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-1087/ieu-a-1087.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-1087/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 09:03:18 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-1087/ieu-a-1087.vcf.gz ...
Read summary statistics for 15576362 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, 1265508 SNPs remain.
After merging with regression SNP LD, 1265508 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.102 (0.0058)
Lambda GC: 1.2245
Mean Chi^2: 1.2644
Intercept: 1.0118 (0.0074)
Ratio: 0.0447 (0.028)
Analysis finished at Wed Feb  5 09:05:51 2020
Total time elapsed: 2.0m:33.26s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9579,
    "inflation_factor": 1.0966,
    "mean_EFFECT": -0,
    "n": 128266,
    "n_snps": 15576362,
    "n_clumped_hits": 12,
    "n_p_sig": 297,
    "n_mono": 0,
    "n_ns": 909748,
    "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": 281592,
    "n_est": 125522.6026,
    "ratio_se_n": 0.9892,
    "mean_diff": -2.7544e-06,
    "ratio_diff": 0.9525,
    "sd_y_est1": 1.0213,
    "sd_y_est2": 1.0104,
    "r2_sum1": 0.0036,
    "r2_sum2": 0.0034,
    "r2_sum3": 0.0035,
    "r2_sum4": 0.0035,
    "ldsc_nsnp_merge_refpanel_ld": 1265508,
    "ldsc_nsnp_merge_regression_ld": 1265508,
    "ldsc_observed_scale_h2_beta": 0.102,
    "ldsc_observed_scale_h2_se": 0.0058,
    "ldsc_intercept_beta": 1.0118,
    "ldsc_intercept_se": 0.0074,
    "ldsc_lambda_gc": 1.2245,
    "ldsc_mean_chisq": 1.2644,
    "ldsc_ratio": 0.0446
}

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 58 0 15576361 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 52 0 55032 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 51 0 23971 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 8.667351e+00 5.794684e+00 1.00000e+00 4.000000e+00 8.000000e+00 1.300000e+01 2.200000e+01 ▇▅▃▂▂
numeric POS 0 1.0000000 NA NA NA NA NA 7.882425e+07 5.641542e+07 5.60000e+01 3.241606e+07 6.942169e+07 1.145682e+08 2.492405e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA -1.340000e-05 3.480630e-02 -5.84115e-01 -8.847300e-03 -1.700000e-06 8.852300e-03 8.510740e-01 ▁▂▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 2.511590e-02 2.359670e-02 3.88860e-03 5.336200e-03 1.420950e-02 4.179950e-02 4.888090e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.820211e-01 2.939654e-01 0.00000e+00 2.200002e-01 4.799997e-01 7.400005e-01 1.000000e+00 ▇▇▇▆▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.820216e-01 2.939393e-01 0.00000e+00 2.228244e-01 4.762346e-01 7.368355e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA 1.548960e-01 2.436184e-01 1.00000e-03 2.953000e-03 2.204400e-02 2.093150e-01 9.990000e-01 ▇▁▁▁▁
numeric AF_reference 281592 0.9819218 NA NA NA NA NA 1.526512e-01 2.319374e-01 0.00000e+00 1.397800e-03 2.675720e-02 2.158550e-01 1.000000e+00 ▇▁▁▁▁
numeric N 0 1.0000000 NA NA NA NA NA 1.282660e+05 0.000000e+00 1.28266e+05 1.282660e+05 1.282660e+05 1.282660e+05 1.282660e+05 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 10177 rs367896724 A AC 0.0000612 0.0057079 0.9900000 0.9914476 0.399426 0.4253190 128266
1 10352 rs555500075 T TA -0.0124425 0.0059281 0.0359998 0.0358259 0.388667 0.4375000 128266
1 10511 rs534229142 G A 0.1195170 0.0828030 0.1499999 0.1489107 0.001404 0.0001997 128266
1 10616 rs376342519 CCGCCGTTGCAAAGGCGCGCCG C 0.0080714 0.0410826 0.8400000 0.8442434 0.995358 0.9930110 128266
1 11008 rs575272151 C G -0.0002322 0.0098593 0.9800000 0.9812119 0.085430 0.0880591 128266
1 11012 rs544419019 C G -0.0002322 0.0098593 0.9800000 0.9812119 0.085430 0.0880591 128266
1 13110 rs540538026 G A -0.0100804 0.0127003 0.4299995 0.4273622 0.060313 0.0267572 128266
1 13116 rs62635286 T G 0.0195660 0.0077543 0.0120000 0.0116282 0.190300 0.0970447 128266
1 13273 rs531730856 G C -0.0029548 0.0089115 0.7400005 0.7402168 0.134841 0.0950479 128266
1 13453 rs568927457 T C 0.0353173 0.0345276 0.3100002 0.3063686 0.006605 0.0007987 128266
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51238328 rs553081191 A C -0.0405156 0.0533205 0.4500005 0.4473440 0.001952 0.0005990 128266
22 51238364 rs564490465 C G -0.0154327 0.0399460 0.6999999 0.6992456 0.005532 0.0005990 128266
22 51238394 rs149712012 C T -0.0436393 0.0443981 0.3300000 0.3256521 0.003338 0.0033946 128266
22 51239281 rs8138215 G C 0.1073680 0.0648230 0.0980009 0.0976559 0.001443 0.0111821 128266
22 51239296 rs8137179 T C 0.1073680 0.0648230 0.0980009 0.0976559 0.001443 0.0111821 128266
22 51239304 rs8142977 C T 0.1073680 0.0648230 0.0980009 0.0976559 0.001443 0.0111821 128266
22 51239586 rs535432390 T G -0.0696235 0.0610293 0.2500000 0.2539445 0.001765 0.0001997 128266
22 51239794 rs561893765 C A -0.0402305 0.0677275 0.5500004 0.5525085 0.001585 0.0299521 128266
22 51241386 rs568168135 C G 0.0322653 0.0331955 0.3300000 0.3310614 0.009136 0.0139776 128266
22 51244237 rs575160859 C T -0.0183548 0.0242772 0.4500005 0.4496187 0.013204 0.0037939 128266

bcf preview

1   10177   rs367896724 A   AC  .   PASS    AF=0.399426 ES:SE:LP:AF:SS:ID   6.11831e-05:0.00570789:0.00436481:0.399426:128266:rs367896724
1   10352   rs555500075 T   TA  .   PASS    AF=0.388667 ES:SE:LP:AF:SS:ID   -0.0124425:0.00592811:1.4437:0.388667:128266:rs555500075
1   10511   rs534229142 G   A   .   PASS    AF=0.001404 ES:SE:LP:AF:SS:ID   0.119517:0.082803:0.823909:0.001404:128266:rs534229142
1   10616   rs376342519 CCGCCGTTGCAAAGGCGCGCCG  C   .   PASS    AF=0.995358 ES:SE:LP:AF:SS:ID   0.00807144:0.0410826:0.0757207:0.995358:128266:rs376342519
1   11008   rs575272151 C   G   .   PASS    AF=0.08543  ES:SE:LP:AF:SS:ID   -0.000232182:0.00985928:0.00877392:0.08543:128266:rs575272151
1   11012   rs544419019 C   G   .   PASS    AF=0.08543  ES:SE:LP:AF:SS:ID   -0.000232182:0.00985928:0.00877392:0.08543:128266:rs544419019
1   13110   rs540538026 G   A   .   PASS    AF=0.060313 ES:SE:LP:AF:SS:ID   -0.0100804:0.0127003:0.366532:0.060313:128266:rs540538026
1   13116   rs62635286  T   G   .   PASS    AF=0.1903   ES:SE:LP:AF:SS:ID   0.019566:0.00775434:1.92082:0.1903:128266:rs62635286
1   13273   rs531730856 G   C   .   PASS    AF=0.134841 ES:SE:LP:AF:SS:ID   -0.00295475:0.00891149:0.130768:0.134841:128266:rs531730856
1   13453   rs568927457 T   C   .   PASS    AF=0.006605 ES:SE:LP:AF:SS:ID   0.0353173:0.0345276:0.508638:0.006605:128266:rs568927457
1   13483   rs554760071 G   C   .   PASS    AF=0.005168 ES:SE:LP:AF:SS:ID   0.0247417:0.0391168:0.275724:0.005168:128266:rs554760071
1   13494   rs574697788 A   G   .   PASS    AF=0.003437 ES:SE:LP:AF:SS:ID   0.112457:0.0547713:1.39794:0.003437:128266:rs574697788
1   13550   rs554008981 G   A   .   PASS    AF=0.004181 ES:SE:LP:AF:SS:ID   0.0543834:0.0547385:0.49485:0.004181:128266:rs554008981
1   14464   rs546169444 A   T   .   PASS    AF=0.156788 ES:SE:LP:AF:SS:ID   -0.00616068:0.00811432:0.346787:0.156788:128266:rs546169444
1   14604   rs541940975 A   G   .   PASS    AF=0.192696 ES:SE:LP:AF:SS:ID   0.0132302:0.00745136:1.11919:0.192696:128266:rs541940975
1   14933   rs199856693 G   A   .   PASS    AF=0.047723 ES:SE:LP:AF:SS:ID   -6.06116e-05:0.0137659:-0:0.047723:128266:rs199856693
1   15245   rs576044687 C   T   .   PASS    AF=0.001248 ES:SE:LP:AF:SS:ID   0.0252491:0.0764485:0.130768:0.001248:128266:rs576044687
1   15585   rs533630043 G   A   .   PASS    AF=0.008303 ES:SE:LP:AF:SS:ID   0.0340624:0.0357846:0.468521:0.008303:128266:rs533630043
1   15644   rs564003018 G   A   .   PASS    AF=0.003474 ES:SE:LP:AF:SS:ID   -0.0287593:0.051209:0.244125:0.003474:128266:rs564003018
1   15774   rs374029747 G   A   .   PASS    AF=0.005697 ES:SE:LP:AF:SS:ID   -0.0282444:0.0429769:0.29243:0.005697:128266:rs374029747
1   15820   rs2691315   G   T   .   PASS    AF=0.26979  ES:SE:LP:AF:SS:ID   0.00120582:0.0068021:0.0655015:0.26979:128266:rs2691315
1   15903   rs557514207 G   GC  .   PASS    AF=0.407317 ES:SE:LP:AF:SS:ID   -0.00393736:0.00566656:0.309804:0.407317:128266:rs557514207
1   16142   rs548165136 G   A   .   PASS    AF=0.002882 ES:SE:LP:AF:SS:ID   -0.0896244:0.0547471:1:0.002882:128266:rs548165136
1   16949   rs199745162 A   C   .   PASS    AF=0.020683 ES:SE:LP:AF:SS:ID   0.0170368:0.0202916:0.39794:0.020683:128266:rs199745162
1   18643   rs564023708 G   A   .   PASS    AF=0.006363 ES:SE:LP:AF:SS:ID   -0.0616669:0.0373436:1.00436:0.006363:128266:rs564023708
1   18849   rs533090414 C   G   .   PASS    AF=0.975406 ES:SE:LP:AF:SS:ID   0.0231865:0.0172165:0.744727:0.975406:128266:rs533090414
1   30923   rs806731    G   T   .   PASS    AF=0.904756 ES:SE:LP:AF:SS:ID   0.00573953:0.0102205:0.244125:0.904756:128266:rs806731
1   46285   rs545414834 ATAT    A   .   PASS    AF=0.001696 ES:SE:LP:AF:SS:ID   -0.0456837:0.0639185:0.327902:0.001696:128266:rs545414834
1   47159   rs540662756 T   C   .   PASS    AF=0.065849 ES:SE:LP:AF:SS:ID   -0.00236363:0.0121194:0.0705811:0.065849:128266:rs540662756
1   49315   rs567788405 T   A   .   PASS    AF=0.001295 ES:SE:LP:AF:SS:ID   -0.0409599:0.0913716:0.187087:0.001295:128266:rs567788405
1   49318   rs536836601 A   G   .   PASS    AF=0.00149  ES:SE:LP:AF:SS:ID   -0.0770111:0.070613:0.552842:0.00149:128266:rs536836601
1   49343   rs553572247 T   C   .   PASS    AF=0.002073 ES:SE:LP:AF:SS:ID   0.0553912:0.0625381:0.420216:0.002073:128266:rs553572247
1   49554   rs539322794 A   G   .   PASS    AF=0.097761 ES:SE:LP:AF:SS:ID   0.00144164:0.010044:0.05061:0.097761:128266:rs539322794
1   50891   rs542415070 T   C   .   PASS    AF=0.00338  ES:SE:LP:AF:SS:ID   0.0140137:0.0553843:0.09691:0.00338:128266:rs542415070
1   51047   rs559500163 A   T   .   PASS    AF=0.001654 ES:SE:LP:AF:SS:ID   0.0603848:0.0746287:0.376751:0.001654:128266:rs559500163
1   51049   rs528344458 A   C   .   PASS    AF=0.001654 ES:SE:LP:AF:SS:ID   0.0603848:0.0746287:0.376751:0.001654:128266:rs528344458
1   51050   rs551668143 A   T   .   PASS    AF=0.001654 ES:SE:LP:AF:SS:ID   0.0603848:0.0746287:0.376751:0.001654:128266:rs551668143
1   51053   rs565211799 G   T   .   PASS    AF=0.001654 ES:SE:LP:AF:SS:ID   0.0603848:0.0746287:0.376751:0.001654:128266:rs565211799
1   51479   rs116400033 T   A   .   PASS    AF=0.212306 ES:SE:LP:AF:SS:ID   -0.000519758:0.00714346:0.0268721:0.212306:128266:rs116400033
1   51762   rs559190862 A   G   .   PASS    AF=0.008279 ES:SE:LP:AF:SS:ID   0.00428754:0.0328413:0.0457575:0.008279:128266:rs559190862
1   51765   rs575564077 C   G   .   PASS    AF=0.008098 ES:SE:LP:AF:SS:ID   0.0070194:0.0329176:0.0809219:0.008098:128266:rs575564077
1   52152   rs568235219 ATAAT   A   .   PASS    AF=0.001745 ES:SE:LP:AF:SS:ID   -0.0536554:0.0852045:0.275724:0.001745:128266:rs568235219
1   52238   rs2691277   T   G   .   PASS    AF=0.978076 ES:SE:LP:AF:SS:ID   0.0516957:0.0210458:1.85387:0.978076:128266:rs2691277
1   52253   rs530867301 C   G   .   PASS    AF=0.005139 ES:SE:LP:AF:SS:ID   -0.0501321:0.0436511:0.60206:0.005139:128266:rs530867301
1   54353   rs140052487 C   A   .   PASS    AF=0.001764 ES:SE:LP:AF:SS:ID   -0.103328:0.0592187:1.09151:0.001764:128266:rs140052487
1   54354   rs569165477 C   T   .   PASS    AF=0.002378 ES:SE:LP:AF:SS:ID   0.0289274:0.0531973:0.229148:0.002378:128266:rs569165477
1   54490   rs141149254 G   A   .   PASS    AF=0.153507 ES:SE:LP:AF:SS:ID   -0.00638354:0.00801139:0.366532:0.153507:128266:rs141149254
1   54591   rs561234294 A   G   .   PASS    AF=0.002218 ES:SE:LP:AF:SS:ID   -0.0304988:0.059089:0.21467:0.002218:128266:rs561234294
1   54716   rs569128616 C   T   .   PASS    AF=0.427338 ES:SE:LP:AF:SS:ID   -0.0036004:0.00604781:0.259637:0.427338:128266:rs569128616
1   54830   rs552733564 G   A   .   PASS    AF=0.002191 ES:SE:LP:AF:SS:ID   0.0466167:0.0746029:0.275724:0.002191:128266:rs552733564