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_6156_100.vcf.gz --id UKB-b:256 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_6156_100.txt.gz --cohort_cases 11226 --cohort_controls 20091 --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-256/UKB-b-256_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-256/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:42:50 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-256/UKB-b-256_data.vcf.gz ...
Read summary statistics for 6122546 SNPs.
Dropped 2927 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, 1223019 SNPs remain.
After merging with regression SNP LD, 1223019 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0548 (0.0145)
Lambda GC: 1.0379
Mean Chi^2: 1.0362
Intercept: 1.0016 (0.0065)
Ratio: 0.043 (0.1803)
Analysis finished at Thu Oct 17 14:43:57 2019
Total time elapsed: 1.0m:7.36s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9266,
    "inflation_factor": 1.0475,
    "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": 55769,
    "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": 1223019,
    "ldsc_nsnp_merge_regression_ld": 1223019,
    "ldsc_observed_scale_h2_beta": 0.0548,
    "ldsc_observed_scale_h2_se": 0.0145,
    "ldsc_intercept_beta": 1.0016,
    "ldsc_intercept_se": 0.0065,
    "ldsc_lambda_gc": 1.0379,
    "ldsc_mean_chisq": 1.0362,
    "ldsc_ratio": 0.0442
}

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 6119638 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 6122546 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 8.670304e+00 5.762675e+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 7.858583e+07 5.651707e+07 828.0000000 3.200009e+07 6.902439e+07 1.145227e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -1.160000e-05 6.133600e-03 -0.0527096 -3.616400e-03 9.000000e-06 3.604900e-03 5.133360e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 5.713600e-03 2.003000e-03 0.0036610 4.070400e-03 4.926300e-03 6.874000e-03 2.080100e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.943751e-01 2.897512e-01 0.0000018 2.399999e-01 4.899999e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.943771e-01 2.897273e-01 0.0000018 2.426775e-01 4.920234e-01 7.451015e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 3.070637e-01 2.544194e-01 0.0311780 9.170900e-02 2.234420e-01 4.717160e-01 9.688220e-01 ▇▃▂▂▁
numeric AF_reference 55769 0.9908912 NA NA NA NA NA NA NA 3.039063e-01 2.473906e-01 0.0000000 1.008390e-01 2.312300e-01 4.624600e-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 0.0045216 0.0067547 0.5000000 0.5032412 0.623710 0.7821490 NA
1 54676 rs2462492 C T 0.0063996 0.0067223 0.3400001 0.3410992 0.399162 NA NA
1 86028 rs114608975 T C -0.0073862 0.0106372 0.4899999 0.4874504 0.103756 0.0277556 NA
1 91536 rs6702460 G T 0.0074570 0.0066098 0.2599998 0.2592483 0.455304 0.4207270 NA
1 234313 rs8179466 C T -0.0008314 0.0131395 0.9500000 0.9495458 0.073691 NA NA
1 534192 rs6680723 C T 0.0046697 0.0075798 0.5400003 0.5378474 0.240720 NA NA
1 546697 rs12025928 A G 0.0004407 0.0093710 0.9599999 0.9624873 0.912539 NA NA
1 693731 rs12238997 A G 0.0050772 0.0062970 0.4199997 0.4200839 0.118189 0.1417730 NA
1 705882 rs72631875 G A -0.0031224 0.0092012 0.7300002 0.7343470 0.068040 0.0315495 NA
1 706368 rs55727773 A G 0.0074444 0.0046764 0.1100001 0.1114033 0.513640 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51218377 rs2519461 G C -0.0098649 0.0073258 0.1800002 0.1781138 0.073259 0.0826677 NA
22 51219006 rs28729663 G A -0.0036289 0.0056484 0.5199996 0.5205690 0.138957 0.2052720 NA
22 51219387 rs9616832 T C -0.0096495 0.0073465 0.1900002 0.1890179 0.073345 0.0654952 NA
22 51219704 rs147475742 G A -0.0183184 0.0098806 0.0640000 0.0637436 0.041495 0.0473243 NA
22 51221190 rs369304721 G A -0.0074838 0.0098528 0.4500005 0.4475196 0.049369 NA NA
22 51221731 rs115055839 T C -0.0094432 0.0073535 0.2000000 0.1990787 0.072770 0.0625000 NA
22 51222100 rs114553188 G T 0.0074458 0.0085807 0.3900004 0.3855408 0.055203 0.0880591 NA
22 51223637 rs375798137 G A 0.0075991 0.0086227 0.3800004 0.3781644 0.054822 0.0788738 NA
22 51229805 rs9616985 T C -0.0095357 0.0073839 0.2000000 0.1965545 0.072555 0.0730831 NA
22 51237063 rs3896457 T C 0.0012876 0.0045308 0.7800007 0.7762724 0.295451 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.62371  ES:SE:LP:AF:ID  0.00452158:0.00675469:0.30103:0.62371:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399162 ES:SE:LP:AF:ID  0.00639961:0.00672231:0.468521:0.399162:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103756 ES:SE:LP:AF:ID  -0.00738615:0.0106372:0.309804:0.103756:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455304 ES:SE:LP:AF:ID  0.00745701:0.00660983:0.585027:0.455304:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.073691 ES:SE:LP:AF:ID  -0.000831431:0.0131395:0.0222764:0.073691:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.24072  ES:SE:LP:AF:ID  0.00466971:0.00757982:0.267606:0.24072:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912539 ES:SE:LP:AF:ID  0.000440741:0.00937097:0.0177288:0.912539:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.118189 ES:SE:LP:AF:ID  0.00507715:0.00629704:0.376751:0.118189:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.06804  ES:SE:LP:AF:ID  -0.00312243:0.00920124:0.136677:0.06804:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.51364  ES:SE:LP:AF:ID  0.00744441:0.00467638:0.958607:0.51364:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033949 ES:SE:LP:AF:ID  0.00941329:0.0116167:0.376751:0.033949:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037636 ES:SE:LP:AF:ID  0.00508967:0.0105636:0.200659:0.037636:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037783 ES:SE:LP:AF:ID  0.00393297:0.0105195:0.148742:0.037783:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037389 ES:SE:LP:AF:ID  0.00461049:0.0106073:0.180456:0.037389:rs12184279
1   720381  rs116801199 G   T   .   PASS    AF=0.037982 ES:SE:LP:AF:ID  0.00536616:0.0104868:0.21467:0.037982:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.038114 ES:SE:LP:AF:ID  0.00524763:0.0104506:0.207608:0.038114:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.100278 ES:SE:LP:AF:ID  -0.0181063:0.00774596:1.72125:0.100278:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.95763  ES:SE:LP:AF:ID  -0.000205081:0.0100614:0.00877392:0.95763:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.032028 ES:SE:LP:AF:ID  -0.0286368:0.0183767:0.920819:0.032028:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052547 ES:SE:LP:AF:ID  0.0256219:0.0149191:1.0655:0.052547:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037554 ES:SE:LP:AF:ID  0.0040007:0.0105286:0.154902:0.037554:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037858 ES:SE:LP:AF:ID  0.00314278:0.0104395:0.119186:0.037858:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.840353 ES:SE:LP:AF:ID  -0.00433614:0.00544289:0.366532:0.840353:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056243 ES:SE:LP:AF:ID  0.00864749:0.00887947:0.481486:0.056243:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.124107 ES:SE:LP:AF:ID  0.00709444:0.00598419:0.619789:0.124107:rs58276399
1   734349  rs141242758 T   C   .   PASS    AF=0.123373 ES:SE:LP:AF:ID  0.00704163:0.00598666:0.619789:0.123373:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.134053 ES:SE:LP:AF:ID  0.00525043:0.00590458:0.431798:0.134053:rs79010578
1   752478  rs146277091 G   A   .   PASS    AF=0.037655 ES:SE:LP:AF:ID  0.00315944:0.0103518:0.119186:0.037655:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.835828 ES:SE:LP:AF:ID  -0.00475038:0.00528495:0.431798:0.835828:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.835345 ES:SE:LP:AF:ID  -0.00395482:0.00527554:0.346787:0.835345:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.866915 ES:SE:LP:AF:ID  -0.0064853:0.0056549:0.60206:0.866915:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.132666 ES:SE:LP:AF:ID  0.0054809:0.00566935:0.481486:0.132666:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.038158 ES:SE:LP:AF:ID  0.00267815:0.0101789:0.102373:0.038158:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038414 ES:SE:LP:AF:ID  0.00245467:0.0101127:0.091515:0.038414:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.866153 ES:SE:LP:AF:ID  -0.00563378:0.00564042:0.49485:0.866153:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.866318 ES:SE:LP:AF:ID  -0.00573161:0.00564349:0.508638:0.866318:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038339 ES:SE:LP:AF:ID  0.00194046:0.010159:0.0705811:0.038339:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.86615  ES:SE:LP:AF:ID  -0.00549367:0.0056399:0.481486:0.86615:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.834923 ES:SE:LP:AF:ID  -0.00393861:0.00526701:0.346787:0.834923:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.03837  ES:SE:LP:AF:ID  0.00190904:0.0101694:0.0705811:0.03837:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.835586 ES:SE:LP:AF:ID  -0.00414454:0.00528295:0.366532:0.835586:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.836772 ES:SE:LP:AF:ID  -0.00426355:0.00535154:0.366532:0.836772:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.866643 ES:SE:LP:AF:ID  -0.00637327:0.00563799:0.585027:0.866643:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.866271 ES:SE:LP:AF:ID  -0.00665201:0.00562437:0.619789:0.866271:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.864793 ES:SE:LP:AF:ID  -0.00517467:0.00560553:0.443698:0.864793:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.866378 ES:SE:LP:AF:ID  -0.00662888:0.00562941:0.619789:0.866378:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.866402 ES:SE:LP:AF:ID  -0.00662354:0.00562976:0.619789:0.866402:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.866407 ES:SE:LP:AF:ID  -0.0066213:0.0056299:0.619789:0.866407:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.86679  ES:SE:LP:AF:ID  -0.00632141:0.00564452:0.585027:0.86679:rs3131954
1   759036  rs114525117 G   A   .   PASS    AF=0.038174 ES:SE:LP:AF:ID  0.00187251:0.0101416:0.0705811:0.038174:rs114525117