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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    "FORMAT.5": "<ID=EZ,Number=A,Type=Float,Description=\"Z-score provided if it was used to derive the EFFECT and SE fields\">",
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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_4196.vcf.gz --id UKB-b:8778 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_4196.txt.gz --cohort_controls 151466 --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-8778/UKB-b-8778_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-8778/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:42:06 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-8778/UKB-b-8778_data.vcf.gz ...
Read summary statistics for 9326514 SNPs.
Dropped 10427 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, 1288004 SNPs remain.
After merging with regression SNP LD, 1288004 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0362 (0.004)
Lambda GC: 1.1102
Mean Chi^2: 1.1265
Intercept: 1.0191 (0.0072)
Ratio: 0.1513 (0.0571)
Analysis finished at Thu Oct 17 14:44:32 2019
Total time elapsed: 2.0m:26.59s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9488,
    "inflation_factor": 1.0966,
    "mean_EFFECT": 0.0001,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 3,
    "n_p_sig": 128,
    "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": 113672,
    "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": 1288004,
    "ldsc_nsnp_merge_regression_ld": 1288004,
    "ldsc_observed_scale_h2_beta": 0.0362,
    "ldsc_observed_scale_h2_se": 0.004,
    "ldsc_intercept_beta": 1.0191,
    "ldsc_intercept_se": 0.0072,
    "ldsc_lambda_gc": 1.1102,
    "ldsc_mean_chisq": 1.1265,
    "ldsc_ratio": 0.151
}

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 9316139 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 9326514 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 8.634471e+00 5.753957e+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 7.881316e+07 5.630867e+07 828.0000000 3.250544e+07 6.939288e+07 1.145427e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.000000 NA NA NA NA NA NA NA 6.260000e-05 1.353460e-02 -0.1738130 -4.936100e-03 2.270000e-05 5.036900e-03 1.489400e-01 ▁▁▇▁▁
numeric SE 0 1.000000 NA NA NA NA NA NA NA 1.043530e-02 8.281400e-03 0.0034821 4.190600e-03 6.632700e-03 1.430070e-02 1.210390e-01 ▇▁▁▁▁
numeric PVAL 0 1.000000 NA NA NA NA NA NA NA 4.871403e-01 2.919709e-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 4.871411e-01 2.919444e-01 0.0000000 2.312457e-01 4.834917e-01 7.396963e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.000000 NA NA NA NA NA NA NA 2.133722e-01 2.577666e-01 0.0023110 1.731600e-02 9.193900e-02 3.349500e-01 9.976890e-01 ▇▂▁▁▁
numeric AF_reference 113672 0.987812 NA NA NA NA NA NA NA 2.144438e-01 2.495479e-01 0.0000000 1.477640e-02 1.104230e-01 3.348640e-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 0.0045450 0.0064198 0.4799997 0.4789656 0.623785 0.7821490 NA
1 54676 rs2462492 C T -0.0103112 0.0063758 0.1100001 0.1058264 0.399259 NA NA
1 86028 rs114608975 T C -0.0131564 0.0101596 0.2000000 0.1953298 0.103820 0.0277556 NA
1 91536 rs6702460 G T -0.0021966 0.0062824 0.7300002 0.7266119 0.456280 0.4207270 NA
1 234313 rs8179466 C T -0.0133651 0.0124039 0.2800000 0.2812606 0.074491 NA NA
1 534192 rs6680723 C T 0.0045846 0.0071812 0.5199996 0.5232028 0.241086 NA NA
1 546697 rs12025928 A G 0.0016460 0.0089096 0.8499999 0.8534254 0.913073 NA NA
1 693731 rs12238997 A G 0.0078893 0.0059873 0.1900002 0.1876161 0.116899 0.1417730 NA
1 705882 rs72631875 G A -0.0018574 0.0087591 0.8300000 0.8320630 0.067599 0.0315495 NA
1 706368 rs55727773 A G -0.0043691 0.0044326 0.3200000 0.3242918 0.514977 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0040251 0.0053673 0.4500005 0.4532957 0.137087 0.2052720 NA
22 51219387 rs9616832 T C 0.0010578 0.0069809 0.8800001 0.8795646 0.072744 0.0654952 NA
22 51219704 rs147475742 G A -0.0036496 0.0093197 0.6999999 0.6953505 0.041672 0.0473243 NA
22 51221190 rs369304721 G A -0.0013764 0.0093408 0.8800001 0.8828536 0.049116 NA NA
22 51221731 rs115055839 T C -0.0003937 0.0069866 0.9599999 0.9550585 0.072216 0.0625000 NA
22 51222100 rs114553188 G T -0.0035213 0.0081783 0.6700003 0.6667847 0.054449 0.0880591 NA
22 51223637 rs375798137 G A -0.0034246 0.0082204 0.6800001 0.6769738 0.054059 0.0788738 NA
22 51229805 rs9616985 T C -0.0008277 0.0070123 0.9100000 0.9060377 0.072072 0.0730831 NA
22 51232488 rs376461333 A G -0.0049241 0.0164269 0.7600007 0.7643624 0.020166 NA NA
22 51237063 rs3896457 T C -0.0063269 0.0042619 0.1400000 0.1376664 0.297598 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623785 ES:SE:LP:AF:ID  0.00454498:0.00641976:0.318759:0.623785:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399259 ES:SE:LP:AF:ID  -0.0103112:0.0063758:0.958607:0.399259:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.10382  ES:SE:LP:AF:ID  -0.0131564:0.0101596:0.69897:0.10382:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.45628  ES:SE:LP:AF:ID  -0.00219656:0.00628242:0.136677:0.45628:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074491 ES:SE:LP:AF:ID  -0.0133651:0.0124039:0.552842:0.074491:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241086 ES:SE:LP:AF:ID  0.00458462:0.00718124:0.283997:0.241086:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913073 ES:SE:LP:AF:ID  0.00164604:0.00890957:0.0705811:0.913073:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116899 ES:SE:LP:AF:ID  0.00788929:0.00598733:0.721246:0.116899:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067599 ES:SE:LP:AF:ID  -0.00185743:0.00875913:0.0809219:0.067599:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.514977 ES:SE:LP:AF:ID  -0.00436912:0.00443261:0.49485:0.514977:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033558 ES:SE:LP:AF:ID  -0.00332846:0.0110754:0.119186:0.033558:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037257 ES:SE:LP:AF:ID  -0.000199807:0.0100592:0.00877392:0.037257:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037384 ES:SE:LP:AF:ID  -0.000112361:0.01002:0.00436481:0.037384:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037042 ES:SE:LP:AF:ID  0.000562426:0.0100959:0.0177288:0.037042:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016374 ES:SE:LP:AF:ID  0.00436938:0.015749:0.107905:0.016374:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037627 ES:SE:LP:AF:ID  0.000345415:0.00997965:0.0132283:0.037627:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037722 ES:SE:LP:AF:ID  -2.01528e-05:0.00994754:-0:0.037722:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101565 ES:SE:LP:AF:ID  0.00466773:0.00730103:0.283997:0.101565:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958311 ES:SE:LP:AF:ID  -0.00281999:0.0095955:0.113509:0.958311:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031686 ES:SE:LP:AF:ID  0.0216201:0.0175519:0.657577:0.031686:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052655 ES:SE:LP:AF:ID  0.0131413:0.0141561:0.455932:0.052655:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037196 ES:SE:LP:AF:ID  -0.00146698:0.0100162:0.0555173:0.037196:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.03752  ES:SE:LP:AF:ID  0.00109912:0.00992743:0.0409586:0.03752:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841994 ES:SE:LP:AF:ID  -0.00764159:0.00518116:0.853872:0.841994:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056084 ES:SE:LP:AF:ID  0.0117079:0.00841435:0.79588:0.056084:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122823 ES:SE:LP:AF:ID  0.00886969:0.00568281:0.920819:0.122823:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025779 ES:SE:LP:AF:ID  -0.01036:0.0139654:0.337242:0.025779:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122043 ES:SE:LP:AF:ID  0.0087615:0.0056856:0.920819:0.122043:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.133317 ES:SE:LP:AF:ID  0.00754408:0.0055924:0.744727:0.133317:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011238 ES:SE:LP:AF:ID  -0.0266938:0.020274:0.721246:0.011238:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005845 ES:SE:LP:AF:ID  0.00546756:0.0259007:0.0809219:0.005845:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037469 ES:SE:LP:AF:ID  0.00148593:0.00982514:0.0555173:0.037469:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837756 ES:SE:LP:AF:ID  -0.00723974:0.00501723:0.823909:0.837756:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.837368 ES:SE:LP:AF:ID  -0.00767232:0.00501158:0.886057:0.837368:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869077 ES:SE:LP:AF:ID  -0.00842194:0.00538057:0.920819:0.869077:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130629 ES:SE:LP:AF:ID  0.00961654:0.00539117:1.13077:0.130629:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.03796  ES:SE:LP:AF:ID  -0.00107088:0.00966352:0.0409586:0.03796:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038213 ES:SE:LP:AF:ID  -0.000320993:0.00960221:0.0132283:0.038213:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868399 ES:SE:LP:AF:ID  -0.00918879:0.00536972:1.06048:0.868399:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868507 ES:SE:LP:AF:ID  -0.00916606:0.00537216:1.05552:0.868507:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038145 ES:SE:LP:AF:ID  -0.00162002:0.00964382:0.0604807:0.038145:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.868395 ES:SE:LP:AF:ID  -0.00908628:0.00536937:1.04096:0.868395:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005106 ES:SE:LP:AF:ID  0.034704:0.0277242:0.677781:0.005106:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005076 ES:SE:LP:AF:ID  0.0350809:0.0277856:0.677781:0.005076:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.836852 ES:SE:LP:AF:ID  -0.00731536:0.00499852:0.853872:0.836852:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038157 ES:SE:LP:AF:ID  -0.00108497:0.00965691:0.0409586:0.038157:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.837485 ES:SE:LP:AF:ID  -0.00767959:0.00501241:0.886057:0.837485:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013263 ES:SE:LP:AF:ID  -0.00580826:0.0179276:0.124939:0.013263:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005458 ES:SE:LP:AF:ID  -0.0101157:0.0272389:0.148742:0.005458:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838713 ES:SE:LP:AF:ID  -0.00748463:0.00508108:0.853872:0.838713:rs3131965