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_5105.vcf.gz --id UKB-b:2846 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_5105.txt.gz --cohort_controls 83410 --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-2846/UKB-b-2846_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-2846/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:43:05 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-2846/UKB-b-2846_data.vcf.gz ...
Read summary statistics for 8878036 SNPs.
Dropped 8206 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, 1286800 SNPs remain.
After merging with regression SNP LD, 1286800 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0688 (0.0076)
Lambda GC: 1.1032
Mean Chi^2: 1.1176
Intercept: 1.004 (0.0073)
Ratio: 0.0338 (0.0621)
Analysis finished at Thu Oct 17 14:44:44 2019
Total time elapsed: 1.0m:38.89s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9474,
    "inflation_factor": 1.0475,
    "mean_EFFECT": -0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 1,
    "n_p_sig": 27,
    "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": 88966,
    "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": 1286800,
    "ldsc_nsnp_merge_regression_ld": 1286800,
    "ldsc_observed_scale_h2_beta": 0.0688,
    "ldsc_observed_scale_h2_se": 0.0076,
    "ldsc_intercept_beta": 1.004,
    "ldsc_intercept_se": 0.0073,
    "ldsc_lambda_gc": 1.1032,
    "ldsc_mean_chisq": 1.1176,
    "ldsc_ratio": 0.034
}

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 8869869 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 8878036 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 8.646674e+00 5.759678e+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 7.877942e+07 5.634531e+07 828.0000000 3.241435e+07 6.933640e+07 1.145509e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -4.490000e-05 1.570630e-02 -0.1736890 -6.406900e-03 -2.900000e-06 6.353300e-03 1.680050e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.260670e-02 8.998400e-03 0.0047276 5.612500e-03 8.480400e-03 1.723000e-02 1.079100e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.885555e-01 2.918472e-01 0.0000000 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 4.885561e-01 2.918222e-01 0.0000000 2.330210e-01 4.851345e-01 7.412430e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.231463e-01 2.588611e-01 0.0041970 2.201400e-02 1.057010e-01 3.522500e-01 9.958030e-01 ▇▂▁▁▁
numeric AF_reference 88966 0.9899791 NA NA NA NA NA NA NA 2.232303e-01 2.508099e-01 0.0000000 1.936900e-02 1.228040e-01 3.502400e-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 0.0094023 0.0087276 0.2800000 0.2813401 0.623505 0.7821490 NA
1 54676 rs2462492 C T -0.0080900 0.0086649 0.3500000 0.3504852 0.399158 NA NA
1 86028 rs114608975 T C 0.0115883 0.0137489 0.4000000 0.3993107 0.103979 0.0277556 NA
1 91536 rs6702460 G T -0.0006846 0.0085172 0.9400001 0.9359318 0.455954 0.4207270 NA
1 234313 rs8179466 C T -0.0036494 0.0167640 0.8300000 0.8276667 0.074560 NA NA
1 534192 rs6680723 C T -0.0184978 0.0097555 0.0580003 0.0579417 0.240218 NA NA
1 546697 rs12025928 A G -0.0216534 0.0120897 0.0729995 0.0732834 0.912844 NA NA
1 693731 rs12238997 A G -0.0143281 0.0081174 0.0779992 0.0775450 0.117537 0.1417730 NA
1 705882 rs72631875 G A 0.0051188 0.0119009 0.6700003 0.6671087 0.067410 0.0315495 NA
1 706368 rs55727773 A G 0.0008418 0.0060192 0.8900000 0.8887780 0.515046 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0038838 0.0072758 0.5900000 0.5934835 0.137482 0.2052720 NA
22 51219387 rs9616832 T C -0.0113893 0.0094746 0.2300001 0.2293315 0.072750 0.0654952 NA
22 51219704 rs147475742 G A -0.0258490 0.0126175 0.0400000 0.0404951 0.041805 0.0473243 NA
22 51221190 rs369304721 G A -0.0124424 0.0126994 0.3300000 0.3272032 0.049124 NA NA
22 51221731 rs115055839 T C -0.0103584 0.0094760 0.2700001 0.2743429 0.072308 0.0625000 NA
22 51222100 rs114553188 G T 0.0062097 0.0111022 0.5800000 0.5759441 0.054502 0.0880591 NA
22 51223637 rs375798137 G A 0.0062860 0.0111580 0.5700002 0.5731898 0.054135 0.0788738 NA
22 51229805 rs9616985 T C -0.0102353 0.0095114 0.2800000 0.2818807 0.072184 0.0730831 NA
22 51232488 rs376461333 A G -0.0018797 0.0224706 0.9299999 0.9333328 0.020055 NA NA
22 51237063 rs3896457 T C -0.0041633 0.0057991 0.4700002 0.4728026 0.297913 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623505 ES:SE:LP:AF:ID  0.00940231:0.00872755:0.552842:0.623505:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399158 ES:SE:LP:AF:ID  -0.00808999:0.00866492:0.455932:0.399158:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103979 ES:SE:LP:AF:ID  0.0115883:0.0137489:0.39794:0.103979:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455954 ES:SE:LP:AF:ID  -0.000684648:0.00851721:0.0268721:0.455954:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.07456  ES:SE:LP:AF:ID  -0.00364944:0.016764:0.0809219:0.07456:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240218 ES:SE:LP:AF:ID  -0.0184978:0.00975551:1.23657:0.240218:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912844 ES:SE:LP:AF:ID  -0.0216534:0.0120897:1.13668:0.912844:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117537 ES:SE:LP:AF:ID  -0.0143281:0.00811739:1.10791:0.117537:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.06741  ES:SE:LP:AF:ID  0.00511881:0.0119009:0.173925:0.06741:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515046 ES:SE:LP:AF:ID  0.000841782:0.00601915:0.05061:0.515046:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033464 ES:SE:LP:AF:ID  0.0255993:0.0150487:1.05061:0.033464:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037138 ES:SE:LP:AF:ID  0.0264749:0.0136788:1.27572:0.037138:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037222 ES:SE:LP:AF:ID  0.02652:0.013634:1.284:0.037222:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036892 ES:SE:LP:AF:ID  0.0269382:0.0137341:1.30103:0.036892:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016769 ES:SE:LP:AF:ID  -0.0113032:0.021101:0.229148:0.016769:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037477 ES:SE:LP:AF:ID  0.0267265:0.013578:1.3098:0.037477:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037563 ES:SE:LP:AF:ID  0.0270754:0.013535:1.34679:0.037563:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.100858 ES:SE:LP:AF:ID  0.0086403:0.0099799:0.408935:0.100858:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958184 ES:SE:LP:AF:ID  -0.0222947:0.0130136:1.06048:0.958184:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.03191  ES:SE:LP:AF:ID  -0.0296895:0.023669:0.677781:0.03191:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052678 ES:SE:LP:AF:ID  -0.00158862:0.019142:0.0315171:0.052678:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037038 ES:SE:LP:AF:ID  0.0256679:0.0136272:1.22185:0.037038:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037361 ES:SE:LP:AF:ID  0.0264083:0.0135106:1.29243:0.037361:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841197 ES:SE:LP:AF:ID  0.00452827:0.00702657:0.283997:0.841197:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055916 ES:SE:LP:AF:ID  -0.0320734:0.0114646:2.29243:0.055916:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.12353  ES:SE:LP:AF:ID  -0.0134865:0.00770394:1.09691:0.12353:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025888 ES:SE:LP:AF:ID  -0.015649:0.0189328:0.387216:0.025888:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122691 ES:SE:LP:AF:ID  -0.0139802:0.00770863:1.1549:0.122691:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.133313 ES:SE:LP:AF:ID  -0.00412741:0.00759975:0.229148:0.133313:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.01125  ES:SE:LP:AF:ID  -0.0187469:0.0274846:0.30103:0.01125:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.006038 ES:SE:LP:AF:ID  -0.0124768:0.0345484:0.142668:0.006038:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037341 ES:SE:LP:AF:ID  0.0266588:0.0133573:1.33724:0.037341:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837078 ES:SE:LP:AF:ID  0.00158904:0.00680175:0.0861861:0.837078:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.836668 ES:SE:LP:AF:ID  0.00109487:0.00679465:0.0604807:0.836668:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868267 ES:SE:LP:AF:ID  0.00941389:0.0072942:0.69897:0.868267:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131381 ES:SE:LP:AF:ID  -0.00836159:0.00730921:0.60206:0.131381:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037784 ES:SE:LP:AF:ID  0.0256412:0.0131481:1.29243:0.037784:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038035 ES:SE:LP:AF:ID  0.0256689:0.0130668:1.3098:0.038035:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867594 ES:SE:LP:AF:ID  0.0086449:0.00728057:0.619789:0.867594:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.867673 ES:SE:LP:AF:ID  0.00872025:0.00728322:0.638272:0.867673:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037976 ES:SE:LP:AF:ID  0.0260537:0.0131212:1.3279:0.037976:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.867592 ES:SE:LP:AF:ID  0.00863795:0.0072801:0.619789:0.867592:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00506  ES:SE:LP:AF:ID  0.0597938:0.0377981:0.958607:0.00506:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005027 ES:SE:LP:AF:ID  0.0608542:0.0379077:0.958607:0.005027:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.836191 ES:SE:LP:AF:ID  0.00061207:0.00677924:0.0315171:0.836191:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037994 ES:SE:LP:AF:ID  0.0273891:0.0131384:1.4318:0.037994:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836792 ES:SE:LP:AF:ID  0.000374504:0.00679777:0.0177288:0.836792:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013255 ES:SE:LP:AF:ID  -0.0360886:0.0243056:0.853872:0.013255:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005432 ES:SE:LP:AF:ID  0.019742:0.0371352:0.229148:0.005432:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838    ES:SE:LP:AF:ID  0.00103441:0.00688866:0.0555173:0.838:rs3131965