   ES:SE:LP:AF:SS:ID   0.023328:0.11538:0.0742538:0.00906921:10805:rs188486692
1   88172   rs940551    G   A   .   PASS    AF=0.0574383    ES:SE:LP:AF:SS:ID   0.067333:0.049293:0.743679:0.0574383:10805:rs940551
1   88177   rs143215837 G   C   .   PASS    AF=0.0560064    ES:SE:LP:AF:SS:ID   0.067536:0.060514:0.562552:0.0560064:10805:rs143215837
1   88188   rs148331237 C   A   .   PASS    AF=0.00445505   ES:SE:LP:AF:SS:ID   -0.016036:0.165891:0.0341076:0.00445505:10805:rs148331237
1   88236   rs186918018 C   T   .   PASS    AF=0.00731901   ES:SE:LP:AF:SS:ID   -0.039761:0.118384:0.129642:0.00731901:10805:rs186918018
1   88316   rs113759966 G   A   .   PASS    AF=0.0566428    ES:SE:LP:AF:SS:ID   0.036384:0.048549:0.334926:0.0566428:10805:rs113759966
1   89946   rs138808727 A   T   .   PASS    AF=0.170485 ES:SE:LP:AF:SS:ID   0.003064:0.028898:0.0375497:0.170485:10805:rs138808727
1   91190   rs143856811 G   A   .   PASS    AF=0.0647574    ES:SE:LP:AF:SS:ID   -0.050084:0.139914:0.139297:0.0647574:10805:rs143856811
1   92858   rs147061536 G   T   .   PASS    AF=0.183055 ES:SE:LP:AF:SS:ID   0.007526:0.02897:0.0974847:0.183055:10805:rs147061536
1   94986   rs185004859 C   T   .   PASS    .   ES:SE:LP:SS:ID  0.090435:0.176619:0.210665:10805:rs185004859
1   98929   rs12184306  A   G   .   PASS    .   ES:SE:LP:SS:ID  0.001548:0.026582:0.0202523:10805:rs12184306
1   98974   rs12184307  A   G   .   PASS    .   ES:SE:LP:SS:ID  0.002037:0.026766:0.0266366:10805:rs12184307
1   99687   rs139153227 C   T   .   PASS    AF=0.0501989    ES:SE:LP:AF:SS:ID   -0.103589:0.065884:0.909573:0.0501989:10805:rs139153227
1   115746  rs147538909 C   T   .   PASS    AF=0.0416866    ES:SE:LP:AF:SS:ID   -0.162753:0.16628:0.472128:0.0416866:10805:rs147538909
1   120705  rs4117992   T   C   .   PASS    .   ES:SE:LP:SS:ID  0.012482:0.049894:0.0935486:10805:rs4117992
1   120983  rs182468771 C   T   .   PASS    .   ES:SE:LP:SS:ID  -0.018056:0.079521:0.0841735:10805:rs182468771
1   126113  rs79114531  C   A   .   PASS    .   ES:SE:LP:SS:ID  0.013998:0.049199:0.107759:10805:rs79114531
1   135203  rs147502335 G   A   .   PASS    AF=0.040891 ES:SE:LP:AF:SS:ID   0.052634:0.188998:0.105238:0.040891:10805:rs147502335
1   137825  rs147252685 G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.015148:0.048149:0.120492:10805:rs147252685
1   162463  rs143399298 T   C   .   PASS    .   ES:SE:LP:SS:ID  -0.022617:0.104315:0.0800652:10805:rs143399298
1   171529  rs191891026 A   G   .   PASS    .   ES:SE:LP:SS:ID  0.201309:0.122017:0.975822:10805:rs191891026
1   172595  rs187071114 G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.004894:0.026202:0.0681934:10805:rs187071114
1   173709  rs192722547 A   C   .   PASS    .   ES:SE:LP:SS:ID  0.011598:0.026576:0.174747:10805:rs192722547