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-20405_1/ukb-d-20405_1.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-20405_1/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 16:46:32 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-20405_1/ukb-d-20405_1.vcf.gz ...
Read summary statistics for 12203570 SNPs.
Dropped 10750 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, 1283152 SNPs remain.
After merging with regression SNP LD, 1283152 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0155 (0.0038)
Lambda GC: 1.0318
Mean Chi^2: 1.0359
Intercept: 1.0001 (0.0059)
Ratio: 0.0021 (0.1658)
Analysis finished at Mon Nov 25 16:48:36 2019
Total time elapsed: 2.0m:4.38s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9495,
    "inflation_factor": 1.0275,
    "mean_EFFECT": 5.3818e-06,
    "n": 117880,
    "n_snps": 12203570,
    "n_clumped_hits": 1,
    "n_p_sig": 7,
    "n_mono": 0,
    "n_ns": 1193524,
    "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": 305359,
    "n_est": 118224.7906,
    "ratio_se_n": 1.0015,
    "mean_diff": 0,
    "ratio_diff": 117.3921,
    "sd_y_est1": 0.21,
    "sd_y_est2": 0.2103,
    "r2_sum1": 0,
    "r2_sum2": 0.0005,
    "r2_sum3": 0.0005,
    "r2_sum4": 0.0005,
    "ldsc_nsnp_merge_refpanel_ld": 1283152,
    "ldsc_nsnp_merge_regression_ld": 1283152,
    "ldsc_observed_scale_h2_beta": 0.0155,
    "ldsc_observed_scale_h2_se": 0.0038,
    "ldsc_intercept_beta": 1.0001,
    "ldsc_intercept_se": 0.0059,
    "ldsc_lambda_gc": 1.0318,
    "ldsc_mean_chisq": 1.0359,
    "ldsc_ratio": 0.0028
}

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 12193445 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 100 0 53881 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 342 0 32312 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 9.066137e+00 6.184643e+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 7.891620e+07 5.596936e+07 3.02000e+02 3.273128e+07 6.995246e+07 1.147852e+08 2.492309e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA 5.400000e-06 3.586500e-03 -4.01029e-02 -1.196400e-03 -4.600000e-06 1.178000e-03 4.643290e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 2.726900e-03 2.306900e-03 7.17000e-04 9.998000e-04 1.615100e-03 3.783900e-03 1.344190e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.956539e-01 2.898865e-01 0.00000e+00 2.439782e-01 4.941741e-01 7.467909e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.956525e-01 2.898873e-01 0.00000e+00 2.439762e-01 4.941724e-01 7.467907e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA 2.112675e-01 2.587944e-01 2.28020e-03 1.399520e-02 8.751840e-02 3.349500e-01 9.977200e-01 ▇▂▁▁▁
numeric AF_reference 305359 0.9749779 NA NA NA NA NA 2.130041e-01 2.503878e-01 0.00000e+00 1.078270e-02 1.076280e-01 3.382590e-01 1.000000e+00 ▇▂▁▁▁
numeric N 0 1.0000000 NA NA NA NA NA 1.178800e+05 0.000000e+00 1.17880e+05 1.178800e+05 1.178800e+05 1.178800e+05 1.178800e+05 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 692794 rs530212009 CA C -0.0017256 0.0015077 0.2524103 0.2524077 0.1116960 0.1894970 117880
1 693731 rs12238997 A G -0.0011374 0.0014278 0.4256729 0.4256717 0.1167130 0.1417730 117880
1 707522 rs371890604 G C -0.0007689 0.0016022 0.6312916 0.6312916 0.0983344 0.1293930 117880
1 717587 rs144155419 G A -0.0031232 0.0038397 0.4159978 0.4159960 0.0157581 0.0045926 117880
1 730087 rs148120343 T C -0.0012390 0.0019871 0.5329421 0.5329413 0.0568842 0.0127796 117880
1 731718 rs142557973 T C -0.0011306 0.0013547 0.4039727 0.4039704 0.1226360 0.1543530 117880
1 732032 rs61770163 A C -0.0013464 0.0014439 0.3511018 0.3510990 0.1222430 0.1555510 117880
1 734349 rs141242758 T C -0.0011064 0.0013554 0.4143773 0.4143724 0.1218910 0.1525560 117880
1 740284 rs61770167 C T 0.0076225 0.0061850 0.2177950 0.2177921 0.0058634 0.0023962 117880
1 749963 rs529266287 T TAA 0.0016061 0.0013356 0.2291638 0.2291603 0.8690250 0.7641770 117880
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T 0.0007475 0.0015930 0.6388766 0.6388752 0.0555055 0.0309934 117880
23 154923374 rs111332691 T A 0.0010637 0.0017364 0.5401595 0.5401609 0.0447871 0.0116556 117880
23 154925045 rs509981 C T 0.0001428 0.0008363 0.8644270 0.8644258 0.2446550 0.3634440 117880
23 154925895 rs538470 C T -0.0000585 0.0008552 0.9455049 0.9455047 0.2410780 0.3634440 117880
23 154927581 rs644138 G A 0.0003103 0.0007869 0.6933316 0.6933302 0.3005220 0.4635760 117880
23 154929412 rs557132 C T 0.0001462 0.0008365 0.8612380 0.8612375 0.2445300 0.3568210 117880
23 154929637 rs35185538 CT C 0.0004355 0.0008738 0.6182085 0.6182077 0.2287520 0.3011920 117880
23 154929952 rs4012982 CAA C -0.0001965 0.0008804 0.8233900 0.8233900 0.2386140 0.3165560 117880
23 154930230 rs781880 A G 0.0000670 0.0008363 0.9361771 0.9361773 0.2449530 0.3618540 117880
23 154930487 rs781879 T A 0.0013390 0.0028414 0.6374630 0.6374633 0.0196917 0.1263580 117880

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.111696 ES:SE:LP:AF:SS:ID   -0.00172564:0.00150774:0.597893:0.111696:117880:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.116713 ES:SE:LP:AF:SS:ID   -0.00113743:0.00142782:0.370924:0.116713:117880:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0983344    ES:SE:LP:AF:SS:ID   -0.000768897:0.00160217:0.19977:0.0983344:117880:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.0157581    ES:SE:LP:AF:SS:ID   -0.00312318:0.00383972:0.380909:0.0157581:117880:rs144155419
1   730087  rs148120343 T   C   .   PASS    AF=0.0568842    ES:SE:LP:AF:SS:ID   -0.00123902:0.00198713:0.27332:0.0568842:117880:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122636 ES:SE:LP:AF:SS:ID   -0.0011306:0.00135474:0.393648:0.122636:117880:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.122243 ES:SE:LP:AF:SS:ID   -0.00134636:0.00144388:0.454567:0.122243:117880:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.121891 ES:SE:LP:AF:SS:ID   -0.00110635:0.00135545:0.382604:0.121891:117880:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.00586344   ES:SE:LP:AF:SS:ID   0.00762252:0.006185:0.661952:0.00586344:117880:rs61770167
1   749963  rs529266287 T   TAA .   PASS    AF=0.869025 ES:SE:LP:AF:SS:ID   0.00160608:0.00133559:0.639854:0.869025:117880:rs529266287
1   751343  rs28544273  T   A   .   PASS    AF=0.123676 ES:SE:LP:AF:SS:ID   -0.002115:0.00132329:0.958686:0.123676:117880:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.143312 ES:SE:LP:AF:SS:ID   -0.00117937:0.00130874:0.434727:0.143312:117880:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.123798 ES:SE:LP:AF:SS:ID   -0.00207371:0.00132146:0.933342:0.123798:117880:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.869794 ES:SE:LP:AF:SS:ID   0.0017697:0.00128691:0.77189:0.869794:117880:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.874573 ES:SE:LP:AF:SS:ID   0.00195728:0.00130853:0.870591:0.874573:117880:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.129665 ES:SE:LP:AF:SS:ID   -0.00169786:0.00129051:0.725164:0.129665:117880:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0365244    ES:SE:LP:AF:SS:ID   0.00255632:0.00234372:0.560031:0.0365244:117880:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869388 ES:SE:LP:AF:SS:ID   0.00175563:0.00128569:0.764239:0.869388:117880:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869459 ES:SE:LP:AF:SS:ID   0.0017675:0.00128617:0.771164:0.869459:117880:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.869387 ES:SE:LP:AF:SS:ID   0.00176082:0.00128565:0.767466:0.869387:117880:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00511644   ES:SE:LP:AF:SS:ID   -0.00339226:0.00657747:0.217501:0.00511644:117880:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.0050855    ES:SE:LP:AF:SS:ID   -0.00343873:0.006592:0.220466:0.0050855:117880:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.00568415   ES:SE:LP:AF:SS:ID   0.000557733:0.00633875:0.0315703:0.00568415:117880:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.869461 ES:SE:LP:AF:SS:ID   0.00175338:0.0012833:0.764858:0.869461:117880:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.126942 ES:SE:LP:AF:SS:ID   -0.00173871:0.00130996:0.734216:0.126942:117880:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.868981 ES:SE:LP:AF:SS:ID   0.00174929:0.00127993:0.765182:0.868981:117880:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.868381 ES:SE:LP:AF:SS:ID   0.00174877:0.00127944:0.765275:0.868381:117880:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.86917  ES:SE:LP:AF:SS:ID   0.00174794:0.0012812:0.763271:0.86917:117880:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869177 ES:SE:LP:AF:SS:ID   0.00174934:0.00128129:0.76406:0.869177:117880:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.86918  ES:SE:LP:AF:SS:ID   0.00174828:0.0012813:0.763402:0.86918:117880:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869615 ES:SE:LP:AF:SS:ID   0.00175845:0.00128458:0.76691:0.869615:117880:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.0995696    ES:SE:LP:AF:SS:ID   -0.00152508:0.00149326:0.512706:0.0995696:117880:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.00653662   ES:SE:LP:AF:SS:ID   0.00229988:0.00575777:0.161422:0.00653662:117880:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.873673 ES:SE:LP:AF:SS:ID   0.00186359:0.00130425:0.81517:0.873673:117880:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.862961 ES:SE:LP:AF:SS:ID   0.00143805:0.00127908:0.583536:0.862961:117880:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.868361 ES:SE:LP:AF:SS:ID   0.00158114:0.0012914:0.655964:0.868361:117880:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.865372 ES:SE:LP:AF:SS:ID   0.00173747:0.00129159:0.748218:0.865372:117880:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0997403    ES:SE:LP:AF:SS:ID   -0.00187092:0.00154341:0.646971:0.0997403:117880:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.870662 ES:SE:LP:AF:SS:ID   0.00189509:0.00130685:0.832603:0.870662:117880:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.870662 ES:SE:LP:AF:SS:ID   0.00189466:0.00130685:0.832331:0.870662:117880:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.87066  ES:SE:LP:AF:SS:ID   0.00189397:0.00130685:0.831892:0.87066:117880:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.870982 ES:SE:LP:AF:SS:ID   0.00182348:0.0013074:0.787549:0.870982:117880:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.126535 ES:SE:LP:AF:SS:ID   -0.00180958:0.00130853:0.778077:0.126535:117880:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.106314 ES:SE:LP:AF:SS:ID   -0.00139846:0.00142204:0.487578:0.106314:117880:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.854398 ES:SE:LP:AF:SS:ID   0.00194134:0.00127871:0.889525:0.854398:117880:rs2519015
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.83908  ES:SE:LP:AF:SS:ID   0.000449104:0.001267:0.140866:0.83908:117880:rs376645387
1   768253  rs2977608   A   C   .   PASS    AF=0.763032 ES:SE:LP:AF:SS:ID   0.000909545:0.00101546:0.43131:0.763032:117880:rs2977608
1   768448  rs12562034  G   A   .   PASS    AF=0.105264 ES:SE:LP:AF:SS:ID   0.000327217:0.00140478:0.0884078:0.105264:117880:rs12562034
1   768819  rs12562811  C   T   .   PASS    AF=0.00774254   ES:SE:LP:AF:SS:ID   0.0117388:0.00518507:1.62749:0.00774254:117880:rs12562811
1   769138  rs59306077  CAT C   .   PASS    AF=0.129947 ES:SE:LP:AF:SS:ID   -0.00172384:0.00128983:0.741382:0.129947:117880:rs762168062