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_100230.vcf.gz --id UKB-b:4955 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_100230.txt.gz --cohort_controls 64941 --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-4955/UKB-b-4955_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4955/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:44:56 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4955/UKB-b-4955_data.vcf.gz ...
Read summary statistics for 8624990 SNPs.
Dropped 7372 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, 1285751 SNPs remain.
After merging with regression SNP LD, 1285751 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0172 (0.0067)
Lambda GC: 1.0242
Mean Chi^2: 1.0308
Intercept: 1.0087 (0.0056)
Ratio: 0.282 (0.181)
Analysis finished at Thu Oct 17 14:46:27 2019
Total time elapsed: 1.0m:31.21s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9462,
    "inflation_factor": 1.0475,
    "mean_EFFECT": 0.0001,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 3,
    "n_p_sig": 12,
    "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": 83090,
    "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": 1285751,
    "ldsc_nsnp_merge_regression_ld": 1285751,
    "ldsc_observed_scale_h2_beta": 0.0172,
    "ldsc_observed_scale_h2_se": 0.0067,
    "ldsc_intercept_beta": 1.0087,
    "ldsc_intercept_se": 0.0056,
    "ldsc_lambda_gc": 1.0242,
    "ldsc_mean_chisq": 1.0308,
    "ldsc_ratio": 0.2825
}

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 8617652 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 8624990 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 8.650336e+00 5.760981e+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 7.877022e+07 5.637165e+07 828.0000000 3.238557e+07 6.929221e+07 1.145689e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 6.240000e-05 6.602900e-03 -0.0612173 -2.747900e-03 -9.000000e-06 2.736700e-03 8.129260e-02 ▁▂▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 5.407400e-03 3.691100e-03 0.0021477 2.529100e-03 3.728900e-03 7.309700e-03 3.702060e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.962967e-01 2.898779e-01 0.0000000 2.399999e-01 4.899999e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.962971e-01 2.898510e-01 0.0000000 2.442513e-01 4.949677e-01 7.476709e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.291173e-01 2.594883e-01 0.0053900 2.518900e-02 1.138970e-01 3.626260e-01 9.946100e-01 ▇▂▁▁▁
numeric AF_reference 83090 0.9903664 NA NA NA NA NA NA NA 2.288663e-01 2.514591e-01 0.0000000 2.316290e-02 1.303910e-01 3.596250e-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 0.0016668 0.0039579 0.6700003 0.6736536 0.623821 0.7821490 NA
1 54676 rs2462492 C T -0.0040349 0.0039465 0.3100002 0.3066027 0.399128 NA NA
1 86028 rs114608975 T C 0.0105613 0.0062822 0.0929994 0.0927370 0.103537 0.0277556 NA
1 91536 rs6702460 G T 0.0017397 0.0038814 0.6499995 0.6539934 0.455912 0.4207270 NA
1 234313 rs8179466 C T -0.0107515 0.0076764 0.1600000 0.1613379 0.074453 NA NA
1 534192 rs6680723 C T 0.0046132 0.0044207 0.2999998 0.2966980 0.242061 NA NA
1 546697 rs12025928 A G -0.0051673 0.0054851 0.3500000 0.3461650 0.912855 NA NA
1 693731 rs12238997 A G 0.0055813 0.0036863 0.1299999 0.1300102 0.117306 0.1417730 NA
1 705882 rs72631875 G A 0.0018985 0.0053728 0.7199992 0.7238293 0.067706 0.0315495 NA
1 706368 rs55727773 A G -0.0073691 0.0027362 0.0071000 0.0070768 0.513304 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0071234 0.0033283 0.0320000 0.0323327 0.136310 0.2052720 NA
22 51219387 rs9616832 T C -0.0010515 0.0043377 0.8100000 0.8084615 0.071798 0.0654952 NA
22 51219704 rs147475742 G A -0.0047379 0.0057706 0.4100001 0.4116176 0.041188 0.0473243 NA
22 51221190 rs369304721 G A 0.0040504 0.0058110 0.4899999 0.4857920 0.048378 NA NA
22 51221731 rs115055839 T C -0.0009465 0.0043387 0.8300000 0.8273138 0.071349 0.0625000 NA
22 51222100 rs114553188 G T -0.0112948 0.0050279 0.0250000 0.0246757 0.054843 0.0880591 NA
22 51223637 rs375798137 G A -0.0116792 0.0050541 0.0210000 0.0208421 0.054463 0.0788738 NA
22 51229805 rs9616985 T C -0.0010470 0.0043521 0.8100000 0.8098768 0.071254 0.0730831 NA
22 51232488 rs376461333 A G -0.0131926 0.0100259 0.1900002 0.1882237 0.020453 NA NA
22 51237063 rs3896457 T C 0.0018429 0.0026274 0.4799997 0.4830507 0.298397 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623821 ES:SE:LP:AF:ID  0.00166683:0.00395791:0.173925:0.623821:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399128 ES:SE:LP:AF:ID  -0.00403485:0.00394654:0.508638:0.399128:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103537 ES:SE:LP:AF:ID  0.0105613:0.00628225:1.03152:0.103537:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455912 ES:SE:LP:AF:ID  0.00173974:0.00388143:0.187087:0.455912:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074453 ES:SE:LP:AF:ID  -0.0107515:0.00767643:0.79588:0.074453:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.242061 ES:SE:LP:AF:ID  0.00461318:0.0044207:0.522879:0.242061:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912855 ES:SE:LP:AF:ID  -0.00516726:0.0054851:0.455932:0.912855:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117306 ES:SE:LP:AF:ID  0.0055813:0.00368631:0.886057:0.117306:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067706 ES:SE:LP:AF:ID  0.00189847:0.00537284:0.142668:0.067706:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.513304 ES:SE:LP:AF:ID  -0.00736907:0.00273617:2.14874:0.513304:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033675 ES:SE:LP:AF:ID  -0.00478963:0.0068203:0.318759:0.033675:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037455 ES:SE:LP:AF:ID  -0.00360153:0.00618571:0.251812:0.037455:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037641 ES:SE:LP:AF:ID  -0.00367042:0.00615335:0.259637:0.037641:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037218 ES:SE:LP:AF:ID  -0.00433572:0.00620957:0.309804:0.037218:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016284 ES:SE:LP:AF:ID  0.00338059:0.00973918:0.136677:0.016284:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037858 ES:SE:LP:AF:ID  -0.00337642:0.00613231:0.236572:0.037858:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037952 ES:SE:LP:AF:ID  -0.00381419:0.00611286:0.275724:0.037952:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.102734 ES:SE:LP:AF:ID  0.00446859:0.00446514:0.49485:0.102734:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958092 ES:SE:LP:AF:ID  0.00182017:0.00590406:0.119186:0.958092:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031694 ES:SE:LP:AF:ID  0.00999779:0.0108104:0.443698:0.031694:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052717 ES:SE:LP:AF:ID  -0.022315:0.00870914:2:0.052717:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037446 ES:SE:LP:AF:ID  -0.00431658:0.006153:0.318759:0.037446:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037715 ES:SE:LP:AF:ID  -0.00386411:0.00610203:0.275724:0.037715:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841452 ES:SE:LP:AF:ID  -0.00641891:0.00318796:1.35655:0.841452:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056325 ES:SE:LP:AF:ID  0.00415106:0.00517901:0.376751:0.056325:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123062 ES:SE:LP:AF:ID  0.0070368:0.00350164:1.35655:0.123062:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025121 ES:SE:LP:AF:ID  0.00487937:0.00872186:0.236572:0.025121:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122315 ES:SE:LP:AF:ID  0.00724073:0.00350277:1.40894:0.122315:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.134118 ES:SE:LP:AF:ID  0.0053188:0.00343856:0.920819:0.134118:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011559 ES:SE:LP:AF:ID  0.0284146:0.0122539:1.69897:0.011559:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.006101 ES:SE:LP:AF:ID  0.0121319:0.015561:0.356547:0.006101:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037597 ES:SE:LP:AF:ID  -0.00354329:0.00604549:0.251812:0.037597:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837048 ES:SE:LP:AF:ID  -0.0067054:0.00308398:1.52288:0.837048:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.836752 ES:SE:LP:AF:ID  -0.00682002:0.00308172:1.56864:0.836752:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868577 ES:SE:LP:AF:ID  -0.00824137:0.00331149:1.88606:0.868577:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.13099  ES:SE:LP:AF:ID  0.00832075:0.00332:1.92082:0.13099:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.038042 ES:SE:LP:AF:ID  -0.00477652:0.00595065:0.376751:0.038042:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038289 ES:SE:LP:AF:ID  -0.00469986:0.00591377:0.366532:0.038289:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.86799  ES:SE:LP:AF:ID  -0.00848789:0.00330616:2:0.86799:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868064 ES:SE:LP:AF:ID  -0.00854897:0.00330752:2.01323:0.868064:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038196 ES:SE:LP:AF:ID  -0.0047869:0.00594108:0.376751:0.038196:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.868001 ES:SE:LP:AF:ID  -0.0083769:0.00330608:1.95861:0.868001:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005397 ES:SE:LP:AF:ID  0.0116833:0.0165686:0.318759:0.005397:rs150578204
1   754503  rs3115859   G   A   .   PASS    AF=0.836177 ES:SE:LP:AF:ID  -0.00673882:0.0030727:1.55284:0.836177:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.0382   ES:SE:LP:AF:ID  -0.00416609:0.00594975:0.318759:0.0382:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836811 ES:SE:LP:AF:ID  -0.006518:0.00308112:1.46852:0.836811:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013031 ES:SE:LP:AF:ID  -0.0104448:0.0111463:0.455932:0.013031:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005662 ES:SE:LP:AF:ID  0.00191355:0.0165469:0.0409586:0.005662:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838128 ES:SE:LP:AF:ID  -0.00703519:0.00312448:1.61979:0.838128:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.868242 ES:SE:LP:AF:ID  -0.00839487:0.00330182:1.95861:0.868242:rs3115858