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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    "gwas_harmonisation_command": "--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/vcf_09_19b/bgzip_vcf/data.batch_4100.vcf.gz --id UKB-b:2122 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_4100.txt.gz --cohort_controls 146226 --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-2122/UKB-b-2122_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-2122/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:42:18 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-2122/UKB-b-2122_data.vcf.gz ...
Read summary statistics for 9301974 SNPs.
Dropped 10288 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, 1287947 SNPs remain.
After merging with regression SNP LD, 1287947 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.2906 (0.0136)
Lambda GC: 1.5912
Mean Chi^2: 1.9279
Intercept: 1.0945 (0.0123)
Ratio: 0.1019 (0.0132)
Analysis finished at Thu Oct 17 14:43:54 2019
Total time elapsed: 1.0m:36.16s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9487,
    "inflation_factor": 1.4295,
    "mean_EFFECT": -4.2907e-06,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 197,
    "n_p_sig": 18481,
    "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": 111647,
    "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": 1287947,
    "ldsc_nsnp_merge_regression_ld": 1287947,
    "ldsc_observed_scale_h2_beta": 0.2906,
    "ldsc_observed_scale_h2_se": 0.0136,
    "ldsc_intercept_beta": 1.0945,
    "ldsc_intercept_se": 0.0123,
    "ldsc_lambda_gc": 1.5912,
    "ldsc_mean_chisq": 1.9279,
    "ldsc_ratio": 0.1018
}

Flags

name value
af_correlation FALSE
inflation_factor TRUE
n TRUE
is_snpid_non_unique FALSE
mean_EFFECT_nonfinite FALSE
mean_EFFECT_05 FALSE
mean_EFFECT_01 FALSE
mean_chisq TRUE
n_p_sig TRUE
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 9291738 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 9301974 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 8.635421e+00 5.754200e+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 7.880990e+07 5.630848e+07 828.0000000 3.250016e+07 6.939227e+07 1.145403e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -4.300000e-06 1.236030e-02 -0.1544080 -5.008300e-03 1.390000e-05 4.997500e-03 1.443630e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 8.860000e-03 6.983600e-03 0.0029709 3.580300e-03 5.653200e-03 1.214710e-02 7.465810e-02 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.434233e-01 3.041164e-01 0.0000000 1.700000e-01 4.199997e-01 7.099994e-01 1.000000e+00 ▇▆▅▅▅
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.434240e-01 3.040926e-01 0.0000000 1.654500e-01 4.228100e-01 7.069434e-01 9.999991e-01 ▇▆▅▅▅
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.138806e-01 2.578296e-01 0.0023940 1.754400e-02 9.266250e-02 3.358530e-01 9.976060e-01 ▇▂▁▁▁
numeric AF_reference 111647 0.9879975 NA NA NA NA NA NA NA 2.148773e-01 2.496214e-01 0.0000000 1.477640e-02 1.110220e-01 3.356630e-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 0.0104685 0.0054785 0.0560003 0.0560265 0.623783 0.7821490 NA
1 54676 rs2462492 C T -0.0006901 0.0054396 0.9000000 0.8990505 0.399281 NA NA
1 86028 rs114608975 T C -0.0091029 0.0086654 0.2900000 0.2934970 0.103822 0.0277556 NA
1 91536 rs6702460 G T -0.0001115 0.0053591 0.9800000 0.9834016 0.456049 0.4207270 NA
1 234313 rs8179466 C T 0.0114795 0.0105898 0.2800000 0.2783582 0.074426 NA NA
1 534192 rs6680723 C T -0.0065627 0.0061208 0.2800000 0.2836340 0.241234 NA NA
1 546697 rs12025928 A G -0.0079722 0.0075996 0.2900000 0.2941649 0.913029 NA NA
1 693731 rs12238997 A G -0.0062222 0.0051107 0.2200002 0.2234229 0.116832 0.1417730 NA
1 705882 rs72631875 G A 0.0070404 0.0074597 0.3500000 0.3452810 0.067746 0.0315495 NA
1 706368 rs55727773 A G -0.0026135 0.0037819 0.4899999 0.4895389 0.515132 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0015371 0.0046052 0.7400005 0.7385533 0.137093 0.2052720 NA
22 51219387 rs9616832 T C -0.0025419 0.0059875 0.6700003 0.6711735 0.072776 0.0654952 NA
22 51219704 rs147475742 G A 0.0014775 0.0079935 0.8499999 0.8533581 0.041651 0.0473243 NA
22 51221190 rs369304721 G A -0.0050892 0.0080104 0.5300002 0.5252219 0.049132 NA NA
22 51221731 rs115055839 T C -0.0026644 0.0059920 0.6600001 0.6565673 0.072255 0.0625000 NA
22 51222100 rs114553188 G T -0.0013553 0.0070193 0.8499999 0.8468979 0.054400 0.0880591 NA
22 51223637 rs375798137 G A -0.0017260 0.0070558 0.8100000 0.8067471 0.054006 0.0788738 NA
22 51229805 rs9616985 T C -0.0021924 0.0060137 0.7199992 0.7154395 0.072125 0.0730831 NA
22 51232488 rs376461333 A G 0.0078367 0.0140921 0.5800000 0.5781389 0.020154 NA NA
22 51237063 rs3896457 T C 0.0048602 0.0036572 0.1800002 0.1838581 0.297636 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623783 ES:SE:LP:AF:ID  0.0104685:0.00547851:1.25181:0.623783:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399281 ES:SE:LP:AF:ID  -0.000690074:0.00543961:0.0457575:0.399281:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103822 ES:SE:LP:AF:ID  -0.00910287:0.00866543:0.537602:0.103822:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456049 ES:SE:LP:AF:ID  -0.000111493:0.00535908:0.00877392:0.456049:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074426 ES:SE:LP:AF:ID  0.0114795:0.0105898:0.552842:0.074426:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241234 ES:SE:LP:AF:ID  -0.00656266:0.00612079:0.552842:0.241234:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913029 ES:SE:LP:AF:ID  -0.00797223:0.00759963:0.537602:0.913029:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116832 ES:SE:LP:AF:ID  -0.00622217:0.00511071:0.657577:0.116832:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067746 ES:SE:LP:AF:ID  0.00704036:0.00745973:0.455932:0.067746:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515132 ES:SE:LP:AF:ID  -0.00261346:0.00378191:0.309804:0.515132:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033488 ES:SE:LP:AF:ID  1.99817e-05:0.00945992:-0:0.033488:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037195 ES:SE:LP:AF:ID  -0.00069596:0.00858842:0.0268721:0.037195:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037322 ES:SE:LP:AF:ID  -0.000189714:0.00855467:0.00877392:0.037322:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036979 ES:SE:LP:AF:ID  -0.00163013:0.00861974:0.0705811:0.036979:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.01633  ES:SE:LP:AF:ID  -0.00848175:0.0134529:0.275724:0.01633:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037556 ES:SE:LP:AF:ID  -0.00158316:0.00852119:0.0705811:0.037556:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037647 ES:SE:LP:AF:ID  -0.00190462:0.00849467:0.0861861:0.037647:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101556 ES:SE:LP:AF:ID  0.00747812:0.00623085:0.638272:0.101556:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958405 ES:SE:LP:AF:ID  0.00104481:0.00819576:0.0457575:0.958405:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.03177  ES:SE:LP:AF:ID  -0.0127406:0.0149324:0.408935:0.03177:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052678 ES:SE:LP:AF:ID  -0.00562743:0.012087:0.19382:0.052678:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.03713  ES:SE:LP:AF:ID  -0.00190625:0.00855265:0.0861861:0.03713:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037443 ES:SE:LP:AF:ID  -0.00110192:0.00847815:0.0457575:0.037443:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.84215  ES:SE:LP:AF:ID  0.00452479:0.00442349:0.508638:0.84215:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056062 ES:SE:LP:AF:ID  -0.00259806:0.00718197:0.142668:0.056062:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122752 ES:SE:LP:AF:ID  -0.00535084:0.00485067:0.568636:0.122752:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025686 ES:SE:LP:AF:ID  0.019342:0.0119387:0.958607:0.025686:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121975 ES:SE:LP:AF:ID  -0.00495187:0.00485305:0.508638:0.121975:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.133228 ES:SE:LP:AF:ID  -0.0033465:0.0047733:0.318759:0.133228:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011238 ES:SE:LP:AF:ID  -0.00770321:0.0172972:0.180456:0.011238:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005839 ES:SE:LP:AF:ID  0.00612049:0.0221063:0.107905:0.005839:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037388 ES:SE:LP:AF:ID  0.000522122:0.00839138:0.0222764:0.037388:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837789 ES:SE:LP:AF:ID  0.00521804:0.00428116:0.657577:0.837789:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.837406 ES:SE:LP:AF:ID  0.0050325:0.0042762:0.619789:0.837406:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869058 ES:SE:LP:AF:ID  0.00595151:0.00459051:0.721246:0.869058:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130635 ES:SE:LP:AF:ID  -0.00627889:0.00459964:0.769551:0.130635:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037882 ES:SE:LP:AF:ID  -0.00116993:0.00825374:0.05061:0.037882:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.03814  ES:SE:LP:AF:ID  -0.00113322:0.00820075:0.05061:0.03814:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868393 ES:SE:LP:AF:ID  0.00548423:0.00458135:0.638272:0.868393:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868495 ES:SE:LP:AF:ID  0.00539673:0.00458313:0.619789:0.868495:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038064 ES:SE:LP:AF:ID  -0.000894714:0.0082372:0.0409586:0.038064:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.868386 ES:SE:LP:AF:ID  0.00554249:0.00458105:0.638272:0.868386:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005181 ES:SE:LP:AF:ID  -0.0238703:0.0234578:0.508638:0.005181:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005152 ES:SE:LP:AF:ID  -0.0239153:0.0235078:0.508638:0.005152:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.8369   ES:SE:LP:AF:ID  0.00465682:0.00426531:0.568636:0.8369:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038074 ES:SE:LP:AF:ID  -0.000424928:0.0082484:0.0177288:0.038074:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.837533 ES:SE:LP:AF:ID  0.0046612:0.00427724:0.552842:0.837533:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013254 ES:SE:LP:AF:ID  -0.0222186:0.0152922:0.823909:0.013254:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005491 ES:SE:LP:AF:ID  0.025147:0.023171:0.552842:0.005491:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838766 ES:SE:LP:AF:ID  0.00516215:0.00433616:0.638272:0.838766:rs3131965