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-M13_MENISCUSDERANGEMENTS/ukb-d-M13_MENISCUSDERANGEMENTS.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-M13_MENISCUSDERANGEMENTS/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 14:55:43 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-M13_MENISCUSDERANGEMENTS/ukb-d-M13_MENISCUSDERANGEMENTS.vcf.gz ...
Read summary statistics for 13405757 SNPs.
Dropped 12475 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, 1283471 SNPs remain.
After merging with regression SNP LD, 1283471 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0121 (0.0018)
Lambda GC: 1.0766
Mean Chi^2: 1.0946
Intercept: 1.0069 (0.0067)
Ratio: 0.073 (0.0711)
Analysis finished at Mon Nov 25 14:57:51 2019
Total time elapsed: 2.0m:7.39s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9518,
    "inflation_factor": 1.0593,
    "mean_EFFECT": -1.8516e-06,
    "n": 361194,
    "n_snps": 13405757,
    "n_clumped_hits": 2,
    "n_p_sig": 256,
    "n_mono": 0,
    "n_ns": 1245862,
    "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": 522384,
    "n_est": 362480.3701,
    "ratio_se_n": 1.0018,
    "mean_diff": -8.5784e-06,
    "ratio_diff": 22.7411,
    "sd_y_est1": 0.171,
    "sd_y_est2": 0.1713,
    "r2_sum1": 7.4159e-06,
    "r2_sum2": 0.0003,
    "r2_sum3": 0.0003,
    "r2_sum4": 0.0003,
    "ldsc_nsnp_merge_refpanel_ld": 1283471,
    "ldsc_nsnp_merge_regression_ld": 1283471,
    "ldsc_observed_scale_h2_beta": 0.0121,
    "ldsc_observed_scale_h2_se": 0.0018,
    "ldsc_intercept_beta": 1.0069,
    "ldsc_intercept_se": 0.0067,
    "ldsc_lambda_gc": 1.0766,
    "ldsc_mean_chisq": 1.0946,
    "ldsc_ratio": 0.0729
}

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 13393943 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 100 0 56749 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 342 0 33243 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 9.054072e+00 6.185764e+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 7.900982e+07 5.592228e+07 3.02000e+02 3.289997e+07 7.011438e+07 1.148388e+08 2.492309e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA -1.900000e-06 2.266700e-03 -2.29506e-02 -6.390000e-04 -5.500000e-06 6.235000e-04 3.064910e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 1.627600e-03 1.546800e-03 3.30900e-04 4.792000e-04 8.671000e-04 2.331200e-03 8.115700e-03 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.906584e-01 2.911332e-01 0.00000e+00 2.360772e-01 4.875543e-01 7.430191e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.906579e-01 2.911335e-01 0.00000e+00 2.360760e-01 4.875532e-01 7.430199e-01 9.999998e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA 1.932218e-01 2.549826e-01 1.15410e-03 8.065100e-03 6.302310e-02 3.001450e-01 9.988460e-01 ▇▂▁▁▁
numeric AF_reference 522384 0.9610329 NA NA NA NA NA 1.981343e-01 2.474951e-01 0.00000e+00 5.990400e-03 8.666130e-02 3.115020e-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 -0.0005094 0.0007048 0.4697784 0.4697773 0.1106400 0.1894970 361194
1 693731 rs12238997 A G -0.0007161 0.0006659 0.2822299 0.2822294 0.1158300 0.1417730 361194
1 707522 rs371890604 G C -0.0004996 0.0007486 0.5045288 0.5045286 0.0973034 0.1293930 361194
1 717587 rs144155419 G A 0.0016775 0.0017864 0.3477067 0.3477051 0.0156880 0.0045926 361194
1 723329 rs189787166 A T -0.0040269 0.0052705 0.4448473 0.4448466 0.0017336 0.0003994 361194
1 730087 rs148120343 T C -0.0011206 0.0009277 0.2270691 0.2270688 0.0564602 0.0127796 361194
1 731718 rs142557973 T C -0.0005248 0.0006315 0.4059878 0.4059870 0.1217380 0.1543530 361194
1 732032 rs61770163 A C -0.0004054 0.0006737 0.5473322 0.5473322 0.1211710 0.1555510 361194
1 734349 rs141242758 T C -0.0005587 0.0006319 0.3765833 0.3765829 0.1209650 0.1525560 361194
1 740284 rs61770167 C T 0.0031945 0.0028909 0.2691448 0.2691435 0.0057851 0.0023962 361194
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T 0.0001678 0.0007296 0.8181079 0.8181083 0.0561667 0.0309934 361194
23 154923374 rs111332691 T A 0.0006403 0.0008018 0.4245941 0.4245936 0.0447862 0.0116556 361194
23 154925045 rs509981 C T 0.0008565 0.0003858 0.0264113 0.0264102 0.2456060 0.3634440 361194
23 154925895 rs538470 C T 0.0008805 0.0003947 0.0256756 0.0256746 0.2419150 0.3634440 361194
23 154927581 rs644138 G A 0.0007970 0.0003628 0.0280524 0.0280515 0.3021620 0.4635760 361194
23 154929412 rs557132 C T 0.0008634 0.0003859 0.0252645 0.0252640 0.2454590 0.3568210 361194
23 154929637 rs35185538 CT C 0.0007167 0.0004028 0.0751571 0.0751567 0.2296970 0.3011920 361194
23 154929952 rs4012982 CAA C 0.0010664 0.0004059 0.0086082 0.0086077 0.2394250 0.3165560 361194
23 154930230 rs781880 A G 0.0009028 0.0003859 0.0193010 0.0193007 0.2458690 0.3618540 361194
23 154930487 rs781879 T A 0.0008100 0.0013237 0.5406174 0.5406176 0.0195623 0.1263580 361194

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.11064  ES:SE:LP:AF:SS:ID   -0.000509424:0.000704752:0.328107:0.11064:361194:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.11583  ES:SE:LP:AF:SS:ID   -0.000716063:0.000665904:0.549397:0.11583:361194:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0973034    ES:SE:LP:AF:SS:ID   -0.000499611:0.000748614:0.297114:0.0973034:361194:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.015688 ES:SE:LP:AF:SS:ID   0.0016775:0.00178638:0.458787:0.015688:361194:rs144155419
1   723329  rs189787166 A   T   .   PASS    AF=0.00173363   ES:SE:LP:AF:SS:ID   -0.00402686:0.00527052:0.351789:0.00173363:361194:rs189787166
1   730087  rs148120343 T   C   .   PASS    AF=0.0564602    ES:SE:LP:AF:SS:ID   -0.0011206:0.000927692:0.643842:0.0564602:361194:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.121738 ES:SE:LP:AF:SS:ID   -0.000524799:0.000631545:0.391487:0.121738:361194:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.121171 ES:SE:LP:AF:SS:ID   -0.000405387:0.000673666:0.261749:0.121171:361194:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.120965 ES:SE:LP:AF:SS:ID   -0.000558719:0.000631885:0.424139:0.120965:361194:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.00578512   ES:SE:LP:AF:SS:ID   0.00319453:0.00289088:0.570014:0.00578512:361194:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.00187787   ES:SE:LP:AF:SS:ID   0.00488371:0.00545972:0.430561:0.00187787:361194:rs112573343
1   749963  rs529266287 T   TAA .   PASS    AF=0.869742 ES:SE:LP:AF:SS:ID   0.000419426:0.000622939:0.300375:0.869742:361194:rs529266287
1   750230  rs190826124 G   C   .   PASS    AF=0.00153021   ES:SE:LP:AF:SS:ID   -0.00318006:0.00563944:0.241978:0.00153021:361194:rs190826124
1   751343  rs28544273  T   A   .   PASS    AF=0.122916 ES:SE:LP:AF:SS:ID   -0.000183924:0.000616939:0.115993:0.122916:361194:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.142712 ES:SE:LP:AF:SS:ID   -0.000515187:0.000609811:0.399892:0.142712:361194:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.123031 ES:SE:LP:AF:SS:ID   -0.000177056:0.00061608:0.111363:0.123031:361194:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.87089  ES:SE:LP:AF:SS:ID   0.000438218:0.00060035:0.332147:0.87089:361194:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.875472 ES:SE:LP:AF:SS:ID   0.000192511:0.000610299:0.123534:0.875472:361194:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.128631 ES:SE:LP:AF:SS:ID   -0.000398966:0.000601855:0.29465:0.128631:361194:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0363613    ES:SE:LP:AF:SS:ID   -0.00188121:0.0010919:1.07103:0.0363613:361194:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.870484 ES:SE:LP:AF:SS:ID   0.000444733:0.000599726:0.338799:0.870484:361194:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.870589 ES:SE:LP:AF:SS:ID   0.000456709:0.000599999:0.350132:0.870589:361194:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.870481 ES:SE:LP:AF:SS:ID   0.000445721:0.000599703:0.339761:0.870481:361194:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00504367   ES:SE:LP:AF:SS:ID   0.005294:0.00307876:1.06793:0.00504367:361194:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00501064   ES:SE:LP:AF:SS:ID   0.00533987:0.003087:1.07744:0.00501064:361194:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.00558665   ES:SE:LP:AF:SS:ID   -0.00273986:0.00298186:0.445901:0.00558665:361194:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.870563 ES:SE:LP:AF:SS:ID   0.000432792:0.000598648:0.328168:0.870563:361194:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.125985 ES:SE:LP:AF:SS:ID   -0.000360467:0.000610841:0.255619:0.125985:361194:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.870127 ES:SE:LP:AF:SS:ID   0.000460089:0.000597207:0.3555:0.870127:361194:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.869414 ES:SE:LP:AF:SS:ID   0.000444286:0.000596663:0.340556:0.869414:361194:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.870281 ES:SE:LP:AF:SS:ID   0.000459165:0.000597721:0.35421:0.870281:361194:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.870287 ES:SE:LP:AF:SS:ID   0.000458736:0.000597764:0.35376:0.870287:361194:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.870295 ES:SE:LP:AF:SS:ID   0.00045598:0.000597782:0.351064:0.870295:361194:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.870739 ES:SE:LP:AF:SS:ID   0.000422417:0.000599305:0.317939:0.870739:361194:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.0988442    ES:SE:LP:AF:SS:ID   -0.00103836:0.000696427:0.866573:0.0988442:361194:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.00647588   ES:SE:LP:AF:SS:ID   -0.00256624:0.00269382:0.467535:0.00647588:361194:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.874635 ES:SE:LP:AF:SS:ID   0.000262357:0.000608237:0.176382:0.874635:361194:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.864027 ES:SE:LP:AF:SS:ID   0.000487658:0.000596466:0.383422:0.864027:361194:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.869425 ES:SE:LP:AF:SS:ID   0.000339074:0.000602257:0.241518:0.869425:361194:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.866371 ES:SE:LP:AF:SS:ID   0.000307742:0.000602379:0.21507:0.866371:361194:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0987831    ES:SE:LP:AF:SS:ID   -0.000167565:0.000721161:0.0881699:0.0987831:361194:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.871556 ES:SE:LP:AF:SS:ID   0.000179217:0.000609242:0.114281:0.871556:361194:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.871556 ES:SE:LP:AF:SS:ID   0.000179182:0.000609242:0.114256:0.871556:361194:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.871555 ES:SE:LP:AF:SS:ID   0.000178035:0.000609254:0.113442:0.871555:361194:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.871929 ES:SE:LP:AF:SS:ID   0.000178583:0.000609601:0.113758:0.871929:361194:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.125562 ES:SE:LP:AF:SS:ID   -0.000257022:0.000610082:0.171635:0.125562:361194:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.105428 ES:SE:LP:AF:SS:ID   -0.000992578:0.000664156:0.869515:0.105428:361194:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.855376 ES:SE:LP:AF:SS:ID   0.0003156:0.000596242:0.224326:0.855376:361194:rs2519015
1   767393  rs538667473 A   C   .   PASS    AF=0.00165444   ES:SE:LP:AF:SS:ID   -0.00345454:0.00528185:0.28981:0.00165444:361194:rs538667473
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.838358 ES:SE:LP:AF:SS:ID   0.000580134:0.000587405:0.490342:0.838358:361194:rs376645387