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/sun/processed/PROT-a-1660/PROT-a-1660.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/sun/processed/PROT-a-1660/ldsc.txt \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ 

Beginning analysis at Mon Oct 28 21:00:12 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/sun/processed/PROT-a-1660/PROT-a-1660.vcf.gz ...
Read summary statistics for 10265284 SNPs.
Dropped 20 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, 1280408 SNPs remain.
After merging with regression SNP LD, 1280408 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: -0.1393 (0.1364)
Lambda GC: 0.9979
Mean Chi^2: 0.9994
Intercept: 1.0085 (0.0059)
Ratio: NA (mean chi^2 < 1)
Analysis finished at Mon Oct 28 21:01:44 2019
Total time elapsed: 1.0m:31.91s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9467,
    "inflation_factor": 0.9945,
    "mean_EFFECT": -0.0001,
    "n": "-Inf",
    "n_snps": 10265284,
    "n_clumped_hits": 1,
    "n_p_sig": 29,
    "n_mono": 0,
    "n_ns": 884453,
    "n_mac": 0,
    "is_snpid_unique": false,
    "n_miss_EFFECT": 0,
    "n_miss_SE": 0,
    "n_miss_PVAL": 0,
    "n_miss_AF": 277,
    "n_miss_AF_reference": 477442,
    "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": 1280408,
    "ldsc_nsnp_merge_regression_ld": 1280408,
    "ldsc_observed_scale_h2_beta": "NA",
    "ldsc_observed_scale_h2_se": "NA",
    "ldsc_intercept_beta": 1.0085,
    "ldsc_intercept_se": 0.0059,
    "ldsc_lambda_gc": 0.9979,
    "ldsc_mean_chisq": 0.9994,
    "ldsc_ratio": -14.1667
}

Flags

name value
af_correlation FALSE
inflation_factor FALSE
n TRUE
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 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 58 0 10265275 0 NA NA NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 97 0 38413 0 NA NA NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 412 0 24018 0 NA NA NA NA NA NA NA NA NA NA
logical N 10265284 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 8.643747e+00 5.756573e+00 1.00000 4.000000e+00 8.000000e+00 1.300000e+01 2.200000e+01 ▇▅▅▂▂
numeric POS 0 1.0000000 NA NA NA NA NA NA NA 7.877894e+07 5.630825e+07 302.00000 3.256329e+07 6.917355e+07 1.143686e+08 2.492405e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -1.124000e-04 7.282890e-02 -0.90850 -2.980000e-02 -1.000000e-04 2.970000e-02 8.703000e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 5.959380e-02 4.190000e-02 0.02390 2.810000e-02 4.050000e-02 7.890000e-02 2.524000e-01 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.999925e-01 2.889359e-01 0.00000 2.511892e-01 5.011872e-01 7.585776e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.999571e-01 2.888644e-01 0.00000 2.493820e-01 5.003330e-01 7.500690e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 277 0.9999730 NA NA NA NA NA NA NA 2.512646e-01 2.751608e-01 0.00242 2.907000e-02 1.321000e-01 4.053500e-01 9.988300e-01 ▇▂▂▁▁
numeric AF_reference 477442 0.9534896 NA NA NA NA NA NA NA 2.332106e-01 2.519924e-01 0.00000 2.535940e-02 1.361820e-01 3.694090e-01 1.000000e+00 ▇▂▂▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 10177 rs367896724 AC A 0.0248 0.0299 0.4073803 0.4068603 0.62806 NA NA
1 10616 rs376342519 C CCGCCGTTGCAAAGGCGCGCCG 0.0990 0.1646 0.5495409 0.5475349 0.00751 NA NA
1 11008 rs575272151 C G -0.0306 0.0518 0.5495409 0.5546989 0.08306 0.0880591 NA
1 13273 rs531730856 G C 0.0115 0.0434 0.7943282 0.7910272 0.13251 0.0950479 NA
1 13483 rs554760071 G C 0.2310 0.1958 0.2398827 0.2380896 0.00504 0.0019968 NA
1 14604 rs541940975 A G -0.0117 0.0370 0.7585776 0.7518384 0.18665 0.1475640 NA
1 14933 rs199856693 G A 0.1048 0.0677 0.1230269 0.1216209 0.04746 0.0283546 NA
1 18849 rs533090414 C G 0.0064 0.0797 0.9332541 0.9359978 0.96874 0.9518770 NA
1 30923 rs806731 G T 0.0000 0.0464 1.0000000 1.0000000 0.89387 0.8724040 NA
1 47159 rs540662756 T C 0.0878 0.0594 0.1380381 0.1393771 0.06190 0.0273562 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51237063 rs3896457 T C 0.0269 0.0268 0.3162278 0.3155081 0.28870 0.2050720 NA
22 51237364 rs200607599 A G -0.0098 0.1161 0.9332541 0.9327305 0.01151 0.0187700 NA
22 51237712 rs370652263 G A -0.0093 0.0513 0.8511380 0.8561427 0.06180 0.0690895 NA
22 51238394 rs149712012 C T -0.1115 0.1834 0.5370318 0.5432135 0.00454 0.0033946 NA
22 51239281 rs8138215 G C -0.0198 0.2011 0.9120110 0.9215682 0.00379 0.0111821 NA
22 51239296 rs8137179 T C -0.0198 0.2011 0.9120110 0.9215682 0.00379 0.0111821 NA
22 51239304 rs8142977 C T -0.0198 0.2011 0.9120110 0.9215682 0.00379 0.0111821 NA
22 51239794 rs561893765 C A 0.1107 0.2141 0.6025596 0.6051226 0.00333 0.0299521 NA
22 51241386 rs568168135 C G -0.0686 0.1654 0.6760830 0.6783234 0.00560 0.0139776 NA
22 51244237 rs575160859 C T -0.0617 0.1169 0.6025596 0.5976371 0.01136 0.0037939 NA

bcf preview

1   10177   rs367896724 AC  A   .   PASS    AF=0.62806  ES:SE:LP:AF:ID  0.0248:0.0299:0.39:0.62806:rs367896724
1   10616   rs376342519 C   CCGCCGTTGCAAAGGCGCGCCG  .   PASS    AF=0.00751  ES:SE:LP:AF:ID  0.099:0.1646:0.26:0.00751:rs376342519
1   11008   rs575272151 C   G   .   PASS    AF=0.08306  ES:SE:LP:AF:ID  -0.0306:0.0518:0.26:0.08306:rs575272151
1   13273   rs531730856 G   C   .   PASS    AF=0.13251  ES:SE:LP:AF:ID  0.0115:0.0434:0.1:0.13251:rs531730856
1   13483   rs554760071 G   C   .   PASS    AF=0.00504  ES:SE:LP:AF:ID  0.231:0.1958:0.620001:0.00504:rs554760071
1   14604   rs541940975 A   G   .   PASS    AF=0.18665  ES:SE:LP:AF:ID  -0.0117:0.037:0.12:0.18665:rs541940975
1   14933   rs199856693 G   A   .   PASS    AF=0.04746  ES:SE:LP:AF:ID  0.1048:0.0677:0.91:0.04746:rs199856693
1   18849   rs533090414 C   G   .   PASS    AF=0.96874  ES:SE:LP:AF:ID  0.0064:0.0797:0.0300001:0.96874:rs533090414
1   30923   rs806731    G   T   .   PASS    AF=0.89387  ES:SE:LP:AF:ID  0:0.0464:-0:0.89387:rs806731
1   47159   rs540662756 T   C   .   PASS    AF=0.0619   ES:SE:LP:AF:ID  0.0878:0.0594:0.860001:0.0619:rs540662756
1   49554   rs539322794 A   G   .   PASS    AF=0.08749  ES:SE:LP:AF:ID  -0.0583:0.0524:0.58:0.08749:rs539322794
1   54712   rs568927205 TTTTC   T   .   PASS    AF=0.43775  ES:SE:LP:AF:ID  -0.036:0.0283:0.69:0.43775:rs568927205
1   54945   rs569799965 C   A   .   PASS    AF=0.00751  ES:SE:LP:AF:ID  0.0044:0.1698:0.0100001:0.00751:rs569799965
1   55249   rs200769871 C   CTATGG  .   PASS    AF=0.01031  ES:SE:LP:AF:ID  0.0192:0.142:0.05:0.01031:rs200769871
1   59040   rs62637815  T   C   .   PASS    AF=0.08548  ES:SE:LP:AF:ID  -0.0425:0.0528:0.38:0.08548:rs62637815
1   60351   rs62637817  A   G   .   PASS    AF=0.08009  ES:SE:LP:AF:ID  -0.0553:0.0542:0.509999:0.08009:rs62637817
1   61208   rs527362206 T   C   .   PASS    AF=0.00728  ES:SE:LP:AF:ID  0.0234:0.1709:0.05:0.00728:rs527362206
1   61743   rs184286948 G   C   .   PASS    AF=0.00759  ES:SE:LP:AF:ID  0.031:0.1685:0.07:0.00759:rs184286948
1   63093   rs200092917 G   A   .   PASS    AF=0.02888  ES:SE:LP:AF:ID  -0.1093:0.0875:0.67:0.02888:rs200092917
1   63671   rs80011619  G   A   .   PASS    AF=0.15883  ES:SE:LP:AF:ID  -0.0486:0.04:0.65:0.15883:rs80011619
1   64649   rs181431124 A   C   .   PASS    AF=0.02531  ES:SE:LP:AF:ID  0.0153:0.0932:0.0599998:0.02531:rs181431124
1   64931   rs62639104  G   A   .   PASS    AF=0.07866  ES:SE:LP:AF:ID  -0.0459:0.0543:0.4:0.07866:rs62639104
1   66219   rs181028663 A   T   .   PASS    AF=0.01917  ES:SE:LP:AF:ID  0.0023:0.1018:0.0100001:0.01917:rs181028663
1   67107   rs545950684 A   G   .   PASS    AF=0.00703  ES:SE:LP:AF:ID  0.0336:0.1733:0.07:0.00703:rs545950684
1   68082   rs367789441 T   C   .   PASS    AF=0.07119  ES:SE:LP:AF:ID  -0.0121:0.0573:0.0799999:0.07119:rs367789441
1   69428   rs140739101 T   G   .   PASS    AF=0.03289  ES:SE:LP:AF:ID  0.0692:0.0809:0.41:0.03289:rs140739101
1   69761   rs200505207 A   T   .   PASS    AF=0.07372  ES:SE:LP:AF:ID  -0.0248:0.0566:0.18:0.07372:rs200505207
1   69897   rs200676709 T   C   .   PASS    AF=0.71804  ES:SE:LP:AF:ID  0.0223:0.0316:0.32:0.71804:rs200676709
1   77866   rs563593912 C   T   .   PASS    AF=0.0757   ES:SE:LP:AF:ID  -0.0501:0.0548:0.44:0.0757:rs563593912
1   81587   rs536406113 C   T   .   PASS    AF=0.06385  ES:SE:LP:AF:ID  0.0246:0.0597:0.17:0.06385:rs536406113
1   82163   rs139113303 G   A   .   PASS    AF=0.07738  ES:SE:LP:AF:ID  0.0937:0.0529:1.12:0.07738:rs139113303
1   82609   rs149189449 C   G   .   PASS    AF=0.07539  ES:SE:LP:AF:ID  0.0856:0.0537:0.959999:0.07539:rs149189449
1   86065   rs116504101 G   C   .   PASS    AF=0.0764   ES:SE:LP:AF:ID  0.094:0.0536:1.1:0.0764:rs116504101
1   87409   rs139490478 C   T   .   PASS    AF=0.07661  ES:SE:LP:AF:ID  0.0905:0.0534:1.04:0.07661:rs139490478
1   88169   rs940550    C   T   .   PASS    AF=0.20467  ES:SE:LP:AF:ID  -0.0311:0.0361:0.41:0.20467:rs940550
1   88172   rs940551    G   A   .   PASS    AF=0.07528  ES:SE:LP:AF:ID  -0.0065:0.0553:0.0399999:0.07528:rs940551
1   88177   rs143215837 G   C   .   PASS    AF=0.07235  ES:SE:LP:AF:ID  -0.009:0.0564:0.0599998:0.07235:rs143215837
1   88316   rs113759966 G   A   .   PASS    AF=0.07124  ES:SE:LP:AF:ID  -0.0164:0.0571:0.11:0.07124:rs113759966
1   88338   rs55700207  G   A   .   PASS    AF=0.07793  ES:SE:LP:AF:ID  -0.0853:0.0534:0.959999:0.07793:rs55700207
1   88710   rs186575039 C   G   .   PASS    AF=0.07789  ES:SE:LP:AF:ID  0.0834:0.053:0.940001:0.07789:rs186575039
1   89599   rs375955515 A   T   .   PASS    AF=0.07642  ES:SE:LP:AF:ID  0.0937:0.0532:1.11:0.07642:rs375955515
1   91190   rs143856811 G   A   .   PASS    AF=0.08117  ES:SE:LP:AF:ID  0.0905:0.0517:1.1:0.08117:rs143856811
1   91340   rs376250562 C   G   .   PASS    AF=0.01521  ES:SE:LP:AF:ID  0.0905:0.1199:0.35:0.01521:rs376250562
1   91358   rs370712517 G   T   .   PASS    AF=0.01478  ES:SE:LP:AF:ID  0.1513:0.1208:0.679999:0.01478:rs370712517
1   91421   rs28619159  T   C   .   PASS    AF=0.01861  ES:SE:LP:AF:ID  0.1941:0.1033:1.22:0.01861:rs28619159
1   91515   rs376723915 A   C   .   PASS    AF=0.54508  ES:SE:LP:AF:ID  0.0104:0.029:0.14:0.54508:rs376723915
1   91536   rs6702460   G   T   .   PASS    AF=0.56854  ES:SE:LP:AF:ID  0.0243:0.0291:0.4:0.56854:rs6702460
1   92858   rs147061536 G   T   .   PASS    AF=0.22951  ES:SE:LP:AF:ID  -0.0426:0.0348:0.65:0.22951:rs147061536
1   99687   rs139153227 C   T   .   PASS    AF=0.06852  ES:SE:LP:AF:ID  -0.0419:0.0578:0.33:0.06852:rs139153227
1   104281  rs576842010 T   C   .   PASS    AF=0.04001  ES:SE:LP:AF:ID  -0.0825:0.0748:0.570001:0.04001:rs576842010