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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    "gwas_harmonisation_command": "--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/vcf_09_19b/bgzip_vcf/data.batch_20159.vcf.gz --id UKB-b:15625 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20159.txt.gz --cohort_controls 113106 --ref /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/human_g1k_v37.fasta --json /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/ukb_gwas.json; 1.1.1",
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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-15625/UKB-b-15625_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-15625/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:44:14 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-15625/UKB-b-15625_data.vcf.gz ...
Read summary statistics for 9112835 SNPs.
Dropped 9231 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, 1287453 SNPs remain.
After merging with regression SNP LD, 1287453 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.1031 (0.0064)
Lambda GC: 1.2206
Mean Chi^2: 1.2545
Intercept: 1.0241 (0.0073)
Ratio: 0.0946 (0.0285)
Analysis finished at Thu Oct 17 14:45:55 2019
Total time elapsed: 1.0m:41.09s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.948,
    "inflation_factor": 1.1474,
    "mean_EFFECT": -0.0001,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 3,
    "n_p_sig": 102,
    "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": 98844,
    "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": 1287453,
    "ldsc_nsnp_merge_regression_ld": 1287453,
    "ldsc_observed_scale_h2_beta": 0.1031,
    "ldsc_observed_scale_h2_se": 0.0064,
    "ldsc_intercept_beta": 1.0241,
    "ldsc_intercept_se": 0.0073,
    "ldsc_lambda_gc": 1.2206,
    "ldsc_mean_chisq": 1.2545,
    "ldsc_ratio": 0.0947
}

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 58 0 9103647 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 9112835 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 8.640380e+00 5.756823e+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 7.879320e+07 5.632621e+07 828.0000000 3.245601e+07 6.936318e+07 1.145266e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -8.880000e-05 1.473040e-02 -0.1707350 -5.955600e-03 -6.910000e-05 5.812100e-03 1.448010e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.137870e-02 8.548400e-03 0.0040249 4.811200e-03 7.449300e-03 1.561940e-02 9.084020e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.747698e-01 2.954625e-01 0.0000000 2.099999e-01 4.700002e-01 7.300002e-01 1.000000e+00 ▇▆▆▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.747711e-01 2.954372e-01 0.0000000 2.122062e-01 4.664682e-01 7.303898e-01 1.000000e+00 ▇▇▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.178328e-01 2.582729e-01 0.0030950 1.935300e-02 9.819700e-02 3.431680e-01 9.969050e-01 ▇▂▁▁▁
numeric AF_reference 98844 0.9891533 NA NA NA NA NA NA NA 2.183346e-01 2.501390e-01 0.0000000 1.657350e-02 1.160140e-01 3.418530e-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 0.0095432 0.0073832 0.2000000 0.1961617 0.623784 0.7821490 NA
1 54676 rs2462492 C T -0.0086650 0.0073243 0.2399999 0.2367947 0.399419 NA NA
1 86028 rs114608975 T C -0.0032152 0.0117435 0.7800007 0.7842495 0.103377 0.0277556 NA
1 91536 rs6702460 G T -0.0055606 0.0072129 0.4400003 0.4407451 0.456405 0.4207270 NA
1 234313 rs8179466 C T 0.0518851 0.0143283 0.0002900 0.0002933 0.074082 NA NA
1 534192 rs6680723 C T 0.0079775 0.0082260 0.3300000 0.3321502 0.241026 NA NA
1 546697 rs12025928 A G 0.0111425 0.0102490 0.2800000 0.2769576 0.913000 NA NA
1 693731 rs12238997 A G -0.0018898 0.0068946 0.7800007 0.7840102 0.117151 0.1417730 NA
1 705882 rs72631875 G A -0.0008071 0.0100550 0.9400001 0.9360276 0.067813 0.0315495 NA
1 706368 rs55727773 A G -0.0011581 0.0051166 0.8200001 0.8209339 0.515935 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0010949 0.0062101 0.8600001 0.8600488 0.136566 0.2052720 NA
22 51219387 rs9616832 T C 0.0035444 0.0080664 0.6600001 0.6603703 0.072760 0.0654952 NA
22 51219704 rs147475742 G A 0.0007848 0.0108515 0.9400001 0.9423448 0.041188 0.0473243 NA
22 51221190 rs369304721 G A 0.0079585 0.0107912 0.4600002 0.4608178 0.049189 NA NA
22 51221731 rs115055839 T C 0.0037121 0.0080739 0.6499995 0.6456869 0.072202 0.0625000 NA
22 51222100 rs114553188 G T -0.0048561 0.0094833 0.6100002 0.6086060 0.054219 0.0880591 NA
22 51223637 rs375798137 G A -0.0039534 0.0095315 0.6800001 0.6783109 0.053853 0.0788738 NA
22 51229805 rs9616985 T C 0.0036800 0.0081030 0.6499995 0.6497167 0.072029 0.0730831 NA
22 51232488 rs376461333 A G -0.0098585 0.0191647 0.6100002 0.6069651 0.019780 NA NA
22 51237063 rs3896457 T C -0.0083210 0.0049096 0.0899995 0.0901004 0.298491 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623784 ES:SE:LP:AF:ID  0.00954321:0.00738316:0.69897:0.623784:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399419 ES:SE:LP:AF:ID  -0.00866495:0.00732433:0.619789:0.399419:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103377 ES:SE:LP:AF:ID  -0.0032152:0.0117435:0.107905:0.103377:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456405 ES:SE:LP:AF:ID  -0.00556065:0.00721286:0.356547:0.456405:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074082 ES:SE:LP:AF:ID  0.0518851:0.0143283:3.5376:0.074082:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241026 ES:SE:LP:AF:ID  0.00797749:0.00822598:0.481486:0.241026:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913    ES:SE:LP:AF:ID  0.0111425:0.010249:0.552842:0.913:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117151 ES:SE:LP:AF:ID  -0.00188978:0.00689457:0.107905:0.117151:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067813 ES:SE:LP:AF:ID  -0.000807051:0.010055:0.0268721:0.067813:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515935 ES:SE:LP:AF:ID  -0.0011581:0.00511655:0.0861861:0.515935:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.03278  ES:SE:LP:AF:ID  -0.010701:0.0129552:0.387216:0.03278:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036374 ES:SE:LP:AF:ID  -0.00432417:0.0117641:0.148742:0.036374:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036491 ES:SE:LP:AF:ID  -0.00449917:0.0117178:0.154902:0.036491:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036187 ES:SE:LP:AF:ID  -0.00538116:0.0118043:0.187087:0.036187:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016694 ES:SE:LP:AF:ID  0.0209853:0.0179366:0.619789:0.016694:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036722 ES:SE:LP:AF:ID  -0.00395846:0.0116723:0.136677:0.036722:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.036836 ES:SE:LP:AF:ID  -0.00400586:0.0116316:0.136677:0.036836:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101768 ES:SE:LP:AF:ID  0.0119973:0.00842195:0.823909:0.101768:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959502 ES:SE:LP:AF:ID  0.00603432:0.0112355:0.229148:0.959502:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031205 ES:SE:LP:AF:ID  -0.0114794:0.0205477:0.236572:0.031205:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053329 ES:SE:LP:AF:ID  0.022649:0.0161139:0.79588:0.053329:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036327 ES:SE:LP:AF:ID  -0.00512327:0.0117061:0.180456:0.036327:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036636 ES:SE:LP:AF:ID  -0.0039383:0.011601:0.136677:0.036636:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.842942 ES:SE:LP:AF:ID  0.00177431:0.00599123:0.113509:0.842942:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055982 ES:SE:LP:AF:ID  -0.00370479:0.00969868:0.154902:0.055982:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123085 ES:SE:LP:AF:ID  -0.00136779:0.00654729:0.0809219:0.123085:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.024787 ES:SE:LP:AF:ID  -0.00277401:0.0164125:0.0604807:0.024787:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122313 ES:SE:LP:AF:ID  -0.00162502:0.00655099:0.09691:0.122313:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132531 ES:SE:LP:AF:ID  -0.00462867:0.00646149:0.327902:0.132531:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011071 ES:SE:LP:AF:ID  -0.0429641:0.0236236:1.16115:0.011071:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005649 ES:SE:LP:AF:ID  0.0179052:0.0305634:0.251812:0.005649:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.036548 ES:SE:LP:AF:ID  -0.00602628:0.0114834:0.221849:0.036548:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838665 ES:SE:LP:AF:ID  0.00445189:0.00580228:0.356547:0.838665:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838311 ES:SE:LP:AF:ID  0.00428826:0.00579642:0.337242:0.838311:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869087 ES:SE:LP:AF:ID  0.00418737:0.00621075:0.30103:0.869087:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130574 ES:SE:LP:AF:ID  -0.00385409:0.00622403:0.267606:0.130574:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037053 ES:SE:LP:AF:ID  -0.00786911:0.0112877:0.309804:0.037053:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037298 ES:SE:LP:AF:ID  -0.00701314:0.0112143:0.275724:0.037298:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868426 ES:SE:LP:AF:ID  0.00381184:0.00619914:0.267606:0.868426:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868523 ES:SE:LP:AF:ID  0.0040137:0.0062021:0.283997:0.868523:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037258 ES:SE:LP:AF:ID  -0.00696449:0.011265:0.267606:0.037258:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.868434 ES:SE:LP:AF:ID  0.00386919:0.00619902:0.275724:0.868434:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005155 ES:SE:LP:AF:ID  -0.016657:0.0317579:0.221849:0.005155:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005126 ES:SE:LP:AF:ID  -0.0156871:0.0318129:0.207608:0.005126:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.837739 ES:SE:LP:AF:ID  0.00427599:0.00578019:0.337242:0.837739:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037296 ES:SE:LP:AF:ID  -0.00686403:0.011278:0.267606:0.037296:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838335 ES:SE:LP:AF:ID  0.00444493:0.00579565:0.356547:0.838335:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013583 ES:SE:LP:AF:ID  -0.00112446:0.020362:0.0177288:0.013583:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005501 ES:SE:LP:AF:ID  0.0122181:0.0312284:0.154902:0.005501:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.839456 ES:SE:LP:AF:ID  0.00472088:0.00587245:0.376751:0.839456:rs3131965