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_3647.vcf.gz --id UKB-b:4512 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_3647.txt.gz --cohort_controls 220399 --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-4512/UKB-b-4512_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4512/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:44:25 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4512/UKB-b-4512_data.vcf.gz ...
Read summary statistics for 9562644 SNPs.
Dropped 12119 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, 1288502 SNPs remain.
After merging with regression SNP LD, 1288502 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0165 (0.0023)
Lambda GC: 1.0873
Mean Chi^2: 1.0968
Intercept: 1.027 (0.0063)
Ratio: 0.2789 (0.0652)
Analysis finished at Thu Oct 17 14:46:01 2019
Total time elapsed: 1.0m:36.76s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9492,
    "inflation_factor": 1.0475,
    "mean_EFFECT": -0.0002,
    "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": 141411,
    "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": 1288502,
    "ldsc_nsnp_merge_regression_ld": 1288502,
    "ldsc_observed_scale_h2_beta": 0.0165,
    "ldsc_observed_scale_h2_se": 0.0023,
    "ldsc_intercept_beta": 1.027,
    "ldsc_intercept_se": 0.0063,
    "ldsc_lambda_gc": 1.0873,
    "ldsc_mean_chisq": 1.0968,
    "ldsc_ratio": 0.2789
}

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 9550587 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 9562644 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 8.627898e+00 5.751079e+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 7.883357e+07 5.630184e+07 828.0000000 3.254531e+07 6.942453e+07 1.145698e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -2.375000e-04 1.568960e-02 -0.2062040 -5.421300e-03 -5.540000e-05 5.213400e-03 2.221490e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.177300e-02 1.006520e-02 0.0036506 4.423800e-03 7.184700e-03 1.596800e-02 1.303510e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.906462e-01 2.918190e-01 0.0000001 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 4.906450e-01 2.917930e-01 0.0000001 2.348107e-01 4.881545e-01 7.434740e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.086262e-01 2.572034e-01 0.0015890 1.528500e-02 8.524300e-02 3.261480e-01 9.984110e-01 ▇▂▁▁▁
numeric AF_reference 141411 0.9852121 NA NA NA NA NA NA NA 2.105609e-01 2.488916e-01 0.0000000 1.297920e-02 1.052320e-01 3.278750e-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 0.0019651 0.0066981 0.7700005 0.7692283 0.623431 0.7821490 NA
1 54676 rs2462492 C T -0.0064270 0.0066363 0.3300000 0.3328163 0.400772 NA NA
1 86028 rs114608975 T C -0.0009603 0.0106079 0.9299999 0.9278668 0.103649 0.0277556 NA
1 91536 rs6702460 G T 0.0059120 0.0065421 0.3700002 0.3661676 0.456851 0.4207270 NA
1 234313 rs8179466 C T -0.0015254 0.0129283 0.9100000 0.9060787 0.074338 NA NA
1 534192 rs6680723 C T -0.0041741 0.0074797 0.5800000 0.5768077 0.240989 NA NA
1 546697 rs12025928 A G 0.0131510 0.0093306 0.1600000 0.1587044 0.913346 NA NA
1 693731 rs12238997 A G -0.0062181 0.0062696 0.3200000 0.3213013 0.116330 0.1417730 NA
1 705882 rs72631875 G A -0.0060065 0.0091492 0.5099998 0.5114991 0.067658 0.0315495 NA
1 706368 rs55727773 A G 0.0067544 0.0046474 0.1499999 0.1461243 0.515768 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219704 rs147475742 G A 0.0036594 0.0097944 0.7099994 0.7086885 0.041605 0.0473243 NA
22 51219766 rs182321900 C T -0.0911168 0.0458297 0.0470002 0.0467939 0.001903 NA NA
22 51220146 rs868950473 C T -0.0896289 0.0453589 0.0479999 0.0481555 0.001954 NA NA
22 51221190 rs369304721 G A -0.0139413 0.0097792 0.1499999 0.1539830 0.049326 NA NA
22 51221731 rs115055839 T C -0.0062376 0.0073123 0.3900004 0.3936371 0.072580 0.0625000 NA
22 51222100 rs114553188 G T 0.0039509 0.0085825 0.6499995 0.6452677 0.054011 0.0880591 NA
22 51223637 rs375798137 G A 0.0040381 0.0086237 0.6400000 0.6396040 0.053649 0.0788738 NA
22 51229805 rs9616985 T C -0.0072439 0.0073356 0.3200000 0.3233986 0.072449 0.0730831 NA
22 51232488 rs376461333 A G 0.0094022 0.0172042 0.5800000 0.5847181 0.019953 NA NA
22 51237063 rs3896457 T C 0.0016127 0.0044596 0.7199992 0.7176376 0.298144 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623431 ES:SE:LP:AF:ID  0.00196513:0.00669814:0.113509:0.623431:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400772 ES:SE:LP:AF:ID  -0.00642695:0.00663628:0.481486:0.400772:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103649 ES:SE:LP:AF:ID  -0.000960323:0.0106079:0.0315171:0.103649:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456851 ES:SE:LP:AF:ID  0.00591197:0.00654214:0.431798:0.456851:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074338 ES:SE:LP:AF:ID  -0.00152536:0.0129283:0.0409586:0.074338:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240989 ES:SE:LP:AF:ID  -0.00417408:0.00747971:0.236572:0.240989:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913346 ES:SE:LP:AF:ID  0.013151:0.00933064:0.79588:0.913346:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.11633  ES:SE:LP:AF:ID  -0.00621813:0.00626962:0.49485:0.11633:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067658 ES:SE:LP:AF:ID  -0.00600653:0.00914925:0.29243:0.067658:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515768 ES:SE:LP:AF:ID  0.00675441:0.00464745:0.823909:0.515768:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033111 ES:SE:LP:AF:ID  -0.0113845:0.0116871:0.481486:0.033111:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036762 ES:SE:LP:AF:ID  -0.00711003:0.0106143:0.30103:0.036762:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036917 ES:SE:LP:AF:ID  -0.00787481:0.0105684:0.337242:0.036917:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036577 ES:SE:LP:AF:ID  -0.0090952:0.0106496:0.408935:0.036577:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016384 ES:SE:LP:AF:ID  -0.0271064:0.0164627:1:0.016384:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037156 ES:SE:LP:AF:ID  -0.00725153:0.0105256:0.309804:0.037156:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037266 ES:SE:LP:AF:ID  -0.00913541:0.0104872:0.420216:0.037266:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101278 ES:SE:LP:AF:ID  -0.00597508:0.0076452:0.366532:0.101278:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958929 ES:SE:LP:AF:ID  0.00963973:0.0101198:0.468521:0.958929:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031444 ES:SE:LP:AF:ID  0.00867144:0.0183637:0.19382:0.031444:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053272 ES:SE:LP:AF:ID  0.0134999:0.0146253:0.443698:0.053272:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036798 ES:SE:LP:AF:ID  -0.0064566:0.010551:0.267606:0.036798:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037112 ES:SE:LP:AF:ID  -0.00781957:0.0104555:0.346787:0.037112:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.842992 ES:SE:LP:AF:ID  0.00608063:0.00543026:0.585027:0.842992:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056084 ES:SE:LP:AF:ID  0.00177964:0.00876498:0.0757207:0.056084:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122246 ES:SE:LP:AF:ID  -0.00549318:0.0059472:0.443698:0.122246:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.02551  ES:SE:LP:AF:ID  0.0130636:0.0146687:0.431798:0.02551:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.1215   ES:SE:LP:AF:ID  -0.00607045:0.00594912:0.508638:0.1215:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132603 ES:SE:LP:AF:ID  -0.00316441:0.00585735:0.229148:0.132603:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011064 ES:SE:LP:AF:ID  0.0181964:0.0213933:0.39794:0.011064:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005716 ES:SE:LP:AF:ID  -0.00539659:0.0274091:0.0757207:0.005716:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.002246 ES:SE:LP:AF:ID  -0.0417306:0.0465033:0.431798:0.002246:rs112573343
1   752478  rs146277091 G   A   .   PASS    AF=0.037004 ES:SE:LP:AF:ID  -0.00788031:0.0103542:0.346787:0.037004:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838624 ES:SE:LP:AF:ID  0.00744228:0.00525615:0.79588:0.838624:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838286 ES:SE:LP:AF:ID  0.00698732:0.00525145:0.744727:0.838286:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869745 ES:SE:LP:AF:ID  0.00811626:0.00563637:0.823909:0.869745:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129882 ES:SE:LP:AF:ID  -0.00785094:0.00564881:0.79588:0.129882:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.03752  ES:SE:LP:AF:ID  -0.00699352:0.0101784:0.309804:0.03752:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037752 ES:SE:LP:AF:ID  -0.00759548:0.0101168:0.346787:0.037752:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869116 ES:SE:LP:AF:ID  0.00782722:0.0056261:0.79588:0.869116:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869213 ES:SE:LP:AF:ID  0.00799398:0.00562833:0.79588:0.869213:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037725 ES:SE:LP:AF:ID  -0.00726529:0.0101586:0.327902:0.037725:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869124 ES:SE:LP:AF:ID  0.00783821:0.00562609:0.79588:0.869124:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005219 ES:SE:LP:AF:ID  -0.0343226:0.0286066:0.638272:0.005219:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005185 ES:SE:LP:AF:ID  -0.0352878:0.0286854:0.657577:0.005185:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.837716 ES:SE:LP:AF:ID  0.00735824:0.00523655:0.79588:0.837716:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.03774  ES:SE:LP:AF:ID  -0.00739831:0.0101726:0.327902:0.03774:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838355 ES:SE:LP:AF:ID  0.00758952:0.00525148:0.823909:0.838355:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013722 ES:SE:LP:AF:ID  0.0143308:0.0183933:0.356547:0.013722:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005525 ES:SE:LP:AF:ID  0.0057715:0.0283239:0.0757207:0.005525:rs184270342