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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    "gwas_harmonisation_command": "--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/vcf_09_19b/bgzip_vcf/data.batch_100290.vcf.gz --id UKB-b:8374 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_100290.txt.gz --cohort_controls 64942 --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-8374/UKB-b-8374_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-8374/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:41:36 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-8374/UKB-b-8374_data.vcf.gz ...
Read summary statistics for 8624939 SNPs.
Dropped 7370 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, 1285747 SNPs remain.
After merging with regression SNP LD, 1285747 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: -0.0086 (0.007)
Lambda GC: 1.021
Mean Chi^2: 1.0204
Intercept: 1.0314 (0.0061)
Ratio: 1.5405 (0.2999)
Analysis finished at Thu Oct 17 14:43:51 2019
Total time elapsed: 2.0m:15.15s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9462,
    "inflation_factor": 1,
    "mean_EFFECT": 0.0001,
    "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": 83093,
    "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": 1285747,
    "ldsc_nsnp_merge_regression_ld": 1285747,
    "ldsc_observed_scale_h2_beta": "NA",
    "ldsc_observed_scale_h2_se": "NA",
    "ldsc_intercept_beta": 1.0314,
    "ldsc_intercept_se": 0.0061,
    "ldsc_lambda_gc": 1.021,
    "ldsc_mean_chisq": 1.0204,
    "ldsc_ratio": 1.5392
}

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 TRUE
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 8617603 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 8624939 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 8.650346e+00 5.760986e+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 7.877021e+07 5.637169e+07 828.0000000 3.238546e+07 6.929208e+07 1.145691e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.000000 NA NA NA NA NA NA NA 8.180000e-05 9.060800e-03 -0.0891844 -3.752200e-03 -6.800000e-06 3.770600e-03 1.098060e-01 ▁▁▇▁▁
numeric SE 0 1.000000 NA NA NA NA NA NA NA 7.445100e-03 5.082000e-03 0.0029571 3.482200e-03 5.134100e-03 1.006410e-02 5.097280e-02 ▇▂▁▁▁
numeric PVAL 0 1.000000 NA NA NA NA NA NA NA 4.975224e-01 2.889436e-01 0.0000001 2.500000e-01 5.000000e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.000000 NA NA NA NA NA NA NA 4.975217e-01 2.889172e-01 0.0000001 2.468115e-01 4.967657e-01 7.474018e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.000000 NA NA NA NA NA NA NA 2.291185e-01 2.594884e-01 0.0053900 2.519000e-02 1.139000e-01 3.626220e-01 9.946100e-01 ▇▂▁▁▁
numeric AF_reference 83093 0.990366 NA NA NA NA NA NA NA 2.288676e-01 2.514592e-01 0.0000000 2.316290e-02 1.303910e-01 3.596250e-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 -0.0064957 0.0054495 0.2300001 0.2332689 0.623811 0.7821490 NA
1 54676 rs2462492 C T 0.0056500 0.0054336 0.2999998 0.2984214 0.399147 NA NA
1 86028 rs114608975 T C 0.0123919 0.0086494 0.1499999 0.1519453 0.103539 0.0277556 NA
1 91536 rs6702460 G T -0.0027288 0.0053445 0.6100002 0.6096386 0.455906 0.4207270 NA
1 234313 rs8179466 C T 0.0069268 0.0105691 0.5099998 0.5122235 0.074455 NA NA
1 534192 rs6680723 C T 0.0037087 0.0060871 0.5400003 0.5423487 0.242047 NA NA
1 546697 rs12025928 A G 0.0079663 0.0075526 0.2900000 0.2915259 0.912860 NA NA
1 693731 rs12238997 A G -0.0037509 0.0050758 0.4600002 0.4599141 0.117303 0.1417730 NA
1 705882 rs72631875 G A -0.0080737 0.0073979 0.2800000 0.2751201 0.067702 0.0315495 NA
1 706368 rs55727773 A G -0.0049388 0.0037674 0.1900002 0.1898823 0.513314 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0010878 0.0045827 0.8100000 0.8123729 0.136306 0.2052720 NA
22 51219387 rs9616832 T C -0.0017066 0.0059736 0.7800007 0.7751155 0.071782 0.0654952 NA
22 51219704 rs147475742 G A 0.0004281 0.0079470 0.9599999 0.9570380 0.041175 0.0473243 NA
22 51221190 rs369304721 G A -0.0039875 0.0080040 0.6200004 0.6183495 0.048356 NA NA
22 51221731 rs115055839 T C -0.0019839 0.0059750 0.7400005 0.7398598 0.071333 0.0625000 NA
22 51222100 rs114553188 G T 0.0052072 0.0069220 0.4500005 0.4518909 0.054856 0.0880591 NA
22 51223637 rs375798137 G A 0.0053490 0.0069581 0.4400003 0.4420505 0.054476 0.0788738 NA
22 51229805 rs9616985 T C -0.0021599 0.0059934 0.7199992 0.7185636 0.071237 0.0730831 NA
22 51232488 rs376461333 A G 0.0024646 0.0138005 0.8600001 0.8582631 0.020462 NA NA
22 51237063 rs3896457 T C -0.0065996 0.0036175 0.0680002 0.0680987 0.298401 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623811 ES:SE:LP:AF:ID  -0.00649565:0.00544946:0.638272:0.623811:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399147 ES:SE:LP:AF:ID  0.00564996:0.00543357:0.522879:0.399147:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103539 ES:SE:LP:AF:ID  0.0123919:0.00864937:0.823909:0.103539:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455906 ES:SE:LP:AF:ID  -0.00272883:0.00534447:0.21467:0.455906:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074455 ES:SE:LP:AF:ID  0.00692677:0.0105691:0.29243:0.074455:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.242047 ES:SE:LP:AF:ID  0.00370867:0.00608712:0.267606:0.242047:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.91286  ES:SE:LP:AF:ID  0.00796629:0.00755256:0.537602:0.91286:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117303 ES:SE:LP:AF:ID  -0.00375092:0.00507575:0.337242:0.117303:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067702 ES:SE:LP:AF:ID  -0.0080737:0.00739792:0.552842:0.067702:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.513314 ES:SE:LP:AF:ID  -0.00493884:0.00376744:0.721246:0.513314:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033675 ES:SE:LP:AF:ID  -0.00863423:0.0093907:0.443698:0.033675:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037454 ES:SE:LP:AF:ID  -0.00673044:0.00851711:0.366532:0.037454:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037641 ES:SE:LP:AF:ID  -0.00715327:0.00847255:0.39794:0.037641:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037218 ES:SE:LP:AF:ID  -0.0065273:0.0085499:0.346787:0.037218:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016284 ES:SE:LP:AF:ID  0.00685291:0.01341:0.21467:0.016284:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037857 ES:SE:LP:AF:ID  -0.00653449:0.00844359:0.356547:0.037857:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037952 ES:SE:LP:AF:ID  -0.00640615:0.00841679:0.346787:0.037952:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.102744 ES:SE:LP:AF:ID  0.012119:0.00614779:1.3098:0.102744:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958092 ES:SE:LP:AF:ID  0.00262792:0.00812927:0.124939:0.958092:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031692 ES:SE:LP:AF:ID  -0.0201279:0.0148849:0.744727:0.031692:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052725 ES:SE:LP:AF:ID  0.00264218:0.0119894:0.0809219:0.052725:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037445 ES:SE:LP:AF:ID  -0.00672827:0.00847211:0.366532:0.037445:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037714 ES:SE:LP:AF:ID  -0.00587229:0.00840191:0.318759:0.037714:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841455 ES:SE:LP:AF:ID  0.00379106:0.00438953:0.408935:0.841455:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056321 ES:SE:LP:AF:ID  -0.00543827:0.0071311:0.346787:0.056321:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123068 ES:SE:LP:AF:ID  -0.00337332:0.00482134:0.318759:0.123068:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025132 ES:SE:LP:AF:ID  0.00441843:0.0120059:0.148742:0.025132:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.12232  ES:SE:LP:AF:ID  -0.00294573:0.0048229:0.267606:0.12232:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.134123 ES:SE:LP:AF:ID  -0.00547759:0.00473451:0.60206:0.134123:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011559 ES:SE:LP:AF:ID  -0.0242787:0.0168726:0.823909:0.011559:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.006101 ES:SE:LP:AF:ID  0.0109912:0.0214261:0.21467:0.006101:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037596 ES:SE:LP:AF:ID  -0.00568859:0.00832408:0.309804:0.037596:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837043 ES:SE:LP:AF:ID  0.00129055:0.00424632:0.119186:0.837043:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.836747 ES:SE:LP:AF:ID  0.00115612:0.0042432:0.102373:0.836747:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868571 ES:SE:LP:AF:ID  0.000865197:0.00455955:0.0705811:0.868571:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130995 ES:SE:LP:AF:ID  -0.000697538:0.00457126:0.0555173:0.130995:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.038042 ES:SE:LP:AF:ID  -0.00338477:0.00819352:0.167491:0.038042:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038288 ES:SE:LP:AF:ID  -0.00312483:0.00814274:0.154902:0.038288:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867985 ES:SE:LP:AF:ID  0.000587755:0.00455222:0.0457575:0.867985:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868059 ES:SE:LP:AF:ID  0.000646327:0.00455408:0.05061:0.868059:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038196 ES:SE:LP:AF:ID  -0.00303668:0.00818034:0.148742:0.038196:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.867996 ES:SE:LP:AF:ID  0.000564688:0.0045521:0.0457575:0.867996:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005397 ES:SE:LP:AF:ID  0.031349:0.0228135:0.769551:0.005397:rs150578204
1   754503  rs3115859   G   A   .   PASS    AF=0.836172 ES:SE:LP:AF:ID  0.000869986:0.00423078:0.0757207:0.836172:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038199 ES:SE:LP:AF:ID  -0.00315218:0.00819229:0.154902:0.038199:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836806 ES:SE:LP:AF:ID  0.000821165:0.00424237:0.0705811:0.836806:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013024 ES:SE:LP:AF:ID  -0.0109938:0.0153516:0.327902:0.013024:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005662 ES:SE:LP:AF:ID  0.00735406:0.0227836:0.124939:0.005662:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838121 ES:SE:LP:AF:ID  0.000812366:0.00430206:0.0705811:0.838121:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.868237 ES:SE:LP:AF:ID  0.000402082:0.00454624:0.0315171:0.868237:rs3115858