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_4814.vcf.gz --id UKB-b:14254 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_4814.txt.gz --cohort_controls 43349 --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-14254/UKB-b-14254_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-14254/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:43:24 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-14254/UKB-b-14254_data.vcf.gz ...
Read summary statistics for 8039645 SNPs.
Dropped 6197 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, 1282809 SNPs remain.
After merging with regression SNP LD, 1282809 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0216 (0.0102)
Lambda GC: 1.0275
Mean Chi^2: 1.0279
Intercept: 1.0096 (0.0056)
Ratio: 0.3443 (0.2025)
Analysis finished at Thu Oct 17 14:44:52 2019
Total time elapsed: 1.0m:28.1s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9432,
    "inflation_factor": 1,
    "mean_EFFECT": 0.0003,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 1,
    "n_p_sig": 2,
    "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": 75096,
    "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": 1282809,
    "ldsc_nsnp_merge_regression_ld": 1282809,
    "ldsc_observed_scale_h2_beta": 0.0216,
    "ldsc_observed_scale_h2_se": 0.0102,
    "ldsc_intercept_beta": 1.0096,
    "ldsc_intercept_se": 0.0056,
    "ldsc_lambda_gc": 1.0275,
    "ldsc_mean_chisq": 1.0279,
    "ldsc_ratio": 0.3441
}

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 8033475 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 8039645 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 8.660292e+00 5.763887e+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 7.870393e+07 5.640956e+07 828.0000000 3.227088e+07 6.917086e+07 1.145337e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 2.640000e-04 6.944060e-02 -0.7708540 -3.178160e-02 6.400000e-06 3.209460e-02 6.874720e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 5.911140e-02 3.587190e-02 0.0265542 3.090470e-02 4.319770e-02 7.788510e-02 3.210570e-01 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.965705e-01 2.892938e-01 0.0000000 2.500000e-01 5.000000e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.965692e-01 2.892677e-01 0.0000000 2.451892e-01 4.956675e-01 7.467637e-01 9.999997e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.444662e-01 2.606883e-01 0.0080750 3.467100e-02 1.350300e-01 3.871990e-01 9.919250e-01 ▇▂▂▁▁
numeric AF_reference 75096 0.9906593 NA NA NA NA NA NA NA 2.437695e-01 2.525318e-01 0.0000000 3.634190e-02 1.505590e-01 3.825880e-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 0.0117308 0.0487393 0.8100000 0.8097996 0.623234 0.7821490 NA
1 54676 rs2462492 C T 0.0227986 0.0485893 0.6400000 0.6389193 0.399460 NA NA
1 86028 rs114608975 T C -0.0373659 0.0769709 0.6300007 0.6273537 0.104002 0.0277556 NA
1 91536 rs6702460 G T -0.0385653 0.0480358 0.4199997 0.4220644 0.455698 0.4207270 NA
1 234313 rs8179466 C T 0.1193200 0.0951749 0.2099999 0.2099540 0.074002 NA NA
1 534192 rs6680723 C T 0.0296460 0.0550147 0.5900000 0.5899737 0.240433 NA NA
1 546697 rs12025928 A G 0.0038493 0.0683886 0.9599999 0.9551144 0.913556 NA NA
1 693731 rs12238997 A G 0.0238833 0.0457358 0.5999997 0.6015301 0.118031 0.1417730 NA
1 705882 rs72631875 G A -0.0048321 0.0670312 0.9400001 0.9425322 0.067831 0.0315495 NA
1 706368 rs55727773 A G -0.0162565 0.0337755 0.6300007 0.6302959 0.514681 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0598097 0.0410699 0.1499999 0.1453124 0.137820 0.2052720 NA
22 51219387 rs9616832 T C -0.0821351 0.0532036 0.1199999 0.1226396 0.073350 0.0654952 NA
22 51219704 rs147475742 G A -0.1100930 0.0711475 0.1199999 0.1217690 0.041887 0.0473243 NA
22 51221190 rs369304721 G A -0.1071610 0.0713751 0.1299999 0.1332578 0.049234 NA NA
22 51221731 rs115055839 T C -0.0833955 0.0532707 0.1199999 0.1174648 0.072730 0.0625000 NA
22 51222100 rs114553188 G T -0.0143621 0.0628405 0.8200001 0.8192199 0.054473 0.0880591 NA
22 51223637 rs375798137 G A -0.0124883 0.0631615 0.8400000 0.8432640 0.054079 0.0788738 NA
22 51229805 rs9616985 T C -0.0840861 0.0534747 0.1199999 0.1158471 0.072518 0.0730831 NA
22 51232488 rs376461333 A G 0.0481527 0.1277120 0.7099994 0.7061429 0.019991 NA NA
22 51237063 rs3896457 T C -0.0064388 0.0325974 0.8400000 0.8434180 0.299022 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623234 ES:SE:LP:AF:ID  0.0117308:0.0487393:0.091515:0.623234:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.39946  ES:SE:LP:AF:ID  0.0227986:0.0485893:0.19382:0.39946:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.104002 ES:SE:LP:AF:ID  -0.0373659:0.0769709:0.200659:0.104002:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455698 ES:SE:LP:AF:ID  -0.0385653:0.0480358:0.376751:0.455698:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074002 ES:SE:LP:AF:ID  0.11932:0.0951749:0.677781:0.074002:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240433 ES:SE:LP:AF:ID  0.029646:0.0550147:0.229148:0.240433:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913556 ES:SE:LP:AF:ID  0.00384928:0.0683886:0.0177288:0.913556:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.118031 ES:SE:LP:AF:ID  0.0238833:0.0457358:0.221849:0.118031:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067831 ES:SE:LP:AF:ID  -0.00483212:0.0670312:0.0268721:0.067831:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.514681 ES:SE:LP:AF:ID  -0.0162565:0.0337755:0.200659:0.514681:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033571 ES:SE:LP:AF:ID  0.189619:0.0848348:1.60206:0.033571:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037382 ES:SE:LP:AF:ID  0.1828:0.0768695:1.76955:0.037382:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037522 ES:SE:LP:AF:ID  0.181947:0.0765598:1.76955:0.037522:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037134 ES:SE:LP:AF:ID  0.187832:0.0772322:1.82391:0.037134:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.01631  ES:SE:LP:AF:ID  0.0178041:0.120572:0.0555173:0.01631:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037733 ES:SE:LP:AF:ID  0.178969:0.0763396:1.72125:0.037733:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037836 ES:SE:LP:AF:ID  0.175721:0.0760556:1.67778:0.037836:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.10186  ES:SE:LP:AF:ID  -0.0112554:0.0557029:0.0757207:0.10186:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958419 ES:SE:LP:AF:ID  -0.149289:0.0735661:1.37675:0.958419:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031659 ES:SE:LP:AF:ID  -0.144112:0.13456:0.552842:0.031659:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052792 ES:SE:LP:AF:ID  -0.115762:0.10754:0.552842:0.052792:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037267 ES:SE:LP:AF:ID  0.169171:0.0766354:1.56864:0.037267:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037649 ES:SE:LP:AF:ID  0.172264:0.0759077:1.63827:0.037649:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.840814 ES:SE:LP:AF:ID  -0.0760836:0.0396864:1.25964:0.840814:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056415 ES:SE:LP:AF:ID  0.0075793:0.0640992:0.0409586:0.056415:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.124084 ES:SE:LP:AF:ID  0.0442171:0.0434417:0.508638:0.124084:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.02573  ES:SE:LP:AF:ID  0.0236434:0.107133:0.0809219:0.02573:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.123212 ES:SE:LP:AF:ID  0.0440731:0.0434782:0.508638:0.123212:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.13483  ES:SE:LP:AF:ID  0.0723812:0.0426976:1.04576:0.13483:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.010962 ES:SE:LP:AF:ID  0.0606622:0.157483:0.154902:0.010962:rs181876450
1   752478  rs146277091 G   A   .   PASS    AF=0.037493 ES:SE:LP:AF:ID  0.158337:0.0752605:1.45593:0.037493:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.836047 ES:SE:LP:AF:ID  -0.0668001:0.0383497:1.08619:0.836047:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.83574  ES:SE:LP:AF:ID  -0.0671525:0.0383066:1.09691:0.83574:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.867671 ES:SE:LP:AF:ID  -0.0350647:0.0410638:0.408935:0.867671:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131998 ES:SE:LP:AF:ID  0.0365711:0.0411384:0.431798:0.131998:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.038076 ES:SE:LP:AF:ID  0.141724:0.0739283:1.25964:0.038076:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038375 ES:SE:LP:AF:ID  0.138995:0.0733944:1.23657:0.038375:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867109 ES:SE:LP:AF:ID  -0.0329529:0.0409887:0.376751:0.867109:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.867198 ES:SE:LP:AF:ID  -0.0323167:0.0410026:0.366532:0.867198:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038245 ES:SE:LP:AF:ID  0.143967:0.0737836:1.29243:0.038245:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.867103 ES:SE:LP:AF:ID  -0.0335039:0.0409856:0.387216:0.867103:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.835263 ES:SE:LP:AF:ID  -0.0625396:0.038224:1:0.835263:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038256 ES:SE:LP:AF:ID  0.146268:0.0738636:1.31876:0.038256:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.83586  ES:SE:LP:AF:ID  -0.0602955:0.0383233:0.920819:0.83586:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013154 ES:SE:LP:AF:ID  -0.0417961:0.13767:0.119186:0.013154:rs181660517
1   755775  rs3131965   A   G   .   PASS    AF=0.837075 ES:SE:LP:AF:ID  -0.0621979:0.0388544:0.958607:0.837075:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.867269 ES:SE:LP:AF:ID  -0.0364855:0.0409379:0.431798:0.867269:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.86679  ES:SE:LP:AF:ID  -0.0344934:0.0408274:0.39794:0.86679:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.865905 ES:SE:LP:AF:ID  -0.0329005:0.0407476:0.376751:0.865905:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.866948 ES:SE:LP:AF:ID  -0.0350885:0.0408688:0.408935:0.866948:rs4951929