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_6142_3.vcf.gz --id UKB-b:9056 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_6142_3.txt.gz --cohort_cases 23212 --cohort_controls 438030 --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-9056/UKB-b-9056_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-9056/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:42:17 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-9056/UKB-b-9056_data.vcf.gz ...
Read summary statistics for 7145418 SNPs.
Dropped 4517 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, 1267886 SNPs remain.
After merging with regression SNP LD, 1267886 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0065 (0.0011)
Lambda GC: 1.0821
Mean Chi^2: 1.0817
Intercept: 1.0217 (0.0065)
Ratio: 0.2656 (0.0798)
Analysis finished at Thu Oct 17 14:44:15 2019
Total time elapsed: 1.0m:58.56s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.937,
    "inflation_factor": 1.0966,
    "mean_EFFECT": 1.9176e-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": 65860,
    "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": 1267886,
    "ldsc_nsnp_merge_regression_ld": 1267886,
    "ldsc_observed_scale_h2_beta": 0.0065,
    "ldsc_observed_scale_h2_se": 0.0011,
    "ldsc_intercept_beta": 1.0217,
    "ldsc_intercept_se": 0.0065,
    "ldsc_lambda_gc": 1.0821,
    "ldsc_mean_chisq": 1.0817,
    "ldsc_ratio": 0.2656
}

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 7140923 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 7145418 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 8.664117e+00 5.763904e+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 7.864019e+07 5.646141e+07 828.0000000 3.215146e+07 6.906942e+07 1.145269e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 1.900000e-06 9.162000e-04 -0.0084387 -4.839000e-04 -2.200000e-06 4.821000e-04 8.174300e-03 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 8.033000e-04 3.903000e-04 0.0004333 4.911000e-04 6.384000e-04 1.016100e-03 4.701100e-03 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.884364e-01 2.917994e-01 0.0000004 2.300001e-01 4.799997e-01 7.400005e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.884349e-01 2.917746e-01 0.0000004 2.327233e-01 4.839389e-01 7.415917e-01 9.999997e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.711359e-01 2.601844e-01 0.0150790 5.559800e-02 1.728020e-01 4.262550e-01 9.849210e-01 ▇▃▂▁▁
numeric AF_reference 65860 0.9907829 NA NA NA NA NA NA NA 2.695975e-01 2.521299e-01 0.0000000 6.269970e-02 1.853040e-01 4.191290e-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 -0.0006906 0.0007975 0.3900004 0.3865468 0.623778 0.7821490 NA
1 54676 rs2462492 C T -0.0013611 0.0007900 0.0850002 0.0848946 0.400378 NA NA
1 86028 rs114608975 T C 0.0001629 0.0012631 0.9000000 0.8973870 0.103557 0.0277556 NA
1 91536 rs6702460 G T -0.0004919 0.0007779 0.5300002 0.5272092 0.456860 0.4207270 NA
1 234313 rs8179466 C T -0.0006563 0.0015334 0.6700003 0.6686368 0.074523 NA NA
1 534192 rs6680723 C T -0.0000898 0.0008886 0.9199999 0.9194911 0.240951 NA NA
1 546697 rs12025928 A G -0.0025077 0.0011084 0.0239999 0.0236711 0.913472 NA NA
1 693731 rs12238997 A G 0.0010533 0.0007446 0.1600000 0.1571868 0.116347 0.1417730 NA
1 705882 rs72631875 G A 0.0009556 0.0010913 0.3800004 0.3811927 0.067274 0.0315495 NA
1 706368 rs55727773 A G 0.0001919 0.0005516 0.7300002 0.7279772 0.515631 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0002459 0.0006654 0.7099994 0.7117746 0.137948 0.2052720 NA
22 51219387 rs9616832 T C -0.0007329 0.0008639 0.4000000 0.3962422 0.073728 0.0654952 NA
22 51219704 rs147475742 G A -0.0014712 0.0011577 0.2000000 0.2037975 0.041940 0.0473243 NA
22 51221190 rs369304721 G A -0.0007964 0.0011557 0.4899999 0.4907432 0.049716 NA NA
22 51221731 rs115055839 T C -0.0007173 0.0008644 0.4100001 0.4066767 0.073220 0.0625000 NA
22 51222100 rs114553188 G T 0.0010049 0.0010174 0.3200000 0.3233167 0.054470 0.0880591 NA
22 51223637 rs375798137 G A 0.0010690 0.0010223 0.2999998 0.2957497 0.054101 0.0788738 NA
22 51229805 rs9616985 T C -0.0007680 0.0008675 0.3800004 0.3760225 0.073055 0.0730831 NA
22 51232488 rs376461333 A G 0.0033713 0.0020428 0.0990011 0.0988778 0.020050 NA NA
22 51237063 rs3896457 T C -0.0006430 0.0005305 0.2300001 0.2254956 0.297948 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623778 ES:SE:LP:AF:ID  -0.00069057:0.00079752:0.408935:0.623778:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400378 ES:SE:LP:AF:ID  -0.00136109:0.000789969:1.07058:0.400378:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103557 ES:SE:LP:AF:ID  0.000162893:0.0012631:0.0457575:0.103557:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.45686  ES:SE:LP:AF:ID  -0.000491874:0.000777947:0.275724:0.45686:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074523 ES:SE:LP:AF:ID  -0.000656304:0.00153335:0.173925:0.074523:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240951 ES:SE:LP:AF:ID  -8.98112e-05:0.000888562:0.0362122:0.240951:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913472 ES:SE:LP:AF:ID  -0.00250774:0.00110843:1.61979:0.913472:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116347 ES:SE:LP:AF:ID  0.00105331:0.000744601:0.79588:0.116347:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067274 ES:SE:LP:AF:ID  0.000955626:0.00109127:0.420216:0.067274:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515631 ES:SE:LP:AF:ID  0.00019186:0.000551611:0.136677:0.515631:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.032996 ES:SE:LP:AF:ID  0.00182853:0.0013908:0.721246:0.032996:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036612 ES:SE:LP:AF:ID  0.00159424:0.00126331:0.677781:0.036612:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036728 ES:SE:LP:AF:ID  0.00148028:0.00125853:0.619789:0.036728:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036428 ES:SE:LP:AF:ID  0.00150152:0.00126758:0.619789:0.036428:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016411 ES:SE:LP:AF:ID  0.000195862:0.00195128:0.0362122:0.016411:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036967 ES:SE:LP:AF:ID  0.00145997:0.00125354:0.619789:0.036967:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037064 ES:SE:LP:AF:ID  0.00159739:0.00124926:0.69897:0.037064:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101197 ES:SE:LP:AF:ID  0.00052765:0.000910099:0.251812:0.101197:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959109 ES:SE:LP:AF:ID  -0.000845149:0.00120493:0.318759:0.959109:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031452 ES:SE:LP:AF:ID  0.00168096:0.00218666:0.356547:0.031452:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053261 ES:SE:LP:AF:ID  0.000733788:0.00173933:0.173925:0.053261:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.03658  ES:SE:LP:AF:ID  0.00135473:0.00125738:0.552842:0.03658:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036898 ES:SE:LP:AF:ID  0.00138717:0.00124589:0.568636:0.036898:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843206 ES:SE:LP:AF:ID  -0.000629167:0.000645338:0.481486:0.843206:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055925 ES:SE:LP:AF:ID  0.00135332:0.00104488:0.69897:0.055925:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.12233  ES:SE:LP:AF:ID  0.000767237:0.000706324:0.552842:0.12233:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025712 ES:SE:LP:AF:ID  0.00120635:0.00173789:0.309804:0.025712:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121571 ES:SE:LP:AF:ID  0.000690682:0.000706632:0.481486:0.121571:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132336 ES:SE:LP:AF:ID  0.000829727:0.000696489:0.638272:0.132336:rs79010578
1   752478  rs146277091 G   A   .   PASS    AF=0.03681  ES:SE:LP:AF:ID  0.00152741:0.00123335:0.657577:0.03681:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838943 ES:SE:LP:AF:ID  -0.000753424:0.000624959:0.638272:0.838943:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838571 ES:SE:LP:AF:ID  -0.000678205:0.000624286:0.552842:0.838571:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.86976  ES:SE:LP:AF:ID  -0.000564756:0.000669876:0.39794:0.86976:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129892 ES:SE:LP:AF:ID  0.0005037:0.000671231:0.346787:0.129892:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.03732  ES:SE:LP:AF:ID  0.00142777:0.00121245:0.619789:0.03732:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037563 ES:SE:LP:AF:ID  0.00141525:0.00120479:0.619789:0.037563:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869102 ES:SE:LP:AF:ID  -0.000463081:0.000668562:0.309804:0.869102:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869201 ES:SE:LP:AF:ID  -0.000458649:0.000668828:0.309804:0.869201:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037522 ES:SE:LP:AF:ID  0.00135588:0.00120999:0.585027:0.037522:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869105 ES:SE:LP:AF:ID  -0.000475953:0.000668548:0.318759:0.869105:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.838023 ES:SE:LP:AF:ID  -0.000664598:0.000622548:0.537602:0.838023:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037534 ES:SE:LP:AF:ID  0.00124371:0.00121171:0.522879:0.037534:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838653 ES:SE:LP:AF:ID  -0.000699396:0.000624296:0.585027:0.838653:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.839766 ES:SE:LP:AF:ID  -0.000744076:0.000632738:0.619789:0.839766:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.869384 ES:SE:LP:AF:ID  -0.000605105:0.000667776:0.443698:0.869384:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.868929 ES:SE:LP:AF:ID  -0.000650739:0.000666093:0.481486:0.868929:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867885 ES:SE:LP:AF:ID  -0.000487441:0.00066482:0.337242:0.867885:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869073 ES:SE:LP:AF:ID  -0.000646836:0.00066664:0.481486:0.869073:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869082 ES:SE:LP:AF:ID  -0.000648868:0.000666691:0.481486:0.869082:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869089 ES:SE:LP:AF:ID  -0.000649206:0.000666706:0.481486:0.869089:rs3131956