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_4570.vcf.gz --id UKB-b:3304 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_4570.txt.gz --cohort_controls 151232 --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-3304/UKB-b-3304_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-3304/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:43:24 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-3304/UKB-b-3304_data.vcf.gz ...
Read summary statistics for 9324349 SNPs.
Dropped 10416 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, 1287994 SNPs remain.
After merging with regression SNP LD, 1287994 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0586 (0.0041)
Lambda GC: 1.1762
Mean Chi^2: 1.1956
Intercept: 1.0218 (0.0064)
Ratio: 0.1116 (0.0325)
Analysis finished at Thu Oct 17 14:45:09 2019
Total time elapsed: 1.0m:45.02s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9488,
    "inflation_factor": 1.1474,
    "mean_EFFECT": 0.0001,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 4,
    "n_p_sig": 32,
    "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": 113584,
    "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": 1287994,
    "ldsc_nsnp_merge_regression_ld": 1287994,
    "ldsc_observed_scale_h2_beta": 0.0586,
    "ldsc_observed_scale_h2_se": 0.0041,
    "ldsc_intercept_beta": 1.0218,
    "ldsc_intercept_se": 0.0064,
    "ldsc_lambda_gc": 1.1762,
    "ldsc_mean_chisq": 1.1956,
    "ldsc_ratio": 0.1115
}

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 9313985 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 9324349 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 8.634526e+00 5.753879e+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 7.881203e+07 5.630810e+07 828.0000000 3.250459e+07 6.939324e+07 1.145407e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 1.012000e-04 1.014750e-02 -0.1182570 -3.747100e-03 3.700000e-05 3.863400e-03 1.301170e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 7.758900e-03 6.154900e-03 0.0025901 3.117200e-03 4.933200e-03 1.063280e-02 9.020710e-02 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.799423e-01 2.941363e-01 0.0000000 2.200002e-01 4.700002e-01 7.300002e-01 1.000000e+00 ▇▆▇▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.799427e-01 2.941103e-01 0.0000000 2.203052e-01 4.726080e-01 7.349445e-01 1.000000e+00 ▇▇▇▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.134132e-01 2.577723e-01 0.0023150 1.733300e-02 9.199400e-02 3.350310e-01 9.976850e-01 ▇▂▁▁▁
numeric AF_reference 113584 0.9878186 NA NA NA NA NA NA NA 2.144757e-01 2.495554e-01 0.0000000 1.477640e-02 1.106230e-01 3.350640e-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 0.0087431 0.0047748 0.0669993 0.0670853 0.623836 0.7821490 NA
1 54676 rs2462492 C T -0.0067822 0.0047423 0.1499999 0.1526725 0.399260 NA NA
1 86028 rs114608975 T C 0.0070815 0.0075604 0.3500000 0.3489339 0.103756 0.0277556 NA
1 91536 rs6702460 G T -0.0073977 0.0046734 0.1100001 0.1134337 0.456290 0.4207270 NA
1 234313 rs8179466 C T 0.0051537 0.0092232 0.5800000 0.5763163 0.074501 NA NA
1 534192 rs6680723 C T 0.0009756 0.0053388 0.8499999 0.8549997 0.241305 NA NA
1 546697 rs12025928 A G 0.0024069 0.0066223 0.7199992 0.7162688 0.912967 NA NA
1 693731 rs12238997 A G -0.0002082 0.0044488 0.9599999 0.9626802 0.117060 0.1417730 NA
1 705882 rs72631875 G A -0.0056050 0.0065078 0.3900004 0.3890815 0.067693 0.0315495 NA
1 706368 rs55727773 A G -0.0030611 0.0032957 0.3500000 0.3529767 0.514876 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0104456 0.0039932 0.0089000 0.0089000 0.136984 0.2052720 NA
22 51219387 rs9616832 T C -0.0084779 0.0051929 0.1000000 0.1025529 0.072713 0.0654952 NA
22 51219704 rs147475742 G A -0.0124528 0.0069320 0.0719996 0.0724278 0.041651 0.0473243 NA
22 51221190 rs369304721 G A -0.0060674 0.0069486 0.3800004 0.3825615 0.049098 NA NA
22 51221731 rs115055839 T C -0.0086429 0.0051967 0.0959997 0.0962862 0.072190 0.0625000 NA
22 51222100 rs114553188 G T -0.0095790 0.0060882 0.1199999 0.1156317 0.054366 0.0880591 NA
22 51223637 rs375798137 G A -0.0096017 0.0061192 0.1199999 0.1166192 0.053983 0.0788738 NA
22 51229805 rs9616985 T C -0.0084830 0.0052158 0.1000000 0.1038653 0.072044 0.0730831 NA
22 51232488 rs376461333 A G -0.0080273 0.0122298 0.5099998 0.5115837 0.020124 NA NA
22 51237063 rs3896457 T C 0.0008830 0.0031714 0.7800007 0.7806849 0.297470 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623836 ES:SE:LP:AF:ID  0.00874309:0.00477477:1.17393:0.623836:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.39926  ES:SE:LP:AF:ID  -0.00678221:0.00474229:0.823909:0.39926:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103756 ES:SE:LP:AF:ID  0.0070815:0.00756038:0.455932:0.103756:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.45629  ES:SE:LP:AF:ID  -0.00739771:0.00467338:0.958607:0.45629:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074501 ES:SE:LP:AF:ID  0.00515368:0.0092232:0.236572:0.074501:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241305 ES:SE:LP:AF:ID  0.000975626:0.00533879:0.0705811:0.241305:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912967 ES:SE:LP:AF:ID  0.00240686:0.00662226:0.142668:0.912967:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.11706  ES:SE:LP:AF:ID  -0.000208164:0.00444885:0.0177288:0.11706:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067693 ES:SE:LP:AF:ID  -0.00560504:0.00650777:0.408935:0.067693:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.514876 ES:SE:LP:AF:ID  -0.00306112:0.00329567:0.455932:0.514876:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033447 ES:SE:LP:AF:ID  -0.00561835:0.00825184:0.30103:0.033447:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037143 ES:SE:LP:AF:ID  -0.00595652:0.00749244:0.366532:0.037143:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.03727  ES:SE:LP:AF:ID  -0.00687324:0.00746334:0.443698:0.03727:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036924 ES:SE:LP:AF:ID  -0.00596665:0.00752023:0.366532:0.036924:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.01636  ES:SE:LP:AF:ID  -0.00037362:0.0117225:0.0132283:0.01636:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037505 ES:SE:LP:AF:ID  -0.00687381:0.00743398:0.443698:0.037505:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037602 ES:SE:LP:AF:ID  -0.00707098:0.00740979:0.468521:0.037602:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101622 ES:SE:LP:AF:ID  0.00296657:0.00542528:0.236572:0.101622:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958448 ES:SE:LP:AF:ID  0.0082721:0.00714965:0.60206:0.958448:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031684 ES:SE:LP:AF:ID  -0.00972195:0.013053:0.337242:0.031684:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052631 ES:SE:LP:AF:ID  -0.000521988:0.0105372:0.0177288:0.052631:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037072 ES:SE:LP:AF:ID  -0.00663339:0.00746166:0.431798:0.037072:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037403 ES:SE:LP:AF:ID  -0.00696786:0.00739467:0.455932:0.037403:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841933 ES:SE:LP:AF:ID  0.00158542:0.00385229:0.167491:0.841933:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056144 ES:SE:LP:AF:ID  0.00405698:0.00625511:0.283997:0.056144:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122971 ES:SE:LP:AF:ID  0.00058223:0.00422271:0.05061:0.122971:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025762 ES:SE:LP:AF:ID  0.00936135:0.0103902:0.431798:0.025762:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122189 ES:SE:LP:AF:ID  0.0005135:0.00422479:0.0457575:0.122189:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.133387 ES:SE:LP:AF:ID  -0.00192203:0.0041571:0.19382:0.133387:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011253 ES:SE:LP:AF:ID  -0.0025142:0.015072:0.0604807:0.011253:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005842 ES:SE:LP:AF:ID  0.0185721:0.0192724:0.468521:0.005842:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037341 ES:SE:LP:AF:ID  -0.00606925:0.00731922:0.387216:0.037341:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837676 ES:SE:LP:AF:ID  0.00182176:0.00372987:0.200659:0.837676:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.837289 ES:SE:LP:AF:ID  0.00176032:0.00372565:0.19382:0.837289:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868921 ES:SE:LP:AF:ID  0.000283041:0.00399865:0.0268721:0.868921:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130782 ES:SE:LP:AF:ID  -0.000435811:0.00400646:0.0409586:0.130782:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037829 ES:SE:LP:AF:ID  -0.00605259:0.00719955:0.39794:0.037829:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038081 ES:SE:LP:AF:ID  -0.00597902:0.00715378:0.39794:0.038081:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868243 ES:SE:LP:AF:ID  0.000473846:0.00399062:0.0409586:0.868243:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868352 ES:SE:LP:AF:ID  0.000515833:0.00399243:0.0457575:0.868352:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038017 ES:SE:LP:AF:ID  -0.00575465:0.00718457:0.376751:0.038017:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.86824  ES:SE:LP:AF:ID  0.000323685:0.00399036:0.0268721:0.86824:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005144 ES:SE:LP:AF:ID  -0.00014843:0.0205323:0.00436481:0.005144:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005114 ES:SE:LP:AF:ID  0.000478393:0.0205774:0.00877392:0.005114:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.836771 ES:SE:LP:AF:ID  0.00161704:0.00371608:0.180456:0.836771:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038028 ES:SE:LP:AF:ID  -0.00591287:0.00719455:0.387216:0.038028:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.837402 ES:SE:LP:AF:ID  0.00180522:0.00372636:0.200659:0.837402:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013254 ES:SE:LP:AF:ID  -0.0155472:0.0133345:0.619789:0.013254:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005451 ES:SE:LP:AF:ID  0.0260032:0.0202654:0.69897:0.005451:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838616 ES:SE:LP:AF:ID  0.00130878:0.00377721:0.136677:0.838616:rs3131965