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/ieu-a-import/processed/ieu-a-980/ieu-a-980.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/ieu-a-import/processed/ieu-a-980/ldsc.txt \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ 

Beginning analysis at Wed Feb  5 09:13:42 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-980/ieu-a-980.vcf.gz ...
Read summary statistics for 9280966 SNPs.
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, 1170827 SNPs remain.
After merging with regression SNP LD, 1170827 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.1269 (0.0145)
Lambda GC: 1.1069
Mean Chi^2: 1.1463
Intercept: 1.0228 (0.0079)
Ratio: 0.1555 (0.0537)
Analysis finished at Wed Feb  5 09:15:12 2020
Total time elapsed: 1.0m:30.15s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9825,
    "inflation_factor": 1.029,
    "mean_EFFECT": -0.0001,
    "n": 49988,
    "n_snps": 9280966,
    "n_clumped_hits": 21,
    "n_p_sig": 869,
    "n_mono": 0,
    "n_ns": 0,
    "n_mac": 0,
    "is_snpid_unique": true,
    "n_miss_EFFECT": 0,
    "n_miss_SE": 0,
    "n_miss_PVAL": 0,
    "n_miss_AF": 253347,
    "n_miss_AF_reference": 309164,
    "n_est": 42800.9641,
    "ratio_se_n": 0.9253,
    "mean_diff": -0,
    "ratio_diff": 0.8153,
    "sd_y_est1": 1.2928,
    "sd_y_est2": 1.1963,
    "r2_sum1": 0.0308,
    "r2_sum2": 0.0184,
    "r2_sum3": 0.0215,
    "r2_sum4": 0.0217,
    "ldsc_nsnp_merge_refpanel_ld": 1170827,
    "ldsc_nsnp_merge_regression_ld": 1170827,
    "ldsc_observed_scale_h2_beta": 0.1269,
    "ldsc_observed_scale_h2_se": 0.0145,
    "ldsc_intercept_beta": 1.0228,
    "ldsc_intercept_se": 0.0079,
    "ldsc_lambda_gc": 1.1069,
    "ldsc_mean_chisq": 1.1463,
    "ldsc_ratio": 0.1558
}

Flags

name value
af_correlation FALSE
inflation_factor FALSE
n FALSE
is_snpid_non_unique FALSE
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 57 0 9280964 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 1 0 4 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 1 0 4 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 8.739298e+00 5.837432e+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 7.828070e+07 5.631068e+07 8.28000e+02 3.209398e+07 6.846330e+07 1.139317e+08 2.492391e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA -6.680000e-05 2.784440e-02 -1.12184e+00 -1.038700e-02 -7.600000e-05 1.023300e-02 1.044640e+00 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 2.123530e-02 1.742150e-02 3.95000e-04 8.873000e-03 1.390800e-02 2.808600e-02 4.253630e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.949877e-01 2.905118e-01 0.00000e+00 2.416490e-01 4.938545e-01 7.469216e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.880778e-01 2.922561e-01 0.00000e+00 2.314686e-01 4.843782e-01 7.415427e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 253347 0.9727025 NA NA NA NA NA 2.338344e-01 2.660501e-01 7.96000e-05 2.458230e-02 1.185360e-01 3.681780e-01 9.999200e-01 ▇▂▁▁▁
numeric AF_reference 309164 0.9666884 NA NA NA NA NA 2.345222e-01 2.642879e-01 0.00000e+00 1.936900e-02 1.279950e-01 3.694090e-01 1.000000e+00 ▇▂▂▁▁
numeric N 0 1.0000000 NA NA NA NA NA 4.998800e+04 0.000000e+00 4.99880e+04 4.998800e+04 4.998800e+04 4.998800e+04 4.998800e+04 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 51479 rs116400033 T A 0.010024 0.018989 0.6055054 0.5975795 0.175736 0.1281950 49988
1 52058 rs62637813 G C 0.105988 0.065444 0.1130420 0.1053350 NA NA 49988
1 54421 rs146477069 A G 0.106444 0.047875 0.0296149 0.0261906 NA NA 49988
1 54490 rs141149254 G A 0.001673 0.019823 0.9341500 0.9327409 0.125457 0.0960463 49988
1 54676 rs2462492 C T 0.033397 0.039605 0.4093172 0.3990875 NA NA 49988
1 54753 rs143174675 T G 0.032019 0.046679 0.5021321 0.4927509 NA NA 49988
1 55164 rs3091274 C A -0.010351 0.016214 0.5322063 0.5232140 0.960700 0.9233230 49988
1 55299 rs10399749 C T -0.023400 0.017149 0.1818210 0.1724068 NA NA 49988
1 55326 rs3107975 T C 0.082553 0.066434 0.2240139 0.2140036 0.028401 0.0459265 49988
1 58814 rs114420996 G A -0.015995 0.032037 0.6252044 0.6175917 0.101034 0.1090260 49988
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51227891 rs6010091 G A 0.019188 0.018546 0.3113738 0.3008480 0.4236280 0.4039540 49988
22 51229805 rs9616985 T C 0.038994 0.023551 0.1052059 0.0977773 0.0715195 0.0730831 49988
22 51229813 rs191731586 T C 0.014295 0.021245 0.5103064 0.5010337 0.0052506 0.0057907 49988
22 51229855 rs144549712 G A 0.008556 0.052992 0.8744470 0.8717324 0.1306280 0.1160140 49988
22 51234677 rs189571549 A C 0.018367 0.028846 0.5332919 0.5243033 0.0098648 0.0227636 49988
23 35747397 rs74987454 T C 0.038693 0.078348 0.6289556 0.6214045 NA NA 49988
23 50040891 rs192267219 C T 0.050430 0.080282 0.5388154 0.5298987 NA 0.0222517 49988
23 75822996 rs147134110 C T 0.021534 0.030359 0.4876655 0.4781309 0.0243437 0.0773510 49988
23 100784211 rs188350543 C A -0.001614 0.010167 0.8765639 0.8738667 0.7770880 0.6498010 49988
23 133445361 rs112934402 T C -0.206403 0.099276 0.0419382 0.0376098 0.0037391 NA 49988

bcf preview

1   51479   rs116400033 T   A   .   PASS    AF=0.175736 ES:SE:LP:AF:SS:ID   0.010024:0.018989:0.217882:0.175736:49988:rs116400033
1   52058   rs62637813  G   C   .   PASS    .   ES:SE:LP:SS:ID  0.105988:0.065444:0.94676:49988:rs62637813
1   54421   rs146477069 A   G   .   PASS    .   ES:SE:LP:SS:ID  0.106444:0.047875:1.52849:49988:rs146477069
1   54490   rs141149254 G   A   .   PASS    AF=0.125457 ES:SE:LP:AF:SS:ID   0.001673:0.019823:0.0295834:0.125457:49988:rs141149254
1   54676   rs2462492   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.033397:0.039605:0.38794:49988:rs2462492
1   54753   rs143174675 T   G   .   PASS    .   ES:SE:LP:SS:ID  0.032019:0.046679:0.299182:49988:rs143174675
1   55164   rs3091274   C   A   .   PASS    AF=0.9607   ES:SE:LP:AF:SS:ID   -0.010351:0.016214:0.27392:0.9607:49988:rs3091274
1   55299   rs10399749  C   T   .   PASS    .   ES:SE:LP:SS:ID  -0.0234:0.017149:0.740356:49988:rs10399749
1   55326   rs3107975   T   C   .   PASS    AF=0.028401 ES:SE:LP:AF:SS:ID   0.082553:0.066434:0.649725:0.028401:49988:rs3107975
1   58814   rs114420996 G   A   .   PASS    AF=0.101034 ES:SE:LP:AF:SS:ID   -0.015995:0.032037:0.203978:0.101034:49988:rs114420996
1   61987   rs76735897  A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.018727:0.01641:0.578163:49988:rs76735897
1   61989   rs77573425  G   C   .   PASS    .   ES:SE:LP:SS:ID  -0.020934:0.016416:0.673459:49988:rs77573425
1   66162   rs62639105  A   T   .   PASS    .   ES:SE:LP:SS:ID  -0.017768:0.01671:0.525602:49988:rs62639105
1   66507   rs12401368  T   A   .   PASS    .   ES:SE:LP:SS:ID  0.029618:0.024993:0.608671:49988:rs12401368
1   73841   rs143773730 C   T   .   PASS    .   ES:SE:LP:SS:ID  0.022118:0.037043:0.252517:49988:rs143773730
1   77961   rs78385339  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.111577:0.0777:0.795926:49988:rs78385339
1   79772   rs147215883 C   G   .   PASS    .   ES:SE:LP:SS:ID  -0.006603:0.029662:0.0821963:49988:rs147215883
1   82163   rs139113303 G   A   .   PASS    AF=0.0603819    ES:SE:LP:AF:SS:ID   -0.143593:0.08818:0.954364:0.0603819:49988:rs139113303
1   82249   rs1851945   A   G   .   PASS    .   ES:SE:LP:SS:ID  0.012287:0.026436:0.187565:49988:rs1851945
1   82303   rs3871776   T   C   .   PASS    .   ES:SE:LP:SS:ID  0.029117:0.032463:0.42005:49988:rs3871776
1   82316   rs4030305   A   C   .   PASS    .   ES:SE:LP:SS:ID  0.058093:0.053918:0.534968:49988:rs4030305
1   82609   rs149189449 C   G   .   PASS    AF=0.0604614    ES:SE:LP:AF:SS:ID   -0.002156:0.034558:0.0216784:0.0604614:49988:rs149189449
1   82676   rs185237834 T   G   .   PASS    .   ES:SE:LP:SS:ID  -0.016548:0.034247:0.196275:49988:rs185237834
1   82734   rs4030331   T   C   .   PASS    .   ES:SE:LP:SS:ID  0.004701:0.021638:0.0800714:49988:rs4030331
1   84002   rs28850140  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.06668:0.036419:1.13537:49988:rs28850140
1   84010   rs186443818 G   A   .   PASS    .   ES:SE:LP:SS:ID  0.050587:0.085981:0.248068:49988:rs186443818
1   84244   rs191297051 A   C   .   PASS    .   ES:SE:LP:SS:ID  -0.030961:0.034169:0.425636:49988:rs191297051
1   85597   rs192472955 A   C   .   PASS    .   ES:SE:LP:SS:ID  -0.033066:0.017107:1.23209:49988:rs192472955
1   86018   rs142878000 C   G   .   PASS    .   ES:SE:LP:SS:ID  0.003581:0.032745:0.0386924:49988:rs142878000
1   86028   rs114608975 T   C   .   PASS    AF=0.0540971    ES:SE:LP:AF:SS:ID   0.013489:0.025429:0.219145:0.0540971:49988:rs114608975
1   86065   rs116504101 G   C   .   PASS    AF=0.0630072    ES:SE:LP:AF:SS:ID   0.01292:0.02536:0.208898:0.0630072:49988:rs116504101
1   86303   rs2949417   G   T   .   PASS    .   ES:SE:LP:SS:ID  -0.007598:0.032841:0.0857039:49988:rs2949417
1   86331   rs115209712 A   G   .   PASS    AF=0.110183 ES:SE:LP:AF:SS:ID   -0.006308:0.032861:0.0700725:0.110183:49988:rs115209712
1   87021   rs188486692 T   C   .   PASS    AF=0.00906921   ES:SE:LP:AF:SS:ID   0.092191:0.084298:0.545817:0.00906921:49988:rs188486692
1   87190   rs1524602   G   A   .   PASS    .   ES:SE:LP:SS:ID  0.01431:0.040933:0.135308:49988:rs1524602
1   87409   rs139490478 C   T   .   PASS    AF=0.0637232    ES:SE:LP:AF:SS:ID   -0.112726:0.088673:0.670555:0.0637232:49988:rs139490478
1   88169   rs940550    C   T   .   PASS    AF=0.24463  ES:SE:LP:AF:SS:ID   0.010901:0.041232:0.0991557:0.24463:49988:rs940550
1   88172   rs940551    G   A   .   PASS    AF=0.0574383    ES:SE:LP:AF:SS:ID   0.002437:0.030661:0.0278041:0.0574383:49988:rs940551
1   88177   rs143215837 G   C   .   PASS    AF=0.0560064    ES:SE:LP:AF:SS:ID   -0.007118:0.039711:0.0651144:0.0560064:49988:rs143215837
1   88188   rs148331237 C   A   .   PASS    AF=0.00445505   ES:SE:LP:AF:SS:ID   0.083469:0.078137:0.528855:0.00445505:49988:rs148331237
1   88236   rs186918018 C   T   .   PASS    AF=0.00731901   ES:SE:LP:AF:SS:ID   -0.081816:0.080001:0.498978:0.00731901:49988:rs186918018
1   88316   rs113759966 G   A   .   PASS    AF=0.0566428    ES:SE:LP:AF:SS:ID   0.00986:0.030391:0.124416:0.0566428:49988:rs113759966
1   88338   rs55700207  G   A   .   PASS    AF=0.0881464    ES:SE:LP:AF:SS:ID   -0.007863:0.035621:0.0814518:0.0881464:49988:rs55700207
1   88370   rs185487977 G   A   .   PASS    AF=0.00238663   ES:SE:LP:AF:SS:ID   0.038143:0.135988:0.105829:0.00238663:49988:rs185487977
1   88710   rs186575039 C   G   .   PASS    AF=0.0631663    ES:SE:LP:AF:SS:ID   -0.106486:0.08935:0.613424:0.0631663:49988:rs186575039
1   89946   rs138808727 A   T   .   PASS    AF=0.170485 ES:SE:LP:AF:SS:ID   0.004587:0.018915:0.0902189:0.170485:49988:rs138808727
1   91190   rs143856811 G   A   .   PASS    AF=0.0647574    ES:SE:LP:AF:SS:ID   -0.125627:0.083004:0.858184:0.0647574:49988:rs143856811
1   92633   rs149776517 C   T   .   PASS    AF=0.0186953    ES:SE:LP:AF:SS:ID   -0.109606:0.116984:0.444587:0.0186953:49988:rs149776517
1   92858   rs147061536 G   T   .   PASS    AF=0.183055 ES:SE:LP:AF:SS:ID   0.004052:0.018975:0.0785919:0.183055:49988:rs147061536
1   98929   rs12184306  A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.028247:0.017297:0.958347:49988:rs12184306