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_41246_1040.vcf.gz --id UKB-b:997 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41246_1040.txt.gz --cohort_cases 16489 --cohort_controls 428411 --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-997/UKB-b-997_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-997/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:43:15 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-997/UKB-b-997_data.vcf.gz ...
Read summary statistics for 6658858 SNPs.
Dropped 3782 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, 1250987 SNPs remain.
After merging with regression SNP LD, 1250987 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0056 (0.0011)
Lambda GC: 1.1005
Mean Chi^2: 1.0981
Intercept: 1.0499 (0.0061)
Ratio: 0.5089 (0.0617)
Analysis finished at Thu Oct 17 14:44:44 2019
Total time elapsed: 1.0m:29.21s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9325,
    "inflation_factor": 1.0966,
    "mean_EFFECT": 4.8121e-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": 61145,
    "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": 1250987,
    "ldsc_nsnp_merge_regression_ld": 1250987,
    "ldsc_observed_scale_h2_beta": 0.0056,
    "ldsc_observed_scale_h2_se": 0.0011,
    "ldsc_intercept_beta": 1.0499,
    "ldsc_intercept_se": 0.0061,
    "ldsc_lambda_gc": 1.1005,
    "ldsc_mean_chisq": 1.0981,
    "ldsc_ratio": 0.5087
}

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 TRUE
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 6655098 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 6658858 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 8.664119e+00 5.763900e+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 7.860102e+07 5.648053e+07 828.0000000 3.206694e+07 6.905121e+07 1.144981e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 4.800000e-06 7.382000e-04 -0.0060367 -4.112000e-04 6.000000e-07 4.162000e-04 7.455000e-03 ▁▂▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 6.559000e-04 2.755000e-04 0.0003863 4.329000e-04 5.430000e-04 8.111000e-04 3.382100e-03 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.853851e-01 2.922674e-01 0.0000002 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 4.853858e-01 2.922440e-01 0.0000002 2.286861e-01 4.795487e-01 7.387771e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.874695e-01 2.582906e-01 0.0212270 7.088000e-02 1.959095e-01 4.477970e-01 9.787730e-01 ▇▃▂▂▁
numeric AF_reference 61145 0.9908175 NA NA NA NA NA NA NA 2.852644e-01 2.505332e-01 0.0000000 7.987220e-02 2.064700e-01 4.398960e-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 -0.0008489 0.0007108 0.2300001 0.2323599 0.623745 0.7821490 NA
1 54676 rs2462492 C T 0.0008031 0.0007042 0.2500000 0.2541376 0.400412 NA NA
1 86028 rs114608975 T C -0.0017974 0.0011260 0.1100001 0.1104428 0.103531 0.0277556 NA
1 91536 rs6702460 G T 0.0008508 0.0006933 0.2200002 0.2197731 0.456861 0.4207270 NA
1 234313 rs8179466 C T -0.0015063 0.0013682 0.2700001 0.2709355 0.074430 NA NA
1 534192 rs6680723 C T 0.0004013 0.0007921 0.6100002 0.6123781 0.240925 NA NA
1 546697 rs12025928 A G 0.0007203 0.0009882 0.4700002 0.4660479 0.913480 NA NA
1 693731 rs12238997 A G -0.0008426 0.0006639 0.2000000 0.2044367 0.116346 0.1417730 NA
1 705882 rs72631875 G A -0.0000749 0.0009727 0.9400001 0.9386177 0.067343 0.0315495 NA
1 706368 rs55727773 A G -0.0002955 0.0004918 0.5500004 0.5479256 0.515729 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51218377 rs2519461 G C 0.0008544 0.0007681 0.2700001 0.2660313 0.073664 0.0826677 NA
22 51219006 rs28729663 G A 0.0001940 0.0005931 0.7400005 0.7435632 0.138010 0.2052720 NA
22 51219387 rs9616832 T C 0.0007765 0.0007696 0.3100002 0.3130169 0.073793 0.0654952 NA
22 51219704 rs147475742 G A 0.0021050 0.0010313 0.0409996 0.0412523 0.041979 0.0473243 NA
22 51221190 rs369304721 G A 0.0014493 0.0010294 0.1600000 0.1591706 0.049778 NA NA
22 51221731 rs115055839 T C 0.0008579 0.0007701 0.2700001 0.2652708 0.073286 0.0625000 NA
22 51222100 rs114553188 G T -0.0008820 0.0009070 0.3300000 0.3308070 0.054496 0.0880591 NA
22 51223637 rs375798137 G A -0.0008729 0.0009114 0.3400001 0.3381536 0.054124 0.0788738 NA
22 51229805 rs9616985 T C 0.0009427 0.0007729 0.2200002 0.2226193 0.073123 0.0730831 NA
22 51237063 rs3896457 T C 0.0005356 0.0004729 0.2599998 0.2573714 0.298134 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623745 ES:SE:LP:AF:ID  -0.000848899:0.000710791:0.638272:0.623745:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400412 ES:SE:LP:AF:ID  0.000803053:0.000704212:0.60206:0.400412:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103531 ES:SE:LP:AF:ID  -0.00179736:0.00112602:0.958607:0.103531:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456861 ES:SE:LP:AF:ID  0.000850763:0.000693294:0.657577:0.456861:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.07443  ES:SE:LP:AF:ID  -0.00150631:0.00136824:0.568636:0.07443:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240925 ES:SE:LP:AF:ID  0.000401333:0.000792081:0.21467:0.240925:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.91348  ES:SE:LP:AF:ID  0.000720345:0.00098823:0.327902:0.91348:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116346 ES:SE:LP:AF:ID  -0.000842553:0.000663944:0.69897:0.116346:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067343 ES:SE:LP:AF:ID  -7.49027e-05:0.00097267:0.0268721:0.067343:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515729 ES:SE:LP:AF:ID  -0.000295502:0.000491789:0.259637:0.515729:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033034 ES:SE:LP:AF:ID  -0.00219826:0.00123932:1.11919:0.033034:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036651 ES:SE:LP:AF:ID  -0.00213027:0.00112576:1.23657:0.036651:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036767 ES:SE:LP:AF:ID  -0.00221493:0.00112151:1.31876:0.036767:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036462 ES:SE:LP:AF:ID  -0.00206193:0.00112968:1.16749:0.036462:rs12184279
1   720381  rs116801199 G   T   .   PASS    AF=0.037    ES:SE:LP:AF:ID  -0.00206744:0.0011172:1.19382:0.037:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037103 ES:SE:LP:AF:ID  -0.00211376:0.00111321:1.23657:0.037103:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.10125  ES:SE:LP:AF:ID  0.00112606:0.000811322:0.769551:0.10125:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959084 ES:SE:LP:AF:ID  0.00171914:0.00107397:0.958607:0.959084:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031442 ES:SE:LP:AF:ID  0.00206473:0.0019496:0.537602:0.031442:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053323 ES:SE:LP:AF:ID  0.00291656:0.00154852:1.22185:0.053323:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.03661  ES:SE:LP:AF:ID  -0.00215382:0.0011206:1.25964:0.03661:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036929 ES:SE:LP:AF:ID  -0.00215665:0.00111034:1.284:0.036929:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843161 ES:SE:LP:AF:ID  0.000986752:0.000575369:1.0655:0.843161:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055902 ES:SE:LP:AF:ID  0.000539002:0.000931715:0.251812:0.055902:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122334 ES:SE:LP:AF:ID  -0.000644912:0.000629741:0.508638:0.122334:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025698 ES:SE:LP:AF:ID  0.00139074:0.00154981:0.431798:0.025698:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121577 ES:SE:LP:AF:ID  -0.000575967:0.000630001:0.443698:0.121577:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132377 ES:SE:LP:AF:ID  -0.000855464:0.000620885:0.769551:0.132377:rs79010578
1   752478  rs146277091 G   A   .   PASS    AF=0.036839 ES:SE:LP:AF:ID  -0.00211186:0.00109921:1.25964:0.036839:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838854 ES:SE:LP:AF:ID  0.0010057:0.00055707:1.14874:0.838854:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838474 ES:SE:LP:AF:ID  0.000982919:0.000556448:1.11351:0.838474:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869713 ES:SE:LP:AF:ID  0.000608534:0.000597114:0.508638:0.869713:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129954 ES:SE:LP:AF:ID  -0.000645822:0.000598297:0.552842:0.129954:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037356 ES:SE:LP:AF:ID  -0.0020698:0.00108047:1.25964:0.037356:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.0376   ES:SE:LP:AF:ID  -0.00202713:0.00107364:1.22915:0.0376:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.86905  ES:SE:LP:AF:ID  0.00057724:0.000595924:0.481486:0.86905:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.86915  ES:SE:LP:AF:ID  0.000595971:0.000596166:0.49485:0.86915:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037561 ES:SE:LP:AF:ID  -0.00206765:0.00107825:1.25964:0.037561:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869053 ES:SE:LP:AF:ID  0.000572961:0.000595913:0.468521:0.869053:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.837921 ES:SE:LP:AF:ID  0.000991936:0.000554898:1.13077:0.837921:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037576 ES:SE:LP:AF:ID  -0.00203968:0.00107974:1.22915:0.037576:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838552 ES:SE:LP:AF:ID  0.000970436:0.000556455:1.09151:0.838552:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.839678 ES:SE:LP:AF:ID  0.00102274:0.000564022:1.1549:0.839678:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.869342 ES:SE:LP:AF:ID  0.000615498:0.00059526:0.522879:0.869342:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.868893 ES:SE:LP:AF:ID  0.000601318:0.000593788:0.508638:0.868893:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867835 ES:SE:LP:AF:ID  0.000566069:0.000592611:0.468521:0.867835:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869033 ES:SE:LP:AF:ID  0.000603753:0.000594264:0.508638:0.869033:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869042 ES:SE:LP:AF:ID  0.000602:0.00059431:0.508638:0.869042:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.86905  ES:SE:LP:AF:ID  0.000605618:0.000594324:0.508638:0.86905:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869525 ES:SE:LP:AF:ID  0.000610856:0.000595933:0.508638:0.869525:rs3131954