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-1168/ieu-a-1168.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-1168/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 09:23:25 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-1168/ieu-a-1168.vcf.gz ...
Read summary statistics for 12849762 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, 1286536 SNPs remain.
After merging with regression SNP LD, 1286536 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.3839 (0.034)
Lambda GC: 1.2113
Mean Chi^2: 1.3084
Intercept: 1.0592 (0.0091)
Ratio: 0.192 (0.0296)
Analysis finished at Wed Feb  5 09:25:13 2020
Total time elapsed: 1.0m:48.03s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9595,
    "inflation_factor": 1.1474,
    "mean_EFFECT": 0.0001,
    "n": 33832,
    "n_snps": 12849762,
    "n_clumped_hits": 50,
    "n_p_sig": 4869,
    "n_mono": 0,
    "n_ns": 3624,
    "n_mac": 0,
    "is_snpid_unique": true,
    "n_miss_EFFECT": 0,
    "n_miss_SE": 0,
    "n_miss_PVAL": 0,
    "n_miss_AF": 0,
    "n_miss_AF_reference": 477549,
    "n_est": 34425.1,
    "ratio_se_n": 1.0087,
    "mean_diff": 0.0004,
    "ratio_diff": 0.8804,
    "sd_y_est1": 1.5301,
    "sd_y_est2": 1.5434,
    "r2_sum1": 0.204,
    "r2_sum2": 0.0872,
    "r2_sum3": 0.0857,
    "r2_sum4": 0.1297,
    "ldsc_nsnp_merge_refpanel_ld": 1286536,
    "ldsc_nsnp_merge_regression_ld": 1286536,
    "ldsc_observed_scale_h2_beta": 0.3839,
    "ldsc_observed_scale_h2_se": 0.034,
    "ldsc_intercept_beta": 1.0592,
    "ldsc_intercept_se": 0.0091,
    "ldsc_lambda_gc": 1.2113,
    "ldsc_mean_chisq": 1.3084,
    "ldsc_ratio": 0.192
}

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 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 59 0 12849760 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 26 0 571 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 41 0 301 0 NA NA NA NA NA NA NA NA
numeric CHROM 1 0.9999999 NA NA NA NA NA 8.970766e+00 6.085482e+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 7.885449e+07 5.611371e+07 1.15700e+03 3.258802e+07 6.988654e+07 1.147565e+08 2.492283e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA 1.499000e-04 8.599000e-02 -1.29889e+00 -2.129580e-02 2.240000e-05 2.138610e-02 1.236030e+00 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 5.980270e-02 5.943270e-02 9.46290e-03 1.415840e-02 3.124920e-02 9.145270e-02 3.111010e-01 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.803775e-01 2.939966e-01 0.00000e+00 2.200002e-01 4.700002e-01 7.300002e-01 1.000000e+00 ▇▇▇▆▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.803780e-01 2.939715e-01 0.00000e+00 2.208775e-01 4.740828e-01 7.348129e-01 9.999999e-01 ▇▇▇▇▆
numeric AF 0 1.0000000 NA NA NA NA NA 1.720938e-01 2.480134e-01 7.00800e-04 5.378300e-03 3.993580e-02 2.507800e-01 9.991920e-01 ▇▁▁▁▁
numeric AF_reference 477549 0.9628360 NA NA NA NA NA 1.781956e-01 2.436284e-01 0.00000e+00 3.993600e-03 5.790730e-02 2.669730e-01 1.000000e+00 ▇▂▁▁▁
numeric N 0 1.0000000 NA NA NA NA NA 3.383200e+04 0.000000e+00 3.38320e+04 3.383200e+04 3.383200e+04 3.383200e+04 3.383200e+04 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 55850 rs191890754 C G 0.0841873 0.1097290 0.4400003 0.4429452 0.0050374 NA 33832
1 61743 rs184286948 G C 0.2278570 0.2081040 0.2700001 0.2735522 0.0014630 0.0045926 33832
1 88188 rs148331237 C A 0.1240680 0.0950864 0.1900002 0.1919636 0.0081978 0.0033946 33832
1 99719 rs183898652 C T 0.3410690 0.1830260 0.0619998 0.0623919 0.0021039 0.0089856 33832
1 534540 rs183186584 G T 0.1577950 0.1384060 0.2500000 0.2542497 0.0030403 0.0011981 33832
1 669228 rs187185296 G A 0.2153890 0.1424030 0.1299999 0.1303987 0.0023126 0.0001997 33832
1 701131 rs185335630 A C 0.1366610 0.1541090 0.3800004 0.3751966 0.0029933 0.0041933 33832
1 712583 rs142048655 T C -0.0743700 0.1295310 0.5700002 0.5658675 0.0023907 0.0001997 33832
1 713337 rs184249808 G A -0.2010430 0.1632120 0.2200002 0.2180273 0.0024171 0.0071885 33832
1 715211 rs184426933 C G 0.0103747 0.1215050 0.9299999 0.9319553 0.0031305 NA 33832
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T 0.0222230 0.0344424 0.5199996 0.5187832 0.0395422 0.0309934 33832
23 154925045 rs509981 C T -0.0009008 0.0119417 0.9400001 0.9398690 0.2408650 0.3634440 33832
23 154925895 rs538470 C T -0.0001338 0.0124873 0.9900000 0.9914519 0.2444180 0.3634440 33832
23 154926315 rs145589925 A C 0.0188253 0.1069070 0.8600001 0.8602229 0.0066350 0.0037086 33832
23 154927185 rs185685661 T C -0.0178402 0.0198573 0.3700002 0.3689616 0.1402100 0.1796030 33832
23 154927199 rs645904 C T -0.0006529 0.0119090 0.9599999 0.9562755 0.2391490 0.3674170 33832
23 154927581 rs644138 G A 0.0017749 0.0117434 0.8800001 0.8798678 0.2847020 0.4635760 33832
23 154929412 rs557132 C T -0.0009040 0.0119407 0.9400001 0.9396528 0.2407430 0.3568210 33832
23 154930230 rs781880 A G -0.0013228 0.0120084 0.9100000 0.9122842 0.2408700 0.3618540 33832
NA 3696 rs17052208 C T -0.0231118 0.0844549 0.7800007 0.7843471 0.0077230 NA 33832

bcf preview

1   55850   rs191890754 C   G   .   PASS    AF=0.00503738   ES:SE:LP:AF:SS:ID   0.0841873:0.109729:0.356547:0.00503738:33832:rs191890754
1   61743   rs184286948 G   C   .   PASS    AF=0.00146299   ES:SE:LP:AF:SS:ID   0.227857:0.208104:0.568636:0.00146299:33832:rs184286948
1   88188   rs148331237 C   A   .   PASS    AF=0.00819782   ES:SE:LP:AF:SS:ID   0.124068:0.0950864:0.721246:0.00819782:33832:rs148331237
1   99719   rs183898652 C   T   .   PASS    AF=0.0021039    ES:SE:LP:AF:SS:ID   0.341069:0.183026:1.20761:0.0021039:33832:rs183898652
1   534540  rs183186584 G   T   .   PASS    AF=0.00304029   ES:SE:LP:AF:SS:ID   0.157795:0.138406:0.60206:0.00304029:33832:rs183186584
1   669228  rs187185296 G   A   .   PASS    AF=0.0023126    ES:SE:LP:AF:SS:ID   0.215389:0.142403:0.886057:0.0023126:33832:rs187185296
1   701131  rs185335630 A   C   .   PASS    AF=0.00299333   ES:SE:LP:AF:SS:ID   0.136661:0.154109:0.420216:0.00299333:33832:rs185335630
1   712583  rs142048655 T   C   .   PASS    AF=0.0023907    ES:SE:LP:AF:SS:ID   -0.07437:0.129531:0.244125:0.0023907:33832:rs142048655
1   713337  rs184249808 G   A   .   PASS    AF=0.0024171    ES:SE:LP:AF:SS:ID   -0.201043:0.163212:0.657577:0.0024171:33832:rs184249808
1   715211  rs184426933 C   G   .   PASS    AF=0.00313049   ES:SE:LP:AF:SS:ID   0.0103747:0.121505:0.0315171:0.00313049:33832:rs184426933
1   720468  rs187315357 G   A   .   PASS    AF=0.00377569   ES:SE:LP:AF:SS:ID   0.182004:0.12849:0.79588:0.00377569:33832:rs187315357
1   723379  rs181223789 T   A   .   PASS    AF=0.00241745   ES:SE:LP:AF:SS:ID   -0.0716061:0.129005:0.236572:0.00241745:33832:rs181223789
1   736689  rs181876450 T   C   .   PASS    AF=0.00645792   ES:SE:LP:AF:SS:ID   0.0931135:0.108689:0.408935:0.00645792:33832:rs181876450
1   748332  rs182373484 T   C   .   PASS    AF=0.00411936   ES:SE:LP:AF:SS:ID   -0.104218:0.126618:0.387216:0.00411936:33832:rs182373484
1   748524  rs144265613 C   T   .   PASS    AF=0.0030819    ES:SE:LP:AF:SS:ID   0.230824:0.142328:1:0.0030819:33832:rs144265613
1   755435  rs184270342 T   G   .   PASS    AF=0.00143177   ES:SE:LP:AF:SS:ID   -0.434496:0.180259:1.79588:0.00143177:33832:rs184270342
1   767811  rs140378911 G   C   .   PASS    AF=0.0229196    ES:SE:LP:AF:SS:ID   0.0201796:0.0583518:0.136677:0.0229196:33832:rs140378911
1   770281  rs188583355 T   A   .   PASS    AF=0.00215254   ES:SE:LP:AF:SS:ID   -0.110137:0.160753:0.309804:0.00215254:33832:rs188583355
1   772172  rs141427868 C   G   .   PASS    AF=0.00137714   ES:SE:LP:AF:SS:ID   -0.2036:0.232257:0.420216:0.00137714:33832:rs141427868
1   775426  rs2905037   G   A   .   PASS    AF=0.998058 ES:SE:LP:AF:SS:ID   0.0454384:0.185015:0.091515:0.998058:33832:rs2905037
1   783632  rs193023236 G   A   .   PASS    AF=0.0030342    ES:SE:LP:AF:SS:ID   -0.152729:0.15418:0.49485:0.0030342:33832:rs193023236
1   789256  rs3131939   T   C   .   PASS    AF=0.989984 ES:SE:LP:AF:SS:ID   -0.0363985:0.0847601:0.173925:0.989984:33832:rs3131939
1   791932  rs149451138 C   T   .   PASS    AF=0.0035666    ES:SE:LP:AF:SS:ID   -0.148874:0.146634:0.508638:0.0035666:33832:rs149451138
1   793470  rs184843908 G   A   .   PASS    AF=0.0103438    ES:SE:LP:AF:SS:ID   -0.019121:0.0860253:0.0861861:0.0103438:33832:rs184843908
1   794319  rs186495636 G   A   .   PASS    AF=0.00144783   ES:SE:LP:AF:SS:ID   -0.140782:0.214841:0.29243:0.00144783:33832:rs186495636
1   794368  rs191334345 G   A   .   PASS    AF=0.0018618    ES:SE:LP:AF:SS:ID   -0.134625:0.155663:0.408935:0.0018618:33832:rs191334345
1   794787  rs183239437 A   G   .   PASS    AF=0.00199079   ES:SE:LP:AF:SS:ID   0.0321343:0.176137:0.0655015:0.00199079:33832:rs183239437
1   795131  rs187620961 G   A   .   PASS    AF=0.00216635   ES:SE:LP:AF:SS:ID   -0.109169:0.159573:0.309804:0.00216635:33832:rs187620961
1   796298  rs143060708 C   G   .   PASS    AF=0.00302765   ES:SE:LP:AF:SS:ID   0.043:0.139629:0.119186:0.00302765:33832:rs143060708
1   799346  rs184260340 C   A   .   PASS    AF=0.00265105   ES:SE:LP:AF:SS:ID   -0.0530107:0.169821:0.124939:0.00265105:33832:rs184260340
1   799389  rs187038627 G   A   .   PASS    AF=0.0091864    ES:SE:LP:AF:SS:ID   -0.019228:0.0888176:0.0809219:0.0091864:33832:rs187038627
1   801136  rs140609992 C   T   .   PASS    AF=0.00129571   ES:SE:LP:AF:SS:ID   -0.0726092:0.218836:0.130768:0.00129571:33832:rs140609992
1   801848  rs114911728 C   A   .   PASS    AF=0.00507533   ES:SE:LP:AF:SS:ID   -0.0183638:0.0999171:0.0705811:0.00507533:33832:rs114911728
1   802671  rs191771620 T   G   .   PASS    AF=0.003486 ES:SE:LP:AF:SS:ID   -0.0502294:0.132353:0.154902:0.003486:33832:rs191771620
1   803192  rs190240504 C   T   .   PASS    AF=0.00267073   ES:SE:LP:AF:SS:ID   0.209364:0.133352:0.920819:0.00267073:33832:rs190240504
1   809001  rs184951212 G   A   .   PASS    AF=0.00322111   ES:SE:LP:AF:SS:ID   0.0103524:0.117872:0.0315171:0.00322111:33832:rs184951212
1   810470  rs181485325 C   A   .   PASS    AF=0.00120633   ES:SE:LP:AF:SS:ID   0.185824:0.238857:0.356547:0.00120633:33832:rs181485325
1   810777  rs143204076 C   G   .   PASS    AF=0.0130197    ES:SE:LP:AF:SS:ID   0.1231:0.0749837:1:0.0130197:33832:rs143204076
1   812717  rs151199172 G   T   .   PASS    AF=0.00173359   ES:SE:LP:AF:SS:ID   -0.0910737:0.160932:0.244125:0.00173359:33832:rs151199172
1   819065  rs188466450 C   T   .   PASS    AF=0.0068528    ES:SE:LP:AF:SS:ID   0.0531956:0.0946198:0.244125:0.0068528:33832:rs188466450
1   819467  rs187253977 T   A   .   PASS    AF=0.00151854   ES:SE:LP:AF:SS:ID   -0.0604025:0.220291:0.107905:0.00151854:33832:rs187253977
1   823790  rs143626389 G   A   .   PASS    AF=0.0209886    ES:SE:LP:AF:SS:ID   -0.00357438:0.059865:0.0222764:0.0209886:33832:rs143626389
1   824357  rs80134645  C   T   .   PASS    AF=0.00289833   ES:SE:LP:AF:SS:ID   0.00511637:0.111692:0.0177288:0.00289833:33832:rs80134645
1   824666  rs140725574 C   A   .   PASS    AF=0.0047316    ES:SE:LP:AF:SS:ID   0.0113683:0.110035:0.0362122:0.0047316:33832:rs140725574
1   830791  rs184031231 C   T   .   PASS    AF=0.00704278   ES:SE:LP:AF:SS:ID   0.193929:0.100482:1.26761:0.00704278:33832:rs184031231
1   834240  rs188158544 G   A   .   PASS    AF=0.00218954   ES:SE:LP:AF:SS:ID   -0.282941:0.173388:1:0.00218954:33832:rs188158544
1   834830  rs116452738 G   A   .   PASS    AF=0.0121869    ES:SE:LP:AF:SS:ID   -0.117473:0.0709605:1.00877:0.0121869:33832:rs116452738
1   837096  rs144333274 C   T   .   PASS    AF=0.00684682   ES:SE:LP:AF:SS:ID   0.189487:0.100812:1.22185:0.00684682:33832:rs144333274
1   837214  rs72631888  G   C   .   PASS    AF=0.0099436    ES:SE:LP:AF:SS:ID   -0.0527937:0.088438:0.259637:0.0099436:33832:rs72631888
1   839682  rs189624598 G   A   .   PASS    AF=0.0222536    ES:SE:LP:AF:SS:ID   0.027885:0.0538492:0.221849:0.0222536:33832:rs189624598