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-22601_11323222/ukb-d-22601_11323222.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-22601_11323222/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 14:58:04 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-22601_11323222/ukb-d-22601_11323222.vcf.gz ...
Read summary statistics for 10599696 SNPs.
Dropped 8522 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, 1281040 SNPs remain.
After merging with regression SNP LD, 1281040 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0089 (0.0048)
Lambda GC: 1.0167
Mean Chi^2: 1.015
Intercept: 0.9993 (0.0061)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Mon Nov 25 15:00:11 2019
Total time elapsed: 2.0m:7.54s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9444,
    "inflation_factor": 1.0152,
    "mean_EFFECT": 5.8947e-06,
    "n": 89866,
    "n_snps": 10599696,
    "n_clumped_hits": 2,
    "n_p_sig": 9,
    "n_mono": 0,
    "n_ns": 1117289,
    "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": 208766,
    "n_est": 89986.6876,
    "ratio_se_n": 1.0007,
    "mean_diff": 0,
    "ratio_diff": 48.7273,
    "sd_y_est1": 0.154,
    "sd_y_est2": 0.1541,
    "r2_sum1": 0,
    "r2_sum2": 0.0007,
    "r2_sum3": 0.0007,
    "r2_sum4": 0.0007,
    "ldsc_nsnp_merge_refpanel_ld": 1281040,
    "ldsc_nsnp_merge_regression_ld": 1281040,
    "ldsc_observed_scale_h2_beta": 0.0089,
    "ldsc_observed_scale_h2_se": 0.0048,
    "ldsc_intercept_beta": 0.9993,
    "ldsc_intercept_se": 0.0061,
    "ldsc_lambda_gc": 1.0167,
    "ldsc_mean_chisq": 1.015,
    "ldsc_ratio": -0.0467
}

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 10591745 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 100 0 49992 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 342 0 30889 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 9.075073e+00 6.178646e+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 7.879784e+07 5.605049e+07 3.02000e+02 3.256715e+07 6.974073e+07 1.147409e+08 2.492309e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA 5.900000e-06 2.024300e-03 -1.76999e-02 -8.622000e-04 -8.800000e-06 8.491000e-04 3.160310e-02 ▁▇▁▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 1.671500e-03 1.133600e-03 6.00700e-04 8.099000e-04 1.148700e-03 2.205400e-03 6.386900e-03 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.976751e-01 2.890933e-01 0.00000e+00 2.470211e-01 4.967616e-01 7.478423e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.976733e-01 2.890944e-01 0.00000e+00 2.470179e-01 4.967588e-01 7.478420e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA 2.413083e-01 2.624950e-01 5.72610e-03 2.937120e-02 1.302520e-01 3.863080e-01 9.942730e-01 ▇▂▂▁▁
numeric AF_reference 208766 0.9803045 NA NA NA NA NA 2.412589e-01 2.534605e-01 0.00000e+00 3.015180e-02 1.471650e-01 3.835860e-01 1.000000e+00 ▇▂▂▁▁
numeric N 0 1.0000000 NA NA NA NA NA 8.986600e+04 0.000000e+00 8.98660e+04 8.986600e+04 8.986600e+04 8.986600e+04 8.986600e+04 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 692794 rs530212009 CA C 0.0014080 0.0012691 0.2672520 0.2672507 0.1114870 0.1894970 89866
1 693731 rs12238997 A G 0.0009381 0.0012022 0.4352232 0.4352192 0.1164980 0.1417730 89866
1 707522 rs371890604 G C 0.0011247 0.0013515 0.4053004 0.4052981 0.0978285 0.1293930 89866
1 717587 rs144155419 G A -0.0013112 0.0032192 0.6837998 0.6837983 0.0158090 0.0045926 89866
1 730087 rs148120343 T C -0.0000700 0.0016670 0.9665001 0.9665003 0.0569661 0.0127796 89866
1 731718 rs142557973 T C 0.0010468 0.0011408 0.3588352 0.3588316 0.1223780 0.1543530 89866
1 732032 rs61770163 A C 0.0006721 0.0012162 0.5805585 0.5805577 0.1220530 0.1555510 89866
1 734349 rs141242758 T C 0.0010772 0.0011417 0.3454300 0.3454280 0.1216170 0.1525560 89866
1 740284 rs61770167 C T 0.0037554 0.0052171 0.4716415 0.4716402 0.0057975 0.0023962 89866
1 749963 rs529266287 T TAA -0.0011636 0.0011232 0.3002120 0.3002088 0.8689590 0.7641770 89866
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T -0.0017783 0.0013353 0.1829359 0.1829339 0.0553576 0.0309934 89866
23 154923374 rs111332691 T A -0.0017565 0.0014537 0.2269368 0.2269323 0.0449113 0.0116556 89866
23 154925045 rs509981 C T 0.0013742 0.0007011 0.0499781 0.0499743 0.2444280 0.3634440 89866
23 154925895 rs538470 C T 0.0013011 0.0007169 0.0695200 0.0695179 0.2408760 0.3634440 89866
23 154927581 rs644138 G A 0.0007673 0.0006595 0.2446262 0.2446228 0.3001910 0.4635760 89866
23 154929412 rs557132 C T 0.0013861 0.0007013 0.0480950 0.0480926 0.2442750 0.3568210 89866
23 154929637 rs35185538 CT C 0.0006347 0.0007323 0.3861268 0.3861243 0.2286930 0.3011920 89866
23 154929952 rs4012982 CAA C 0.0013505 0.0007380 0.0672636 0.0672616 0.2381980 0.3165560 89866
23 154930230 rs781880 A G 0.0014005 0.0007011 0.0457530 0.0457491 0.2447120 0.3618540 89866
23 154930487 rs781879 T A 0.0038366 0.0023782 0.1066891 0.1066866 0.0196791 0.1263580 89866

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.111487 ES:SE:LP:AF:SS:ID   0.00140796:0.0012691:0.573079:0.111487:89866:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.116498 ES:SE:LP:AF:SS:ID   0.000938051:0.00120218:0.361288:0.116498:89866:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0978285    ES:SE:LP:AF:SS:ID   0.00112473:0.00135152:0.392223:0.0978285:89866:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.015809 ES:SE:LP:AF:SS:ID   -0.00131115:0.00321924:0.165071:0.015809:89866:rs144155419
1   730087  rs148120343 T   C   .   PASS    AF=0.0569661    ES:SE:LP:AF:SS:ID   -7.00111e-05:0.00166701:0.0147981:0.0569661:89866:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122378 ES:SE:LP:AF:SS:ID   0.0010468:0.00114081:0.445105:0.122378:89866:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.122053 ES:SE:LP:AF:SS:ID   0.000672058:0.00121624:0.236154:0.122053:89866:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.121617 ES:SE:LP:AF:SS:ID   0.00107715:0.00114166:0.46164:0.121617:89866:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.00579754   ES:SE:LP:AF:SS:ID   0.00375535:0.00521712:0.326388:0.00579754:89866:rs61770167
1   749963  rs529266287 T   TAA .   PASS    AF=0.868959 ES:SE:LP:AF:SS:ID   -0.00116364:0.00112322:0.522572:0.868959:89866:rs529266287
1   751343  rs28544273  T   A   .   PASS    AF=0.123678 ES:SE:LP:AF:SS:ID   0.00105683:0.00111351:0.465246:0.123678:89866:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.143191 ES:SE:LP:AF:SS:ID   0.000684097:0.00110244:0.271718:0.143191:89866:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.123813 ES:SE:LP:AF:SS:ID   0.00105936:0.00111191:0.467597:0.123813:89866:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.869927 ES:SE:LP:AF:SS:ID   -0.000901211:0.0010834:0.392007:0.869927:89866:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.87461  ES:SE:LP:AF:SS:ID   -0.000820531:0.00110106:0.340901:0.87461:89866:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.12951  ES:SE:LP:AF:SS:ID   0.000929622:0.00108671:0.406376:0.12951:89866:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0357365    ES:SE:LP:AF:SS:ID   0.000248544:0.00198815:0.0455095:0.0357365:89866:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869574 ES:SE:LP:AF:SS:ID   -0.000856637:0.00108244:0.367829:0.869574:89866:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869652 ES:SE:LP:AF:SS:ID   -0.000874774:0.00108292:0.377564:0.869652:89866:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.86957  ES:SE:LP:AF:SS:ID   -0.000857723:0.0010824:0.368442:0.86957:89866:rs3131967
1   755890  rs3115858   A   T   .   PASS    AF=0.869566 ES:SE:LP:AF:SS:ID   -0.000948409:0.00108025:0.420248:0.869566:89866:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.126879 ES:SE:LP:AF:SS:ID   0.00100876:0.00110277:0.443306:0.126879:89866:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.869094 ES:SE:LP:AF:SS:ID   -0.000878321:0.00107745:0.381984:0.869094:89866:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.868498 ES:SE:LP:AF:SS:ID   -0.000966209:0.00107679:0.432315:0.868498:89866:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869245 ES:SE:LP:AF:SS:ID   -0.000918385:0.00107844:0.404015:0.869245:89866:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869251 ES:SE:LP:AF:SS:ID   -0.00091942:0.00107851:0.404567:0.869251:89866:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869252 ES:SE:LP:AF:SS:ID   -0.000919532:0.00107852:0.404626:0.869252:89866:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869724 ES:SE:LP:AF:SS:ID   -0.000979156:0.00108138:0.437444:0.869724:89866:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.0991203    ES:SE:LP:AF:SS:ID   0.00145978:0.00125891:0.608656:0.0991203:89866:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.00649047   ES:SE:LP:AF:SS:ID   0.00366408:0.00485865:0.346045:0.00649047:89866:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.873732 ES:SE:LP:AF:SS:ID   -0.000906103:0.00109745:0.388268:0.873732:89866:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.863116 ES:SE:LP:AF:SS:ID   -0.000778873:0.00107643:0.328521:0.863116:89866:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.868511 ES:SE:LP:AF:SS:ID   -0.000939888:0.00108718:0.411946:0.868511:89866:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.86539  ES:SE:LP:AF:SS:ID   -0.000974514:0.00108734:0.431647:0.86539:89866:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0994301    ES:SE:LP:AF:SS:ID   0.00143265:0.00130209:0.566683:0.0994301:89866:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.870696 ES:SE:LP:AF:SS:ID   -0.00075327:0.00109985:0.306784:0.870696:89866:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.870695 ES:SE:LP:AF:SS:ID   -0.000753268:0.00109985:0.306782:0.870695:89866:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.870693 ES:SE:LP:AF:SS:ID   -0.000752834:0.00109986:0.306562:0.870693:89866:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.871061 ES:SE:LP:AF:SS:ID   -0.000687332:0.00110038:0.273915:0.871061:89866:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.126484 ES:SE:LP:AF:SS:ID   0.00105816:0.00110148:0.47273:0.126484:89866:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.106022 ES:SE:LP:AF:SS:ID   0.00185045:0.00119739:0.91274:0.106022:89866:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.854451 ES:SE:LP:AF:SS:ID   -0.00118199:0.00107617:0.565331:0.854451:89866:rs2519015
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.839008 ES:SE:LP:AF:SS:ID   0.00189178:0.00106313:1.12395:0.839008:89866:rs376645387
1   768253  rs2977608   A   C   .   PASS    AF=0.76317  ES:SE:LP:AF:SS:ID   0.000524786:0.000854765:0.268212:0.76317:89866:rs2977608
1   768448  rs12562034  G   A   .   PASS    AF=0.10519  ES:SE:LP:AF:SS:ID   -0.00245842:0.00117797:1.43307:0.10519:89866:rs12562034
1   768819  rs12562811  C   T   .   PASS    AF=0.00773441   ES:SE:LP:AF:SS:ID   -0.00965867:0.00436415:1.57045:0.00773441:89866:rs12562811
1   769138  rs59306077  CAT C   .   PASS    AF=0.129876 ES:SE:LP:AF:SS:ID   0.00114404:0.00108605:0.534373:0.129876:89866:rs762168062
1   769223  rs60320384  C   G   .   PASS    AF=0.129602 ES:SE:LP:AF:SS:ID   0.00114184:0.00108463:0.533929:0.129602:89866:rs60320384
1   769963  rs7518545   G   A   .   PASS    AF=0.104469 ES:SE:LP:AF:SS:ID   -0.0024107:0.00118706:1.37391:0.104469:89866:rs7518545
1   770181  rs146076599 A   G   .   PASS    AF=0.00900456   ES:SE:LP:AF:SS:ID   0.00251762:0.00418901:0.261348:0.00900456:89866:rs146076599