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/ukb-a-import/processed/ukb-a-344/ukb-a-344.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-a-import/processed/ukb-a-344/ldsc.txt \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ 

Beginning analysis at Sun Feb 16 01:58:40 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-a-import/processed/ukb-a-344/ukb-a-344.vcf.gz ...
Read summary statistics for 10877936 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, 1281483 SNPs remain.
After merging with regression SNP LD, 1281483 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.1075 (0.0201)
Lambda GC: 1.0395
Mean Chi^2: 1.0412
Intercept: 0.9914 (0.0061)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Sun Feb 16 02:00:26 2020
Total time elapsed: 1.0m:45.14s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9533,
    "inflation_factor": 1.023,
    "mean_EFFECT": -0.0001,
    "n": 23332,
    "n_snps": 10877936,
    "n_clumped_hits": 4,
    "n_p_sig": 306,
    "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": 356865,
    "n_est": 23314.2604,
    "ratio_se_n": 0.9996,
    "mean_diff": 2.2554e-07,
    "ratio_diff": 0.0287,
    "sd_y_est1": 0.9132,
    "sd_y_est2": 0.9129,
    "r2_sum1": 0.0075,
    "r2_sum2": 0.0089,
    "r2_sum3": 0.0089,
    "r2_sum4": 0.0092,
    "ldsc_nsnp_merge_refpanel_ld": 1281483,
    "ldsc_nsnp_merge_regression_ld": 1281483,
    "ldsc_observed_scale_h2_beta": 0.1075,
    "ldsc_observed_scale_h2_se": 0.0201,
    "ldsc_intercept_beta": 0.9914,
    "ldsc_intercept_se": 0.0061,
    "ldsc_lambda_gc": 1.0395,
    "ldsc_mean_chisq": 1.0412,
    "ldsc_ratio": -0.2087
}

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 10877936 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 8.598638e+00 5.745666e+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 7.911383e+07 5.624931e+07 8.28000e+02 3.287294e+07 6.988889e+07 1.148258e+08 2.492251e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA -8.010000e-05 5.058300e-02 -6.83572e-01 -1.357820e-02 2.130000e-05 1.371770e-02 5.848440e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 3.653260e-02 3.481460e-02 8.30150e-03 1.032020e-02 1.981680e-02 5.293440e-02 2.134190e-01 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.961366e-01 2.892635e-01 0.00000e+00 2.447907e-01 4.951126e-01 7.463044e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.961297e-01 2.892676e-01 0.00000e+00 2.447795e-01 4.951065e-01 7.463023e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA 1.820865e-01 2.503832e-01 1.00090e-03 6.878700e-03 5.191440e-02 2.739880e-01 9.989990e-01 ▇▂▁▁▁
numeric AF_reference 356865 0.9671937 NA NA NA NA NA 1.877709e-01 2.439191e-01 0.00000e+00 5.191700e-03 7.468050e-02 2.873400e-01 1.000000e+00 ▇▂▁▁▁
numeric N 0 1.0000000 NA NA NA NA NA 2.333200e+04 0.000000e+00 2.33320e+04 2.333200e+04 2.333200e+04 2.333200e+04 2.333200e+04 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 693731 rs12238997 A G 0.0042189 0.0139392 0.7621457 0.7621440 0.1169260 0.1417730 23332
1 717587 rs144155419 G A -0.0152777 0.0368352 0.6783237 0.6783189 0.0143862 0.0045926 23332
1 730087 rs148120343 T C 0.0082015 0.0193352 0.6714428 0.6714370 0.0554733 0.0127796 23332
1 731718 rs142557973 T C 0.0094628 0.0131802 0.4727908 0.4727851 0.1216580 0.1543530 23332
1 734349 rs141242758 T C 0.0094931 0.0131913 0.4717512 0.4717433 0.1215130 0.1525560 23332
1 740284 rs61770167 C T -0.0306196 0.0615110 0.6186357 0.6186316 0.0058556 0.0023962 23332
1 742813 rs112573343 C T -0.0359422 0.1296830 0.7816638 0.7816616 0.0015525 0.1030350 23332
1 753405 rs3115860 C A -0.0018966 0.0125695 0.8800659 0.8800651 0.8706760 0.7517970 23332
1 753541 rs2073813 G A 0.0010800 0.0125812 0.9315891 0.9315886 0.1288330 0.3019170 23332
1 754182 rs3131969 A G -0.0018230 0.0125506 0.8845160 0.8845150 0.8703180 0.6785140 23332
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219387 rs9616832 T C 0.0395810 0.0162826 0.0150695 0.0150623 0.0729806 0.0654952 23332
22 51219704 rs147475742 G A 0.0496604 0.0217277 0.0222879 0.0222788 0.0418225 0.0473243 23332
22 51219766 rs182321900 C T 0.1278790 0.1370110 0.3506509 0.3506401 0.0014546 NA 23332
22 51220088 rs566371895 G A -0.0467287 0.0712592 0.5119871 0.5119807 0.0039843 0.0003994 23332
22 51220146 rs868950473 C T 0.0758701 0.1372910 0.5805264 0.5805219 0.0015139 NA 23332
22 51221731 rs115055839 T C 0.0392477 0.0162824 0.0159412 0.0159334 0.0728101 0.0625000 23332
22 51223637 rs375798137 G A -0.0136730 0.0192929 0.4785144 0.4785067 0.0540289 0.0788738 23332
22 51226692 rs150189434 G A 0.1112390 0.1411170 0.4305454 0.4305359 0.0013983 0.0155751 23332
22 51229805 rs9616985 T C 0.0382778 0.0163157 0.0189806 0.0189725 0.0727671 0.0730831 23332
22 51237063 rs3896457 T C -0.0020913 0.0098511 0.8318850 0.8318825 0.2968410 0.2050720 23332

bcf preview

1   693731  rs12238997  A   G   .   PASS    AF=0.116926 ES:SE:LP:AF:SS:ID   0.00421893:0.0139392:0.117962:0.116926:23332:rs12238997
1   717587  rs144155419 G   A   .   PASS    AF=0.0143862    ES:SE:LP:AF:SS:ID   -0.0152777:0.0368352:0.168563:0.0143862:23332:rs144155419
1   730087  rs148120343 T   C   .   PASS    AF=0.0554733    ES:SE:LP:AF:SS:ID   0.00820154:0.0193352:0.172991:0.0554733:23332:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.121658 ES:SE:LP:AF:SS:ID   0.00946279:0.0131802:0.325331:0.121658:23332:rs58276399
1   734349  rs141242758 T   C   .   PASS    AF=0.121513 ES:SE:LP:AF:SS:ID   0.00949306:0.0131913:0.326287:0.121513:23332:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.00585559   ES:SE:LP:AF:SS:ID   -0.0306196:0.061511:0.208565:0.00585559:23332:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.0015525    ES:SE:LP:AF:SS:ID   -0.0359422:0.129683:0.10698:0.0015525:23332:rs112573343
1   753405  rs3115860   C   A   .   PASS    AF=0.870676 ES:SE:LP:AF:SS:ID   -0.00189657:0.0125695:0.0554848:0.870676:23332:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.128833 ES:SE:LP:AF:SS:ID   0.00108005:0.0125812:0.0307756:0.128833:23332:rs2073813
1   754182  rs3131969   A   G   .   PASS    AF=0.870318 ES:SE:LP:AF:SS:ID   -0.00182295:0.0125506:0.0532943:0.870318:23332:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.870409 ES:SE:LP:AF:SS:ID   -0.00165134:0.0125528:0.048012:0.870409:23332:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.870037 ES:SE:LP:AF:SS:ID   -0.00182333:0.0125495:0.053311:0.870037:23332:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00465304   ES:SE:LP:AF:SS:ID   -0.0693201:0.0664634:0.52729:0.00465304:23332:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00464711   ES:SE:LP:AF:SS:ID   -0.0718109:0.0665823:0.551589:0.00464711:23332:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.00590897   ES:SE:LP:AF:SS:ID   -0.0754925:0.0624413:0.644608:0.00590897:23332:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.870329 ES:SE:LP:AF:SS:ID   -0.00121279:0.0125329:0.0348402:0.870329:23332:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.87002  ES:SE:LP:AF:SS:ID   -0.00240127:0.012505:0.071745:0.87002:23332:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.869421 ES:SE:LP:AF:SS:ID   -0.0010247:0.012487:0.0293752:0.869421:23332:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.870219 ES:SE:LP:AF:SS:ID   -0.00175984:0.0125184:0.0514878:0.870219:23332:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.870219 ES:SE:LP:AF:SS:ID   -0.00177881:0.0125193:0.0520695:0.870219:23332:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.870222 ES:SE:LP:AF:SS:ID   -0.00177001:0.0125193:0.0517979:0.870222:23332:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.870396 ES:SE:LP:AF:SS:ID   -0.00155104:0.0125469:0.0449774:0.870396:23332:rs3131954
1   761732  rs2286139   C   T   .   PASS    AF=0.867743 ES:SE:LP:AF:SS:ID   -0.00218435:0.0124873:0.0649267:0.867743:23332:rs2286139
1   766007  rs61768174  A   C   .   PASS    AF=0.106958 ES:SE:LP:AF:SS:ID   0.00980107:0.0139198:0.317518:0.106958:23332:rs61768174
1   768253  rs2977608   A   C   .   PASS    AF=0.763932 ES:SE:LP:AF:SS:ID   0.00416817:0.00993492:0.170812:0.763932:23332:rs2977608
1   768448  rs12562034  G   A   .   PASS    AF=0.105257 ES:SE:LP:AF:SS:ID   -0.00726048:0.0136382:0.225863:0.105257:23332:rs12562034
1   768819  rs12562811  C   T   .   PASS    AF=0.00692766   ES:SE:LP:AF:SS:ID   -0.0714511:0.0519851:0.771307:0.00692766:23332:rs12562811
1   769223  rs60320384  C   G   .   PASS    AF=0.128651 ES:SE:LP:AF:SS:ID   0.00105381:0.0125709:0.0300285:0.128651:23332:rs60320384
1   769224  rs141644775 G   A   .   PASS    AF=0.00143239   ES:SE:LP:AF:SS:ID   0.125967:0.115989:0.556773:0.00143239:23332:rs141644775
1   770181  rs146076599 A   G   .   PASS    AF=0.00907328   ES:SE:LP:AF:SS:ID   -0.0315956:0.0478024:0.293587:0.00907328:23332:rs146076599
1   770377  rs112563271 A   T   .   PASS    AF=0.00692321   ES:SE:LP:AF:SS:ID   -0.0720227:0.0521743:0.776066:0.00692321:23332:rs112563271
1   771823  rs2977605   T   C   .   PASS    AF=0.869934 ES:SE:LP:AF:SS:ID   -0.00217269:0.0125298:0.064323:0.869934:23332:rs2977605
1   771967  rs59066358  G   A   .   PASS    AF=0.128709 ES:SE:LP:AF:SS:ID   0.00128642:0.0125661:0.0369388:0.128709:23332:rs59066358
1   772755  rs2905039   A   C   .   PASS    AF=0.870112 ES:SE:LP:AF:SS:ID   -0.00280365:0.0125292:0.0846324:0.870112:23332:rs2905039
1   774736  rs28830877  A   C   .   PASS    AF=0.998797 ES:SE:LP:AF:SS:ID   -0.0790456:0.156149:0.212746:0.998797:23332:rs28830877
1   776556  rs151160018 C   T   .   PASS    AF=0.00851871   ES:SE:LP:AF:SS:ID   -0.007826:0.0482911:0.0598517:0.00851871:23332:rs151160018
1   777122  rs2980319   A   T   .   PASS    AF=0.871122 ES:SE:LP:AF:SS:ID   -0.00303633:0.0125674:0.0920032:0.871122:23332:rs2980319
1   777232  rs112618790 C   T   .   PASS    AF=0.0961319    ES:SE:LP:AF:SS:ID   -0.00107072:0.0144437:0.0264533:0.0961319:23332:rs112618790
1   778745  rs1055606   A   G   .   PASS    AF=0.127871 ES:SE:LP:AF:SS:ID   0.00224238:0.0125928:0.0661732:0.127871:23332:rs1055606
1   779322  rs4040617   A   G   .   PASS    AF=0.127984 ES:SE:LP:AF:SS:ID   0.00216453:0.0125758:0.0638156:0.127984:23332:rs4040617
1   780785  rs2977612   T   A   .   PASS    AF=0.870455 ES:SE:LP:AF:SS:ID   -0.00296285:0.0125431:0.0897658:0.870455:23332:rs2977612
1   781367  rs149821290 A   C   .   PASS    AF=0.00989772   ES:SE:LP:AF:SS:ID   -0.0656393:0.047568:0.775651:0.00989772:23332:rs149821290
1   781845  rs61768199  A   G   .   PASS    AF=0.1044   ES:SE:LP:AF:SS:ID   0.0101641:0.0141157:0.326519:0.1044:23332:rs61768199
1   782721  rs185280546 G   A   .   PASS    AF=0.0143713    ES:SE:LP:AF:SS:ID   0.00335726:0.0386017:0.0311926:0.0143713:23332:rs185280546
1   782981  rs6594026   C   T   .   PASS    AF=0.128338 ES:SE:LP:AF:SS:ID   0.00188195:0.0125766:0.054999:0.128338:23332:rs6594026
1   783193  rs145767270 G   C   .   PASS    AF=0.0143639    ES:SE:LP:AF:SS:ID   0.00268732:0.0387927:0.024673:0.0143639:23332:rs145767270
1   783194  rs138555831 G   T   .   PASS    AF=0.0143713    ES:SE:LP:AF:SS:ID   0.00245606:0.0387798:0.0225041:0.0143713:23332:rs138555831
1   783711  rs184266993 G   A   .   PASS    AF=0.00541372   ES:SE:LP:AF:SS:ID   -0.03335:0.0608666:0.233771:0.00541372:23332:rs184266993
1   785050  rs2905062   G   A   .   PASS    AF=0.87014  ES:SE:LP:AF:SS:ID   -0.0016527:0.0125357:0.0481226:0.87014:23332:rs2905062
1   785989  rs2980300   T   C   .   PASS    AF=0.87009  ES:SE:LP:AF:SS:ID   -0.000935263:0.0125449:0.0266084:0.87009:23332:rs2980300