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-30650_irnt/ukb-d-30650_irnt.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-30650_irnt/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 15:33:20 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-30650_irnt/ukb-d-30650_irnt.vcf.gz ...
Read summary statistics for 13586008 SNPs.
Dropped 12709 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, 1283504 SNPs remain.
After merging with regression SNP LD, 1283504 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.1249 (0.0107)
Lambda GC: 1.4315
Mean Chi^2: 1.9905
Intercept: 1.1055 (0.0154)
Ratio: 0.1065 (0.0156)
Analysis finished at Mon Nov 25 15:35:39 2019
Total time elapsed: 2.0m:18.48s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9521,
    "inflation_factor": 1.274,
    "mean_EFFECT": -5.0195e-06,
    "n": 342990,
    "n_snps": 13586008,
    "n_clumped_hits": 247,
    "n_p_sig": 62781,
    "n_mono": 0,
    "n_ns": 1251783,
    "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": 569151,
    "n_est": 344310.3338,
    "ratio_se_n": 1.0019,
    "mean_diff": -1.447e-06,
    "ratio_diff": 1.2113,
    "sd_y_est1": 0.9503,
    "sd_y_est2": 0.9521,
    "r2_sum1": 0.0543,
    "r2_sum2": 0.0601,
    "r2_sum3": 0.0599,
    "r2_sum4": 0.0597,
    "ldsc_nsnp_merge_refpanel_ld": 1283504,
    "ldsc_nsnp_merge_regression_ld": 1283504,
    "ldsc_observed_scale_h2_beta": 0.1249,
    "ldsc_observed_scale_h2_se": 0.0107,
    "ldsc_intercept_beta": 1.1055,
    "ldsc_intercept_se": 0.0154,
    "ldsc_lambda_gc": 1.4315,
    "ldsc_mean_chisq": 1.9905,
    "ldsc_ratio": 0.1065
}

Flags

name value
af_correlation FALSE
inflation_factor TRUE
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 TRUE
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 13573963 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 100 0 57081 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 342 0 33337 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 9.051252e+00 6.184978e+00 1.0000e+00 4.000000e+00 8.000000e+00 1.300000e+01 2.300000e+01 ▇▅▅▂▂
numeric POS 0 1.0000000 NA NA NA NA NA 7.902572e+07 5.591523e+07 3.0200e+02 3.293103e+07 7.013951e+07 1.148603e+08 2.492309e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA -5.000000e-06 1.410750e-02 -5.7021e-01 -4.188000e-03 -1.450000e-05 4.183400e-03 3.599300e-01 ▁▁▁▇▁
numeric SE 0 1.0000000 NA NA NA NA NA 9.641400e-03 9.300200e-03 1.8865e-03 2.745100e-03 5.058500e-03 1.385600e-02 4.698800e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.587143e-01 3.020014e-01 0.0000e+00 1.864700e-01 4.464697e-01 7.210195e-01 1.000000e+00 ▇▆▆▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.587139e-01 3.020017e-01 0.0000e+00 1.864732e-01 4.464698e-01 7.210214e-01 9.999999e-01 ▇▆▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA 1.908097e-01 2.544045e-01 1.0000e-03 7.431700e-03 6.004100e-02 2.953200e-01 9.990000e-01 ▇▂▁▁▁
numeric AF_reference 569151 0.9581076 NA NA NA NA NA 1.963389e-01 2.471025e-01 0.0000e+00 5.591000e-03 8.426520e-02 3.081070e-01 1.000000e+00 ▇▂▁▁▁
numeric N 0 1.0000000 NA NA NA NA NA 3.429900e+05 0.000000e+00 3.4299e+05 3.429900e+05 3.429900e+05 3.429900e+05 3.429900e+05 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 692794 rs530212009 CA C -0.0002520 0.0040183 0.9500000 0.9500009 0.1105500 0.1894970 342990
1 693731 rs12238997 A G 0.0005758 0.0037949 0.8794100 0.8794062 0.1158500 0.1417730 342990
1 707522 rs371890604 G C -0.0002675 0.0042686 0.9500300 0.9500299 0.0972770 0.1293930 342990
1 717587 rs144155419 G A 0.0015954 0.0101880 0.8755600 0.8755633 0.0156720 0.0045926 342990
1 723329 rs189787166 A T -0.0616150 0.0299960 0.0399632 0.0399653 0.0017395 0.0003994 342990
1 730087 rs148120343 T C -0.0020606 0.0052887 0.6968094 0.6968152 0.0564430 0.0127796 342990
1 731718 rs142557973 T C 0.0003342 0.0035993 0.9260100 0.9260106 0.1217400 0.1543530 342990
1 732032 rs61770163 A C 0.0004548 0.0038407 0.9057499 0.9057464 0.1211400 0.1555510 342990
1 734349 rs141242758 T C 0.0003872 0.0036012 0.9143901 0.9143855 0.1209600 0.1525560 342990
1 740284 rs61770167 C T -0.0061866 0.0165160 0.7079702 0.7079712 0.0057593 0.0023962 342990
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T -0.0018967 0.0041590 0.6483597 0.6483561 0.056215 0.0309934 342990
23 154923374 rs111332691 T A -0.0009812 0.0045661 0.8298500 0.8298512 0.044874 0.0116556 342990
23 154925045 rs509981 C T 0.0029867 0.0021997 0.1745300 0.1745348 0.245670 0.3634440 342990
23 154925895 rs538470 C T 0.0027208 0.0022502 0.2266000 0.2266102 0.242010 0.3634440 342990
23 154927581 rs644138 G A 0.0021321 0.0020687 0.3027102 0.3027063 0.302270 0.4635760 342990
23 154929412 rs557132 C T 0.0029612 0.0022003 0.1783598 0.1783617 0.245530 0.3568210 342990
23 154929637 rs35185538 CT C 0.0017519 0.0022965 0.4455598 0.4455490 0.229800 0.3011920 342990
23 154929952 rs4012982 CAA C 0.0022148 0.0023141 0.3385003 0.3385223 0.239520 0.3165560 342990
23 154930230 rs781880 A G 0.0027732 0.0022000 0.2074699 0.2074727 0.245930 0.3618540 342990
23 154930487 rs781879 T A -0.0096870 0.0075461 0.1992402 0.1992436 0.019548 0.1263580 342990

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.11055  ES:SE:LP:AF:SS:ID   -0.00025197:0.0040183:0.0222764:0.11055:342990:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.11585  ES:SE:LP:AF:SS:ID   0.00057577:0.0037949:0.0558086:0.11585:342990:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.097277 ES:SE:LP:AF:SS:ID   -0.00026751:0.0042686:0.0222627:0.097277:342990:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.015672 ES:SE:LP:AF:SS:ID   0.0015954:0.010188:0.0577141:0.015672:342990:rs144155419
1   723329  rs189787166 A   T   .   PASS    AF=0.0017395    ES:SE:LP:AF:SS:ID   -0.061615:0.029996:1.39834:0.0017395:342990:rs189787166
1   730087  rs148120343 T   C   .   PASS    AF=0.056443 ES:SE:LP:AF:SS:ID   -0.0020606:0.0052887:0.156886:0.056443:342990:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.12174  ES:SE:LP:AF:SS:ID   0.00033425:0.0035993:0.0333843:0.12174:342990:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.12114  ES:SE:LP:AF:SS:ID   0.00045476:0.0038407:0.0429917:0.12114:342990:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.12096  ES:SE:LP:AF:SS:ID   0.00038716:0.0036012:0.0388685:0.12096:342990:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.0057593    ES:SE:LP:AF:SS:ID   -0.0061866:0.016516:0.149985:0.0057593:342990:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.0018588    ES:SE:LP:AF:SS:ID   0.029442:0.031351:0.458833:0.0018588:342990:rs112573343
1   749963  rs529266287 T   TAA .   PASS    AF=0.86976  ES:SE:LP:AF:SS:ID   -7.2759e-05:0.0035505:0.0071594:0.86976:342990:rs529266287
1   750230  rs190826124 G   C   .   PASS    AF=0.0015222    ES:SE:LP:AF:SS:ID   -0.024314:0.032248:0.345949:0.0015222:342990:rs190826124
1   751343  rs28544273  T   A   .   PASS    AF=0.12292  ES:SE:LP:AF:SS:ID   0.00047983:0.0035159:0.049903:0.12292:342990:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.14274  ES:SE:LP:AF:SS:ID   -0.00028214:0.0034749:0.0290537:0.14274:342990:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.12304  ES:SE:LP:AF:SS:ID   0.0005255:0.003511:0.0550142:0.12304:342990:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.87093  ES:SE:LP:AF:SS:ID   -0.0006796:0.003422:0.0743889:0.87093:342990:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.87548  ES:SE:LP:AF:SS:ID   -0.0010096:0.0034782:0.112602:0.87548:342990:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.12861  ES:SE:LP:AF:SS:ID   0.00042213:0.0034304:0.0447646:0.12861:342990:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.036317 ES:SE:LP:AF:SS:ID   0.010726:0.0062294:1.07014:0.036317:342990:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.87052  ES:SE:LP:AF:SS:ID   -0.00060224:0.0034185:0.0654208:0.87052:342990:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.87062  ES:SE:LP:AF:SS:ID   -0.00060996:0.00342:0.066285:0.87062:342990:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.87052  ES:SE:LP:AF:SS:ID   -0.00061799:0.0034183:0.0672574:0.87052:342990:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.0050449    ES:SE:LP:AF:SS:ID   -0.0031805:0.017545:0.0674501:0.0050449:342990:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.0050118    ES:SE:LP:AF:SS:ID   -0.0029422:0.017591:0.0618958:0.0050118:342990:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.0055444    ES:SE:LP:AF:SS:ID   0.018055:0.017067:0.537437:0.0055444:342990:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.87061  ES:SE:LP:AF:SS:ID   -0.00054754:0.0034125:0.0592246:0.87061:342990:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.12595  ES:SE:LP:AF:SS:ID   0.0003291:0.0034817:0.0340038:0.12595:342990:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.87018  ES:SE:LP:AF:SS:ID   -0.00060559:0.0034044:0.0661029:0.87018:342990:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.86945  ES:SE:LP:AF:SS:ID   -0.00064888:0.0034012:0.0712458:0.86945:342990:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.87032  ES:SE:LP:AF:SS:ID   -0.00065817:0.0034072:0.0722089:0.87032:342990:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.87033  ES:SE:LP:AF:SS:ID   -0.00066446:0.0034074:0.0729429:0.87033:342990:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.87034  ES:SE:LP:AF:SS:ID   -0.00063463:0.0034075:0.069433:0.87034:342990:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.87078  ES:SE:LP:AF:SS:ID   -0.00052196:0.0034161:0.0562286:0.87078:342990:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.098823 ES:SE:LP:AF:SS:ID   0.00043355:0.0039703:0.0395102:0.098823:342990:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.0064308    ES:SE:LP:AF:SS:ID   0.0082017:0.015418:0.225651:0.0064308:342990:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.87466  ES:SE:LP:AF:SS:ID   -0.00055055:0.0034667:0.058578:0.87466:342990:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.86408  ES:SE:LP:AF:SS:ID   -0.001247:0.0034004:0.146399:0.86408:342990:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.86946  ES:SE:LP:AF:SS:ID   -0.00080283:0.0034328:0.0887998:0.86946:342990:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.8664   ES:SE:LP:AF:SS:ID   0.00050185:0.0034335:0.0536509:0.8664:342990:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.098771 ES:SE:LP:AF:SS:ID   0.0023137:0.0041102:0.241474:0.098771:342990:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.87158  ES:SE:LP:AF:SS:ID   9.4151e-05:0.0034727:0.00949687:0.87158:342990:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.87158  ES:SE:LP:AF:SS:ID   9.4062e-05:0.0034727:0.00948799:0.87158:342990:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.87158  ES:SE:LP:AF:SS:ID   7.5854e-05:0.0034728:0.0076365:0.87158:342990:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.87195  ES:SE:LP:AF:SS:ID   -0.0002549:0.0034747:0.0261704:0.87195:342990:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.12555  ES:SE:LP:AF:SS:ID   0.00056842:0.0034771:0.0604109:0.12555:342990:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.10542  ES:SE:LP:AF:SS:ID   0.0002699:0.0037861:0.02541:0.10542:342990:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.85542  ES:SE:LP:AF:SS:ID   0.00022597:0.0033989:0.0236546:0.85542:342990:rs2519015
1   767393  rs538667473 A   C   .   PASS    AF=0.001658 ES:SE:LP:AF:SS:ID   -0.061706:0.030071:1.39608:0.001658:342990:rs538667473
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.83825  ES:SE:LP:AF:SS:ID   -0.0027071:0.0033474:0.378107:0.83825:342990:rs376645387