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_4548.vcf.gz --id UKB-b:2053 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_4548.txt.gz --cohort_controls 152420 --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-2053/UKB-b-2053_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-2053/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:42:12 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-2053/UKB-b-2053_data.vcf.gz ...
Read summary statistics for 9329946 SNPs.
Dropped 10455 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, 1288018 SNPs remain.
After merging with regression SNP LD, 1288018 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0775 (0.0046)
Lambda GC: 1.2092
Mean Chi^2: 1.2509
Intercept: 1.02 (0.0075)
Ratio: 0.0798 (0.0301)
Analysis finished at Thu Oct 17 14:43:51 2019
Total time elapsed: 1.0m:39.42s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9488,
    "inflation_factor": 1.1474,
    "mean_EFFECT": 0.0001,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 2,
    "n_p_sig": 8,
    "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": 114057,
    "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": 1288018,
    "ldsc_nsnp_merge_regression_ld": 1288018,
    "ldsc_observed_scale_h2_beta": 0.0775,
    "ldsc_observed_scale_h2_se": 0.0046,
    "ldsc_intercept_beta": 1.02,
    "ldsc_intercept_se": 0.0075,
    "ldsc_lambda_gc": 1.2092,
    "ldsc_mean_chisq": 1.2509,
    "ldsc_ratio": 0.0797
}

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 9319543 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 9329946 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 8.634389e+00 5.753913e+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 7.881301e+07 5.630809e+07 828.0000000 3.250582e+07 6.939321e+07 1.145420e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 7.580000e-05 1.201940e-02 -0.1777680 -4.473500e-03 4.350000e-05 4.580200e-03 1.523080e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 9.127500e-03 7.251700e-03 0.0030422 3.661700e-03 5.797600e-03 1.250710e-02 1.055280e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.770475e-01 2.955437e-01 0.0000000 2.099999e-01 4.700002e-01 7.300002e-01 1.000000e+00 ▇▆▇▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.770485e-01 2.955192e-01 0.0000000 2.148241e-01 4.695714e-01 7.335100e-01 1.000000e+00 ▇▇▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.132980e-01 2.577576e-01 0.0022970 1.728200e-02 9.183800e-02 3.348300e-01 9.977030e-01 ▇▂▁▁▁
numeric AF_reference 114057 0.9877752 NA NA NA NA NA NA NA 2.143787e-01 2.495377e-01 0.0000000 1.457670e-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 0.0098213 0.0056056 0.0800000 0.0797639 0.623711 0.7821490 NA
1 54676 rs2462492 C T -0.0031698 0.0055686 0.5700002 0.5692060 0.399326 NA NA
1 86028 rs114608975 T C 0.0092107 0.0088763 0.2999998 0.2994243 0.103790 0.0277556 NA
1 91536 rs6702460 G T -0.0036435 0.0054869 0.5099998 0.5066691 0.456251 0.4207270 NA
1 234313 rs8179466 C T 0.0041709 0.0108344 0.6999999 0.7002616 0.074494 NA NA
1 534192 rs6680723 C T -0.0044562 0.0062726 0.4799997 0.4774374 0.241185 NA NA
1 546697 rs12025928 A G -0.0037069 0.0077787 0.6300007 0.6336890 0.913039 NA NA
1 693731 rs12238997 A G 0.0049344 0.0052262 0.3500000 0.3450754 0.116985 0.1417730 NA
1 705882 rs72631875 G A -0.0026103 0.0076438 0.7300002 0.7327323 0.067663 0.0315495 NA
1 706368 rs55727773 A G -0.0060335 0.0038702 0.1199999 0.1189988 0.514948 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0019035 0.0046918 0.6800001 0.6849576 0.137065 0.2052720 NA
22 51219387 rs9616832 T C -0.0047978 0.0061005 0.4299995 0.4315963 0.072732 0.0654952 NA
22 51219704 rs147475742 G A -0.0073630 0.0081406 0.3700002 0.3657428 0.041694 0.0473243 NA
22 51221190 rs369304721 G A -0.0050587 0.0081614 0.5400003 0.5353667 0.049130 NA NA
22 51221731 rs115055839 T C -0.0048700 0.0061050 0.4299995 0.4250384 0.072210 0.0625000 NA
22 51222100 rs114553188 G T 0.0000118 0.0071505 1.0000000 0.9986886 0.054414 0.0880591 NA
22 51223637 rs375798137 G A -0.0000987 0.0071873 0.9900000 0.9890415 0.054024 0.0788738 NA
22 51229805 rs9616985 T C -0.0043902 0.0061275 0.4700002 0.4737003 0.072065 0.0730831 NA
22 51232488 rs376461333 A G 0.0135326 0.0143547 0.3500000 0.3458193 0.020155 NA NA
22 51237063 rs3896457 T C 0.0056908 0.0037249 0.1299999 0.1265704 0.297631 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623711 ES:SE:LP:AF:ID  0.0098213:0.00560558:1.09691:0.623711:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399326 ES:SE:LP:AF:ID  -0.00316976:0.00556859:0.244125:0.399326:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.10379  ES:SE:LP:AF:ID  0.00921069:0.00887633:0.522879:0.10379:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456251 ES:SE:LP:AF:ID  -0.00364347:0.00548688:0.29243:0.456251:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074494 ES:SE:LP:AF:ID  0.00417089:0.0108344:0.154902:0.074494:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241185 ES:SE:LP:AF:ID  -0.00445623:0.00627258:0.318759:0.241185:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913039 ES:SE:LP:AF:ID  -0.00370687:0.0077787:0.200659:0.913039:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116985 ES:SE:LP:AF:ID  0.00493445:0.00522615:0.455932:0.116985:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067663 ES:SE:LP:AF:ID  -0.00261032:0.00764384:0.136677:0.067663:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.514948 ES:SE:LP:AF:ID  -0.00603354:0.00387016:0.920819:0.514948:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.0335   ES:SE:LP:AF:ID  0.0109877:0.00968261:0.585027:0.0335:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037207 ES:SE:LP:AF:ID  0.0083577:0.0087915:0.468521:0.037207:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037337 ES:SE:LP:AF:ID  0.00861228:0.0087568:0.481486:0.037337:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036992 ES:SE:LP:AF:ID  0.0101369:0.00882349:0.60206:0.036992:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016375 ES:SE:LP:AF:ID  0.0256206:0.0137572:1.20066:0.016375:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037575 ES:SE:LP:AF:ID  0.00817846:0.00872209:0.455932:0.037575:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.03767  ES:SE:LP:AF:ID  0.00881195:0.00869401:0.508638:0.03767:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101519 ES:SE:LP:AF:ID  -0.00875191:0.00637553:0.769551:0.101519:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958383 ES:SE:LP:AF:ID  -0.00342198:0.00838928:0.167491:0.958383:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031685 ES:SE:LP:AF:ID  -0.00965796:0.0153275:0.275724:0.031685:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052618 ES:SE:LP:AF:ID  -0.0170629:0.0123763:0.769551:0.052618:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037147 ES:SE:LP:AF:ID  0.00956379:0.00875363:0.568636:0.037147:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037478 ES:SE:LP:AF:ID  0.0087498:0.00867519:0.508638:0.037478:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841954 ES:SE:LP:AF:ID  -0.00493633:0.0045239:0.552842:0.841954:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.05605  ES:SE:LP:AF:ID  0.000635247:0.00735376:0.0315171:0.05605:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122907 ES:SE:LP:AF:ID  0.00498475:0.00496004:0.508638:0.122907:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025751 ES:SE:LP:AF:ID  0.00793468:0.0121998:0.283997:0.025751:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122127 ES:SE:LP:AF:ID  0.00519557:0.00496248:0.522879:0.122127:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.133359 ES:SE:LP:AF:ID  0.00263907:0.00488304:0.229148:0.133359:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011232 ES:SE:LP:AF:ID  -0.0142336:0.0177101:0.376751:0.011232:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005853 ES:SE:LP:AF:ID  0.00209654:0.0226043:0.0315171:0.005853:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037418 ES:SE:LP:AF:ID  0.00792096:0.00858653:0.443698:0.037418:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.83769  ES:SE:LP:AF:ID  -0.00536056:0.0043805:0.657577:0.83769:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.837304 ES:SE:LP:AF:ID  -0.00490015:0.00437562:0.585027:0.837304:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868981 ES:SE:LP:AF:ID  -0.00406845:0.00469677:0.408935:0.868981:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130715 ES:SE:LP:AF:ID  0.00374482:0.00470613:0.366532:0.130715:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037909 ES:SE:LP:AF:ID  0.00720968:0.00844602:0.408935:0.037909:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038161 ES:SE:LP:AF:ID  0.00750582:0.00839248:0.431798:0.038161:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868304 ES:SE:LP:AF:ID  -0.00324005:0.00468733:0.309804:0.868304:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868412 ES:SE:LP:AF:ID  -0.00347707:0.00468944:0.337242:0.868412:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038094 ES:SE:LP:AF:ID  0.00760307:0.00842895:0.431798:0.038094:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.8683   ES:SE:LP:AF:ID  -0.00341602:0.00468702:0.327902:0.8683:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005116 ES:SE:LP:AF:ID  -0.00883987:0.0241809:0.148742:0.005116:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005086 ES:SE:LP:AF:ID  -0.00869689:0.0242343:0.142668:0.005086:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.836781 ES:SE:LP:AF:ID  -0.00477834:0.00436418:0.568636:0.836781:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038103 ES:SE:LP:AF:ID  0.00735909:0.00844076:0.420216:0.038103:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.837415 ES:SE:LP:AF:ID  -0.00464466:0.00437632:0.537602:0.837415:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013292 ES:SE:LP:AF:ID  -0.0169405:0.0156369:0.552842:0.013292:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005447 ES:SE:LP:AF:ID  0.023484:0.0238134:0.49485:0.005447:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838644 ES:SE:LP:AF:ID  -0.00513637:0.00443625:0.60206:0.838644:rs3131965