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_3773.vcf.gz --id UKB-b:8906 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_3773.txt.gz --cohort_cases 76910 --cohort_controls 20979 --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-8906/UKB-b-8906_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-8906/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:42:07 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-8906/UKB-b-8906_data.vcf.gz ...
Read summary statistics for 8802302 SNPs.
Dropped 7919 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, 1286357 SNPs remain.
After merging with regression SNP LD, 1286357 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0219 (0.005)
Lambda GC: 1.0429
Mean Chi^2: 1.0471
Intercept: 1.0055 (0.0061)
Ratio: 0.117 (0.1288)
Analysis finished at Thu Oct 17 14:43:45 2019
Total time elapsed: 1.0m:38.07s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9469,
    "inflation_factor": 1.0475,
    "mean_EFFECT": -3.8669e-06,
    "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": 86967,
    "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": 1286357,
    "ldsc_nsnp_merge_regression_ld": 1286357,
    "ldsc_observed_scale_h2_beta": 0.0219,
    "ldsc_observed_scale_h2_se": 0.005,
    "ldsc_intercept_beta": 1.0055,
    "ldsc_intercept_se": 0.0061,
    "ldsc_lambda_gc": 1.0429,
    "ldsc_mean_chisq": 1.0471,
    "ldsc_ratio": 0.1168
}

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 1.00000 3 58 0 8794420 0 NA NA NA NA NA NA NA NA NA NA
character REF 0 1.00000 1 1 0 4 0 NA NA NA NA NA NA NA NA NA NA
character ALT 0 1.00000 1 1 0 4 0 NA NA NA NA NA NA NA NA NA NA
logical N 8802302 0.00000 NA NA NA NA NA NaN : NA NA NA NA NA NA NA NA
numeric CHROM 0 1.00000 NA NA NA NA NA NA NA 8.648702e+00 5.760357e+00 1.0000000 4.000000e+00 8.000000e+00 1.300000e+01 2.200000e+01 ▇▅▅▂▂
numeric POS 0 1.00000 NA NA NA NA NA NA NA 7.877333e+07 5.635251e+07 828.0000000 3.240357e+07 6.931992e+07 1.145493e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.00000 NA NA NA NA NA NA NA -3.900000e-06 5.771100e-03 -0.0567695 -2.338000e-03 2.100000e-06 2.356100e-03 7.392970e-02 ▁▁▇▁▁
numeric SE 0 1.00000 NA NA NA NA NA NA NA 4.688800e-03 3.302400e-03 0.0017843 2.118100e-03 3.178000e-03 6.387800e-03 3.185890e-02 ▇▂▁▁▁
numeric PVAL 0 1.00000 NA NA NA NA NA NA NA 4.946867e-01 2.902248e-01 0.0000002 2.399999e-01 4.899999e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.00000 NA NA NA NA NA NA NA 4.946876e-01 2.901984e-01 0.0000002 2.419082e-01 4.927846e-01 7.462207e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.00000 NA NA NA NA NA NA NA 2.248478e-01 2.590537e-01 0.0045510 2.290500e-02 1.079380e-01 3.552330e-01 9.954490e-01 ▇▂▁▁▁
numeric AF_reference 86967 0.99012 NA NA NA NA NA NA NA 2.247975e-01 2.510047e-01 0.0000000 2.036740e-02 1.250000e-01 3.528350e-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 -0.0008657 0.0032810 0.7899998 0.7918855 0.623969 0.7821490 NA
1 54676 rs2462492 C T -0.0002720 0.0032502 0.9299999 0.9332961 0.400668 NA NA
1 86028 rs114608975 T C 0.0079370 0.0051965 0.1299999 0.1266721 0.103534 0.0277556 NA
1 91536 rs6702460 G T -0.0008139 0.0032099 0.8000000 0.7998467 0.457423 0.4207270 NA
1 234313 rs8179466 C T 0.0002152 0.0063601 0.9699999 0.9730076 0.074194 NA NA
1 534192 rs6680723 C T -0.0004360 0.0036625 0.9100000 0.9052367 0.241092 NA NA
1 546697 rs12025928 A G -0.0029679 0.0045839 0.5199996 0.5173321 0.913866 NA NA
1 693731 rs12238997 A G 0.0018477 0.0030673 0.5500004 0.5469260 0.116514 0.1417730 NA
1 705882 rs72631875 G A 0.0029239 0.0045097 0.5199996 0.5167497 0.067215 0.0315495 NA
1 706368 rs55727773 A G 0.0001127 0.0022717 0.9599999 0.9604451 0.515071 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0021735 0.0027350 0.4299995 0.4267784 0.139839 0.2052720 NA
22 51219387 rs9616832 T C 0.0049538 0.0035427 0.1600000 0.1620216 0.074739 0.0654952 NA
22 51219704 rs147475742 G A 0.0106699 0.0047507 0.0250000 0.0247051 0.042254 0.0473243 NA
22 51221190 rs369304721 G A 0.0060941 0.0047534 0.2000000 0.1998266 0.050098 NA NA
22 51221731 rs115055839 T C 0.0049307 0.0035443 0.1600000 0.1641686 0.074258 0.0625000 NA
22 51222100 rs114553188 G T -0.0033218 0.0041728 0.4299995 0.4259898 0.055071 0.0880591 NA
22 51223637 rs375798137 G A -0.0029507 0.0041941 0.4799997 0.4817178 0.054686 0.0788738 NA
22 51229805 rs9616985 T C 0.0052510 0.0035564 0.1400000 0.1398149 0.074083 0.0730831 NA
22 51232488 rs376461333 A G -0.0011101 0.0084086 0.8900000 0.8949714 0.020238 NA NA
22 51237063 rs3896457 T C -0.0001882 0.0021880 0.9299999 0.9314443 0.298482 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623969 ES:SE:LP:AF:ID  -0.000865737:0.00328101:0.102373:0.623969:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400668 ES:SE:LP:AF:ID  -0.000272033:0.00325015:0.0315171:0.400668:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103534 ES:SE:LP:AF:ID  0.00793696:0.00519653:0.886057:0.103534:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.457423 ES:SE:LP:AF:ID  -0.000813861:0.00320992:0.09691:0.457423:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074194 ES:SE:LP:AF:ID  0.000215202:0.00636007:0.0132283:0.074194:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241092 ES:SE:LP:AF:ID  -0.000436021:0.00366254:0.0409586:0.241092:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913866 ES:SE:LP:AF:ID  -0.00296792:0.00458392:0.283997:0.913866:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116514 ES:SE:LP:AF:ID  0.00184767:0.00306732:0.259637:0.116514:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067215 ES:SE:LP:AF:ID  0.0029239:0.00450966:0.283997:0.067215:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515071 ES:SE:LP:AF:ID  0.000112663:0.00227166:0.0177288:0.515071:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033197 ES:SE:LP:AF:ID  -0.000225588:0.00572185:0.0132283:0.033197:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036816 ES:SE:LP:AF:ID  -9.20517e-05:0.00519868:0.00436481:0.036816:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036956 ES:SE:LP:AF:ID  -0.000371324:0.00517648:0.0268721:0.036956:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036673 ES:SE:LP:AF:ID  -0.000771004:0.005214:0.0555173:0.036673:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016386 ES:SE:LP:AF:ID  0.00144294:0.00803848:0.0655015:0.016386:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037195 ES:SE:LP:AF:ID  -0.000873551:0.00515787:0.0604807:0.037195:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037336 ES:SE:LP:AF:ID  -0.00108437:0.00513674:0.0809219:0.037336:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.100254 ES:SE:LP:AF:ID  -9.61412e-05:0.00376275:0.00877392:0.100254:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958769 ES:SE:LP:AF:ID  0.00189117:0.00495716:0.154902:0.958769:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.03142  ES:SE:LP:AF:ID  0.00795187:0.00899012:0.420216:0.03142:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.05301  ES:SE:LP:AF:ID  0.000946143:0.00723673:0.0457575:0.05301:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036827 ES:SE:LP:AF:ID  0.000242198:0.00517193:0.0177288:0.036827:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037071 ES:SE:LP:AF:ID  -0.00034374:0.00512951:0.0222764:0.037071:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.842832 ES:SE:LP:AF:ID  -0.000949817:0.00265916:0.142668:0.842832:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055711 ES:SE:LP:AF:ID  0.00597749:0.00431446:0.769551:0.055711:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122509 ES:SE:LP:AF:ID  0.0017957:0.00290895:0.267606:0.122509:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025953 ES:SE:LP:AF:ID  0.00808544:0.00713032:0.585027:0.025953:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121707 ES:SE:LP:AF:ID  0.00182476:0.00291079:0.275724:0.121707:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132624 ES:SE:LP:AF:ID  0.000985625:0.0028706:0.136677:0.132624:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011036 ES:SE:LP:AF:ID  -0.0273334:0.010453:2.05061:0.011036:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005521 ES:SE:LP:AF:ID  -0.0247138:0.0137758:1.13668:0.005521:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037044 ES:SE:LP:AF:ID  -0.00134357:0.00507253:0.102373:0.037044:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838771 ES:SE:LP:AF:ID  -0.00117088:0.00257534:0.187087:0.838771:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838409 ES:SE:LP:AF:ID  -0.000934082:0.00257262:0.142668:0.838409:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.86963  ES:SE:LP:AF:ID  -0.00164481:0.00275798:0.259637:0.86963:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130035 ES:SE:LP:AF:ID  0.000914356:0.00276322:0.130768:0.130035:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037603 ES:SE:LP:AF:ID  -0.00115547:0.00498431:0.0861861:0.037603:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037834 ES:SE:LP:AF:ID  -0.00113084:0.00495477:0.0861861:0.037834:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869005 ES:SE:LP:AF:ID  -0.0011981:0.00275317:0.180456:0.869005:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869082 ES:SE:LP:AF:ID  -0.00114675:0.00275386:0.167491:0.869082:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037795 ES:SE:LP:AF:ID  -0.00115111:0.00497473:0.0861861:0.037795:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869013 ES:SE:LP:AF:ID  -0.00126978:0.00275306:0.19382:0.869013:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005139 ES:SE:LP:AF:ID  0.00961821:0.0141499:0.30103:0.005139:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.0051   ES:SE:LP:AF:ID  0.0110207:0.0141946:0.356547:0.0051:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.837857 ES:SE:LP:AF:ID  -0.00074779:0.00256553:0.113509:0.837857:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.03781  ES:SE:LP:AF:ID  -0.00108119:0.00498148:0.0809219:0.03781:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838573 ES:SE:LP:AF:ID  -0.000806734:0.00257351:0.124939:0.838573:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013581 ES:SE:LP:AF:ID  0.00535721:0.009061:0.259637:0.013581:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005553 ES:SE:LP:AF:ID  -0.0123589:0.0138514:0.431798:0.005553:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.839578 ES:SE:LP:AF:ID  -0.000369423:0.00260792:0.05061:0.839578:rs3131965