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_3720.vcf.gz --id UKB-b:19716 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_3720.txt.gz --cohort_cases 10073 --cohort_controls 56984 --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-19716/UKB-b-19716_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19716/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:41:30 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19716/UKB-b-19716_data.vcf.gz ...
Read summary statistics for 5963736 SNPs.
Dropped 2686 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, 1212530 SNPs remain.
After merging with regression SNP LD, 1212530 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0079 (0.007)
Lambda GC: 1.0125
Mean Chi^2: 1.008
Intercept: 0.9973 (0.0067)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Thu Oct 17 14:42:38 2019
Total time elapsed: 1.0m:7.86s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9243,
    "inflation_factor": 1,
    "mean_EFFECT": -5.9704e-06,
    "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": 53829,
    "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": 1212530,
    "ldsc_nsnp_merge_regression_ld": 1212530,
    "ldsc_observed_scale_h2_beta": 0.0079,
    "ldsc_observed_scale_h2_se": 0.007,
    "ldsc_intercept_beta": 0.9973,
    "ldsc_intercept_se": 0.0067,
    "ldsc_lambda_gc": 1.0125,
    "ldsc_mean_chisq": 1.008,
    "ldsc_ratio": -0.3375
}

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 5961067 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 5963736 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 8.672034e+00 5.762329e+00 1.0000000 4.000000e+00 8.00000e+00 1.300000e+01 2.200000e+01 ▇▅▅▂▂
numeric POS 0 1.0000000 NA NA NA NA NA NA NA 7.857284e+07 5.652158e+07 828.0000000 3.197950e+07 6.90235e+07 1.144946e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -6.000000e-06 3.027900e-03 -0.0223535 -1.807200e-03 -2.90000e-06 1.798700e-03 2.890080e-02 ▁▂▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 2.858900e-03 9.462000e-04 0.0018793 2.079600e-03 2.49140e-03 3.413300e-03 9.855100e-03 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.983960e-01 2.890631e-01 0.0000010 2.500000e-01 5.00000e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.983935e-01 2.890370e-01 0.0000010 2.478675e-01 4.97021e-01 7.489544e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 3.131607e-01 2.529399e-01 0.0347470 9.853200e-02 2.31637e-01 4.785130e-01 9.652530e-01 ▇▃▂▂▁
numeric AF_reference 53829 0.9909739 NA NA NA NA NA NA NA 3.096132e-01 2.461839e-01 0.0000000 1.074280e-01 2.39018e-01 4.690500e-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 0.0001880 0.0034664 0.9599999 0.9567510 0.623026 0.7821490 NA
1 54676 rs2462492 C T -0.0038817 0.0034352 0.2599998 0.2584934 0.400986 NA NA
1 86028 rs114608975 T C 0.0047014 0.0054845 0.3900004 0.3913242 0.103725 0.0277556 NA
1 91536 rs6702460 G T -0.0006338 0.0033775 0.8499999 0.8511497 0.457415 0.4207270 NA
1 234313 rs8179466 C T -0.0064712 0.0066915 0.3300000 0.3335045 0.074190 NA NA
1 534192 rs6680723 C T -0.0006570 0.0038485 0.8600001 0.8644555 0.242063 NA NA
1 546697 rs12025928 A G 0.0014854 0.0048547 0.7600007 0.7596246 0.914041 NA NA
1 693731 rs12238997 A G 0.0006549 0.0032275 0.8400000 0.8391966 0.116898 0.1417730 NA
1 705882 rs72631875 G A -0.0072040 0.0047640 0.1299999 0.1304916 0.066254 0.0315495 NA
1 706368 rs55727773 A G 0.0011662 0.0023936 0.6300007 0.6261008 0.515314 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51218377 rs2519461 G C -0.0000156 0.0037378 1.0000000 0.9966611 0.074017 0.0826677 NA
22 51219006 rs28729663 G A 0.0017343 0.0028897 0.5500004 0.5483868 0.139332 0.2052720 NA
22 51219387 rs9616832 T C 0.0004787 0.0037477 0.9000000 0.8983565 0.074043 0.0654952 NA
22 51219704 rs147475742 G A -0.0014435 0.0049904 0.7700005 0.7723851 0.042687 0.0473243 NA
22 51221190 rs369304721 G A 0.0000629 0.0050221 0.9900000 0.9900029 0.049797 NA NA
22 51221731 rs115055839 T C 0.0001903 0.0037496 0.9599999 0.9595328 0.073521 0.0625000 NA
22 51222100 rs114553188 G T 0.0028791 0.0044045 0.5099998 0.5133191 0.055220 0.0880591 NA
22 51223637 rs375798137 G A 0.0029258 0.0044232 0.5099998 0.5083091 0.054910 0.0788738 NA
22 51229805 rs9616985 T C 0.0001293 0.0037624 0.9699999 0.9725952 0.073413 0.0730831 NA
22 51237063 rs3896457 T C -0.0001141 0.0023133 0.9599999 0.9606694 0.295420 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623026 ES:SE:LP:AF:ID  0.000187984:0.00346635:0.0177288:0.623026:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400986 ES:SE:LP:AF:ID  -0.00388167:0.00343523:0.585027:0.400986:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103725 ES:SE:LP:AF:ID  0.00470144:0.00548453:0.408935:0.103725:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.457415 ES:SE:LP:AF:ID  -0.000633786:0.00337746:0.0705811:0.457415:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.07419  ES:SE:LP:AF:ID  -0.00647121:0.0066915:0.481486:0.07419:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.242063 ES:SE:LP:AF:ID  -0.000656954:0.00384847:0.0655015:0.242063:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.914041 ES:SE:LP:AF:ID  0.00148542:0.00485473:0.119186:0.914041:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116898 ES:SE:LP:AF:ID  0.000654918:0.00322745:0.0757207:0.116898:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.066254 ES:SE:LP:AF:ID  -0.00720402:0.00476404:0.886057:0.066254:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515314 ES:SE:LP:AF:ID  0.00116623:0.00239363:0.200659:0.515314:rs12029736
1   715265  rs12184267  C   T   .   PASS    AF=0.037136 ES:SE:LP:AF:ID  0.00756909:0.00545618:0.769551:0.037136:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037239 ES:SE:LP:AF:ID  0.00690016:0.00543976:0.69897:0.037239:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036909 ES:SE:LP:AF:ID  0.00768335:0.00547984:0.79588:0.036909:rs12184279
1   720381  rs116801199 G   T   .   PASS    AF=0.03748  ES:SE:LP:AF:ID  0.00759702:0.00541809:0.79588:0.03748:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037584 ES:SE:LP:AF:ID  0.0077899:0.00539899:0.823909:0.037584:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.100445 ES:SE:LP:AF:ID  -0.005968:0.00397299:0.886057:0.100445:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958353 ES:SE:LP:AF:ID  -0.00581129:0.00519442:0.585027:0.958353:rs2977670
1   725060  rs865924913 A   T   .   PASS    AF=0.053424 ES:SE:LP:AF:ID  0.00266738:0.00751134:0.142668:0.053424:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037042 ES:SE:LP:AF:ID  0.00725388:0.00543586:0.744727:0.037042:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037323 ES:SE:LP:AF:ID  0.00716101:0.00538818:0.744727:0.037323:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.84206  ES:SE:LP:AF:ID  -0.00246565:0.00279725:0.420216:0.84206:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056144 ES:SE:LP:AF:ID  0.00251475:0.00454394:0.236572:0.056144:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122891 ES:SE:LP:AF:ID  0.000905377:0.00306197:0.113509:0.122891:rs58276399
1   734349  rs141242758 T   C   .   PASS    AF=0.122086 ES:SE:LP:AF:ID  0.00072621:0.00306348:0.091515:0.122086:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132889 ES:SE:LP:AF:ID  0.00472566:0.00302162:0.920819:0.132889:rs79010578
1   752478  rs146277091 G   A   .   PASS    AF=0.037294 ES:SE:LP:AF:ID  0.00755969:0.00533047:0.79588:0.037294:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837897 ES:SE:LP:AF:ID  -0.00228457:0.00270671:0.39794:0.837897:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.837474 ES:SE:LP:AF:ID  -0.0021707:0.002703:0.376751:0.837474:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869041 ES:SE:LP:AF:ID  -0.000600394:0.00289981:0.0757207:0.869041:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130696 ES:SE:LP:AF:ID  0.000474654:0.00290462:0.0604807:0.130696:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037754 ES:SE:LP:AF:ID  0.00750804:0.00524138:0.823909:0.037754:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038022 ES:SE:LP:AF:ID  0.00752142:0.00520635:0.823909:0.038022:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.86829  ES:SE:LP:AF:ID  -0.000460684:0.0028928:0.0604807:0.86829:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868369 ES:SE:LP:AF:ID  -0.000369379:0.00289391:0.0457575:0.868369:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037968 ES:SE:LP:AF:ID  0.00790752:0.0052309:0.886057:0.037968:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.868298 ES:SE:LP:AF:ID  -0.000454335:0.00289268:0.0555173:0.868298:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.83688  ES:SE:LP:AF:ID  -0.00243716:0.00269604:0.431798:0.83688:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037967 ES:SE:LP:AF:ID  0.0080912:0.00524082:0.920819:0.037967:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.837523 ES:SE:LP:AF:ID  -0.00244097:0.00270361:0.431798:0.837523:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.838715 ES:SE:LP:AF:ID  -0.00230947:0.00274004:0.39794:0.838715:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.86863  ES:SE:LP:AF:ID  -0.000730461:0.00289002:0.09691:0.86863:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.868158 ES:SE:LP:AF:ID  -0.000641108:0.00288244:0.0861861:0.868158:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867082 ES:SE:LP:AF:ID  -0.00053509:0.00287696:0.0705811:0.867082:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.868324 ES:SE:LP:AF:ID  -0.000698385:0.00288519:0.091515:0.868324:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.868331 ES:SE:LP:AF:ID  -0.000696423:0.0028854:0.091515:0.868331:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.868338 ES:SE:LP:AF:ID  -0.000704106:0.00288547:0.091515:0.868338:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.868854 ES:SE:LP:AF:ID  -0.00080602:0.00289347:0.107905:0.868854:rs3131954
1   759036  rs114525117 G   A   .   PASS    AF=0.037962 ES:SE:LP:AF:ID  0.00863449:0.00521011:1.01323:0.037962:rs114525117
1   760912  rs1048488   C   T   .   PASS    AF=0.837249 ES:SE:LP:AF:ID  -0.00283746:0.00269151:0.537602:0.837249:rs1048488
1   760998  rs148828841 C   A   .   PASS    AF=0.037487 ES:SE:LP:AF:ID  0.00829218:0.00530623:0.920819:0.037487:rs148828841