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_5156.vcf.gz --id UKB-b:17859 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_5156.txt.gz --cohort_controls 94008 --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-17859/UKB-b-17859_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-17859/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:40:28 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-17859/UKB-b-17859_data.vcf.gz ...
Read summary statistics for 8976477 SNPs.
Dropped 8607 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, 1287137 SNPs remain.
After merging with regression SNP LD, 1287137 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0235 (0.0053)
Lambda GC: 1.0481
Mean Chi^2: 1.0504
Intercept: 1.0072 (0.0062)
Ratio: 0.1438 (0.1235)
Analysis finished at Thu Oct 17 14:42:10 2019
Total time elapsed: 1.0m:41.45s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9478,
    "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": 92296,
    "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": 1287137,
    "ldsc_nsnp_merge_regression_ld": 1287137,
    "ldsc_observed_scale_h2_beta": 0.0235,
    "ldsc_observed_scale_h2_se": 0.0053,
    "ldsc_intercept_beta": 1.0072,
    "ldsc_intercept_se": 0.0062,
    "ldsc_lambda_gc": 1.0481,
    "ldsc_mean_chisq": 1.0504,
    "ldsc_ratio": 0.1429
}

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 8967909 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 8976477 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 8.643621e+00 5.758322e+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 7.879069e+07 5.634265e+07 828.0000000 3.242932e+07 6.935455e+07 1.145518e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.000000 NA NA NA NA NA NA NA 2.640000e-05 1.506580e-02 -0.1731080 -5.931800e-03 -1.320000e-05 5.913400e-03 2.320500e-01 ▁▁▇▁▁
numeric SE 0 1.000000 NA NA NA NA NA NA NA 1.208350e-02 8.796200e-03 0.0044250 5.270500e-03 8.044600e-03 1.656330e-02 1.034810e-01 ▇▁▁▁▁
numeric PVAL 0 1.000000 NA NA NA NA NA NA NA 4.948165e-01 2.901731e-01 0.0000001 2.399999e-01 4.899999e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.000000 NA NA NA NA NA NA NA 4.948167e-01 2.901458e-01 0.0000001 2.418051e-01 4.932954e-01 7.461680e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.000000 NA NA NA NA NA NA NA 2.209132e-01 2.586181e-01 0.0037240 2.086600e-02 1.024940e-01 3.484670e-01 9.962760e-01 ▇▂▁▁▁
numeric AF_reference 92296 0.989718 NA NA NA NA NA NA NA 2.211678e-01 2.505329e-01 0.0000000 1.817090e-02 1.198080e-01 3.468450e-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 0.0086312 0.0081727 0.2900000 0.2909201 0.623858 0.7821490 NA
1 54676 rs2462492 C T -0.0083135 0.0081148 0.3100002 0.3056064 0.398882 NA NA
1 86028 rs114608975 T C -0.0048075 0.0129030 0.7099994 0.7094553 0.103853 0.0277556 NA
1 91536 rs6702460 G T 0.0030613 0.0079740 0.6999999 0.7010409 0.455860 0.4207270 NA
1 234313 rs8179466 C T 0.0103600 0.0156277 0.5099998 0.5073783 0.074807 NA NA
1 534192 rs6680723 C T 0.0076487 0.0091299 0.4000000 0.4021613 0.240481 NA NA
1 546697 rs12025928 A G -0.0038119 0.0113182 0.7400005 0.7362730 0.912884 NA NA
1 693731 rs12238997 A G -0.0065451 0.0075880 0.3900004 0.3883774 0.117829 0.1417730 NA
1 705882 rs72631875 G A 0.0033157 0.0111151 0.7700005 0.7654669 0.067640 0.0315495 NA
1 706368 rs55727773 A G 0.0061025 0.0056311 0.2800000 0.2784860 0.514400 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0107982 0.0068111 0.1100001 0.1128805 0.137320 0.2052720 NA
22 51219387 rs9616832 T C -0.0126717 0.0088688 0.1499999 0.1530617 0.072762 0.0654952 NA
22 51219704 rs147475742 G A -0.0158809 0.0117927 0.1800002 0.1780859 0.041904 0.0473243 NA
22 51221190 rs369304721 G A -0.0245961 0.0118645 0.0379997 0.0381645 0.049211 NA NA
22 51221731 rs115055839 T C -0.0121545 0.0088707 0.1700000 0.1706282 0.072311 0.0625000 NA
22 51222100 rs114553188 G T -0.0070312 0.0103906 0.5000000 0.4986054 0.054444 0.0880591 NA
22 51223637 rs375798137 G A -0.0064790 0.0104441 0.5400003 0.5350260 0.054065 0.0788738 NA
22 51229805 rs9616985 T C -0.0112797 0.0089029 0.2099999 0.2051643 0.072175 0.0730831 NA
22 51232488 rs376461333 A G -0.0171485 0.0210183 0.4100001 0.4145663 0.020021 NA NA
22 51237063 rs3896457 T C 0.0108250 0.0054219 0.0460002 0.0458748 0.298236 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623858 ES:SE:LP:AF:ID  0.0086312:0.00817267:0.537602:0.623858:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.398882 ES:SE:LP:AF:ID  -0.00831354:0.00811485:0.508638:0.398882:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103853 ES:SE:LP:AF:ID  -0.0048075:0.012903:0.148742:0.103853:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.45586  ES:SE:LP:AF:ID  0.00306134:0.00797399:0.154902:0.45586:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074807 ES:SE:LP:AF:ID  0.01036:0.0156277:0.29243:0.074807:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240481 ES:SE:LP:AF:ID  0.0076487:0.00912986:0.39794:0.240481:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912884 ES:SE:LP:AF:ID  -0.00381189:0.0113182:0.130768:0.912884:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117829 ES:SE:LP:AF:ID  -0.00654515:0.00758802:0.408935:0.117829:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.06764  ES:SE:LP:AF:ID  0.00331574:0.0111151:0.113509:0.06764:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.5144   ES:SE:LP:AF:ID  0.00610253:0.00563106:0.552842:0.5144:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033512 ES:SE:LP:AF:ID  -0.0130757:0.0140797:0.455932:0.033512:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037154 ES:SE:LP:AF:ID  -0.0192663:0.0128052:0.886057:0.037154:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037237 ES:SE:LP:AF:ID  -0.0195797:0.0127637:0.886057:0.037237:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036918 ES:SE:LP:AF:ID  -0.018876:0.0128553:0.853872:0.036918:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016768 ES:SE:LP:AF:ID  -0.0115991:0.0197488:0.251812:0.016768:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037493 ES:SE:LP:AF:ID  -0.0189674:0.0127097:0.853872:0.037493:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037578 ES:SE:LP:AF:ID  -0.0172981:0.012671:0.769551:0.037578:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101102 ES:SE:LP:AF:ID  0.0107982:0.00932033:0.60206:0.101102:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958226 ES:SE:LP:AF:ID  0.00721059:0.0121919:0.259637:0.958226:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031905 ES:SE:LP:AF:ID  0.0142009:0.0221654:0.283997:0.031905:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052657 ES:SE:LP:AF:ID  -0.00354026:0.0179296:0.0757207:0.052657:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037062 ES:SE:LP:AF:ID  -0.016723:0.0127557:0.721246:0.037062:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037368 ES:SE:LP:AF:ID  -0.0163136:0.0126489:0.69897:0.037368:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841035 ES:SE:LP:AF:ID  0.00745535:0.00657357:0.585027:0.841035:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.0561   ES:SE:LP:AF:ID  -0.0156895:0.0107071:0.853872:0.0561:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123762 ES:SE:LP:AF:ID  -0.00763378:0.0072031:0.537602:0.123762:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025886 ES:SE:LP:AF:ID  0.00291903:0.017713:0.0604807:0.025886:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122948 ES:SE:LP:AF:ID  -0.00743742:0.00720673:0.522879:0.122948:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.133522 ES:SE:LP:AF:ID  -0.0103156:0.00710816:0.823909:0.133522:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011227 ES:SE:LP:AF:ID  -0.0121955:0.0257975:0.19382:0.011227:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.006057 ES:SE:LP:AF:ID  0.0127738:0.0322953:0.161151:0.006057:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037332 ES:SE:LP:AF:ID  -0.0143566:0.0125094:0.60206:0.037332:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.836785 ES:SE:LP:AF:ID  0.00799232:0.00636015:0.677781:0.836785:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.836365 ES:SE:LP:AF:ID  0.00772349:0.00635339:0.657577:0.836365:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.867944 ES:SE:LP:AF:ID  0.00645516:0.00681387:0.468521:0.867944:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131736 ES:SE:LP:AF:ID  -0.00730244:0.00682856:0.552842:0.131736:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037769 ES:SE:LP:AF:ID  -0.0160096:0.0123126:0.721246:0.037769:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038018 ES:SE:LP:AF:ID  -0.0149288:0.0122362:0.657577:0.038018:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867248 ES:SE:LP:AF:ID  0.00583448:0.00680041:0.408935:0.867248:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.867349 ES:SE:LP:AF:ID  0.0059835:0.00680366:0.420216:0.867349:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037965 ES:SE:LP:AF:ID  -0.0156173:0.0122856:0.69897:0.037965:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.867246 ES:SE:LP:AF:ID  0.00578141:0.00679998:0.39794:0.867246:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005119 ES:SE:LP:AF:ID  -0.0506203:0.035149:0.823909:0.005119:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005086 ES:SE:LP:AF:ID  -0.0507157:0.0352488:0.823909:0.005086:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.83589  ES:SE:LP:AF:ID  0.00737522:0.00633926:0.619789:0.83589:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037982 ES:SE:LP:AF:ID  -0.0155957:0.0123018:0.69897:0.037982:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836493 ES:SE:LP:AF:ID  0.00740809:0.00635633:0.619789:0.836493:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013213 ES:SE:LP:AF:ID  -0.00141348:0.0227987:0.0222764:0.013213:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005497 ES:SE:LP:AF:ID  0.00949591:0.0345224:0.107905:0.005497:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.837744 ES:SE:LP:AF:ID  0.00780902:0.00644215:0.638272:0.837744:rs3131965