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-1062/ieu-a-1062.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-1062/ldsc.txt \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ 

Beginning analysis at Tue Feb  4 12:44:47 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-1062/ieu-a-1062.vcf.gz ...
Read summary statistics for 2435585 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, 1076250 SNPs remain.
After merging with regression SNP LD, 1076250 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: -0.0131 (0.364)
Lambda GC: 0.9882
Mean Chi^2: 0.9929
Intercept: 0.9931 (0.0057)
Ratio: NA (mean chi^2 < 1)
Analysis finished at Tue Feb  4 12:45:10 2020
Total time elapsed: 22.86s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9245,
    "inflation_factor": 0.9844,
    "mean_EFFECT": -0.0001,
    "n": 991,
    "n_snps": 2435585,
    "n_clumped_hits": 0,
    "n_p_sig": 0,
    "n_mono": 0,
    "n_ns": 0,
    "n_mac": 13582,
    "is_snpid_unique": true,
    "n_miss_EFFECT": 0,
    "n_miss_SE": 0,
    "n_miss_PVAL": 0,
    "n_miss_AF": 3,
    "n_miss_AF_reference": 33234,
    "n_est": 987.1495,
    "ratio_se_n": 0.9981,
    "mean_diff": -9.6036e-06,
    "ratio_diff": 1.9459,
    "sd_y_est1": 0.9645,
    "sd_y_est2": 0.9626,
    "r2_sum1": 0,
    "r2_sum2": 0,
    "r2_sum3": 0,
    "r2_sum4": 0,
    "ldsc_nsnp_merge_refpanel_ld": 1076250,
    "ldsc_nsnp_merge_regression_ld": 1076250,
    "ldsc_observed_scale_h2_beta": "NA",
    "ldsc_observed_scale_h2_se": "NA",
    "ldsc_intercept_beta": 0.9931,
    "ldsc_intercept_se": 0.0057,
    "ldsc_lambda_gc": 0.9882,
    "ldsc_mean_chisq": 0.9929,
    "ldsc_ratio": 0.9718
}

Flags

name value
af_correlation FALSE
inflation_factor FALSE
n TRUE
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 TRUE
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 42 0 2435585 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 8.614998e+00 5.674087e+00 1.0000e+00 4.000000e+00 8.000000e+00 1.200000e+01 2.400000e+01 ▇▆▃▃▁
numeric POS 0 1.0000000 NA NA NA NA NA 7.878840e+07 5.571011e+07 1.1523e+04 3.258525e+07 7.016216e+07 1.143272e+08 2.492190e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA -1.207000e-04 1.152820e-01 -3.3840e+00 -4.045000e-02 -1.876000e-04 4.032000e-02 3.141000e+00 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 7.837810e-02 8.552610e-02 4.0610e-02 4.551000e-02 5.351000e-02 7.578000e-02 9.639000e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 5.017561e-01 2.884986e-01 2.0000e-07 2.522092e-01 5.033685e-01 7.513962e-01 9.999993e-01 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 5.017562e-01 2.884986e-01 2.0000e-07 2.521545e-01 5.033696e-01 7.514055e-01 9.999993e-01 ▇▇▇▇▇
numeric AF 3 0.9999988 NA NA NA NA NA 3.503389e-01 2.765281e-01 1.0000e-04 1.116000e-01 2.816000e-01 5.490000e-01 9.997000e-01 ▇▅▃▂▂
numeric AF_reference 33234 0.9863548 NA NA NA NA NA 3.539464e-01 2.586615e-01 0.0000e+00 1.371810e-01 2.881390e-01 5.377400e-01 1.000000e+00 ▇▆▃▃▂
numeric N 0 1.0000000 NA NA NA NA NA 9.910000e+02 0.000000e+00 9.9100e+02 9.910000e+02 9.910000e+02 9.910000e+02 9.910000e+02 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 568527 rs531457238 G A 0.761800 0.96150 0.4281855 0.4281836 0.0097 0.0001997 991
1 721290 rs12565286 G C 0.043050 0.13440 0.7487417 0.7487314 0.0597 0.0371406 991
1 723819 rs11804171 T A 0.038380 0.13320 0.7733463 0.7732410 0.0597 0.1345850 991
1 723891 rs2977670 G C -0.030340 0.13100 0.8169263 0.8168463 0.9403 0.7799520 991
1 750235 rs12138618 G A 0.086950 0.09420 0.3559549 0.3559884 0.0685 NA 991
1 752566 rs3094315 G T 0.051890 0.06228 0.4047315 0.4047473 0.8416 NA 991
1 754192 rs3131968 A G 0.059750 0.06612 0.3662073 0.3661757 0.8789 0.6785140 991
1 765948 rs2519016 C T 0.336000 0.22200 0.1300259 0.1301493 0.9562 NA 991
1 768448 rs12562034 G A 0.005856 0.06951 0.9328656 0.9328602 0.1117 0.1918930 991
1 775659 rs2905035 A G 0.056290 0.06627 0.3957145 0.3956566 0.8777 0.7450080 991
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51222100 rs114553188 G T 0.063860 0.10400 0.5392809 0.5391899 0.04980 0.0880591 991
22 51223637 rs375798137 G A 0.063860 0.10400 0.5392809 0.5391899 0.04980 0.0788738 991
22 51229805 rs9616985 T C -0.071300 0.09612 0.4582695 0.4582200 0.09730 0.0730831 991
23 35921591 rs2204667 C G 0.002740 0.05660 0.9613896 0.9613896 0.17010 NA 991
23 51666786 rs14115 A G -0.119100 0.11510 0.3006685 0.3007846 0.03582 NA 991
23 70163799 rs1626496 A C -0.009269 0.08250 0.9105062 0.9105447 0.06910 NA 991
23 74963352 rs1053838 T G -0.338000 0.43190 0.4339203 0.4338688 0.99580 NA 991
23 91415872 rs6562597 G A 0.141900 0.11810 0.2297513 0.2295480 0.03370 0.0021192 991
23 118495837 rs12882977 G A 0.104000 0.04221 0.0137012 0.0137446 0.49140 0.2307280 991
24 3631296 rs2176440 T C 0.118800 0.22850 0.6029940 0.6031246 0.04110 NA 991

bcf preview

1   568527  rs11497407  G   A   .   PASS    AF=0.0097   ES:SE:LP:AF:SS:ID   0.7618:0.9615:0.368368:0.0097:991:rs11497407
1   721290  rs12565286  G   C   .   PASS    AF=0.0597   ES:SE:LP:AF:SS:ID   0.04305:0.1344:0.125668:0.0597:991:rs12565286
1   723819  rs11804171  T   A   .   PASS    AF=0.0597   ES:SE:LP:AF:SS:ID   0.03838:0.1332:0.111626:0.0597:991:rs11804171
1   723891  rs2977670   G   C   .   PASS    AF=0.9403   ES:SE:LP:AF:SS:ID   -0.03034:0.131:0.0878171:0.9403:991:rs2977670
1   750235  rs12138618  G   A   .   PASS    AF=0.0685   ES:SE:LP:AF:SS:ID   0.08695:0.0942:0.448605:0.0685:991:rs12138618
1   752566  rs3094315   G   T   .   PASS    AF=0.8416   ES:SE:LP:AF:SS:ID   0.05189:0.06228:0.392833:0.8416:991:rs3094315
1   754192  rs3131968   A   G   .   PASS    AF=0.8789   ES:SE:LP:AF:SS:ID   0.05975:0.06612:0.436273:0.8789:991:rs3131968
1   765948  rs2519016   C   T   .   PASS    AF=0.9562   ES:SE:LP:AF:SS:ID   0.336:0.222:0.88597:0.9562:991:rs2519016
1   768448  rs12562034  G   A   .   PASS    AF=0.1117   ES:SE:LP:AF:SS:ID   0.005856:0.06951:0.0301809:0.1117:991:rs12562034
1   775659  rs2905035   A   G   .   PASS    AF=0.8777   ES:SE:LP:AF:SS:ID   0.05629:0.06627:0.402618:0.8777:991:rs2905035
1   776546  rs12124819  A   G   .   PASS    AF=0.285    ES:SE:LP:AF:SS:ID   0.1879:0.06528:2.39909:0.285:991:rs12124819
1   777122  rs2980319   A   T   .   PASS    AF=0.8777   ES:SE:LP:AF:SS:ID   0.05348:0.06635:0.3765:0.8777:991:rs2980319
1   779322  rs4040617   A   G   .   PASS    AF=0.1223   ES:SE:LP:AF:SS:ID   -0.05348:0.06635:0.3765:0.1223:991:rs4040617
1   780785  rs2977612   T   A   .   PASS    AF=0.8777   ES:SE:LP:AF:SS:ID   0.05348:0.06635:0.3765:0.8777:991:rs2977612
1   785050  rs2905062   G   A   .   PASS    AF=0.8777   ES:SE:LP:AF:SS:ID   0.05348:0.06635:0.3765:0.8777:991:rs2905062
1   785989  rs2980300   T   C   .   PASS    AF=0.8776   ES:SE:LP:AF:SS:ID   0.05348:0.06635:0.3765:0.8776:991:rs2980300
1   798026  rs4951864   C   T   .   PASS    AF=0.8864   ES:SE:LP:AF:SS:ID   0.001507:0.07353:0.00716183:0.8864:991:rs4951864
1   798801  rs12132517  G   A   .   PASS    AF=0.1204   ES:SE:LP:AF:SS:ID   0.0009242:0.07328:0.00439159:0.1204:991:rs12132517
1   798959  rs11240777  G   A   .   PASS    AF=0.211    ES:SE:LP:AF:SS:ID   -0.07994:0.06145:0.713862:0.211:991:rs11240777
1   846864  rs950122    G   C   .   PASS    AF=0.1416   ES:SE:LP:AF:SS:ID   -0.02446:0.09148:0.102834:0.1416:991:rs950122
1   882033  rs2272756   G   A   .   PASS    AF=0.2591   ES:SE:LP:AF:SS:ID   -0.0841:0.06335:0.734762:0.2591:991:rs2272756
1   962210  rs3128126   A   G   .   PASS    AF=0.4211   ES:SE:LP:AF:SS:ID   -0.02604:0.0492:0.224319:0.4211:991:rs3128126
1   990380  rs3121561   C   T   .   PASS    AF=0.2547   ES:SE:LP:AF:SS:ID   -0.1096:0.05433:1.3606:0.2547:991:rs3121561
1   998501  rs3813193   G   C   .   PASS    AF=0.1544   ES:SE:LP:AF:SS:ID   -0.02906:0.06142:0.196446:0.1544:991:rs3813193
1   1003629 rs4075116   C   T   .   PASS    AF=0.7394   ES:SE:LP:AF:SS:ID   0.05012:0.04769:0.532749:0.7394:991:rs4075116
1   1005806 rs3934834   C   T   .   PASS    AF=0.1337   ES:SE:LP:AF:SS:ID   -0.0283:0.06203:0.188212:0.1337:991:rs3934834
1   1017170 rs3766193   C   G   .   PASS    AF=0.5638   ES:SE:LP:AF:SS:ID   0.04574:0.04402:0.524613:0.5638:991:rs3766193
1   1017197 rs3766192   C   T   .   PASS    AF=0.5746   ES:SE:LP:AF:SS:ID   0.04514:0.04354:0.523261:0.5746:991:rs3766192
1   1017587 rs3766191   C   T   .   PASS    AF=0.1323   ES:SE:LP:AF:SS:ID   0.001032:0.06353:0.00566396:0.1323:991:rs3766191
1   1018562 rs9442371   C   T   .   PASS    AF=0.5838   ES:SE:LP:AF:SS:ID   0.04514:0.04354:0.523261:0.5838:991:rs9442371
1   1018704 rs9442372   A   G   .   PASS    AF=0.5832   ES:SE:LP:AF:SS:ID   0.04514:0.04354:0.523261:0.5832:991:rs9442372
1   1021346 rs10907177  A   G   .   PASS    AF=0.1342   ES:SE:LP:AF:SS:ID   -0.02853:0.06163:0.191495:0.1342:991:rs10907177
1   1021415 rs3737728   A   C   .   PASS    AF=0.725    ES:SE:LP:AF:SS:ID   0.02442:0.04752:0.216631:0.725:991:rs3737728
1   1021583 rs10907178  A   C   .   PASS    AF=0.134    ES:SE:LP:AF:SS:ID   -0.02853:0.06163:0.191495:0.134:991:rs10907178
1   1021695 rs9442398   A   G   .   PASS    AF=0.7326   ES:SE:LP:AF:SS:ID   0.02263:0.04783:0.196446:0.7326:991:rs9442398
1   1022037 rs6701114   C   T   .   PASS    AF=0.5752   ES:SE:LP:AF:SS:ID   0.03975:0.04346:0.443276:0.5752:991:rs6701114
1   1025301 rs9442400   T   C   .   PASS    AF=0.9475   ES:SE:LP:AF:SS:ID   0.07653:0.1448:0.223965:0.9475:991:rs9442400
1   1026707 rs4074137   C   A   .   PASS    AF=0.6092   ES:SE:LP:AF:SS:ID   -0.03148:0.04402:0.323721:0.6092:991:rs4074137
1   1030565 rs6687776   C   T   .   PASS    AF=0.1529   ES:SE:LP:AF:SS:ID   0.06542:0.05959:0.565105:0.1529:991:rs6687776
1   1030633 rs6678318   G   A   .   PASS    AF=0.1503   ES:SE:LP:AF:SS:ID   0.06542:0.05959:0.565105:0.1503:991:rs6678318
1   1031540 rs9651273   A   G   .   PASS    AF=0.7333   ES:SE:LP:AF:SS:ID   0.01614:0.04951:0.128179:0.7333:991:rs9651273
1   1036959 rs11579015  T   C   .   PASS    AF=0.1008   ES:SE:LP:AF:SS:ID   0.08327:0.07029:0.627056:0.1008:991:rs11579015
1   1040026 rs6671356   T   C   .   PASS    AF=0.1357   ES:SE:LP:AF:SS:ID   0.08343:0.06407:0.714631:0.1357:991:rs6671356
1   1046164 rs6666280   C   T   .   PASS    AF=0.1264   ES:SE:LP:AF:SS:ID   0.08343:0.06407:0.714631:0.1264:991:rs6666280
1   1048955 rs4970405   A   G   .   PASS    AF=0.0996   ES:SE:LP:AF:SS:ID   0.08327:0.07029:0.627056:0.0996:991:rs4970405
1   1049950 rs12726255  A   G   .   PASS    AF=0.1299   ES:SE:LP:AF:SS:ID   0.08293:0.06358:0.716171:0.1299:991:rs12726255
1   1053452 rs4970409   G   A   .   PASS    AF=0.103    ES:SE:LP:AF:SS:ID   0.07702:0.06915:0.576297:0.103:991:rs4970409
1   1060174 rs7548798   C   T   .   PASS    AF=0.3137   ES:SE:LP:AF:SS:ID   -0.01626:0.0477:0.134745:0.3137:991:rs7548798
1   1060235 rs7540009   G   A   .   PASS    AF=0.026    ES:SE:LP:AF:SS:ID   0.06998:0.1359:0.217081:0.026:991:rs7540009
1   1060608 rs17160824  G   A   .   PASS    AF=0.1157   ES:SE:LP:AF:SS:ID   0.07374:0.06788:0.55677:0.1157:991:rs17160824