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_4233.vcf.gz --id UKB-b:13501 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_4233.txt.gz --cohort_controls 148579 --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-13501/UKB-b-13501_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-13501/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:42:47 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-13501/UKB-b-13501_data.vcf.gz ...
Read summary statistics for 9312189 SNPs.
Dropped 10350 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: 0.0152 (0.0035)
Lambda GC: 1.0783
Mean Chi^2: 1.0913
Intercept: 1.0468 (0.0066)
Ratio: 0.5128 (0.0718)
Analysis finished at Thu Oct 17 14:44:34 2019
Total time elapsed: 1.0m:46.4s

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": 112587,
    "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": 0.0152,
    "ldsc_observed_scale_h2_se": 0.0035,
    "ldsc_intercept_beta": 1.0468,
    "ldsc_intercept_se": 0.0066,
    "ldsc_lambda_gc": 1.0783,
    "ldsc_mean_chisq": 1.0913,
    "ldsc_ratio": 0.5126
}

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 9301891 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 9312189 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 8.634869e+00 5.754023e+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 7.881166e+07 5.630719e+07 828.0000000 3.250313e+07 6.939521e+07 1.145415e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 1.965000e-04 1.359270e-02 -0.1751800 -4.817000e-03 7.810000e-05 5.043500e-03 1.622920e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.039960e-02 8.222000e-03 0.0034835 4.191000e-03 6.623800e-03 1.425520e-02 1.205800e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.892128e-01 2.921345e-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 4.892132e-01 2.921084e-01 0.0000001 2.328837e-01 4.860892e-01 7.423416e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.136612e-01 2.578007e-01 0.0023560 1.744300e-02 9.236100e-02 3.354530e-01 9.976440e-01 ▇▂▁▁▁
numeric AF_reference 112587 0.9879097 NA NA NA NA NA NA NA 2.146869e-01 2.495900e-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 0.0004078 0.0064197 0.9500000 0.9493553 0.623818 0.7821490 NA
1 54676 rs2462492 C T -0.0044724 0.0063777 0.4799997 0.4831370 0.399257 NA NA
1 86028 rs114608975 T C 0.0054784 0.0101712 0.5900000 0.5901499 0.103711 0.0277556 NA
1 91536 rs6702460 G T 0.0066601 0.0062840 0.2900000 0.2892118 0.456340 0.4207270 NA
1 234313 rs8179466 C T -0.0004984 0.0123889 0.9699999 0.9679108 0.074578 NA NA
1 534192 rs6680723 C T 0.0016328 0.0071848 0.8200001 0.8202190 0.241173 NA NA
1 546697 rs12025928 A G 0.0093806 0.0089136 0.2900000 0.2926161 0.913098 NA NA
1 693731 rs12238997 A G 0.0019742 0.0059842 0.7400005 0.7414698 0.117055 0.1417730 NA
1 705882 rs72631875 G A -0.0075161 0.0087619 0.3900004 0.3909930 0.067502 0.0315495 NA
1 706368 rs55727773 A G -0.0005717 0.0044327 0.9000000 0.8973874 0.514761 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0045370 0.0053688 0.4000000 0.3980732 0.137156 0.2052720 NA
22 51219387 rs9616832 T C 0.0135071 0.0069840 0.0530005 0.0531110 0.072778 0.0654952 NA
22 51219704 rs147475742 G A 0.0110043 0.0093144 0.2399999 0.2374324 0.041741 0.0473243 NA
22 51221190 rs369304721 G A 0.0141533 0.0093418 0.1299999 0.1297608 0.049164 NA NA
22 51221731 rs115055839 T C 0.0136165 0.0069890 0.0510000 0.0513810 0.072251 0.0625000 NA
22 51222100 rs114553188 G T -0.0045518 0.0081776 0.5800000 0.5777860 0.054492 0.0880591 NA
22 51223637 rs375798137 G A -0.0054681 0.0082184 0.5099998 0.5058300 0.054111 0.0788738 NA
22 51229805 rs9616985 T C 0.0140691 0.0070146 0.0449997 0.0448880 0.072104 0.0730831 NA
22 51232488 rs376461333 A G -0.0256392 0.0164106 0.1199999 0.1182041 0.020185 NA NA
22 51237063 rs3896457 T C -0.0029312 0.0042664 0.4899999 0.4920681 0.297499 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623818 ES:SE:LP:AF:ID  0.000407758:0.00641973:0.0222764:0.623818:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399257 ES:SE:LP:AF:ID  -0.00447245:0.00637768:0.318759:0.399257:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103711 ES:SE:LP:AF:ID  0.0054784:0.0101712:0.229148:0.103711:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.45634  ES:SE:LP:AF:ID  0.00666009:0.00628398:0.537602:0.45634:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074578 ES:SE:LP:AF:ID  -0.000498389:0.0123889:0.0132283:0.074578:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241173 ES:SE:LP:AF:ID  0.00163283:0.00718475:0.0861861:0.241173:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913098 ES:SE:LP:AF:ID  0.00938063:0.00891356:0.537602:0.913098:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117055 ES:SE:LP:AF:ID  0.00197423:0.00598419:0.130768:0.117055:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067502 ES:SE:LP:AF:ID  -0.00751608:0.00876186:0.408935:0.067502:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.514761 ES:SE:LP:AF:ID  -0.000571654:0.00443272:0.0457575:0.514761:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033496 ES:SE:LP:AF:ID  0.000152265:0.0110914:0.00436481:0.033496:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037184 ES:SE:LP:AF:ID  0.00102719:0.0100743:0.0362122:0.037184:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037316 ES:SE:LP:AF:ID  0.000315266:0.0100341:0.0132283:0.037316:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.03697  ES:SE:LP:AF:ID  0.000305576:0.0101106:0.00877392:0.03697:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016423 ES:SE:LP:AF:ID  -0.000577682:0.0157324:0.0132283:0.016423:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037551 ES:SE:LP:AF:ID  0.000150005:0.00999511:0.00436481:0.037551:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037648 ES:SE:LP:AF:ID  -0.000420611:0.00996248:0.0132283:0.037648:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101584 ES:SE:LP:AF:ID  -0.00750392:0.00729707:0.522879:0.101584:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958416 ES:SE:LP:AF:ID  0.00144942:0.00961273:0.0555173:0.958416:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031724 ES:SE:LP:AF:ID  -0.00712872:0.0175425:0.167491:0.031724:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052626 ES:SE:LP:AF:ID  -0.0302619:0.0141661:1.48149:0.052626:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037121 ES:SE:LP:AF:ID  0.000278196:0.0100316:0.00877392:0.037121:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037448 ES:SE:LP:AF:ID  -6.07156e-05:0.00994229:-0:0.037448:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841906 ES:SE:LP:AF:ID  -0.00105884:0.00518144:0.0757207:0.841906:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056167 ES:SE:LP:AF:ID  0.0030225:0.00841286:0.142668:0.056167:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123001 ES:SE:LP:AF:ID  0.00209807:0.00567952:0.148742:0.123001:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025794 ES:SE:LP:AF:ID  -0.00540073:0.0139597:0.154902:0.025794:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122226 ES:SE:LP:AF:ID  0.00172481:0.00568177:0.119186:0.122226:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.133391 ES:SE:LP:AF:ID  0.00196848:0.00559281:0.142668:0.133391:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011263 ES:SE:LP:AF:ID  0.0235208:0.0202591:0.60206:0.011263:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005876 ES:SE:LP:AF:ID  0.00405034:0.0258388:0.0555173:0.005876:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.0374   ES:SE:LP:AF:ID  0.00108683:0.00983842:0.0409586:0.0374:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837578 ES:SE:LP:AF:ID  0.000994804:0.00501624:0.0757207:0.837578:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.837197 ES:SE:LP:AF:ID  0.000693634:0.00501093:0.05061:0.837197:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868822 ES:SE:LP:AF:ID  0.00072408:0.0053765:0.05061:0.868822:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.13085  ES:SE:LP:AF:ID  -0.000314528:0.0053879:0.0222764:0.13085:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037889 ES:SE:LP:AF:ID  0.00221492:0.00967743:0.0861861:0.037889:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038148 ES:SE:LP:AF:ID  0.00141199:0.00961475:0.0555173:0.038148:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868153 ES:SE:LP:AF:ID  0.000539622:0.00536612:0.0362122:0.868153:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.86826  ES:SE:LP:AF:ID  0.000652876:0.00536854:0.0457575:0.86826:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038079 ES:SE:LP:AF:ID  0.0017546:0.00965699:0.0655015:0.038079:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.868149 ES:SE:LP:AF:ID  0.000580004:0.00536575:0.0409586:0.868149:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005179 ES:SE:LP:AF:ID  -0.0309842:0.0275304:0.585027:0.005179:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005149 ES:SE:LP:AF:ID  -0.0313179:0.0275907:0.585027:0.005149:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.836683 ES:SE:LP:AF:ID  0.00121714:0.00499797:0.091515:0.836683:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038093 ES:SE:LP:AF:ID  0.00132524:0.00966996:0.05061:0.038093:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.837313 ES:SE:LP:AF:ID  0.000928478:0.00501177:0.0705811:0.837313:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013269 ES:SE:LP:AF:ID  -0.0357345:0.0179264:1.33724:0.013269:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.00546  ES:SE:LP:AF:ID  0.024178:0.027241:0.431798:0.00546:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838524 ES:SE:LP:AF:ID  0.00193111:0.0050797:0.154902:0.838524:rs3131965