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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    "FORMAT.5": "<ID=EZ,Number=A,Type=Float,Description=\"Z-score provided if it was used to derive the EFFECT and SE fields\">",
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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_3680.vcf.gz --id UKB-b:9791 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_3680.txt.gz --cohort_controls 17236 --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-9791/UKB-b-9791_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-9791/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:42:51 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-9791/UKB-b-9791_data.vcf.gz ...
Read summary statistics for 6722847 SNPs.
Dropped 3882 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, 1253771 SNPs remain.
After merging with regression SNP LD, 1253771 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0365 (0.0255)
Lambda GC: 1.0105
Mean Chi^2: 1.0114
Intercept: 0.9987 (0.0066)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Thu Oct 17 14:44:00 2019
Total time elapsed: 1.0m:8.61s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9333,
    "inflation_factor": 1,
    "mean_EFFECT": -0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 0,
    "n_p_sig": 0,
    "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": 61722,
    "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": 1253771,
    "ldsc_nsnp_merge_regression_ld": 1253771,
    "ldsc_observed_scale_h2_beta": 0.0365,
    "ldsc_observed_scale_h2_se": 0.0255,
    "ldsc_intercept_beta": 0.9987,
    "ldsc_intercept_se": 0.0066,
    "ldsc_lambda_gc": 1.0105,
    "ldsc_mean_chisq": 1.0114,
    "ldsc_ratio": -0.114
}

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 6718987 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 6722847 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 8.664102e+00 5.764702e+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 7.860809e+07 5.647695e+07 828.0000000 3.208243e+07 6.905292e+07 1.144997e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -4.110000e-05 1.774520e-02 -0.1610710 -9.777200e-03 -4.840000e-05 9.710000e-03 1.599540e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.626330e-02 6.968100e-03 0.0094209 1.063360e-02 1.339240e-02 2.017420e-02 8.567760e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.994141e-01 2.893451e-01 0.0000005 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 4.994140e-01 2.893198e-01 0.0000005 2.486576e-01 4.991602e-01 7.504943e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.852581e-01 2.585864e-01 0.0203070 6.870500e-02 1.928330e-01 4.448630e-01 9.796930e-01 ▇▃▂▂▁
numeric AF_reference 61722 0.9908191 NA NA NA NA NA NA NA 2.831551e-01 2.507852e-01 0.0000000 7.767570e-02 2.034740e-01 4.371010e-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 0.0039352 0.0173873 0.8200001 0.8209500 0.624429 0.7821490 NA
1 54676 rs2462492 C T 0.0105982 0.0171231 0.5400003 0.5359548 0.402376 NA NA
1 86028 rs114608975 T C -0.0068179 0.0279649 0.8100000 0.8073833 0.102245 0.0277556 NA
1 91536 rs6702460 G T 0.0273715 0.0169744 0.1100001 0.1068496 0.459469 0.4207270 NA
1 234313 rs8179466 C T 0.0131620 0.0331604 0.6899999 0.6914270 0.074979 NA NA
1 534192 rs6680723 C T 0.0097299 0.0193751 0.6200004 0.6155365 0.240357 NA NA
1 546697 rs12025928 A G 0.0102926 0.0240979 0.6700003 0.6692948 0.913052 NA NA
1 693731 rs12238997 A G 0.0241408 0.0162121 0.1400000 0.1364714 0.117045 0.1417730 NA
1 705882 rs72631875 G A -0.0162640 0.0238811 0.5000000 0.4958458 0.067888 0.0315495 NA
1 706368 rs55727773 A G -0.0265803 0.0120728 0.0280001 0.0276888 0.515076 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0034709 0.0143727 0.8100000 0.8091734 0.141293 0.2052720 NA
22 51219387 rs9616832 T C 0.0067623 0.0189217 0.7199992 0.7208033 0.072938 0.0654952 NA
22 51219704 rs147475742 G A 0.0024442 0.0253749 0.9199999 0.9232633 0.041172 0.0473243 NA
22 51221190 rs369304721 G A 0.0003370 0.0251831 0.9900000 0.9893244 0.049622 NA NA
22 51221731 rs115055839 T C 0.0069807 0.0189153 0.7099994 0.7120905 0.072661 0.0625000 NA
22 51222100 rs114553188 G T -0.0053208 0.0217224 0.8100000 0.8064995 0.057161 0.0880591 NA
22 51223637 rs375798137 G A -0.0049582 0.0218171 0.8200001 0.8202192 0.056792 0.0788738 NA
22 51229805 rs9616985 T C 0.0080641 0.0190041 0.6700003 0.6713209 0.072371 0.0730831 NA
22 51232488 rs376461333 A G -0.0256610 0.0430356 0.5500004 0.5509923 0.021361 NA NA
22 51237063 rs3896457 T C -0.0022711 0.0116616 0.8499999 0.8455891 0.296989 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.624429 ES:SE:LP:AF:ID  0.00393515:0.0173873:0.0861861:0.624429:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.402376 ES:SE:LP:AF:ID  0.0105982:0.0171231:0.267606:0.402376:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.102245 ES:SE:LP:AF:ID  -0.00681793:0.0279649:0.091515:0.102245:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.459469 ES:SE:LP:AF:ID  0.0273715:0.0169744:0.958607:0.459469:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074979 ES:SE:LP:AF:ID  0.013162:0.0331604:0.161151:0.074979:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240357 ES:SE:LP:AF:ID  0.00972991:0.0193751:0.207608:0.240357:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913052 ES:SE:LP:AF:ID  0.0102926:0.0240979:0.173925:0.913052:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117045 ES:SE:LP:AF:ID  0.0241408:0.0162121:0.853872:0.117045:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067888 ES:SE:LP:AF:ID  -0.016264:0.0238811:0.30103:0.067888:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515076 ES:SE:LP:AF:ID  -0.0265803:0.0120728:1.55284:0.515076:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.034033 ES:SE:LP:AF:ID  0.020749:0.0300402:0.309804:0.034033:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037791 ES:SE:LP:AF:ID  0.0147418:0.0272468:0.229148:0.037791:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.038026 ES:SE:LP:AF:ID  0.0155776:0.0270914:0.244125:0.038026:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037666 ES:SE:LP:AF:ID  0.0169848:0.0272852:0.275724:0.037666:rs12184279
1   720381  rs116801199 G   T   .   PASS    AF=0.03822  ES:SE:LP:AF:ID  0.0157479:0.0270077:0.251812:0.03822:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.038365 ES:SE:LP:AF:ID  0.0121604:0.0268856:0.187087:0.038365:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101229 ES:SE:LP:AF:ID  0.0127544:0.0198936:0.283997:0.101229:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.957604 ES:SE:LP:AF:ID  -0.00695469:0.0259122:0.102373:0.957604:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031521 ES:SE:LP:AF:ID  0.0449138:0.0476385:0.455932:0.031521:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053502 ES:SE:LP:AF:ID  -0.0515211:0.0374026:0.769551:0.053502:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.03799  ES:SE:LP:AF:ID  0.0160006:0.0270095:0.259637:0.03799:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.038439 ES:SE:LP:AF:ID  0.0141596:0.0267226:0.221849:0.038439:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.839942 ES:SE:LP:AF:ID  -0.0199983:0.0139288:0.823909:0.839942:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.057001 ES:SE:LP:AF:ID  0.0421646:0.0225952:1.20761:0.057001:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.124049 ES:SE:LP:AF:ID  0.0225133:0.0152835:0.853872:0.124049:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025688 ES:SE:LP:AF:ID  0.0149532:0.0381427:0.154902:0.025688:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.123294 ES:SE:LP:AF:ID  0.0214177:0.0152783:0.79588:0.123294:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.134473 ES:SE:LP:AF:ID  0.0201373:0.0150777:0.744727:0.134473:rs79010578
1   752478  rs146277091 G   A   .   PASS    AF=0.038107 ES:SE:LP:AF:ID  0.0169008:0.026555:0.283997:0.038107:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.834827 ES:SE:LP:AF:ID  -0.0197538:0.0135246:0.853872:0.834827:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.834387 ES:SE:LP:AF:ID  -0.0194467:0.0135002:0.823909:0.834387:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.867193 ES:SE:LP:AF:ID  -0.0204832:0.0145249:0.79588:0.867193:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.132308 ES:SE:LP:AF:ID  0.0202503:0.0145576:0.79588:0.132308:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.038891 ES:SE:LP:AF:ID  0.0173964:0.026004:0.30103:0.038891:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.039191 ES:SE:LP:AF:ID  0.0175803:0.0258168:0.30103:0.039191:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.866528 ES:SE:LP:AF:ID  -0.0202925:0.0144867:0.79588:0.866528:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.866727 ES:SE:LP:AF:ID  -0.0189942:0.0145042:0.721246:0.866727:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.039156 ES:SE:LP:AF:ID  0.0189533:0.0259341:0.337242:0.039156:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.86653  ES:SE:LP:AF:ID  -0.0203366:0.0144854:0.79588:0.86653:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.833599 ES:SE:LP:AF:ID  -0.0203293:0.0134476:0.886057:0.833599:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.039156 ES:SE:LP:AF:ID  0.0188609:0.025982:0.327902:0.039156:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.834245 ES:SE:LP:AF:ID  -0.0201958:0.0134835:0.886057:0.834245:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.835662 ES:SE:LP:AF:ID  -0.0210754:0.0136766:0.920819:0.835662:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.86676  ES:SE:LP:AF:ID  -0.0198717:0.01448:0.769551:0.86676:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.86621  ES:SE:LP:AF:ID  -0.0202626:0.0144397:0.79588:0.86621:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.865248 ES:SE:LP:AF:ID  -0.0198855:0.0144084:0.769551:0.865248:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.866505 ES:SE:LP:AF:ID  -0.0193958:0.0144593:0.744727:0.866505:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.866525 ES:SE:LP:AF:ID  -0.0193278:0.01446:0.744727:0.866525:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.866526 ES:SE:LP:AF:ID  -0.0192938:0.0144603:0.744727:0.866526:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.866932 ES:SE:LP:AF:ID  -0.0192594:0.0144923:0.744727:0.866932:rs3131954