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_5108.vcf.gz --id UKB-b:1732 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_5108.txt.gz --cohort_controls 94258 --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-1732/UKB-b-1732_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-1732/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:40:18 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-1732/UKB-b-1732_data.vcf.gz ...
Read summary statistics for 8978516 SNPs.
Dropped 8612 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, 1287135 SNPs remain.
After merging with regression SNP LD, 1287135 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0454 (0.0053)
Lambda GC: 1.0853
Mean Chi^2: 1.0976
Intercept: 1.0127 (0.0067)
Ratio: 0.1302 (0.0691)
Analysis finished at Thu Oct 17 14:41:55 2019
Total time elapsed: 1.0m:37.15s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9478,
    "inflation_factor": 1.0475,
    "mean_EFFECT": -0.0001,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 1,
    "n_p_sig": 20,
    "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": 92370,
    "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": 1287135,
    "ldsc_nsnp_merge_regression_ld": 1287135,
    "ldsc_observed_scale_h2_beta": 0.0454,
    "ldsc_observed_scale_h2_se": 0.0053,
    "ldsc_intercept_beta": 1.0127,
    "ldsc_intercept_se": 0.0067,
    "ldsc_lambda_gc": 1.0853,
    "ldsc_mean_chisq": 1.0976,
    "ldsc_ratio": 0.1301
}

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 8969943 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 8978516 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 8.643635e+00 5.758321e+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 7.878927e+07 5.634231e+07 828.0000000 3.242774e+07 6.935301e+07 1.145510e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -8.630000e-05 1.513530e-02 -0.1645130 -6.054800e-03 1.390000e-05 6.023100e-03 1.725570e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.211420e-02 8.822300e-03 0.0044341 5.281400e-03 8.063100e-03 1.660520e-02 1.030250e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.903464e-01 2.912760e-01 0.0000000 2.399999e-01 4.899999e-01 7.400005e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.903472e-01 2.912500e-01 0.0000000 2.358471e-01 4.878343e-01 7.421635e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.208670e-01 2.586138e-01 0.0037140 2.083700e-02 1.024190e-01 3.483690e-01 9.962860e-01 ▇▂▁▁▁
numeric AF_reference 92370 0.9897121 NA NA NA NA NA NA NA 2.211248e-01 2.505279e-01 0.0000000 1.817090e-02 1.198080e-01 3.466450e-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 0.0030747 0.0081896 0.7099994 0.7073355 0.623534 0.7821490 NA
1 54676 rs2462492 C T -0.0044768 0.0081368 0.5800000 0.5821873 0.398844 NA NA
1 86028 rs114608975 T C 0.0186767 0.0128827 0.1499999 0.1471281 0.104166 0.0277556 NA
1 91536 rs6702460 G T -0.0017000 0.0079894 0.8300000 0.8315018 0.455509 0.4207270 NA
1 234313 rs8179466 C T -0.0071612 0.0156551 0.6499995 0.6473561 0.074787 NA NA
1 534192 rs6680723 C T 0.0000154 0.0091452 1.0000000 0.9986582 0.240657 NA NA
1 546697 rs12025928 A G 0.0130958 0.0113363 0.2500000 0.2480047 0.912794 NA NA
1 693731 rs12238997 A G -0.0060592 0.0075970 0.4299995 0.4251186 0.117974 0.1417730 NA
1 705882 rs72631875 G A -0.0109982 0.0111410 0.3200000 0.3235532 0.067587 0.0315495 NA
1 706368 rs55727773 A G -0.0034412 0.0056394 0.5400003 0.5417336 0.514361 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0043732 0.0068256 0.5199996 0.5217163 0.137223 0.2052720 NA
22 51219387 rs9616832 T C -0.0063538 0.0088952 0.4799997 0.4750484 0.072665 0.0654952 NA
22 51219704 rs147475742 G A -0.0126738 0.0118352 0.2800000 0.2842340 0.041805 0.0473243 NA
22 51221190 rs369304721 G A -0.0129143 0.0118956 0.2800000 0.2776398 0.049163 NA NA
22 51221731 rs115055839 T C -0.0062108 0.0088975 0.4899999 0.4851521 0.072196 0.0625000 NA
22 51222100 rs114553188 G T 0.0001368 0.0104168 0.9900000 0.9895237 0.054418 0.0880591 NA
22 51223637 rs375798137 G A 0.0000728 0.0104719 0.9900000 0.9944538 0.054029 0.0788738 NA
22 51229805 rs9616985 T C -0.0069877 0.0089296 0.4299995 0.4339004 0.072063 0.0730831 NA
22 51232488 rs376461333 A G 0.0287644 0.0210532 0.1700000 0.1718536 0.020031 NA NA
22 51237063 rs3896457 T C 0.0022184 0.0054291 0.6800001 0.6828210 0.298582 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623534 ES:SE:LP:AF:ID  0.00307469:0.00818963:0.148742:0.623534:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.398844 ES:SE:LP:AF:ID  -0.00447679:0.00813677:0.236572:0.398844:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.104166 ES:SE:LP:AF:ID  0.0186767:0.0128827:0.823909:0.104166:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455509 ES:SE:LP:AF:ID  -0.00169996:0.00798944:0.0809219:0.455509:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074787 ES:SE:LP:AF:ID  -0.00716124:0.0156551:0.187087:0.074787:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240657 ES:SE:LP:AF:ID  1.53797e-05:0.00914517:-0:0.240657:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912794 ES:SE:LP:AF:ID  0.0130958:0.0113363:0.60206:0.912794:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117974 ES:SE:LP:AF:ID  -0.00605915:0.00759699:0.366532:0.117974:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067587 ES:SE:LP:AF:ID  -0.0109982:0.011141:0.49485:0.067587:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.514361 ES:SE:LP:AF:ID  -0.00344115:0.00563944:0.267606:0.514361:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033593 ES:SE:LP:AF:ID  -0.0135643:0.0140948:0.468521:0.033593:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.03724  ES:SE:LP:AF:ID  -0.0130208:0.0128181:0.508638:0.03724:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037327 ES:SE:LP:AF:ID  -0.0134915:0.0127758:0.537602:0.037327:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037028 ES:SE:LP:AF:ID  -0.0130706:0.0128627:0.508638:0.037028:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.01679  ES:SE:LP:AF:ID  0.00568989:0.019787:0.113509:0.01679:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037589 ES:SE:LP:AF:ID  -0.0134524:0.0127201:0.537602:0.037589:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037684 ES:SE:LP:AF:ID  -0.0127977:0.0126792:0.508638:0.037684:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101102 ES:SE:LP:AF:ID  -0.00415801:0.00933677:0.180456:0.101102:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958107 ES:SE:LP:AF:ID  0.00729306:0.0121939:0.259637:0.958107:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031876 ES:SE:LP:AF:ID  -0.00825011:0.0222182:0.148742:0.031876:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052629 ES:SE:LP:AF:ID  0.0334609:0.0179645:1.20066:0.052629:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037162 ES:SE:LP:AF:ID  -0.0134165:0.0127643:0.537602:0.037162:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037472 ES:SE:LP:AF:ID  -0.0129895:0.0126569:0.522879:0.037472:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.840839 ES:SE:LP:AF:ID  0.00711825:0.0065848:0.552842:0.840839:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056187 ES:SE:LP:AF:ID  -0.00579794:0.0107161:0.229148:0.056187:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123793 ES:SE:LP:AF:ID  -0.00587537:0.00721642:0.376751:0.123793:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025864 ES:SE:LP:AF:ID  -0.00773966:0.0177341:0.180456:0.025864:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.12299  ES:SE:LP:AF:ID  -0.00654761:0.00722006:0.443698:0.12299:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.133692 ES:SE:LP:AF:ID  -0.00980104:0.00711951:0.769551:0.133692:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011256 ES:SE:LP:AF:ID  0.0300056:0.0257969:0.619789:0.011256:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.006103 ES:SE:LP:AF:ID  -0.024239:0.0322256:0.346787:0.006103:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037439 ES:SE:LP:AF:ID  -0.0124038:0.0125179:0.49485:0.037439:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.836692 ES:SE:LP:AF:ID  0.00537629:0.00637391:0.39794:0.836692:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.836247 ES:SE:LP:AF:ID  0.00494345:0.00636631:0.356547:0.836247:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.867958 ES:SE:LP:AF:ID  0.00300625:0.00682907:0.180456:0.867958:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131694 ES:SE:LP:AF:ID  -0.00234288:0.0068434:0.136677:0.131694:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037883 ES:SE:LP:AF:ID  -0.014848:0.0123203:0.638272:0.037883:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038129 ES:SE:LP:AF:ID  -0.0134924:0.012244:0.568636:0.038129:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867255 ES:SE:LP:AF:ID  0.0029696:0.00681526:0.180456:0.867255:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.867355 ES:SE:LP:AF:ID  0.00264656:0.00681853:0.154902:0.867355:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038075 ES:SE:LP:AF:ID  -0.014118:0.0122936:0.60206:0.038075:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.867253 ES:SE:LP:AF:ID  0.00297817:0.00681483:0.180456:0.867253:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005077 ES:SE:LP:AF:ID  0.0151332:0.0354054:0.173925:0.005077:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005044 ES:SE:LP:AF:ID  0.0149708:0.0355041:0.173925:0.005044:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.835784 ES:SE:LP:AF:ID  0.00548914:0.00635269:0.408935:0.835784:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038092 ES:SE:LP:AF:ID  -0.0129094:0.0123105:0.537602:0.038092:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836393 ES:SE:LP:AF:ID  0.00566074:0.00636995:0.431798:0.836393:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013143 ES:SE:LP:AF:ID  0.0171804:0.0229054:0.346787:0.013143:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005517 ES:SE:LP:AF:ID  0.020941:0.0345362:0.267606:0.005517:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.837701 ES:SE:LP:AF:ID  0.00679799:0.00645725:0.537602:0.837701:rs3131965