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_4244.vcf.gz --id UKB-b:4776 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_4244.txt.gz --cohort_controls 148549 --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-4776/UKB-b-4776_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4776/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:44:41 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4776/UKB-b-4776_data.vcf.gz ...
Read summary statistics for 9312000 SNPs.
Dropped 10347 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, 1287971 SNPs remain.
After merging with regression SNP LD, 1287971 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0135 (0.0035)
Lambda GC: 1.0806
Mean Chi^2: 1.088
Intercept: 1.0489 (0.0068)
Ratio: 0.5551 (0.0775)
Analysis finished at Thu Oct 17 14:46:25 2019
Total time elapsed: 1.0m:44.49s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9487,
    "inflation_factor": 1.0475,
    "mean_EFFECT": 0.0002,
    "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": 112560,
    "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": 1287971,
    "ldsc_nsnp_merge_regression_ld": 1287971,
    "ldsc_observed_scale_h2_beta": 0.0135,
    "ldsc_observed_scale_h2_se": 0.0035,
    "ldsc_intercept_beta": 1.0489,
    "ldsc_intercept_se": 0.0068,
    "ldsc_lambda_gc": 1.0806,
    "ldsc_mean_chisq": 1.088,
    "ldsc_ratio": 0.5557
}

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 1.0000000 3 58 0 9301705 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 9312000 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 8.634885e+00 5.754012e+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 7.881133e+07 5.630726e+07 828.0000000 3.250308e+07 6.939439e+07 1.145403e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 1.836000e-04 1.353750e-02 -0.1659860 -4.822700e-03 5.980000e-05 5.015600e-03 1.789800e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.039270e-02 8.216000e-03 0.0034810 4.188600e-03 6.620200e-03 1.424540e-02 1.202080e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.897836e-01 2.917726e-01 0.0000001 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 4.897834e-01 2.917451e-01 0.0000001 2.343319e-01 4.864661e-01 7.421984e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.136640e-01 2.577996e-01 0.0023570 1.744500e-02 9.236200e-02 3.354760e-01 9.976430e-01 ▇▂▁▁▁
numeric AF_reference 112560 0.9879124 NA NA NA NA NA NA NA 2.146897e-01 2.495893e-01 0.0000000 1.477640e-02 1.108230e-01 3.354630e-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 -0.0001681 0.0064134 0.9800000 0.9790851 0.623732 0.7821490 NA
1 54676 rs2462492 C T 0.0024712 0.0063732 0.6999999 0.6981958 0.399191 NA NA
1 86028 rs114608975 T C -0.0026228 0.0101571 0.8000000 0.7962315 0.103777 0.0277556 NA
1 91536 rs6702460 G T -0.0045365 0.0062785 0.4700002 0.4699613 0.456304 0.4207270 NA
1 234313 rs8179466 C T 0.0134434 0.0123818 0.2800000 0.2775946 0.074561 NA NA
1 534192 rs6680723 C T 0.0014309 0.0071772 0.8400000 0.8419782 0.241196 NA NA
1 546697 rs12025928 A G -0.0010775 0.0089013 0.9000000 0.9036514 0.913007 NA NA
1 693731 rs12238997 A G 0.0017266 0.0059847 0.7700005 0.7729582 0.116945 0.1417730 NA
1 705882 rs72631875 G A 0.0064138 0.0087520 0.4600002 0.4636596 0.067589 0.0315495 NA
1 706368 rs55727773 A G -0.0052310 0.0044301 0.2399999 0.2376895 0.514798 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0034528 0.0053669 0.5199996 0.5199908 0.137088 0.2052720 NA
22 51219387 rs9616832 T C 0.0080696 0.0069860 0.2500000 0.2480408 0.072609 0.0654952 NA
22 51219704 rs147475742 G A 0.0049180 0.0093232 0.5999997 0.5978460 0.041599 0.0473243 NA
22 51221190 rs369304721 G A 0.0035390 0.0093501 0.7099994 0.7050632 0.049026 NA NA
22 51221731 rs115055839 T C 0.0077780 0.0069910 0.2700001 0.2658957 0.072082 0.0625000 NA
22 51222100 rs114553188 G T -0.0045876 0.0081700 0.5700002 0.5744459 0.054564 0.0880591 NA
22 51223637 rs375798137 G A -0.0045886 0.0082113 0.5800000 0.5762911 0.054179 0.0788738 NA
22 51229805 rs9616985 T C 0.0076239 0.0070174 0.2800000 0.2772895 0.071927 0.0730831 NA
22 51232488 rs376461333 A G -0.0243734 0.0163932 0.1400000 0.1370678 0.020217 NA NA
22 51237063 rs3896457 T C 0.0005597 0.0042646 0.9000000 0.8955784 0.297520 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623732 ES:SE:LP:AF:ID  -0.000168134:0.00641342:0.00877392:0.623732:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399191 ES:SE:LP:AF:ID  0.00247125:0.00637319:0.154902:0.399191:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103777 ES:SE:LP:AF:ID  -0.00262284:0.0101571:0.09691:0.103777:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456304 ES:SE:LP:AF:ID  -0.00453648:0.0062785:0.327902:0.456304:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074561 ES:SE:LP:AF:ID  0.0134434:0.0123818:0.552842:0.074561:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241196 ES:SE:LP:AF:ID  0.00143088:0.00717723:0.0757207:0.241196:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913007 ES:SE:LP:AF:ID  -0.0010775:0.00890128:0.0457575:0.913007:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116945 ES:SE:LP:AF:ID  0.00172662:0.00598466:0.113509:0.116945:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067589 ES:SE:LP:AF:ID  0.00641375:0.00875198:0.337242:0.067589:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.514798 ES:SE:LP:AF:ID  -0.00523098:0.0044301:0.619789:0.514798:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033514 ES:SE:LP:AF:ID  -0.00610505:0.0110796:0.236572:0.033514:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037204 ES:SE:LP:AF:ID  -0.00537599:0.0100637:0.229148:0.037204:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037331 ES:SE:LP:AF:ID  -0.00547492:0.0100239:0.236572:0.037331:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036986 ES:SE:LP:AF:ID  -0.0054381:0.0101003:0.229148:0.036986:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016416 ES:SE:LP:AF:ID  0.00882284:0.0157208:0.244125:0.016416:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037562 ES:SE:LP:AF:ID  -0.00591608:0.00998598:0.259637:0.037562:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.03766  ES:SE:LP:AF:ID  -0.00584702:0.00995322:0.251812:0.03766:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101519 ES:SE:LP:AF:ID  0.00134295:0.00729641:0.0705811:0.101519:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958413 ES:SE:LP:AF:ID  0.00705923:0.00960523:0.337242:0.958413:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031742 ES:SE:LP:AF:ID  -0.00949221:0.0175222:0.229148:0.031742:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052586 ES:SE:LP:AF:ID  -0.0132762:0.0141666:0.455932:0.052586:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037133 ES:SE:LP:AF:ID  -0.00620268:0.0100221:0.267606:0.037133:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037461 ES:SE:LP:AF:ID  -0.00470006:0.0099329:0.19382:0.037461:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.842031 ES:SE:LP:AF:ID  -5.38366e-05:0.00518108:0.00436481:0.842031:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056096 ES:SE:LP:AF:ID  0.00422599:0.00841662:0.207608:0.056096:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122874 ES:SE:LP:AF:ID  0.00289656:0.00567987:0.21467:0.122874:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025803 ES:SE:LP:AF:ID  0.0165997:0.0139486:0.638272:0.025803:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122097 ES:SE:LP:AF:ID  0.00303121:0.0056826:0.229148:0.122097:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.133294 ES:SE:LP:AF:ID  -0.000417039:0.00559228:0.0268721:0.133294:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011292 ES:SE:LP:AF:ID  0.00913766:0.0202084:0.187087:0.011292:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.00587  ES:SE:LP:AF:ID  -0.0506645:0.0258339:1.30103:0.00587:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037404 ES:SE:LP:AF:ID  -0.00435959:0.00983146:0.180456:0.037404:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837717 ES:SE:LP:AF:ID  -0.00184462:0.0050163:0.148742:0.837717:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.837332 ES:SE:LP:AF:ID  -0.00170741:0.00501083:0.136677:0.837332:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868976 ES:SE:LP:AF:ID  -0.00358089:0.00537747:0.29243:0.868976:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130709 ES:SE:LP:AF:ID  0.00313729:0.0053887:0.251812:0.130709:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037892 ES:SE:LP:AF:ID  -0.0021321:0.00967063:0.0809219:0.037892:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038153 ES:SE:LP:AF:ID  -0.00238442:0.00960798:0.09691:0.038153:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868302 ES:SE:LP:AF:ID  -0.00322539:0.00536689:0.259637:0.868302:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868409 ES:SE:LP:AF:ID  -0.00310265:0.00536931:0.251812:0.868409:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038083 ES:SE:LP:AF:ID  -0.00326357:0.00965024:0.130768:0.038083:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.868298 ES:SE:LP:AF:ID  -0.00318311:0.00536653:0.259637:0.868298:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005151 ES:SE:LP:AF:ID  0.0188012:0.0275916:0.30103:0.005151:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005122 ES:SE:LP:AF:ID  0.0200902:0.0276516:0.327902:0.005122:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.836815 ES:SE:LP:AF:ID  -0.0010077:0.00499788:0.0757207:0.836815:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038096 ES:SE:LP:AF:ID  -0.00383383:0.00966328:0.161151:0.038096:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.83744  ES:SE:LP:AF:ID  -0.000794637:0.00501164:0.0604807:0.83744:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013242 ES:SE:LP:AF:ID  -0.0274368:0.0179336:0.886057:0.013242:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005465 ES:SE:LP:AF:ID  0.0404059:0.0272056:0.853872:0.005465:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838661 ES:SE:LP:AF:ID  -0.000668062:0.00507972:0.0457575:0.838661:rs3131965