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_757.vcf.gz --id UKB-b:19756 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_757.txt.gz --cohort_controls 249516 --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-19756/UKB-b-19756_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19756/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:41:34 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19756/UKB-b-19756_data.vcf.gz ...
Read summary statistics for 9645452 SNPs.
Dropped 12817 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, 1288672 SNPs remain.
After merging with regression SNP LD, 1288672 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0113 (0.002)
Lambda GC: 1.0952
Mean Chi^2: 1.0945
Intercept: 1.0374 (0.0066)
Ratio: 0.3961 (0.0696)
Analysis finished at Thu Oct 17 14:43:14 2019
Total time elapsed: 1.0m:40.18s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9495,
    "inflation_factor": 1.0966,
    "mean_EFFECT": -0.0004,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 0,
    "n_p_sig": 1,
    "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": 152653,
    "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": 1288672,
    "ldsc_nsnp_merge_regression_ld": 1288672,
    "ldsc_observed_scale_h2_beta": 0.0113,
    "ldsc_observed_scale_h2_se": 0.002,
    "ldsc_intercept_beta": 1.0374,
    "ldsc_intercept_se": 0.0066,
    "ldsc_lambda_gc": 1.0952,
    "ldsc_mean_chisq": 1.0945,
    "ldsc_ratio": 0.3958
}

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 9632697 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 9645452 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 8.626900e+00 5.750457e+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 7.883874e+07 5.629485e+07 828.0000000 3.255817e+07 6.944264e+07 1.145697e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -3.987000e-04 1.247490e-02 -0.2167890 -4.196100e-03 -1.149000e-04 3.849000e-03 1.886720e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 9.042400e-03 7.942100e-03 0.0027276 3.315200e-03 5.432200e-03 1.220860e-02 1.437900e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.887448e-01 2.915362e-01 0.0000000 2.300001e-01 4.799997e-01 7.400005e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.887449e-01 2.915097e-01 0.0000000 2.344495e-01 4.843342e-01 7.411597e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.071057e-01 2.570715e-01 0.0014030 1.465700e-02 8.310600e-02 3.233960e-01 9.985970e-01 ▇▂▁▁▁
numeric AF_reference 152653 0.9841736 NA NA NA NA NA NA NA 2.094164e-01 2.486910e-01 0.0000000 1.277960e-02 1.036340e-01 3.256790e-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 -0.0033183 0.0050243 0.5099998 0.5089684 0.623834 0.7821490 NA
1 54676 rs2462492 C T 0.0007418 0.0049730 0.8800001 0.8814190 0.400756 NA NA
1 86028 rs114608975 T C 0.0154971 0.0079409 0.0510000 0.0509899 0.103563 0.0277556 NA
1 91536 rs6702460 G T -0.0067887 0.0048979 0.1700000 0.1657312 0.456747 0.4207270 NA
1 234313 rs8179466 C T -0.0024586 0.0096282 0.8000000 0.7984472 0.074745 NA NA
1 534192 rs6680723 C T 0.0105355 0.0055945 0.0599998 0.0596734 0.241115 NA NA
1 546697 rs12025928 A G 0.0114356 0.0069842 0.1000000 0.1015592 0.913628 NA NA
1 693731 rs12238997 A G -0.0062242 0.0046812 0.1800002 0.1836442 0.116246 0.1417730 NA
1 705882 rs72631875 G A -0.0090402 0.0068792 0.1900002 0.1887979 0.067038 0.0315495 NA
1 706368 rs55727773 A G 0.0080782 0.0034690 0.0200000 0.0198758 0.515580 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219704 rs147475742 G A -0.0081983 0.0072596 0.2599998 0.2587689 0.042223 0.0473243 NA
22 51219766 rs182321900 C T 0.0309982 0.0336337 0.3599996 0.3567158 0.001977 NA NA
22 51220146 rs868950473 C T 0.0363560 0.0333052 0.2800000 0.2750084 0.002027 NA NA
22 51221190 rs369304721 G A -0.0047848 0.0072484 0.5099998 0.5091789 0.049995 NA NA
22 51221731 rs115055839 T C -0.0034774 0.0054290 0.5199996 0.5218392 0.073541 0.0625000 NA
22 51222100 rs114553188 G T -0.0127275 0.0064151 0.0470002 0.0472558 0.054407 0.0880591 NA
22 51223637 rs375798137 G A -0.0126718 0.0064454 0.0490004 0.0492964 0.054051 0.0788738 NA
22 51229805 rs9616985 T C -0.0039505 0.0054493 0.4700002 0.4684811 0.073368 0.0730831 NA
22 51232488 rs376461333 A G -0.0183185 0.0128732 0.1499999 0.1547376 0.019989 NA NA
22 51237063 rs3896457 T C 0.0038170 0.0033398 0.2500000 0.2530805 0.297668 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623834 ES:SE:LP:AF:ID  -0.00331828:0.00502431:0.29243:0.623834:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400756 ES:SE:LP:AF:ID  0.00074183:0.00497303:0.0555173:0.400756:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103563 ES:SE:LP:AF:ID  0.0154971:0.00794086:1.29243:0.103563:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456747 ES:SE:LP:AF:ID  -0.0067887:0.00489787:0.769551:0.456747:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074745 ES:SE:LP:AF:ID  -0.00245863:0.0096282:0.09691:0.074745:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241115 ES:SE:LP:AF:ID  0.0105355:0.00559447:1.22185:0.241115:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913628 ES:SE:LP:AF:ID  0.0114356:0.00698425:1:0.913628:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116246 ES:SE:LP:AF:ID  -0.00622422:0.00468121:0.744727:0.116246:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067038 ES:SE:LP:AF:ID  -0.00904023:0.00687918:0.721246:0.067038:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.51558  ES:SE:LP:AF:ID  0.00807816:0.00346898:1.69897:0.51558:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033242 ES:SE:LP:AF:ID  -0.0114464:0.00871715:0.721246:0.033242:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036838 ES:SE:LP:AF:ID  -0.00917359:0.00792601:0.60206:0.036838:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036947 ES:SE:LP:AF:ID  -0.00924734:0.0078977:0.619789:0.036947:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036657 ES:SE:LP:AF:ID  -0.00874701:0.00795208:0.568636:0.036657:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.01645  ES:SE:LP:AF:ID  -0.0133483:0.0122533:0.552842:0.01645:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037201 ES:SE:LP:AF:ID  -0.00855866:0.00786428:0.552842:0.037201:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.0373   ES:SE:LP:AF:ID  -0.00904332:0.00783767:0.60206:0.0373:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101118 ES:SE:LP:AF:ID  0.00151517:0.00573181:0.102373:0.101118:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958793 ES:SE:LP:AF:ID  0.0115937:0.00754896:0.920819:0.958793:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031496 ES:SE:LP:AF:ID  0.0273835:0.0137285:1.33724:0.031496:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053247 ES:SE:LP:AF:ID  -5.50215e-05:0.0109256:-0:0.053247:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036801 ES:SE:LP:AF:ID  -0.00933768:0.00788982:0.619789:0.036801:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.03711  ES:SE:LP:AF:ID  -0.00794455:0.00781794:0.508638:0.03711:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.842962 ES:SE:LP:AF:ID  0.00808907:0.00405222:1.33724:0.842962:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055814 ES:SE:LP:AF:ID  -0.00706402:0.00658001:0.552842:0.055814:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122296 ES:SE:LP:AF:ID  -0.00729316:0.00443814:1:0.122296:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025756 ES:SE:LP:AF:ID  0.00170582:0.0109121:0.0555173:0.025756:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121509 ES:SE:LP:AF:ID  -0.00706066:0.00444029:0.958607:0.121509:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132442 ES:SE:LP:AF:ID  -0.00640957:0.00437636:0.853872:0.132442:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011229 ES:SE:LP:AF:ID  0.0212441:0.0158955:0.744727:0.011229:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005737 ES:SE:LP:AF:ID  0.000236724:0.0204967:0.00436481:0.005737:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.002314 ES:SE:LP:AF:ID  -0.0515627:0.0341466:0.886057:0.002314:rs112573343
1   752478  rs146277091 G   A   .   PASS    AF=0.037054 ES:SE:LP:AF:ID  -0.00771814:0.00773561:0.49485:0.037054:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838758 ES:SE:LP:AF:ID  0.0083838:0.00392607:1.48149:0.838758:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838344 ES:SE:LP:AF:ID  0.00844829:0.00392119:1.50864:0.838344:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869736 ES:SE:LP:AF:ID  0.00835295:0.00420903:1.3279:0.869736:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129926 ES:SE:LP:AF:ID  -0.00807939:0.00421693:1.25964:0.129926:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.03755  ES:SE:LP:AF:ID  -0.00787699:0.00760618:0.522879:0.03755:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.0378   ES:SE:LP:AF:ID  -0.00826637:0.00755798:0.568636:0.0378:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869046 ES:SE:LP:AF:ID  0.00827697:0.00420006:1.3098:0.869046:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869146 ES:SE:LP:AF:ID  0.00836594:0.00420171:1.33724:0.869146:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.03776  ES:SE:LP:AF:ID  -0.00829298:0.00759066:0.568636:0.03776:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869048 ES:SE:LP:AF:ID  0.00827375:0.00420009:1.3098:0.869048:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005138 ES:SE:LP:AF:ID  0.024845:0.0215336:0.60206:0.005138:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005104 ES:SE:LP:AF:ID  0.0236223:0.0215905:0.568636:0.005104:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.83779  ES:SE:LP:AF:ID  0.00860628:0.00391058:1.55284:0.83779:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037775 ES:SE:LP:AF:ID  -0.00836051:0.00760133:0.568636:0.037775:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838442 ES:SE:LP:AF:ID  0.00871858:0.00392174:1.58503:0.838442:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013709 ES:SE:LP:AF:ID  0.00805897:0.0137491:0.251812:0.013709:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005518 ES:SE:LP:AF:ID  0.0461281:0.0212506:1.52288:0.005518:rs184270342