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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    "FORMAT.1": "<ID=SE,Number=A,Type=Float,Description=\"Standard error of effect size estimate\">",
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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-L12_ACTINKERA/ukb-d-L12_ACTINKERA.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-L12_ACTINKERA/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 14:43:38 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-L12_ACTINKERA/ukb-d-L12_ACTINKERA.vcf.gz ...
Read summary statistics for 9790361 SNPs.
Dropped 7447 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, 1276396 SNPs remain.
After merging with regression SNP LD, 1276396 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0031 (0.0015)
Lambda GC: 1.023
Mean Chi^2: 1.0229
Intercept: 1. (0.0068)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Mon Nov 25 14:45:06 2019
Total time elapsed: 1.0m:28.28s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9405,
    "inflation_factor": 1.0215,
    "mean_EFFECT": -5.802e-07,
    "n": 361194,
    "n_snps": 9790361,
    "n_clumped_hits": 2,
    "n_p_sig": 64,
    "n_mono": 0,
    "n_ns": 1069778,
    "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": 194503,
    "n_est": 361089.255,
    "ratio_se_n": 0.9999,
    "mean_diff": -9.3977e-06,
    "ratio_diff": 19.2333,
    "sd_y_est1": 0.0612,
    "sd_y_est2": 0.0611,
    "r2_sum1": 8.0528e-07,
    "r2_sum2": 0.0002,
    "r2_sum3": 0.0002,
    "r2_sum4": 0.0002,
    "ldsc_nsnp_merge_refpanel_ld": 1276396,
    "ldsc_nsnp_merge_regression_ld": 1276396,
    "ldsc_observed_scale_h2_beta": 0.0031,
    "ldsc_observed_scale_h2_se": 0.0015,
    "ldsc_intercept_beta": 1,
    "ldsc_intercept_se": 0.0068,
    "ldsc_lambda_gc": 1.023,
    "ldsc_mean_chisq": 1.0229,
    "ldsc_ratio": 0
}

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 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 numeric.mean numeric.sd numeric.p0 numeric.p25 numeric.p50 numeric.p75 numeric.p100 numeric.hist
character ID 0 1.0000000 3 94 0 9783462 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 98 0 47456 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 342 0 29840 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 9.079564e+00 6.175783e+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 7.874787e+07 5.610550e+07 3.02000e+02 3.244739e+07 6.964315e+07 1.147469e+08 2.492297e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA -6.000000e-07 3.323000e-04 -3.01360e-03 -1.599000e-04 -2.700000e-06 1.560000e-04 4.048100e-03 ▁▂▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 2.862000e-04 1.667000e-04 1.18400e-04 1.578000e-04 2.120000e-04 3.676000e-04 9.273000e-04 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.968136e-01 2.890808e-01 0.00000e+00 2.461592e-01 4.954194e-01 7.468476e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.968132e-01 2.890811e-01 0.00000e+00 2.461591e-01 4.954185e-01 7.468470e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA 2.594505e-01 2.629118e-01 9.26620e-03 4.229130e-02 1.565040e-01 4.131680e-01 9.907330e-01 ▇▂▂▁▁
numeric AF_reference 194503 0.9801332 NA NA NA NA NA 2.589919e-01 2.537297e-01 0.00000e+00 4.772360e-02 1.717250e-01 4.089460e-01 1.000000e+00 ▇▃▂▁▁
numeric N 0 1.0000000 NA NA NA NA NA 3.611940e+05 0.000000e+00 3.61194e+05 3.611940e+05 3.611940e+05 3.611940e+05 3.611940e+05 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 692794 rs530212009 CA C 0.0002444 0.0002522 0.3325049 0.3325058 0.1106400 0.1894970 361194
1 693731 rs12238997 A G 0.0001466 0.0002383 0.5382376 0.5382396 0.1158300 0.1417730 361194
1 707522 rs371890604 G C 0.0003243 0.0002679 0.2259238 0.2259234 0.0973034 0.1293930 361194
1 717587 rs144155419 G A 0.0004770 0.0006392 0.4554881 0.4554877 0.0156880 0.0045926 361194
1 730087 rs148120343 T C 0.0003259 0.0003319 0.3261580 0.3261568 0.0564602 0.0127796 361194
1 731718 rs142557973 T C 0.0002194 0.0002260 0.3315988 0.3315990 0.1217380 0.1543530 361194
1 732032 rs61770163 A C 0.0002608 0.0002410 0.2793168 0.2793168 0.1211710 0.1555510 361194
1 734349 rs141242758 T C 0.0002018 0.0002261 0.3720249 0.3720249 0.1209650 0.1525560 361194
1 749963 rs529266287 T TAA -0.0001685 0.0002229 0.4497219 0.4497220 0.8697420 0.7641770 361194
1 751343 rs28544273 T A 0.0001814 0.0002207 0.4113022 0.4113024 0.1229160 0.2426120 361194
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T 0.0002397 0.0002611 0.3585279 0.3585281 0.0561667 0.0309934 361194
23 154923374 rs111332691 T A -0.0001378 0.0002869 0.6309210 0.6309189 0.0447862 0.0116556 361194
23 154925045 rs509981 C T -0.0001316 0.0001380 0.3405203 0.3405203 0.2456060 0.3634440 361194
23 154925895 rs538470 C T -0.0001059 0.0001412 0.4531072 0.4531076 0.2419150 0.3634440 361194
23 154927581 rs644138 G A -0.0000393 0.0001298 0.7620053 0.7620053 0.3021620 0.4635760 361194
23 154929412 rs557132 C T -0.0001298 0.0001381 0.3473929 0.3473923 0.2454590 0.3568210 361194
23 154929637 rs35185538 CT C -0.0001291 0.0001441 0.3703318 0.3703319 0.2296970 0.3011920 361194
23 154929952 rs4012982 CAA C -0.0000917 0.0001452 0.5276221 0.5276226 0.2394250 0.3165560 361194
23 154930230 rs781880 A G -0.0001235 0.0001381 0.3710377 0.3710372 0.2458690 0.3618540 361194
23 154930487 rs781879 T A -0.0006936 0.0004736 0.1430659 0.1430651 0.0195623 0.1263580 361194

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.11064  ES:SE:LP:AF:SS:ID   0.000244361:0.000252158:0.478202:0.11064:361194:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.11583  ES:SE:LP:AF:SS:ID   0.000146643:0.000238259:0.269026:0.11583:361194:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0973034    ES:SE:LP:AF:SS:ID   0.000324348:0.000267851:0.646038:0.0973034:361194:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.015688 ES:SE:LP:AF:SS:ID   0.000477005:0.000639162:0.341523:0.015688:361194:rs144155419
1   730087  rs148120343 T   C   .   PASS    AF=0.0564602    ES:SE:LP:AF:SS:ID   0.000325912:0.000331925:0.486572:0.0564602:361194:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.121738 ES:SE:LP:AF:SS:ID   0.000219389:0.000225965:0.479387:0.121738:361194:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.121171 ES:SE:LP:AF:SS:ID   0.000260765:0.000241035:0.553903:0.121171:361194:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.120965 ES:SE:LP:AF:SS:ID   0.000201824:0.000226086:0.429428:0.120965:361194:rs141242758
1   749963  rs529266287 T   TAA .   PASS    AF=0.869742 ES:SE:LP:AF:SS:ID   -0.000168474:0.000222885:0.347056:0.869742:361194:rs529266287
1   751343  rs28544273  T   A   .   PASS    AF=0.122916 ES:SE:LP:AF:SS:ID   0.00018136:0.000220739:0.385839:0.122916:361194:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.142712 ES:SE:LP:AF:SS:ID   0.000231887:0.000218188:0.540789:0.142712:361194:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.123031 ES:SE:LP:AF:SS:ID   0.00018032:0.000220431:0.383694:0.123031:361194:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.87089  ES:SE:LP:AF:SS:ID   -0.000127425:0.000214803:0.257246:0.87089:361194:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.875472 ES:SE:LP:AF:SS:ID   -0.000175639:0.000218363:0.375515:0.875472:361194:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.128631 ES:SE:LP:AF:SS:ID   0.0001398:0.000215342:0.287175:0.128631:361194:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0363613    ES:SE:LP:AF:SS:ID   -0.000368106:0.000390679:0.460822:0.0363613:361194:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.870484 ES:SE:LP:AF:SS:ID   -0.000135712:0.00021458:0.278115:0.870484:361194:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.870589 ES:SE:LP:AF:SS:ID   -0.000139714:0.000214678:0.288048:0.870589:361194:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.870481 ES:SE:LP:AF:SS:ID   -0.00013586:0.000214572:0.278499:0.870481:361194:rs3131967
1   755890  rs3115858   A   T   .   PASS    AF=0.870563 ES:SE:LP:AF:SS:ID   -0.000117631:0.000214194:0.234419:0.870563:361194:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.125985 ES:SE:LP:AF:SS:ID   0.000144529:0.000218557:0.293771:0.125985:361194:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.870127 ES:SE:LP:AF:SS:ID   -0.000103998:0.000213679:0.2031:0.870127:361194:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.869414 ES:SE:LP:AF:SS:ID   -0.000130768:0.000213484:0.267463:0.869414:361194:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.870281 ES:SE:LP:AF:SS:ID   -0.000109516:0.000213863:0.215674:0.870281:361194:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.870287 ES:SE:LP:AF:SS:ID   -0.000109538:0.000213878:0.215707:0.870287:361194:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.870295 ES:SE:LP:AF:SS:ID   -0.000109831:0.000213885:0.216384:0.870295:361194:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.870739 ES:SE:LP:AF:SS:ID   -0.000110044:0.000214429:0.216229:0.870739:361194:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.0988442    ES:SE:LP:AF:SS:ID   0.000301027:0.000249179:0.643938:0.0988442:361194:rs10157329
1   759837  rs3115851   T   A   .   PASS    AF=0.874635 ES:SE:LP:AF:SS:ID   -0.000165013:0.000217625:0.348425:0.874635:361194:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.864027 ES:SE:LP:AF:SS:ID   -0.000134817:0.000213414:0.277717:0.864027:361194:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.869425 ES:SE:LP:AF:SS:ID   -0.000133792:0.000215486:0.271912:0.869425:361194:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.866371 ES:SE:LP:AF:SS:ID   -0.000126412:0.000215529:0.253733:0.866371:361194:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0987831    ES:SE:LP:AF:SS:ID   5.44774e-05:0.000258029:0.0794661:0.0987831:361194:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.871556 ES:SE:LP:AF:SS:ID   -0.000141072:0.000217985:0.286068:0.871556:361194:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.871556 ES:SE:LP:AF:SS:ID   -0.00014107:0.000217985:0.286063:0.871556:361194:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.871555 ES:SE:LP:AF:SS:ID   -0.000141063:0.000217989:0.28604:0.871555:361194:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.871929 ES:SE:LP:AF:SS:ID   -0.000139778:0.000218113:0.282645:0.871929:361194:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.125562 ES:SE:LP:AF:SS:ID   0.000160872:0.000218285:0.336172:0.125562:361194:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.105428 ES:SE:LP:AF:SS:ID   0.000151006:0.000237633:0.279733:0.105428:361194:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.855376 ES:SE:LP:AF:SS:ID   -0.00016124:0.000213333:0.347017:0.855376:361194:rs2519015
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.838358 ES:SE:LP:AF:SS:ID   -0.00011509:0.000210172:0.233612:0.838358:361194:rs376645387
1   768253  rs2977608   A   C   .   PASS    AF=0.763351 ES:SE:LP:AF:SS:ID   -8.28663e-05:0.000169149:0.204673:0.763351:361194:rs2977608
1   768448  rs12562034  G   A   .   PASS    AF=0.105921 ES:SE:LP:AF:SS:ID   -8.78447e-06:0.000233046:0.0132587:0.105921:361194:rs12562034
1   769138  rs59306077  CAT C   .   PASS    AF=0.128938 ES:SE:LP:AF:SS:ID   0.000127921:0.000215232:0.257837:0.128938:361194:rs762168062
1   769223  rs60320384  C   G   .   PASS    AF=0.128608 ES:SE:LP:AF:SS:ID   0.00011826:0.000215033:0.23482:0.128608:361194:rs60320384
1   769963  rs7518545   G   A   .   PASS    AF=0.105213 ES:SE:LP:AF:SS:ID   9.103e-06:0.000234806:0.0136426:0.105213:361194:rs7518545
1   770886  rs371458725 G   A   .   PASS    AF=0.104268 ES:SE:LP:AF:SS:ID   -1.13769e-06:0.000236683:0.00166872:0.104268:361194:rs371458725
1   771410  rs2519006   C   T   .   PASS    AF=0.830106 ES:SE:LP:AF:SS:ID   -0.000116484:0.000209524:0.237885:0.830106:361194:rs2519006
1   771823  rs2977605   T   C   .   PASS    AF=0.87012  ES:SE:LP:AF:SS:ID   -0.000110052:0.00021407:0.216677:0.87012:361194:rs2977605
1   771967  rs59066358  G   A   .   PASS    AF=0.128668 ES:SE:LP:AF:SS:ID   0.000120442:0.000214932:0.240162:0.128668:361194:rs59066358