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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}

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/dev/ukb-d-import/processed/ukb-d-22601_31193034/ukb-d-22601_31193034.vcf.gz \
--ref-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-22601_31193034/ldsc.txt \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ 

Beginning analysis at Mon Nov 25 15:33:46 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-22601_31193034/ukb-d-22601_31193034.vcf.gz ...
Read summary statistics for 9855759 SNPs.
Dropped 7533 SNPs with duplicated rs numbers.
Reading reference panel LD Score from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/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 /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/[1-22] ...
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 1276989 SNPs remain.
After merging with regression SNP LD, 1276989 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: -0.0015 (0.0043)
Lambda GC: 0.9994
Mean Chi^2: 1.0015
Intercept: 1.0042 (0.0055)
Ratio: 2.7954 (3.6374)
Analysis finished at Mon Nov 25 15:35:25 2019
Total time elapsed: 1.0m:38.63s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9409,
    "inflation_factor": 1.0024,
    "mean_EFFECT": 1.2825e-06,
    "n": 89866,
    "n_snps": 9855759,
    "n_clumped_hits": 2,
    "n_p_sig": 2,
    "n_mono": 0,
    "n_ns": 1073888,
    "n_mac": 0,
    "is_snpid_unique": false,
    "n_miss_EFFECT": 0,
    "n_miss_SE": 0,
    "n_miss_PVAL": 0,
    "n_miss_AF": 0,
    "n_miss_AF_reference": 195513,
    "n_est": 89862.4794,
    "ratio_se_n": 1,
    "mean_diff": 7.6584e-06,
    "ratio_diff": 5.5695,
    "sd_y_est1": 0.1243,
    "sd_y_est2": 0.1243,
    "r2_sum1": 0,
    "r2_sum2": 0.0007,
    "r2_sum3": 0.0007,
    "r2_sum4": 0.0007,
    "ldsc_nsnp_merge_refpanel_ld": 1276989,
    "ldsc_nsnp_merge_regression_ld": 1276989,
    "ldsc_observed_scale_h2_beta": "NA",
    "ldsc_observed_scale_h2_se": "NA",
    "ldsc_intercept_beta": 1.0042,
    "ldsc_intercept_se": 0.0055,
    "ldsc_lambda_gc": 0.9994,
    "ldsc_mean_chisq": 1.0015,
    "ldsc_ratio": 2.8
}

Flags

name value
af_correlation FALSE
inflation_factor FALSE
n FALSE
is_snpid_non_unique TRUE
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 numeric.mean numeric.sd numeric.p0 numeric.p25 numeric.p50 numeric.p75 numeric.p100 numeric.hist
character ID 0 1.0000000 3 94 0 9848773 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 98 0 47679 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 342 0 29935 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 9.080095e+00 6.176579e+00 1.00000e+00 4.000000e+00 8.000000e+00 1.300000e+01 2.300000e+01 ▇▅▅▂▂
numeric POS 0 1.0000000 NA NA NA NA NA 7.875032e+07 5.609887e+07 3.02000e+02 3.245476e+07 6.964884e+07 1.147409e+08 2.492297e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA 1.300000e-06 1.365800e-03 -1.21554e-02 -6.496000e-04 -1.120000e-05 6.320000e-04 1.558740e-02 ▁▂▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 1.179600e-03 6.962000e-04 4.84900e-04 6.441000e-04 8.688000e-04 1.519100e-03 3.888500e-03 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.996939e-01 2.884345e-01 0.00000e+00 2.502770e-01 4.994822e-01 7.494989e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.996922e-01 2.884356e-01 0.00000e+00 2.502740e-01 4.994801e-01 7.494986e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA 2.578968e-01 2.629296e-01 8.91580e-03 4.104100e-02 1.542520e-01 4.108930e-01 9.910840e-01 ▇▂▂▁▁
numeric AF_reference 195513 0.9801626 NA NA NA NA NA 2.574820e-01 2.537487e-01 0.00000e+00 4.612620e-02 1.697280e-01 4.069490e-01 1.000000e+00 ▇▃▂▁▁
numeric N 0 1.0000000 NA NA NA NA NA 8.986600e+04 0.000000e+00 8.98660e+04 8.986600e+04 8.986600e+04 8.986600e+04 8.986600e+04 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 692794 rs530212009 CA C 0.0011659 0.0010244 0.2550721 0.2550669 0.1114870 0.1894970 89866
1 693731 rs12238997 A G 0.0001687 0.0009704 0.8619850 0.8619845 0.1164980 0.1417730 89866
1 707522 rs371890604 G C 0.0007492 0.0010909 0.4922435 0.4922426 0.0978285 0.1293930 89866
1 717587 rs144155419 G A 0.0014640 0.0025986 0.5731786 0.5731779 0.0158090 0.0045926 89866
1 730087 rs148120343 T C 0.0005507 0.0013456 0.6823717 0.6823703 0.0569661 0.0127796 89866
1 731718 rs142557973 T C 0.0002444 0.0009209 0.7907132 0.7907137 0.1223780 0.1543530 89866
1 732032 rs61770163 A C 0.0003941 0.0009817 0.6881086 0.6881081 0.1220530 0.1555510 89866
1 734349 rs141242758 T C 0.0002610 0.0009215 0.7770393 0.7770388 0.1216170 0.1525560 89866
1 749963 rs529266287 T TAA -0.0003278 0.0009067 0.7176736 0.7176722 0.8689590 0.7641770 89866
1 751343 rs28544273 T A 0.0003408 0.0008988 0.7045681 0.7045671 0.1236780 0.2426120 89866
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T 0.0006627 0.0010778 0.5386703 0.5386661 0.0553576 0.0309934 89866
23 154923374 rs111332691 T A -0.0005514 0.0011734 0.6384193 0.6384198 0.0449113 0.0116556 89866
23 154925045 rs509981 C T 0.0002897 0.0005659 0.6086519 0.6086510 0.2444280 0.3634440 89866
23 154925895 rs538470 C T 0.0001363 0.0005787 0.8138360 0.8138351 0.2408760 0.3634440 89866
23 154927581 rs644138 G A 0.0003924 0.0005323 0.4610542 0.4610525 0.3001910 0.4635760 89866
23 154929412 rs557132 C T 0.0002990 0.0005661 0.5973433 0.5973413 0.2442750 0.3568210 89866
23 154929637 rs35185538 CT C 0.0005081 0.0005911 0.3900336 0.3900325 0.2286930 0.3011920 89866
23 154929952 rs4012982 CAA C 0.0002385 0.0005957 0.6889505 0.6889498 0.2381980 0.3165560 89866
23 154930230 rs781880 A G 0.0002729 0.0005659 0.6296526 0.6296518 0.2447120 0.3618540 89866
23 154930487 rs781879 T A 0.0001910 0.0019197 0.9207319 0.9207323 0.0196791 0.1263580 89866

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.111487 ES:SE:LP:AF:SS:ID   0.00116591:0.00102441:0.593337:0.111487:89866:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.116498 ES:SE:LP:AF:SS:ID   0.000168703:0.000970402:0.0645003:0.116498:89866:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0978285    ES:SE:LP:AF:SS:ID   0.000749199:0.00109094:0.30782:0.0978285:89866:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.015809 ES:SE:LP:AF:SS:ID   0.00146397:0.00259856:0.24171:0.015809:89866:rs144155419
1   730087  rs148120343 T   C   .   PASS    AF=0.0569661    ES:SE:LP:AF:SS:ID   0.000550661:0.0013456:0.165979:0.0569661:89866:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122378 ES:SE:LP:AF:SS:ID   0.000244382:0.000920862:0.101981:0.122378:89866:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.122053 ES:SE:LP:AF:SS:ID   0.000394095:0.000981743:0.162343:0.122053:89866:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.121617 ES:SE:LP:AF:SS:ID   0.000260964:0.000921548:0.109557:0.121617:89866:rs141242758
1   749963  rs529266287 T   TAA .   PASS    AF=0.868959 ES:SE:LP:AF:SS:ID   -0.000327824:0.000906664:0.144073:0.868959:89866:rs529266287
1   751343  rs28544273  T   A   .   PASS    AF=0.123678 ES:SE:LP:AF:SS:ID   0.000340802:0.000898828:0.152077:0.123678:89866:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.143191 ES:SE:LP:AF:SS:ID   0.000382081:0.00088989:0.175442:0.143191:89866:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.123813 ES:SE:LP:AF:SS:ID   0.000336157:0.000897531:0.149962:0.123813:89866:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.869927 ES:SE:LP:AF:SS:ID   -0.000126901:0.000874516:0.0532408:0.869927:89866:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.87461  ES:SE:LP:AF:SS:ID   -9.6502e-05:0.000888774:0.0392739:0.87461:89866:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.12951  ES:SE:LP:AF:SS:ID   0.000160831:0.00087719:0.0682752:0.12951:89866:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0357365    ES:SE:LP:AF:SS:ID   0.00212536:0.00160481:0.731928:0.0357365:89866:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869574 ES:SE:LP:AF:SS:ID   -6.3726e-05:0.000873746:0.0260141:0.869574:89866:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869652 ES:SE:LP:AF:SS:ID   -7.534e-05:0.000874135:0.0309025:0.869652:89866:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.86957  ES:SE:LP:AF:SS:ID   -6.16998e-05:0.000873711:0.0251651:0.86957:89866:rs3131967
1   755890  rs3115858   A   T   .   PASS    AF=0.869566 ES:SE:LP:AF:SS:ID   -7.81734e-06:0.000871979:0.00311767:0.869566:89866:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.126879 ES:SE:LP:AF:SS:ID   0.000213854:0.000890154:0.0914394:0.126879:89866:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.869094 ES:SE:LP:AF:SS:ID   1.11721e-05:0.000869718:0.00447405:0.869094:89866:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.868498 ES:SE:LP:AF:SS:ID   -6.39852e-05:0.000869186:0.0262641:0.868498:89866:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869245 ES:SE:LP:AF:SS:ID   -9.96459e-06:0.000870513:0.00398464:0.869245:89866:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869251 ES:SE:LP:AF:SS:ID   -1.23173e-05:0.000870572:0.00493019:0.869251:89866:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869252 ES:SE:LP:AF:SS:ID   -8.33805e-06:0.000870578:0.00333162:0.869252:89866:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869724 ES:SE:LP:AF:SS:ID   -1.52678e-05:0.000872893:0.00610326:0.869724:89866:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.0991203    ES:SE:LP:AF:SS:ID   0.000350423:0.00101619:0.136549:0.0991203:89866:rs10157329
1   759837  rs3115851   T   A   .   PASS    AF=0.873732 ES:SE:LP:AF:SS:ID   -0.000100312:0.00088586:0.0410335:0.873732:89866:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.863116 ES:SE:LP:AF:SS:ID   -0.000403338:0.000868892:0.192122:0.863116:89866:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.868511 ES:SE:LP:AF:SS:ID   -0.000185431:0.000877573:0.0795354:0.868511:89866:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.86539  ES:SE:LP:AF:SS:ID   -0.000107304:0.000877702:0.044458:0.86539:89866:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0994301    ES:SE:LP:AF:SS:ID   -0.000381362:0.00105105:0.144647:0.0994301:89866:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.870696 ES:SE:LP:AF:SS:ID   -0.000157339:0.000887798:0.0658385:0.870696:89866:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.870695 ES:SE:LP:AF:SS:ID   -0.000157329:0.000887798:0.065834:0.870695:89866:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.870693 ES:SE:LP:AF:SS:ID   -0.000156934:0.000887801:0.0656571:0.870693:89866:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.871061 ES:SE:LP:AF:SS:ID   -0.000183538:0.00088822:0.0776405:0.871061:89866:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.126484 ES:SE:LP:AF:SS:ID   0.000230694:0.000889117:0.0994816:0.126484:89866:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.106022 ES:SE:LP:AF:SS:ID   0.000129712:0.000966544:0.0490299:0.106022:89866:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.854451 ES:SE:LP:AF:SS:ID   -0.000361805:0.000868685:0.169381:0.854451:89866:rs2519015
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.839008 ES:SE:LP:AF:SS:ID   -0.00059329:0.000858168:0.31038:0.839008:89866:rs376645387
1   768253  rs2977608   A   C   .   PASS    AF=0.76317  ES:SE:LP:AF:SS:ID   -0.000387767:0.000689963:0.241006:0.76317:89866:rs2977608
1   768448  rs12562034  G   A   .   PASS    AF=0.10519  ES:SE:LP:AF:SS:ID   0.000369074:0.000950876:0.156199:0.10519:89866:rs12562034
1   769138  rs59306077  CAT C   .   PASS    AF=0.129876 ES:SE:LP:AF:SS:ID   0.000249895:0.000876663:0.11036:0.129876:89866:rs762168062
1   769223  rs60320384  C   G   .   PASS    AF=0.129602 ES:SE:LP:AF:SS:ID   0.000200042:0.000875516:0.0865729:0.129602:89866:rs60320384
1   769963  rs7518545   G   A   .   PASS    AF=0.104469 ES:SE:LP:AF:SS:ID   0.000418446:0.000958208:0.178924:0.104469:89866:rs7518545
1   770181  rs146076599 A   G   .   PASS    AF=0.00900456   ES:SE:LP:AF:SS:ID   0.00313252:0.00338134:0.45071:0.00900456:89866:rs146076599
1   770886  rs371458725 G   A   .   PASS    AF=0.103468 ES:SE:LP:AF:SS:ID   0.000449312:0.000966522:0.19245:0.103468:89866:rs371458725
1   771410  rs2519006   C   T   .   PASS    AF=0.829371 ES:SE:LP:AF:SS:ID   -0.000348795:0.00085457:0.165476:0.829371:89866:rs2519006
1   771823  rs2977605   T   C   .   PASS    AF=0.869112 ES:SE:LP:AF:SS:ID   -0.000145318:0.000871292:0.0617105:0.869112:89866:rs2977605