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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    "FORMAT.1": "<ID=SE,Number=A,Type=Float,Description=\"Standard error of effect size estimate\">",
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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_3436.vcf.gz --id UKB-b:19253 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_3436.txt.gz --cohort_controls 35702 --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-19253/UKB-b-19253_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19253/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:41:06 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19253/UKB-b-19253_data.vcf.gz ...
Read summary statistics for 7763569 SNPs.
Dropped 5662 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, 1280132 SNPs remain.
After merging with regression SNP LD, 1280132 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.1005 (0.0135)
Lambda GC: 1.0777
Mean Chi^2: 1.0748
Intercept: 1.0043 (0.0066)
Ratio: 0.057 (0.0883)
Analysis finished at Thu Oct 17 14:42:29 2019
Total time elapsed: 1.0m:23.68s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9417,
    "inflation_factor": 1.0475,
    "mean_EFFECT": -0,
    "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": 72330,
    "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": 1280132,
    "ldsc_nsnp_merge_regression_ld": 1280132,
    "ldsc_observed_scale_h2_beta": 0.1005,
    "ldsc_observed_scale_h2_se": 0.0135,
    "ldsc_intercept_beta": 1.0043,
    "ldsc_intercept_se": 0.0066,
    "ldsc_lambda_gc": 1.0777,
    "ldsc_mean_chisq": 1.0748,
    "ldsc_ratio": 0.0575
}

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 7757933 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 7763569 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 8.662073e+00 5.764359e+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 7.868372e+07 5.643348e+07 828.0000000 3.221557e+07 6.913954e+07 1.145564e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -2.690000e-05 1.736000e-02 -0.1687740 -8.453300e-03 -2.960000e-05 8.391000e-03 1.758440e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.481480e-02 8.445900e-03 0.0070315 8.145400e-03 1.111860e-02 1.929280e-02 8.655290e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.893891e-01 2.914403e-01 0.0000005 2.300001e-01 4.899999e-01 7.400005e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.893902e-01 2.914135e-01 0.0000005 2.343005e-01 4.857181e-01 7.416431e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.522507e-01 2.608114e-01 0.0098040 4.018400e-02 1.461870e-01 3.989940e-01 9.901960e-01 ▇▂▂▁▁
numeric AF_reference 72330 0.9906834 NA NA NA NA NA NA NA 2.513530e-01 2.526784e-01 0.0000000 4.373000e-02 1.607430e-01 3.935700e-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 -0.0125986 0.0130474 0.3300000 0.3342432 0.622737 0.7821490 NA
1 54676 rs2462492 C T 0.0077233 0.0128943 0.5500004 0.5491918 0.399845 NA NA
1 86028 rs114608975 T C 0.0143519 0.0207691 0.4899999 0.4895519 0.103310 0.0277556 NA
1 91536 rs6702460 G T 0.0114493 0.0127465 0.3700002 0.3690625 0.456458 0.4207270 NA
1 234313 rs8179466 C T -0.0151008 0.0251320 0.5500004 0.5479336 0.074208 NA NA
1 534192 rs6680723 C T 0.0047969 0.0143447 0.7400005 0.7380766 0.244019 NA NA
1 546697 rs12025928 A G 0.0385094 0.0184088 0.0359998 0.0364473 0.914804 NA NA
1 693731 rs12238997 A G 0.0082908 0.0121212 0.4899999 0.4939794 0.116440 0.1417730 NA
1 705882 rs72631875 G A -0.0049947 0.0180134 0.7800007 0.7815692 0.066360 0.0315495 NA
1 706368 rs55727773 A G 0.0102742 0.0090133 0.2500000 0.2543302 0.513083 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0105060 0.0109834 0.3400001 0.3388023 0.135305 0.2052720 NA
22 51219387 rs9616832 T C -0.0023321 0.0142632 0.8700001 0.8701205 0.071772 0.0654952 NA
22 51219704 rs147475742 G A 0.0048558 0.0190725 0.8000000 0.7990328 0.041023 0.0473243 NA
22 51221190 rs369304721 G A -0.0060420 0.0191023 0.7499995 0.7517787 0.048244 NA NA
22 51221731 rs115055839 T C -0.0027868 0.0142681 0.8499999 0.8451449 0.071366 0.0625000 NA
22 51222100 rs114553188 G T -0.0142034 0.0168010 0.4000000 0.3978930 0.053774 0.0880591 NA
22 51223637 rs375798137 G A -0.0149044 0.0168787 0.3800004 0.3772200 0.053390 0.0788738 NA
22 51229805 rs9616985 T C -0.0046508 0.0143066 0.7499995 0.7451210 0.071239 0.0730831 NA
22 51232488 rs376461333 A G -0.0076040 0.0343511 0.8200001 0.8248101 0.019258 NA NA
22 51237063 rs3896457 T C -0.0067831 0.0086592 0.4299995 0.4334269 0.297395 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.622737 ES:SE:LP:AF:ID  -0.0125986:0.0130474:0.481486:0.622737:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399845 ES:SE:LP:AF:ID  0.00772332:0.0128943:0.259637:0.399845:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.10331  ES:SE:LP:AF:ID  0.0143519:0.0207691:0.309804:0.10331:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456458 ES:SE:LP:AF:ID  0.0114493:0.0127465:0.431798:0.456458:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074208 ES:SE:LP:AF:ID  -0.0151008:0.025132:0.259637:0.074208:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.244019 ES:SE:LP:AF:ID  0.00479689:0.0143447:0.130768:0.244019:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.914804 ES:SE:LP:AF:ID  0.0385094:0.0184088:1.4437:0.914804:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.11644  ES:SE:LP:AF:ID  0.00829082:0.0121212:0.309804:0.11644:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.06636  ES:SE:LP:AF:ID  -0.00499466:0.0180134:0.107905:0.06636:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.513083 ES:SE:LP:AF:ID  0.0102742:0.00901329:0.60206:0.513083:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033636 ES:SE:LP:AF:ID  -0.0038128:0.0225089:0.0604807:0.033636:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037305 ES:SE:LP:AF:ID  0.00292851:0.0204297:0.05061:0.037305:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037365 ES:SE:LP:AF:ID  0.00229271:0.020376:0.0409586:0.037365:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037096 ES:SE:LP:AF:ID  0.00398209:0.0205066:0.0705811:0.037096:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016615 ES:SE:LP:AF:ID  0.0422674:0.0316018:0.744727:0.016615:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037667 ES:SE:LP:AF:ID  -0.000207481:0.0202737:0.00436481:0.037667:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.03778  ES:SE:LP:AF:ID  -0.000861218:0.020191:0.0132283:0.03778:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.099591 ES:SE:LP:AF:ID  -0.000397161:0.0149701:0.00877392:0.099591:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958151 ES:SE:LP:AF:ID  0.00236637:0.0193993:0.0457575:0.958151:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.03081  ES:SE:LP:AF:ID  -0.0506276:0.0367075:0.769551:0.03081:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053131 ES:SE:LP:AF:ID  0.0792991:0.0286132:2.25181:0.053131:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037343 ES:SE:LP:AF:ID  0.00260254:0.0203259:0.0457575:0.037343:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037576 ES:SE:LP:AF:ID  0.00356678:0.0201672:0.0655015:0.037576:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841864 ES:SE:LP:AF:ID  -0.000994627:0.0104595:0.0362122:0.841864:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.057213 ES:SE:LP:AF:ID  -0.0123732:0.0168969:0.337242:0.057213:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.12323  ES:SE:LP:AF:ID  0.00105054:0.0114545:0.0315171:0.12323:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025818 ES:SE:LP:AF:ID  0.0106591:0.0284782:0.148742:0.025818:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122412 ES:SE:LP:AF:ID  0.00207699:0.0114614:0.0655015:0.122412:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.133077 ES:SE:LP:AF:ID  -0.00248933:0.0113312:0.0809219:0.133077:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011699 ES:SE:LP:AF:ID  0.0213649:0.0405496:0.221849:0.011699:rs181876450
1   752478  rs146277091 G   A   .   PASS    AF=0.03762  ES:SE:LP:AF:ID  0.00253443:0.0199307:0.0457575:0.03762:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837831 ES:SE:LP:AF:ID  -0.00398035:0.0101523:0.154902:0.837831:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.837423 ES:SE:LP:AF:ID  -0.00303682:0.010137:0.119186:0.837423:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.86896  ES:SE:LP:AF:ID  -0.00193467:0.0108907:0.0655015:0.86896:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130897 ES:SE:LP:AF:ID  0.000931666:0.0109015:0.0315171:0.130897:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.038061 ES:SE:LP:AF:ID  0.00736612:0.0196149:0.148742:0.038061:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038305 ES:SE:LP:AF:ID  0.00779507:0.0195006:0.161151:0.038305:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868336 ES:SE:LP:AF:ID  -0.000624924:0.0108677:0.0222764:0.868336:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868425 ES:SE:LP:AF:ID  -0.000295709:0.0108705:0.00877392:0.868425:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038268 ES:SE:LP:AF:ID  0.00730446:0.0195693:0.148742:0.038268:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.868349 ES:SE:LP:AF:ID  -0.000767531:0.0108672:0.0268721:0.868349:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.836911 ES:SE:LP:AF:ID  -0.00369933:0.0101141:0.148742:0.836911:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038276 ES:SE:LP:AF:ID  0.00669718:0.0195981:0.136677:0.038276:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.837575 ES:SE:LP:AF:ID  -0.0030155:0.0101436:0.113509:0.837575:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013815 ES:SE:LP:AF:ID  0.00042909:0.0357042:0.00436481:0.013815:rs181660517
1   755775  rs3131965   A   G   .   PASS    AF=0.83879  ES:SE:LP:AF:ID  -0.00291028:0.0102817:0.107905:0.83879:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.868575 ES:SE:LP:AF:ID  -0.00208068:0.0108553:0.0705811:0.868575:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.868095 ES:SE:LP:AF:ID  -0.00135779:0.0108291:0.0457575:0.868095:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867038 ES:SE:LP:AF:ID  -0.000878077:0.0108098:0.0268721:0.867038:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.868301 ES:SE:LP:AF:ID  -0.00163003:0.0108393:0.0555173:0.868301:rs4951929