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-30080_irnt/ukb-d-30080_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-30080_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 14:54:31 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-30080_irnt/ukb-d-30080_irnt.vcf.gz ...
Read summary statistics for 13586287 SNPs.
Dropped 12710 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: 0.2508 (0.021)
Lambda GC: 1.6634
Mean Chi^2: 2.9365
Intercept: 1.2228 (0.0204)
Ratio: 0.1151 (0.0106)
Analysis finished at Mon Nov 25 14:56:50 2019
Total time elapsed: 2.0m:18.98s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9521,
    "inflation_factor": 1.4255,
    "mean_EFFECT": 0,
    "n": 350474,
    "n_snps": 13586287,
    "n_clumped_hits": 445,
    "n_p_sig": 108437,
    "n_mono": 0,
    "n_ns": 1251793,
    "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": 569192,
    "n_est": 351809.4824,
    "ratio_se_n": 1.0019,
    "mean_diff": 9.9443e-07,
    "ratio_diff": 1.9441,
    "sd_y_est1": 0.9705,
    "sd_y_est2": 0.9723,
    "r2_sum1": 0.1547,
    "r2_sum2": 0.1643,
    "r2_sum3": 0.1636,
    "r2_sum4": 0.1634,
    "ldsc_nsnp_merge_refpanel_ld": 1283504,
    "ldsc_nsnp_merge_regression_ld": 1283504,
    "ldsc_observed_scale_h2_beta": 0.2508,
    "ldsc_observed_scale_h2_se": 0.021,
    "ldsc_intercept_beta": 1.2228,
    "ldsc_intercept_se": 0.0204,
    "ldsc_lambda_gc": 1.6634,
    "ldsc_mean_chisq": 2.9365,
    "ldsc_ratio": 0.1151
}

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 13574241 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 100 0 57080 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 9.051335e+00 6.184983e+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 7.902521e+07 5.591483e+07 3.02000e+02 3.293109e+07 7.013885e+07 1.148590e+08 2.492309e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA 1.700000e-05 1.488580e-02 -6.85099e-01 -4.540500e-03 3.000000e-07 4.550600e-03 6.845500e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 9.740800e-03 9.396300e-03 1.90520e-03 2.773200e-03 5.110400e-03 1.399860e-02 4.763670e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.404781e-01 3.059202e-01 0.00000e+00 1.605251e-01 4.206520e-01 7.052239e-01 1.000000e+00 ▇▅▅▅▅
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.404776e-01 3.059204e-01 0.00000e+00 1.605244e-01 4.206510e-01 7.052241e-01 9.999999e-01 ▇▅▅▅▅
numeric AF 0 1.0000000 NA NA NA NA NA 1.908060e-01 2.544034e-01 1.00000e-03 7.430500e-03 6.003380e-02 2.953180e-01 9.990000e-01 ▇▂▁▁▁
numeric AF_reference 569192 0.9581054 NA NA NA NA NA 1.963356e-01 2.471015e-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 3.504740e+05 0.000000e+00 3.50474e+05 3.504740e+05 3.504740e+05 3.504740e+05 3.504740e+05 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 692794 rs530212009 CA C 0.0054209 0.0040580 0.1815988 0.1815983 0.1106770 0.1894970 350474
1 693731 rs12238997 A G 0.0051951 0.0038344 0.1754571 0.1754564 0.1158570 0.1417730 350474
1 707522 rs371890604 G C 0.0043136 0.0043120 0.3171283 0.3171276 0.0972839 0.1293930 350474
1 717587 rs144155419 G A 0.0037011 0.0102775 0.7187602 0.7187591 0.0157161 0.0045926 350474
1 723329 rs189787166 A T 0.0055454 0.0304568 0.8555250 0.8555247 0.0017252 0.0003994 350474
1 730087 rs148120343 T C 0.0066532 0.0053402 0.2128080 0.2128075 0.0564938 0.0127796 350474
1 731718 rs142557973 T C 0.0042137 0.0036364 0.2465562 0.2465544 0.1217800 0.1543530 350474
1 732032 rs61770163 A C 0.0038991 0.0038795 0.3148741 0.3148730 0.1211750 0.1555510 350474
1 734349 rs141242758 T C 0.0042852 0.0036383 0.2388779 0.2388782 0.1210090 0.1525560 350474
1 740284 rs61770167 C T 0.0054955 0.0166459 0.7412949 0.7412950 0.0057871 0.0023962 350474
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T 0.0063790 0.0042003 0.1288380 0.1288371 0.0561532 0.0309934 350474
23 154923374 rs111332691 T A 0.0160191 0.0046172 0.0005216 0.0005216 0.0447908 0.0116556 350474
23 154925045 rs509981 C T -0.0022200 0.0022220 0.3177328 0.3177332 0.2454620 0.3634440 350474
23 154925895 rs538470 C T -0.0015913 0.0022728 0.4838425 0.4838410 0.2418090 0.3634440 350474
23 154927581 rs644138 G A -0.0003923 0.0020894 0.8510830 0.8510835 0.3020110 0.4635760 350474
23 154929412 rs557132 C T -0.0020976 0.0022226 0.3453051 0.3453038 0.2453170 0.3568210 350474
23 154929637 rs35185538 CT C -0.0008957 0.0023199 0.6994183 0.6994178 0.2295390 0.3011920 350474
23 154929952 rs4012982 CAA C -0.0028000 0.0023377 0.2310080 0.2310081 0.2392720 0.3165560 350474
23 154930230 rs781880 A G -0.0020568 0.0022222 0.3546892 0.3546879 0.2457280 0.3618540 350474
23 154930487 rs781879 T A -0.0192916 0.0076260 0.0114164 0.0114158 0.0195298 0.1263580 350474

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.110677 ES:SE:LP:AF:SS:ID   0.00542092:0.00405803:0.740887:0.110677:350474:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.115857 ES:SE:LP:AF:SS:ID   0.00519509:0.00383436:0.755829:0.115857:350474:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0972839    ES:SE:LP:AF:SS:ID   0.00431363:0.004312:0.498765:0.0972839:350474:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.0157161    ES:SE:LP:AF:SS:ID   0.00370111:0.0102775:0.143416:0.0157161:350474:rs144155419
1   723329  rs189787166 A   T   .   PASS    AF=0.00172518   ES:SE:LP:AF:SS:ID   0.00554539:0.0304568:0.0677673:0.00172518:350474:rs189787166
1   730087  rs148120343 T   C   .   PASS    AF=0.0564938    ES:SE:LP:AF:SS:ID   0.0066532:0.00534015:0.672012:0.0564938:350474:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.12178  ES:SE:LP:AF:SS:ID   0.0042137:0.00363639:0.608084:0.12178:350474:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.121175 ES:SE:LP:AF:SS:ID   0.00389908:0.00387949:0.501863:0.121175:350474:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.121009 ES:SE:LP:AF:SS:ID   0.00428522:0.00363834:0.621824:0.121009:350474:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.00578711   ES:SE:LP:AF:SS:ID   0.00549546:0.0166459:0.130009:0.00578711:350474:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.00188409   ES:SE:LP:AF:SS:ID   -0.00328724:0.0314167:0.0377884:0.00188409:350474:rs112573343
1   749963  rs529266287 T   TAA .   PASS    AF=0.869714 ES:SE:LP:AF:SS:ID   -0.00573983:0.00358714:0.960292:0.869714:350474:rs529266287
1   750230  rs190826124 G   C   .   PASS    AF=0.00153227   ES:SE:LP:AF:SS:ID   0.0323464:0.0324509:0.496384:0.00153227:350474:rs190826124
1   751343  rs28544273  T   A   .   PASS    AF=0.122937 ES:SE:LP:AF:SS:ID   0.00564459:0.00355239:0.950507:0.122937:350474:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.142738 ES:SE:LP:AF:SS:ID   0.00351477:0.00351157:0.499116:0.142738:350474:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.123054 ES:SE:LP:AF:SS:ID   0.00567417:0.00354741:0.959774:0.123054:350474:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.87085  ES:SE:LP:AF:SS:ID   -0.00547107:0.00345682:0.945031:0.87085:350474:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.87545  ES:SE:LP:AF:SS:ID   -0.00567259:0.00351415:0.972728:0.87545:350474:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.128671 ES:SE:LP:AF:SS:ID   0.00560805:0.00346549:0.976303:0.128671:350474:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0363379    ES:SE:LP:AF:SS:ID   -0.0103048:0.00629185:0.993692:0.0363379:350474:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.870444 ES:SE:LP:AF:SS:ID   -0.00557883:0.00345319:0.973912:0.870444:350474:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.870545 ES:SE:LP:AF:SS:ID   -0.00553676:0.00345471:0.962542:0.870545:350474:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.870439 ES:SE:LP:AF:SS:ID   -0.00560898:0.00345305:0.981712:0.870439:350474:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00504296   ES:SE:LP:AF:SS:ID   0.0272229:0.0177389:0.903538:0.00504296:350474:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00500983   ES:SE:LP:AF:SS:ID   0.0280822:0.0177862:0.941703:0.00500983:350474:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.00558056   ES:SE:LP:AF:SS:ID   0.0036855:0.0171814:0.0808419:0.00558056:350474:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.870523 ES:SE:LP:AF:SS:ID   -0.00569564:0.003447:1.00672:0.870523:350474:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.126014 ES:SE:LP:AF:SS:ID   0.00639764:0.00351748:1.16152:0.126014:350474:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.870086 ES:SE:LP:AF:SS:ID   -0.00569686:0.00343869:1.01063:0.870086:350474:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.869364 ES:SE:LP:AF:SS:ID   -0.0056035:0.00343543:0.987707:0.869364:350474:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.87024  ES:SE:LP:AF:SS:ID   -0.00568:0.00344166:1.00494:0.87024:350474:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.870246 ES:SE:LP:AF:SS:ID   -0.00568142:0.00344191:1.0052:0.870246:350474:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.870255 ES:SE:LP:AF:SS:ID   -0.00568669:0.00344202:1.00653:0.870255:350474:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.870697 ES:SE:LP:AF:SS:ID   -0.00560287:0.00345077:0.981092:0.870697:350474:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.0988383    ES:SE:LP:AF:SS:ID   0.00589852:0.00401101:0.849535:0.0988383:350474:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.00647156   ES:SE:LP:AF:SS:ID   0.00944:0.0155223:0.265132:0.00647156:350474:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.874605 ES:SE:LP:AF:SS:ID   -0.00587269:0.00350229:1.02882:0.874605:350474:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.863994 ES:SE:LP:AF:SS:ID   -0.0049036:0.0034345:0.814274:0.863994:350474:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.869378 ES:SE:LP:AF:SS:ID   -0.00567791:0.00346782:0.993256:0.869378:350474:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.866336 ES:SE:LP:AF:SS:ID   -0.00565825:0.00346862:0.987859:0.866336:350474:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0987492    ES:SE:LP:AF:SS:ID   0.00702602:0.00415381:1.04216:0.0987492:350474:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.87152  ES:SE:LP:AF:SS:ID   -0.00608121:0.00350808:1.08087:0.87152:350474:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.87152  ES:SE:LP:AF:SS:ID   -0.00608068:0.00350808:1.08073:0.87152:350474:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.871519 ES:SE:LP:AF:SS:ID   -0.00607035:0.00350814:1.07796:0.871519:350474:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.87189  ES:SE:LP:AF:SS:ID   -0.00595068:0.00351013:1.04565:0.87189:350474:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.125595 ES:SE:LP:AF:SS:ID   0.00577114:0.00351292:0.998184:0.125595:350474:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.105424 ES:SE:LP:AF:SS:ID   0.00501425:0.00382493:0.721523:0.105424:350474:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.855335 ES:SE:LP:AF:SS:ID   -0.00476824:0.0034336:0.782714:0.855335:350474:rs2519015
1   767393  rs538667473 A   C   .   PASS    AF=0.00164752   ES:SE:LP:AF:SS:ID   0.00109289:0.0305095:0.0125907:0.00164752:350474:rs538667473
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.838288 ES:SE:LP:AF:SS:ID   0.00701399:0.00338217:1.41911:0.838288:350474:rs376645387