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_4559.vcf.gz --id UKB-b:3666 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_4559.txt.gz --cohort_controls 151531 --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-3666/UKB-b-3666_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-3666/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:43:45 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-3666/UKB-b-3666_data.vcf.gz ...
Read summary statistics for 9326196 SNPs.
Dropped 10422 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, 1288007 SNPs remain.
After merging with regression SNP LD, 1288007 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0606 (0.0039)
Lambda GC: 1.1855
Mean Chi^2: 1.2007
Intercept: 1.024 (0.0071)
Ratio: 0.1198 (0.0352)
Analysis finished at Thu Oct 17 14:45:29 2019
Total time elapsed: 1.0m:44.69s

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": 7,
    "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": 113685,
    "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": 1288007,
    "ldsc_nsnp_merge_regression_ld": 1288007,
    "ldsc_observed_scale_h2_beta": 0.0606,
    "ldsc_observed_scale_h2_se": 0.0039,
    "ldsc_intercept_beta": 1.024,
    "ldsc_intercept_se": 0.0071,
    "ldsc_lambda_gc": 1.1855,
    "ldsc_mean_chisq": 1.2007,
    "ldsc_ratio": 0.1196
}

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 9315826 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 9326196 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 8.634432e+00 5.753878e+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 7.881332e+07 5.630824e+07 828.0000000 3.250569e+07 6.939407e+07 1.145433e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 1.202000e-04 1.246050e-02 -0.1588130 -4.610700e-03 4.580000e-05 4.735600e-03 1.669630e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 9.490800e-03 7.532100e-03 0.0031665 3.811200e-03 6.032600e-03 1.300560e-02 1.097320e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.790487e-01 2.944259e-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 4.790513e-01 2.944011e-01 0.0000000 2.182551e-01 4.718982e-01 7.342488e-01 9.999998e-01 ▇▇▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.133755e-01 2.577665e-01 0.0023100 1.731700e-02 9.194500e-02 3.349660e-01 9.976900e-01 ▇▂▁▁▁
numeric AF_reference 113685 0.9878101 NA NA NA NA NA NA NA 2.144439e-01 2.495493e-01 0.0000000 1.477640e-02 1.104230e-01 3.348640e-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 0.0065892 0.0058369 0.2599998 0.2589455 0.623817 0.7821490 NA
1 54676 rs2462492 C T -0.0085466 0.0057976 0.1400000 0.1404390 0.399267 NA NA
1 86028 rs114608975 T C -0.0016370 0.0092390 0.8600001 0.8593636 0.103819 0.0277556 NA
1 91536 rs6702460 G T -0.0026362 0.0057135 0.6400000 0.6445202 0.456279 0.4207270 NA
1 234313 rs8179466 C T 0.0010107 0.0112808 0.9299999 0.9286093 0.074486 NA NA
1 534192 rs6680723 C T 0.0022725 0.0065298 0.7300002 0.7278237 0.241216 NA NA
1 546697 rs12025928 A G -0.0008146 0.0080984 0.9199999 0.9198771 0.913025 NA NA
1 693731 rs12238997 A G -0.0002014 0.0054426 0.9699999 0.9704805 0.116970 0.1417730 NA
1 705882 rs72631875 G A 0.0019218 0.0079607 0.8100000 0.8092386 0.067633 0.0315495 NA
1 706368 rs55727773 A G -0.0024570 0.0040289 0.5400003 0.5419550 0.514982 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0001280 0.0048818 0.9800000 0.9790776 0.137089 0.2052720 NA
22 51219387 rs9616832 T C 0.0019417 0.0063487 0.7600007 0.7597189 0.072740 0.0654952 NA
22 51219704 rs147475742 G A -0.0010062 0.0084799 0.9100000 0.9055440 0.041633 0.0473243 NA
22 51221190 rs369304721 G A 0.0027822 0.0084966 0.7400005 0.7433280 0.049104 NA NA
22 51221731 rs115055839 T C 0.0022722 0.0063535 0.7199992 0.7206244 0.072214 0.0625000 NA
22 51222100 rs114553188 G T -0.0017061 0.0074399 0.8200001 0.8186247 0.054436 0.0880591 NA
22 51223637 rs375798137 G A -0.0015360 0.0074782 0.8400000 0.8372588 0.054046 0.0788738 NA
22 51229805 rs9616985 T C 0.0025357 0.0063768 0.6899999 0.6908931 0.072069 0.0730831 NA
22 51232488 rs376461333 A G 0.0047258 0.0149311 0.7499995 0.7516171 0.020166 NA NA
22 51237063 rs3896457 T C -0.0004189 0.0038772 0.9100000 0.9139551 0.297571 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623817 ES:SE:LP:AF:ID  0.00658919:0.00583689:0.585027:0.623817:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399267 ES:SE:LP:AF:ID  -0.00854658:0.0057976:0.853872:0.399267:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103819 ES:SE:LP:AF:ID  -0.00163701:0.00923902:0.0655015:0.103819:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456279 ES:SE:LP:AF:ID  -0.00263615:0.00571352:0.19382:0.456279:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074486 ES:SE:LP:AF:ID  0.0010107:0.0112808:0.0315171:0.074486:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241216 ES:SE:LP:AF:ID  0.00227251:0.00652979:0.136677:0.241216:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913025 ES:SE:LP:AF:ID  -0.000814601:0.00809835:0.0362122:0.913025:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.11697  ES:SE:LP:AF:ID  -0.000201406:0.00544257:0.0132283:0.11697:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067633 ES:SE:LP:AF:ID  0.00192178:0.0079607:0.091515:0.067633:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.514982 ES:SE:LP:AF:ID  -0.00245705:0.00402888:0.267606:0.514982:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033476 ES:SE:LP:AF:ID  -0.00136229:0.0100842:0.05061:0.033476:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037178 ES:SE:LP:AF:ID  -0.00240539:0.00915611:0.102373:0.037178:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037308 ES:SE:LP:AF:ID  -0.00248595:0.00911999:0.102373:0.037308:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036966 ES:SE:LP:AF:ID  -0.000940651:0.00918878:0.0362122:0.036966:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016381 ES:SE:LP:AF:ID  0.0128292:0.0143195:0.431798:0.016381:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037546 ES:SE:LP:AF:ID  -0.00113075:0.00908398:0.0457575:0.037546:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037642 ES:SE:LP:AF:ID  -0.00139891:0.00905448:0.0555173:0.037642:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101573 ES:SE:LP:AF:ID  0.000911088:0.00663493:0.05061:0.101573:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.9584   ES:SE:LP:AF:ID  0.00103321:0.00873526:0.0409586:0.9584:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031703 ES:SE:LP:AF:ID  0.0114069:0.0159515:0.327902:0.031703:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052658 ES:SE:LP:AF:ID  -0.00670688:0.0128756:0.221849:0.052658:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037119 ES:SE:LP:AF:ID  -0.00153182:0.0091165:0.0604807:0.037119:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037444 ES:SE:LP:AF:ID  -0.00156829:0.00903578:0.0655015:0.037444:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841993 ES:SE:LP:AF:ID  -0.000919437:0.00471072:0.0705811:0.841993:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056151 ES:SE:LP:AF:ID  0.00255382:0.00764802:0.130768:0.056151:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122905 ES:SE:LP:AF:ID  -4.97412e-05:0.00516564:0.00436481:0.122905:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025754 ES:SE:LP:AF:ID  -0.00726089:0.0127009:0.244125:0.025754:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122125 ES:SE:LP:AF:ID  -0.000368337:0.00516809:0.0268721:0.122125:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.133318 ES:SE:LP:AF:ID  -0.00291499:0.00508416:0.244125:0.133318:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011193 ES:SE:LP:AF:ID  -0.0410575:0.0184911:1.58503:0.011193:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005832 ES:SE:LP:AF:ID  0.0222862:0.0235768:0.468521:0.005832:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037391 ES:SE:LP:AF:ID  -0.00173335:0.00894207:0.0705811:0.037391:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837734 ES:SE:LP:AF:ID  -0.0012813:0.00456114:0.107905:0.837734:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.837346 ES:SE:LP:AF:ID  -0.00154568:0.00455597:0.136677:0.837346:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868981 ES:SE:LP:AF:ID  -0.00127544:0.00489078:0.102373:0.868981:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130722 ES:SE:LP:AF:ID  0.00157699:0.00490029:0.124939:0.130722:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.03788  ES:SE:LP:AF:ID  -0.00205923:0.0087958:0.091515:0.03788:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038131 ES:SE:LP:AF:ID  -0.00133972:0.00874013:0.0555173:0.038131:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868302 ES:SE:LP:AF:ID  -0.0016454:0.00488087:0.130768:0.868302:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.86841  ES:SE:LP:AF:ID  -0.00171704:0.00488308:0.136677:0.86841:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038065 ES:SE:LP:AF:ID  -0.00125537:0.00877795:0.05061:0.038065:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.868298 ES:SE:LP:AF:ID  -0.00176047:0.00488055:0.142668:0.868298:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005128 ES:SE:LP:AF:ID  0.0146401:0.0251539:0.251812:0.005128:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005098 ES:SE:LP:AF:ID  0.0150393:0.0252089:0.259637:0.005098:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.836828 ES:SE:LP:AF:ID  -0.00161882:0.00454421:0.142668:0.836828:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038074 ES:SE:LP:AF:ID  -0.00135202:0.00879024:0.0555173:0.038074:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.837458 ES:SE:LP:AF:ID  -0.00146138:0.00455681:0.124939:0.837458:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013261 ES:SE:LP:AF:ID  -0.0133603:0.0162956:0.387216:0.013261:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005455 ES:SE:LP:AF:ID  -0.00158478:0.0247675:0.0222764:0.005455:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838679 ES:SE:LP:AF:ID  -0.00149894:0.00461916:0.124939:0.838679:rs3131965