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_20107_11.vcf.gz --id UKB-b:956 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20107_11.txt.gz --cohort_cases 10106 --cohort_controls 388983 --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-956/UKB-b-956_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-956/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:42:47 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-956/UKB-b-956_data.vcf.gz ...
Read summary statistics for 5965579 SNPs.
Dropped 2691 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, 1212252 SNPs remain.
After merging with regression SNP LD, 1212252 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0042 (0.0013)
Lambda GC: 1.0718
Mean Chi^2: 1.067
Intercept: 1.0337 (0.0066)
Ratio: 0.5027 (0.0986)
Analysis finished at Thu Oct 17 14:43:45 2019
Total time elapsed: 58.51s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.924,
    "inflation_factor": 1.0966,
    "mean_EFFECT": -3.6721e-07,
    "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": 53860,
    "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": 1212252,
    "ldsc_nsnp_merge_regression_ld": 1212252,
    "ldsc_observed_scale_h2_beta": 0.0042,
    "ldsc_observed_scale_h2_se": 0.0013,
    "ldsc_intercept_beta": 1.0337,
    "ldsc_intercept_se": 0.0066,
    "ldsc_lambda_gc": 1.0718,
    "ldsc_mean_chisq": 1.067,
    "ldsc_ratio": 0.503
}

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 5962906 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 5965579 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 8.671726e+00 5.762575e+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 7.857459e+07 5.652126e+07 828.0000000 3.198261e+07 6.902326e+07 1.144902e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -4.000000e-07 5.603000e-04 -0.0046144 -3.361000e-04 -2.500000e-06 3.344000e-04 5.016100e-03 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 5.155000e-04 1.708000e-04 0.0003392 3.748000e-04 4.491000e-04 6.156000e-04 1.750100e-03 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.890144e-01 2.909442e-01 0.0000005 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 4.890138e-01 2.909177e-01 0.0000005 2.349981e-01 4.843386e-01 7.404480e-01 9.999996e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 3.130368e-01 2.529947e-01 0.0346330 9.836750e-02 2.314320e-01 4.784315e-01 9.653670e-01 ▇▃▂▂▁
numeric AF_reference 53860 0.9909715 NA NA NA NA NA NA NA 3.094524e-01 2.462139e-01 0.0000000 1.072280e-01 2.390180e-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 0.0001917 0.0006246 0.7600007 0.7589635 0.623691 0.7821490 NA
1 54676 rs2462492 C T -0.0003702 0.0006186 0.5500004 0.5495405 0.400456 NA NA
1 86028 rs114608975 T C 0.0000852 0.0009895 0.9299999 0.9314116 0.103509 0.0277556 NA
1 91536 rs6702460 G T -0.0006699 0.0006097 0.2700001 0.2718175 0.457026 0.4207270 NA
1 234313 rs8179466 C T -0.0007407 0.0012032 0.5400003 0.5381630 0.074434 NA NA
1 534192 rs6680723 C T -0.0002185 0.0006960 0.7499995 0.7535631 0.241052 NA NA
1 546697 rs12025928 A G 0.0002686 0.0008680 0.7600007 0.7570089 0.913458 NA NA
1 693731 rs12238997 A G 0.0002343 0.0005829 0.6899999 0.6877146 0.116268 0.1417730 NA
1 705882 rs72631875 G A -0.0001509 0.0008539 0.8600001 0.8597566 0.067414 0.0315495 NA
1 706368 rs55727773 A G 0.0005010 0.0004319 0.2500000 0.2460923 0.515690 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51218377 rs2519461 G C 0.0005842 0.0006754 0.3900004 0.3870848 0.073609 0.0826677 NA
22 51219006 rs28729663 G A 0.0000180 0.0005215 0.9699999 0.9724234 0.137844 0.2052720 NA
22 51219387 rs9616832 T C 0.0005887 0.0006767 0.3800004 0.3843341 0.073754 0.0654952 NA
22 51219704 rs147475742 G A 0.0006222 0.0009074 0.4899999 0.4929284 0.041916 0.0473243 NA
22 51221190 rs369304721 G A 0.0004221 0.0009052 0.6400000 0.6410077 0.049706 NA NA
22 51221731 rs115055839 T C 0.0005915 0.0006771 0.3800004 0.3823373 0.073239 0.0625000 NA
22 51222100 rs114553188 G T -0.0007076 0.0007975 0.3700002 0.3749210 0.054369 0.0880591 NA
22 51223637 rs375798137 G A -0.0006611 0.0008014 0.4100001 0.4093604 0.054003 0.0788738 NA
22 51229805 rs9616985 T C 0.0005286 0.0006795 0.4400003 0.4366168 0.073076 0.0730831 NA
22 51237063 rs3896457 T C 0.0000703 0.0004155 0.8700001 0.8656919 0.297923 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623691 ES:SE:LP:AF:ID  0.000191665:0.000624637:0.119186:0.623691:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400456 ES:SE:LP:AF:ID  -0.00037021:0.000618616:0.259637:0.400456:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103509 ES:SE:LP:AF:ID  8.51628e-05:0.000989472:0.0315171:0.103509:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.457026 ES:SE:LP:AF:ID  -0.000669945:0.000609658:0.568636:0.457026:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074434 ES:SE:LP:AF:ID  -0.000740688:0.00120321:0.267606:0.074434:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241052 ES:SE:LP:AF:ID  -0.000218507:0.000696005:0.124939:0.241052:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913458 ES:SE:LP:AF:ID  0.000268583:0.000868046:0.119186:0.913458:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116268 ES:SE:LP:AF:ID  0.000234306:0.000582911:0.161151:0.116268:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067414 ES:SE:LP:AF:ID  -0.000150868:0.000853886:0.0655015:0.067414:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.51569  ES:SE:LP:AF:ID  0.000500995:0.000431932:0.60206:0.51569:rs12029736
1   715265  rs12184267  C   T   .   PASS    AF=0.036692 ES:SE:LP:AF:ID  0.00095996:0.000988283:0.481486:0.036692:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036805 ES:SE:LP:AF:ID  0.000971092:0.000984613:0.49485:0.036805:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036502 ES:SE:LP:AF:ID  0.000981452:0.000991719:0.49485:0.036502:rs12184279
1   720381  rs116801199 G   T   .   PASS    AF=0.037046 ES:SE:LP:AF:ID  0.000986326:0.000980695:0.508638:0.037046:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.03714  ES:SE:LP:AF:ID  0.000906737:0.000977387:0.455932:0.03714:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101352 ES:SE:LP:AF:ID  -0.00047269:0.000711874:0.29243:0.101352:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959113 ES:SE:LP:AF:ID  -0.000802008:0.000943705:0.39794:0.959113:rs2977670
1   725060  rs865924913 A   T   .   PASS    AF=0.053267 ES:SE:LP:AF:ID  -0.000592057:0.00136198:0.180456:0.053267:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036648 ES:SE:LP:AF:ID  0.000992695:0.000983738:0.508638:0.036648:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036972 ES:SE:LP:AF:ID  0.00100602:0.000974715:0.522879:0.036972:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843276 ES:SE:LP:AF:ID  -0.000243946:0.000505194:0.200659:0.843276:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055896 ES:SE:LP:AF:ID  0.000857345:0.000818045:0.537602:0.055896:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122288 ES:SE:LP:AF:ID  -1.25279e-05:0.000552795:0.00877392:0.122288:rs58276399
1   734349  rs141242758 T   C   .   PASS    AF=0.121522 ES:SE:LP:AF:ID  -2.65963e-05:0.000553038:0.0177288:0.121522:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132357 ES:SE:LP:AF:ID  0.000209477:0.000545124:0.154902:0.132357:rs79010578
1   752478  rs146277091 G   A   .   PASS    AF=0.036892 ES:SE:LP:AF:ID  0.000933906:0.000964764:0.481486:0.036892:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838911 ES:SE:LP:AF:ID  -0.000150872:0.00048916:0.119186:0.838911:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838567 ES:SE:LP:AF:ID  -0.000144906:0.000488662:0.113509:0.838567:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869803 ES:SE:LP:AF:ID  0.000167655:0.000524419:0.124939:0.869803:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129823 ES:SE:LP:AF:ID  -0.00016421:0.000525507:0.124939:0.129823:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037415 ES:SE:LP:AF:ID  0.000867695:0.000948253:0.443698:0.037415:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.03765  ES:SE:LP:AF:ID  0.000808368:0.000942427:0.408935:0.03765:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869175 ES:SE:LP:AF:ID  0.00017488:0.00052343:0.130768:0.869175:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869279 ES:SE:LP:AF:ID  0.00018467:0.000523637:0.142668:0.869279:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037614 ES:SE:LP:AF:ID  0.000925721:0.000946388:0.481486:0.037614:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869176 ES:SE:LP:AF:ID  0.000172619:0.00052342:0.130768:0.869176:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.838014 ES:SE:LP:AF:ID  -0.000144464:0.000487295:0.113509:0.838014:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037627 ES:SE:LP:AF:ID  0.000939311:0.000947746:0.49485:0.037627:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838646 ES:SE:LP:AF:ID  -0.000158352:0.000488662:0.124939:0.838646:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.839726 ES:SE:LP:AF:ID  -0.000210113:0.00049521:0.173925:0.839726:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.869427 ES:SE:LP:AF:ID  0.000135587:0.000522771:0.09691:0.869427:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.868972 ES:SE:LP:AF:ID  0.000152778:0.000521441:0.113509:0.868972:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867953 ES:SE:LP:AF:ID  0.000211962:0.000520476:0.167491:0.867953:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869111 ES:SE:LP:AF:ID  0.00015208:0.000521866:0.113509:0.869111:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.86912  ES:SE:LP:AF:ID  0.000152471:0.000521905:0.113509:0.86912:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869128 ES:SE:LP:AF:ID  0.000150195:0.000521918:0.113509:0.869128:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869611 ES:SE:LP:AF:ID  0.000148435:0.000523367:0.107905:0.869611:rs3131954
1   759036  rs114525117 G   A   .   PASS    AF=0.037664 ES:SE:LP:AF:ID  0.00141805:0.00094237:0.886057:0.037664:rs114525117
1   760912  rs1048488   C   T   .   PASS    AF=0.83828  ES:SE:LP:AF:ID  -0.000185619:0.000486349:0.154902:0.83828:rs1048488
1   760998  rs148828841 C   A   .   PASS    AF=0.037105 ES:SE:LP:AF:ID  0.00116747:0.000960043:0.657577:0.037105:rs148828841