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_2926.vcf.gz --id UKB-b:4801 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_2926.txt.gz --cohort_controls 104420 --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-4801/UKB-b-4801_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4801/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:44:41 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4801/UKB-b-4801_data.vcf.gz ...
Read summary statistics for 9050748 SNPs.
Dropped 8947 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, 1287279 SNPs remain.
After merging with regression SNP LD, 1287279 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.047 (0.006)
Lambda GC: 1.1107
Mean Chi^2: 1.1189
Intercept: 1.0229 (0.0074)
Ratio: 0.1927 (0.0619)
Analysis finished at Thu Oct 17 14:46:20 2019
Total time elapsed: 1.0m:39.4s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9478,
    "inflation_factor": 1.0966,
    "mean_EFFECT": 0.0001,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 3,
    "n_p_sig": 509,
    "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": 95748,
    "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": 1287279,
    "ldsc_nsnp_merge_regression_ld": 1287279,
    "ldsc_observed_scale_h2_beta": 0.047,
    "ldsc_observed_scale_h2_se": 0.006,
    "ldsc_intercept_beta": 1.0229,
    "ldsc_intercept_se": 0.0074,
    "ldsc_lambda_gc": 1.1107,
    "ldsc_mean_chisq": 1.1189,
    "ldsc_ratio": 0.1926
}

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 1.000000 3 58 0 9041842 0 NA NA NA NA NA NA NA NA NA NA
character REF 0 1.000000 1 1 0 4 0 NA NA NA NA NA NA NA NA NA NA
character ALT 0 1.000000 1 1 0 4 0 NA NA NA NA NA NA NA NA NA NA
logical N 9050748 0.000000 NA NA NA NA NA NaN : NA NA NA NA NA NA NA NA
numeric CHROM 0 1.000000 NA NA NA NA NA NA NA 8.642429e+00 5.757686e+00 1.0000000 4.000000e+00 8.000000e+00 1.300000e+01 2.200000e+01 ▇▅▃▂▂
numeric POS 0 1.000000 NA NA NA NA NA NA NA 7.879040e+07 5.633131e+07 828.0000000 3.244281e+07 6.936216e+07 1.145311e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.000000 NA NA NA NA NA NA NA 5.640000e-05 1.171120e-02 -0.1361200 -4.570700e-03 4.340000e-05 4.653400e-03 1.293860e-01 ▁▁▇▁▁
numeric SE 0 1.000000 NA NA NA NA NA NA NA 9.219700e-03 6.829200e-03 0.0033071 3.952900e-03 6.082800e-03 1.264880e-02 7.514280e-02 ▇▂▁▁▁
numeric PVAL 0 1.000000 NA NA NA NA NA NA NA 4.861101e-01 2.926549e-01 0.0000000 2.300001e-01 4.799997e-01 7.400005e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.000000 NA NA NA NA NA NA NA 4.861103e-01 2.926280e-01 0.0000000 2.292790e-01 4.814588e-01 7.397965e-01 9.999998e-01 ▇▇▇▇▇
numeric AF 0 1.000000 NA NA NA NA NA NA NA 2.192068e-01 2.584427e-01 0.0033520 2.000300e-02 1.001070e-01 3.456232e-01 9.966480e-01 ▇▂▁▁▁
numeric AF_reference 95748 0.989421 NA NA NA NA NA NA NA 2.195706e-01 2.503276e-01 0.0000000 1.717250e-02 1.176120e-01 3.440500e-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 -0.0049355 0.0060969 0.4199997 0.4182175 0.624733 0.7821490 NA
1 54676 rs2462492 C T 0.0059757 0.0060274 0.3200000 0.3214825 0.400864 NA NA
1 86028 rs114608975 T C 0.0097736 0.0096173 0.3100002 0.3095079 0.103698 0.0277556 NA
1 91536 rs6702460 G T 0.0056054 0.0059337 0.3400001 0.3448245 0.457737 0.4207270 NA
1 234313 rs8179466 C T 0.0135880 0.0116972 0.2500000 0.2453795 0.074683 NA NA
1 534192 rs6680723 C T -0.0033212 0.0068096 0.6300007 0.6257480 0.240783 NA NA
1 546697 rs12025928 A G -0.0000040 0.0084768 1.0000000 0.9996217 0.913589 NA NA
1 693731 rs12238997 A G 0.0052563 0.0057090 0.3599996 0.3572006 0.115772 0.1417730 NA
1 705882 rs72631875 G A -0.0011141 0.0083610 0.8900000 0.8939950 0.067010 0.0315495 NA
1 706368 rs55727773 A G -0.0036204 0.0042187 0.3900004 0.3907949 0.515223 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0042782 0.0050911 0.4000000 0.4007277 0.138187 0.2052720 NA
22 51219387 rs9616832 T C 0.0003341 0.0066062 0.9599999 0.9596687 0.073592 0.0654952 NA
22 51219704 rs147475742 G A -0.0054803 0.0088393 0.5400003 0.5352579 0.041930 0.0473243 NA
22 51221190 rs369304721 G A 0.0007546 0.0088647 0.9299999 0.9321590 0.049388 NA NA
22 51221731 rs115055839 T C -0.0001932 0.0066110 0.9800000 0.9766839 0.073051 0.0625000 NA
22 51222100 rs114553188 G T 0.0049417 0.0077465 0.5199996 0.5235194 0.054829 0.0880591 NA
22 51223637 rs375798137 G A 0.0041851 0.0077842 0.5900000 0.5908232 0.054452 0.0788738 NA
22 51229805 rs9616985 T C -0.0000771 0.0066350 0.9900000 0.9907333 0.072914 0.0730831 NA
22 51232488 rs376461333 A G -0.0053569 0.0156252 0.7300002 0.7317214 0.020163 NA NA
22 51237063 rs3896457 T C -0.0010535 0.0040528 0.7899998 0.7949091 0.299137 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.624733 ES:SE:LP:AF:ID  -0.00493554:0.00609689:0.376751:0.624733:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400864 ES:SE:LP:AF:ID  0.00597569:0.00602743:0.49485:0.400864:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103698 ES:SE:LP:AF:ID  0.00977365:0.00961732:0.508638:0.103698:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.457737 ES:SE:LP:AF:ID  0.00560545:0.00593373:0.468521:0.457737:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074683 ES:SE:LP:AF:ID  0.013588:0.0116972:0.60206:0.074683:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240783 ES:SE:LP:AF:ID  -0.00332117:0.00680958:0.200659:0.240783:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913589 ES:SE:LP:AF:ID  -4.01941e-06:0.00847679:-0:0.913589:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.115772 ES:SE:LP:AF:ID  0.00525632:0.00570897:0.443698:0.115772:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.06701  ES:SE:LP:AF:ID  -0.00111411:0.008361:0.05061:0.06701:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515223 ES:SE:LP:AF:ID  -0.00362039:0.0042187:0.408935:0.515223:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.032995 ES:SE:LP:AF:ID  0.00441133:0.0106283:0.167491:0.032995:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036625 ES:SE:LP:AF:ID  0.00205695:0.00965397:0.0809219:0.036625:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036753 ES:SE:LP:AF:ID  0.00229718:0.00961474:0.091515:0.036753:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036437 ES:SE:LP:AF:ID  0.00228988:0.00968608:0.091515:0.036437:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016501 ES:SE:LP:AF:ID  0.00252215:0.0147847:0.0655015:0.016501:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036993 ES:SE:LP:AF:ID  0.00250511:0.00957544:0.102373:0.036993:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037094 ES:SE:LP:AF:ID  0.00428184:0.00954323:0.187087:0.037094:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.10134  ES:SE:LP:AF:ID  -0.00571313:0.00694622:0.387216:0.10134:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.95902  ES:SE:LP:AF:ID  -0.00152295:0.00920563:0.0604807:0.95902:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031581 ES:SE:LP:AF:ID  -0.0130227:0.0166198:0.366532:0.031581:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053188 ES:SE:LP:AF:ID  -0.0167678:0.0132947:0.677781:0.053188:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036589 ES:SE:LP:AF:ID  0.00297961:0.00960863:0.119186:0.036589:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036827 ES:SE:LP:AF:ID  0.00316114:0.00953265:0.130768:0.036827:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.84352  ES:SE:LP:AF:ID  -0.00515271:0.00493619:0.522879:0.84352:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055307 ES:SE:LP:AF:ID  0.00415137:0.00804086:0.21467:0.055307:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122099 ES:SE:LP:AF:ID  0.00572116:0.00539945:0.537602:0.122099:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025793 ES:SE:LP:AF:ID  -0.00566126:0.0132585:0.173925:0.025793:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121354 ES:SE:LP:AF:ID  0.00539356:0.00540083:0.49485:0.121354:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132021 ES:SE:LP:AF:ID  0.00461082:0.00533755:0.408935:0.132021:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.01103  ES:SE:LP:AF:ID  0.0041592:0.0194717:0.0809219:0.01103:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005894 ES:SE:LP:AF:ID  -0.0037802:0.0246648:0.0555173:0.005894:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.036824 ES:SE:LP:AF:ID  0.00375553:0.00942295:0.161151:0.036824:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.839356 ES:SE:LP:AF:ID  -0.00630508:0.00478518:0.721246:0.839356:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838996 ES:SE:LP:AF:ID  -0.00558942:0.00477986:0.619789:0.838996:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.870005 ES:SE:LP:AF:ID  -0.00609245:0.00512261:0.638272:0.870005:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129643 ES:SE:LP:AF:ID  0.00533871:0.00513293:0.522879:0.129643:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037274 ES:SE:LP:AF:ID  0.00320368:0.00927108:0.136677:0.037274:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037526 ES:SE:LP:AF:ID  0.00329165:0.00921064:0.142668:0.037526:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869331 ES:SE:LP:AF:ID  -0.00551446:0.00511198:0.552842:0.869331:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.86944  ES:SE:LP:AF:ID  -0.00542111:0.00511395:0.537602:0.86944:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037484 ES:SE:LP:AF:ID  0.00268416:0.00925114:0.113509:0.037484:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.86934  ES:SE:LP:AF:ID  -0.00547575:0.00511223:0.552842:0.86934:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.004969 ES:SE:LP:AF:ID  0.0547068:0.0266854:1.39794:0.004969:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00494  ES:SE:LP:AF:ID  0.0537213:0.0267362:1.34679:0.00494:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.838428 ES:SE:LP:AF:ID  -0.00561765:0.00476635:0.619789:0.838428:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037493 ES:SE:LP:AF:ID  0.00234169:0.00926509:0.09691:0.037493:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.839031 ES:SE:LP:AF:ID  -0.00551319:0.0047794:0.60206:0.839031:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013864 ES:SE:LP:AF:ID  0.00519693:0.0165824:0.124939:0.013864:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005573 ES:SE:LP:AF:ID  0.0161315:0.0256276:0.275724:0.005573:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.840081 ES:SE:LP:AF:ID  -0.00597921:0.00484246:0.657577:0.840081:rs3131965