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/public/UKB-b-7356/UKB-b-7356_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-7356/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:40:18 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-7356/UKB-b-7356_data.vcf.gz ...
Read summary statistics for 5482332 SNPs.
Dropped 1991 SNPs with duplicated rs numbers.
Reading reference panel LD Score from ../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 ../reference/eur_w_ld_chr/[1-22] ...
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 1171337 SNPs remain.
After merging with regression SNP LD, 1171337 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 6.3871e-05 (0.001)
Lambda GC: 1.0183
Mean Chi^2: 1.0134
Intercept: 1.0128 (0.0068)
Ratio: 0.9546 (0.5077)
Analysis finished at Thu Oct 17 14:41:17 2019
Total time elapsed: 59.16s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9163,
    "inflation_factor": 1,
    "mean_EFFECT": 6.0262e-07,
    "n": "-Inf",
    "n_snps": 9851866,
    "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": 48004,
    "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": 1171337,
    "ldsc_nsnp_merge_regression_ld": 1171337,
    "ldsc_observed_scale_h2_beta": "NA",
    "ldsc_observed_scale_h2_se": "NA",
    "ldsc_intercept_beta": 1.0128,
    "ldsc_intercept_se": 0.0068,
    "ldsc_lambda_gc": 1.0183,
    "ldsc_mean_chisq": 1.0134,
    "ldsc_ratio": 0.9552
}
 

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 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 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 5480357 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 5482332 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 8.672223e+00 5.762919e+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 7.857363e+07 5.656537e+07 828.0000000 3.197516e+07 6.899417e+07 1.145144e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 6.000000e-07 3.714000e-04 -0.0028367 -2.292000e-04 9.000000e-07 2.308000e-04 2.756800e-03 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 3.558000e-04 9.760000e-05 0.0002506 2.742000e-04 3.194000e-04 4.152000e-04 1.184700e-03 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.972892e-01 2.892694e-01 0.0000011 2.500000e-01 5.000000e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.972858e-01 2.892415e-01 0.0000011 2.461057e-01 4.966515e-01 7.477514e-01 9.999998e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 3.321096e-01 2.468575e-01 0.0482560 1.210110e-01 2.585340e-01 4.989362e-01 9.517430e-01 ▇▃▂▂▂
numeric AF_reference 48004 0.9912439 NA NA NA NA NA NA NA 3.271861e-01 2.414502e-01 0.0000000 1.283950e-01 2.633790e-01 4.888180e-01 1.000000e+00 ▇▆▃▂▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 49298 rs200943160 T C -0.0001373 0.0004613 0.7700005 0.7659497 0.623799 0.7821490 NA
1 54676 rs2462492 C T 0.0000014 0.0004569 1.0000000 0.9976043 0.400435 NA NA
1 86028 rs114608975 T C 0.0005208 0.0007307 0.4799997 0.4759678 0.103546 0.0277556 NA
1 91536 rs6702460 G T -0.0006963 0.0004500 0.1199999 0.1217510 0.456844 0.4207270 NA
1 234313 rs8179466 C T -0.0012314 0.0008872 0.1700000 0.1651569 0.074511 NA NA
1 534192 rs6680723 C T -0.0001389 0.0005140 0.7899998 0.7868980 0.240982 NA NA
1 546697 rs12025928 A G -0.0004274 0.0006413 0.5099998 0.5050907 0.913490 NA NA
1 693731 rs12238997 A G -0.0003397 0.0004306 0.4299995 0.4301095 0.116360 0.1417730 NA
1 705882 rs72631875 G A -0.0000039 0.0006313 1.0000000 0.9950465 0.067257 0.0315495 NA
1 706368 rs55727773 A G 0.0003844 0.0003191 0.2300001 0.2283332 0.515625 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51218224 rs9616975 C A -0.0007147 0.0004993 0.1499999 0.1523344 0.073301 0.0619010 NA
22 51218377 rs2519461 G C -0.0007026 0.0004987 0.1600000 0.1588619 0.073587 0.0826677 NA
22 51219006 rs28729663 G A -0.0004086 0.0003849 0.2900000 0.2885132 0.137940 0.2052720 NA
22 51219387 rs9616832 T C -0.0007390 0.0004997 0.1400000 0.1391413 0.073713 0.0654952 NA
22 51221190 rs369304721 G A -0.0006065 0.0006685 0.3599996 0.3643050 0.049706 NA NA
22 51221731 rs115055839 T C -0.0007201 0.0005000 0.1499999 0.1498087 0.073202 0.0625000 NA
22 51222100 rs114553188 G T 0.0000987 0.0005884 0.8700001 0.8667582 0.054476 0.0880591 NA
22 51223637 rs375798137 G A 0.0000702 0.0005913 0.9100000 0.9054902 0.054106 0.0788738 NA
22 51229805 rs9616985 T C -0.0007434 0.0005018 0.1400000 0.1384897 0.073038 0.0730831 NA
22 51237063 rs3896457 T C 0.0002695 0.0003069 0.3800004 0.3798243 0.297924 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623799 ES:SE:LP:AF:ID  -0.000137308:0.000461265:0.113509:0.623799:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400435 ES:SE:LP:AF:ID  1.37194e-06:0.000456928:-0:0.400435:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103546 ES:SE:LP:AF:ID  0.00052082:0.000730665:0.318759:0.103546:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456844 ES:SE:LP:AF:ID  -0.00069629:0.000449955:0.920819:0.456844:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074511 ES:SE:LP:AF:ID  -0.00123138:0.000887203:0.769551:0.074511:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240982 ES:SE:LP:AF:ID  -0.000138949:0.000513977:0.102373:0.240982:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.91349  ES:SE:LP:AF:ID  -0.000427414:0.00064128:0.29243:0.91349:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.11636  ES:SE:LP:AF:ID  -0.000339735:0.000430587:0.366532:0.11636:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067257 ES:SE:LP:AF:ID  -3.91916e-06:0.000631271:-0:0.067257:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515625 ES:SE:LP:AF:ID  0.000384372:0.00031907:0.638272:0.515625:rs12029736
1   722670  rs116030099 T   C   .   PASS    AF=0.101202 ES:SE:LP:AF:ID  -0.000711833:0.000526383:0.744727:0.101202:rs116030099
1   725060  rs865924913 A   T   .   PASS    AF=0.053274 ES:SE:LP:AF:ID  -0.001585:0.00100576:0.920819:0.053274:rs865924913
1   729679  rs4951859   C   G   .   PASS    AF=0.8432   ES:SE:LP:AF:ID  1.63526e-05:0.000373213:0.0132283:0.8432:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055952 ES:SE:LP:AF:ID  -0.00102207:0.00060411:1.04096:0.055952:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122336 ES:SE:LP:AF:ID  -0.000423136:0.00040846:0.522879:0.122336:rs58276399
1   734349  rs141242758 T   C   .   PASS    AF=0.12158  ES:SE:LP:AF:ID  -0.00037513:0.000408632:0.443698:0.12158:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132365 ES:SE:LP:AF:ID  -0.000100359:0.00040277:0.09691:0.132365:rs79010578
1   752566  rs3094315   G   A   .   PASS    AF=0.838931 ES:SE:LP:AF:ID  0.000126656:0.000361441:0.136677:0.838931:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838565 ES:SE:LP:AF:ID  0.000107969:0.000361056:0.119186:0.838565:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869755 ES:SE:LP:AF:ID  0.000448219:0.000387413:0.60206:0.869755:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129891 ES:SE:LP:AF:ID  -0.000406057:0.000388206:0.522879:0.129891:rs2073813
1   754182  rs3131969   A   G   .   PASS    AF=0.869101 ES:SE:LP:AF:ID  0.000423465:0.000386659:0.568636:0.869101:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869199 ES:SE:LP:AF:ID  0.000411469:0.000386812:0.537602:0.869199:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.869104 ES:SE:LP:AF:ID  0.00041155:0.000386651:0.537602:0.869104:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.838014 ES:SE:LP:AF:ID  7.76486e-05:0.000360046:0.0809219:0.838014:rs3115859
1   754964  rs3131966   C   T   .   PASS    AF=0.838645 ES:SE:LP:AF:ID  8.61416e-05:0.000361059:0.091515:0.838645:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.839752 ES:SE:LP:AF:ID  0.000138415:0.000365939:0.148742:0.839752:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.869378 ES:SE:LP:AF:ID  0.000433532:0.000386199:0.585027:0.869378:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.868926 ES:SE:LP:AF:ID  0.000404918:0.000385227:0.537602:0.868926:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867883 ES:SE:LP:AF:ID  0.000389083:0.00038449:0.508638:0.867883:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869069 ES:SE:LP:AF:ID  0.000424533:0.000385543:0.568636:0.869069:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869078 ES:SE:LP:AF:ID  0.000424457:0.000385572:0.568636:0.869078:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869086 ES:SE:LP:AF:ID  0.000423029:0.000385581:0.568636:0.869086:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869562 ES:SE:LP:AF:ID  0.000423721:0.00038664:0.568636:0.869562:rs3131954
1   760912  rs1048488   C   T   .   PASS    AF=0.838291 ES:SE:LP:AF:ID  8.40615e-05:0.000359368:0.0861861:0.838291:rs1048488
1   761147  rs3115850   T   C   .   PASS    AF=0.838411 ES:SE:LP:AF:ID  8.17159e-05:0.000359621:0.0861861:0.838411:rs3115850
1   761732  rs2286139   C   T   .   PASS    AF=0.862244 ES:SE:LP:AF:ID  0.000317752:0.000384197:0.387216:0.862244:rs2286139
1   763394  rs3115847   G   A   .   PASS    AF=0.706721 ES:SE:LP:AF:ID  0.000337735:0.000374059:0.431798:0.706721:rs3115847
1   766007  rs61768174  A   C   .   PASS    AF=0.105193 ES:SE:LP:AF:ID  -0.000457067:0.000430775:0.537602:0.105193:rs61768174
1   768253  rs2977608   A   C   .   PASS    AF=0.761308 ES:SE:LP:AF:ID  0.000227342:0.000305317:0.337242:0.761308:rs2977608
1   768448  rs12562034  G   A   .   PASS    AF=0.106464 ES:SE:LP:AF:ID  2.18383e-05:0.000420865:0.0177288:0.106464:rs12562034
1   769223  rs60320384  C   G   .   PASS    AF=0.129599 ES:SE:LP:AF:ID  -0.000376103:0.000387969:0.481486:0.129599:rs60320384
1   771823  rs2977605   T   C   .   PASS    AF=0.868887 ES:SE:LP:AF:ID  0.000425529:0.000385901:0.568636:0.868887:rs2977605
1   771967  rs59066358  G   A   .   PASS    AF=0.1297   ES:SE:LP:AF:ID  -0.000397228:0.000387719:0.508638:0.1297:rs59066358
1   772755  rs2905039   A   C   .   PASS    AF=0.868897 ES:SE:LP:AF:ID  0.000431245:0.000385908:0.585027:0.868897:rs2905039
1   776546  rs12124819  A   G   .   PASS    AF=0.265395 ES:SE:LP:AF:ID  0.000207666:0.00034102:0.267606:0.265395:rs12124819
1   777122  rs2980319   A   T   .   PASS    AF=0.870023 ES:SE:LP:AF:ID  0.000434691:0.000386699:0.585027:0.870023:rs2980319
1   777232  rs112618790 C   T   .   PASS    AF=0.095133 ES:SE:LP:AF:ID  0.000163741:0.000448288:0.148742:0.095133:rs112618790
1   778745  rs1055606   A   G   .   PASS    AF=0.128595 ES:SE:LP:AF:ID  -0.000401597:0.000388222:0.522879:0.128595:rs1055606
1   779322  rs4040617   A   G   .   PASS    AF=0.128892 ES:SE:LP:AF:ID  -0.000405646:0.000387565:0.522879:0.128892:rs4040617