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-1468_4/ukb-d-1468_4.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-1468_4/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 17:12:08 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-1468_4/ukb-d-1468_4.vcf.gz ...
Read summary statistics for 13584942 SNPs.
Dropped 12708 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, 1283502 SNPs remain.
After merging with regression SNP LD, 1283502 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0494 (0.0024)
Lambda GC: 1.2829
Mean Chi^2: 1.3108
Intercept: 1.0261 (0.007)
Ratio: 0.0841 (0.0227)
Analysis finished at Mon Nov 25 17:14:27 2019
Total time elapsed: 2.0m:18.68s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9521,
    "inflation_factor": 1.1766,
    "mean_EFFECT": -0,
    "n": 299898,
    "n_snps": 13584942,
    "n_clumped_hits": 11,
    "n_p_sig": 735,
    "n_mono": 0,
    "n_ns": 1251766,
    "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": 568810,
    "n_est": 300980.4511,
    "ratio_se_n": 1.0018,
    "mean_diff": -0,
    "ratio_diff": 12.4803,
    "sd_y_est1": 0.4041,
    "sd_y_est2": 0.4049,
    "r2_sum1": 0.0002,
    "r2_sum2": 0.0013,
    "r2_sum3": 0.0013,
    "r2_sum4": 0.0013,
    "ldsc_nsnp_merge_refpanel_ld": 1283502,
    "ldsc_nsnp_merge_regression_ld": 1283502,
    "ldsc_observed_scale_h2_beta": 0.0494,
    "ldsc_observed_scale_h2_se": 0.0024,
    "ldsc_intercept_beta": 1.0261,
    "ldsc_intercept_se": 0.007,
    "ldsc_lambda_gc": 1.2829,
    "ldsc_mean_chisq": 1.3108,
    "ldsc_ratio": 0.084
}

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 TRUE
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 13572898 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 100 0 57084 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 342 0 33335 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 9.051341e+00 6.184945e+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 7.902534e+07 5.591498e+07 3.02000e+02 3.293099e+07 7.013865e+07 1.148594e+08 2.492309e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA -1.320000e-05 6.228500e-03 -6.88208e-02 -1.794600e-03 -4.900000e-06 1.760900e-03 7.527520e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 4.383300e-03 4.227700e-03 8.58600e-04 1.248200e-03 2.300000e-03 6.298200e-03 2.131450e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.738241e-01 2.960454e-01 0.00000e+00 2.107638e-01 4.643955e-01 7.306207e-01 1.000000e+00 ▇▇▆▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.738235e-01 2.960457e-01 0.00000e+00 2.107627e-01 4.643941e-01 7.306198e-01 9.999999e-01 ▇▇▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA 1.908246e-01 2.544077e-01 1.00000e-03 7.436200e-03 6.006090e-02 2.953230e-01 9.990000e-01 ▇▂▁▁▁
numeric AF_reference 568810 0.9581294 NA NA NA NA NA 1.963494e-01 2.471049e-01 0.00000e+00 5.591000e-03 8.426520e-02 3.081070e-01 1.000000e+00 ▇▂▁▁▁
numeric N 0 1.0000000 NA NA NA NA NA 2.998980e+05 0.000000e+00 2.99898e+05 2.998980e+05 2.998980e+05 2.998980e+05 2.998980e+05 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 692794 rs530212009 CA C 0.0024313 0.0018253 0.1828749 0.1828751 0.1107340 0.1894970 299898
1 693731 rs12238997 A G 0.0022851 0.0017262 0.1855979 0.1855964 0.1157850 0.1417730 299898
1 707522 rs371890604 G C 0.0031718 0.0019394 0.1019570 0.1019563 0.0973319 0.1293930 299898
1 717587 rs144155419 G A 0.0004689 0.0046487 0.9196611 0.9196612 0.0155840 0.0045926 299898
1 723329 rs189787166 A T -0.0031755 0.0133936 0.8125889 0.8125880 0.0017922 0.0003994 299898
1 730087 rs148120343 T C 0.0036537 0.0024022 0.1282590 0.1282589 0.0564910 0.0127796 299898
1 731718 rs142557973 T C 0.0028171 0.0016372 0.0853080 0.0853053 0.1217140 0.1543530 299898
1 732032 rs61770163 A C 0.0028703 0.0017465 0.1002910 0.1002892 0.1211260 0.1555510 299898
1 734349 rs141242758 T C 0.0028177 0.0016380 0.0853965 0.0853952 0.1209330 0.1525560 299898
1 740284 rs61770167 C T 0.0057168 0.0074624 0.4436281 0.4436280 0.0058425 0.0023962 299898
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T -0.0024110 0.0018954 0.2033579 0.2033574 0.0560735 0.0309934 299898
23 154923374 rs111332691 T A -0.0005431 0.0020842 0.7944259 0.7944252 0.0447119 0.0116556 299898
23 154925045 rs509981 C T 0.0006076 0.0010016 0.5441317 0.5441301 0.2454920 0.3634440 299898
23 154925895 rs538470 C T 0.0001780 0.0010244 0.8620271 0.8620266 0.2418830 0.3634440 299898
23 154927581 rs644138 G A -0.0000800 0.0009420 0.9322910 0.9322911 0.3019500 0.4635760 299898
23 154929412 rs557132 C T 0.0005858 0.0010019 0.5588021 0.5588007 0.2453350 0.3568210 299898
23 154929637 rs35185538 CT C 0.0007440 0.0010459 0.4768535 0.4768517 0.2295630 0.3011920 299898
23 154929952 rs4012982 CAA C 0.0005702 0.0010536 0.5883571 0.5883554 0.2393710 0.3165560 299898
23 154930230 rs781880 A G 0.0004887 0.0010017 0.6256494 0.6256486 0.2457780 0.3618540 299898
23 154930487 rs781879 T A 0.0059804 0.0034269 0.0809599 0.0809599 0.0196023 0.1263580 299898

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.110734 ES:SE:LP:AF:SS:ID   0.00243127:0.00182534:0.737846:0.110734:299898:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.115785 ES:SE:LP:AF:SS:ID   0.00228507:0.00172625:0.731427:0.115785:299898:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0973319    ES:SE:LP:AF:SS:ID   0.00317181:0.00193942:0.991583:0.0973319:299898:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.015584 ES:SE:LP:AF:SS:ID   0.000468869:0.00464869:0.0363722:0.015584:299898:rs144155419
1   723329  rs189787166 A   T   .   PASS    AF=0.0017922    ES:SE:LP:AF:SS:ID   -0.00317547:0.0133936:0.0901291:0.0017922:299898:rs189787166
1   730087  rs148120343 T   C   .   PASS    AF=0.056491 ES:SE:LP:AF:SS:ID   0.00365374:0.00240219:0.891912:0.056491:299898:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.121714 ES:SE:LP:AF:SS:ID   0.00281713:0.0016372:1.06901:0.121714:299898:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.121126 ES:SE:LP:AF:SS:ID   0.00287034:0.00174653:0.998738:0.121126:299898:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.120933 ES:SE:LP:AF:SS:ID   0.00281773:0.00163802:1.06856:0.120933:299898:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.00584253   ES:SE:LP:AF:SS:ID   0.00571682:0.00746242:0.352981:0.00584253:299898:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.0018938    ES:SE:LP:AF:SS:ID   -0.0112864:0.014072:0.374149:0.0018938:299898:rs112573343
1   749963  rs529266287 T   TAA .   PASS    AF=0.869748 ES:SE:LP:AF:SS:ID   -0.00182318:0.00161446:0.587068:0.869748:299898:rs529266287
1   750230  rs190826124 G   C   .   PASS    AF=0.0015117    ES:SE:LP:AF:SS:ID   0.00801896:0.0147166:0.232231:0.0015117:299898:rs190826124
1   751343  rs28544273  T   A   .   PASS    AF=0.122872 ES:SE:LP:AF:SS:ID   0.00223975:0.00159918:0.792247:0.122872:299898:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.142608 ES:SE:LP:AF:SS:ID   0.0011901:0.00158097:0.345255:0.142608:299898:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.122986 ES:SE:LP:AF:SS:ID   0.00228599:0.00159694:0.81732:0.122986:299898:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.870851 ES:SE:LP:AF:SS:ID   -0.00269373:0.00155593:1.07881:0.870851:299898:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.875488 ES:SE:LP:AF:SS:ID   -0.00200709:0.00158188:0.689279:0.875488:299898:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.128619 ES:SE:LP:AF:SS:ID   0.00286191:0.00155996:1.17675:0.128619:299898:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0364174    ES:SE:LP:AF:SS:ID   0.00155402:0.00282936:0.234454:0.0364174:299898:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.870456 ES:SE:LP:AF:SS:ID   -0.0026887:0.00155432:1.07746:0.870456:299898:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.870565 ES:SE:LP:AF:SS:ID   -0.00256844:0.00155509:1.00608:0.870565:299898:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.870453 ES:SE:LP:AF:SS:ID   -0.00267403:0.00155427:1.06878:0.870453:299898:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00507322   ES:SE:LP:AF:SS:ID   0.00750304:0.00796172:0.460931:0.00507322:299898:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00504011   ES:SE:LP:AF:SS:ID   0.00710343:0.00798294:0.42764:0.00504011:299898:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.00555847   ES:SE:LP:AF:SS:ID   -0.00160251:0.00774835:0.0777158:0.00555847:299898:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.870536 ES:SE:LP:AF:SS:ID   -0.00263091:0.0015516:1.04594:0.870536:299898:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.125958 ES:SE:LP:AF:SS:ID   0.00251604:0.00158345:0.950507:0.125958:299898:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.870101 ES:SE:LP:AF:SS:ID   -0.00253917:0.00154792:0.995988:0.870101:299898:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.869409 ES:SE:LP:AF:SS:ID   -0.00261713:0.00154653:1.04288:0.869409:299898:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.870249 ES:SE:LP:AF:SS:ID   -0.00263713:0.00154916:1.05207:0.870249:299898:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.870255 ES:SE:LP:AF:SS:ID   -0.00263757:0.00154927:1.05222:0.870255:299898:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.870264 ES:SE:LP:AF:SS:ID   -0.00263487:0.00154932:1.05057:0.870264:299898:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.870702 ES:SE:LP:AF:SS:ID   -0.00260779:0.00155324:1.03074:0.870702:299898:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.0989748    ES:SE:LP:AF:SS:ID   0.00298313:0.00180372:1.00809:0.0989748:299898:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.0064479    ES:SE:LP:AF:SS:ID   -0.00209566:0.00699748:0.116584:0.0064479:299898:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.874623 ES:SE:LP:AF:SS:ID   -0.00224463:0.00157643:0.811114:0.874623:299898:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.864005 ES:SE:LP:AF:SS:ID   -0.00283751:0.00154602:1.17748:0.864005:299898:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.869375 ES:SE:LP:AF:SS:ID   -0.00272363:0.00156082:1.09159:0.869375:299898:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.866335 ES:SE:LP:AF:SS:ID   -0.00279557:0.00156133:1.13445:0.866335:299898:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0988006    ES:SE:LP:AF:SS:ID   0.00207646:0.001869:0.574192:0.0988006:299898:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.871553 ES:SE:LP:AF:SS:ID   -0.00249345:0.00157913:0.941817:0.871553:299898:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.871553 ES:SE:LP:AF:SS:ID   -0.00249326:0.00157913:0.941711:0.871553:299898:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.871552 ES:SE:LP:AF:SS:ID   -0.00247747:0.00157917:0.932989:0.871552:299898:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.871924 ES:SE:LP:AF:SS:ID   -0.00239185:0.00158009:0.885743:0.871924:299898:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.125557 ES:SE:LP:AF:SS:ID   0.00264128:0.00158135:1.02289:0.125557:299898:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.105553 ES:SE:LP:AF:SS:ID   0.00299062:0.00172062:1.08517:0.105553:299898:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.855334 ES:SE:LP:AF:SS:ID   -0.00304446:0.00154526:1.31143:0.855334:299898:rs2519015
1   767393  rs538667473 A   C   .   PASS    AF=0.00171307   ES:SE:LP:AF:SS:ID   -0.00348026:0.0134241:0.0993926:0.00171307:299898:rs538667473
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.838715 ES:SE:LP:AF:SS:ID   0.000220662:0.00152486:0.0530862:0.838715:299898:rs376645387