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}
*********************************************************************
* 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-294/ieu-a-294.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-294/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 21:09:06 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-294/ieu-a-294.vcf.gz ...
Read summary statistics for 125253 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, 50575 SNPs remain.
After merging with regression SNP LD, 50575 SNPs remain.
WARNING: number of SNPs less than 200k; this is almost always bad.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 3.5478 (0.4223)
Lambda GC: 2.5951
Mean Chi^2: 6.5486
Intercept: 1.537 (0.0775)
Ratio: 0.0968 (0.014)
Analysis finished at Tue Feb 4 21:09:16 2020
Total time elapsed: 9.88s
{
"af_correlation": 0.7665,
"inflation_factor": 3.0818,
"mean_EFFECT": 0.0002,
"n": 65642,
"n_snps": 125253,
"n_clumped_hits": 134,
"n_p_sig": 7495,
"n_mono": 0,
"n_ns": 0,
"n_mac": 29,
"is_snpid_unique": true,
"n_miss_EFFECT": 0,
"n_miss_SE": 0,
"n_miss_PVAL": 0,
"n_miss_AF": 0,
"n_miss_AF_reference": 848,
"n_est": 65950.5747,
"ratio_se_n": 1.0023,
"mean_diff": 0.0002,
"ratio_diff": 0.7166,
"sd_y_est1": 1.8494,
"sd_y_est2": 1.8537,
"r2_sum1": 0.633,
"r2_sum2": 0.1851,
"r2_sum3": 0.1842,
"r2_sum4": 0.1836,
"ldsc_nsnp_merge_refpanel_ld": 50575,
"ldsc_nsnp_merge_regression_ld": 50575,
"ldsc_observed_scale_h2_beta": 3.5478,
"ldsc_observed_scale_h2_se": 0.4223,
"ldsc_intercept_beta": 1.537,
"ldsc_intercept_se": 0.0775,
"ldsc_lambda_gc": 2.5951,
"ldsc_mean_chisq": 6.5486,
"ldsc_ratio": 0.0968
}
name | value |
---|---|
af_correlation | FALSE |
inflation_factor | TRUE |
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 | TRUE |
n_clumped_hits | FALSE |
r2_sum1 | TRUE |
r2_sum2 | FALSE |
r2_sum3 | FALSE |
r2_sum4 | FALSE |
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 SNPsn_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:
A, C, G or T
.< 0
or > 1
.<= 0
or = Infinity
).< 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.
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.
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.
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.
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
> 0.5ldsc_intercept_beta
: ldsc_intercept_beta
> 1.5n_clumped_hits
: n_clumped_hits
> 1000r2_sum<*>
: r2_sum<*>
> 0.5Plots
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 | 5 | 25 | 0 | 125253 | 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 | 8.085100e+00 | 5.769473e+00 | 1.0000e+00 | 3.000000e+00 | 6.000000e+00 | 1.200000e+01 | 2.200000e+01 | ▇▅▃▂▂ |
numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | 7.909516e+07 | 5.780924e+07 | 6.3411e+04 | 3.292569e+07 | 6.222801e+07 | 1.176693e+08 | 2.492107e+08 | ▇▅▃▂▁ |
numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | 1.765000e-04 | 5.329470e-02 | -1.1825e+00 | -2.031740e-02 | -4.374000e-04 | 1.986030e-02 | 1.259280e+00 | ▁▁▇▁▁ |
numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | 2.233890e-02 | 2.710410e-02 | 9.7639e-03 | 1.102320e-02 | 1.375100e-02 | 2.206100e-02 | 8.461290e-01 | ▇▁▁▁▁ |
numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | 3.270993e-01 | 3.152972e-01 | 0.0000e+00 | 2.360750e-02 | 2.363845e-01 | 5.857887e-01 | 9.999970e-01 | ▇▂▂▂▂ |
numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | 3.270993e-01 | 3.152972e-01 | 0.0000e+00 | 2.360760e-02 | 2.363839e-01 | 5.857881e-01 | 9.999970e-01 | ▇▂▂▂▂ |
numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | 3.115968e-01 | 2.834389e-01 | 1.4900e-05 | 7.054000e-02 | 2.206000e-01 | 4.996000e-01 | 9.999850e-01 | ▇▃▂▂▂ |
numeric | AF_reference | 848 | 0.9932297 | NA | NA | NA | NA | NA | 2.856280e-01 | 2.579406e-01 | 1.9970e-04 | 7.248400e-02 | 2.046730e-01 | 4.452880e-01 | 1.000000e+00 | ▇▃▂▂▁ |
numeric | N | 0 | 1.0000000 | NA | NA | NA | NA | NA | 6.564200e+04 | 0.000000e+00 | 6.5642e+04 | 6.564200e+04 | 6.564200e+04 | 6.564200e+04 | 6.564200e+04 | ▁▁▇▁▁ |
CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
---|---|---|---|---|---|---|---|---|---|---|---|
1 | 1118275 | rs61733845 | C | T | -0.0050020 | 0.0255474 | 0.8447716 | 0.8447719 | 0.04416 | 0.185703 | 65642 |
1 | 1120431 | rs1320571 | G | A | -0.0072789 | 0.0251118 | 0.7719248 | 0.7719241 | 0.04653 | 0.185304 | 65642 |
1 | 1135242 | rs9729550 | A | C | 0.0474646 | 0.0117498 | 0.0000535 | 0.0000535 | 0.25870 | 0.551917 | 65642 |
1 | 1140435 | rs1815606 | G | T | 0.0417639 | 0.0113960 | 0.0002475 | 0.0002475 | 0.31790 | 0.712061 | 65642 |
1 | 1163804 | rs7515488 | C | T | 0.0874308 | 0.0138634 | 0.0000000 | 0.0000000 | 0.14860 | 0.186901 | 65642 |
1 | 1165310 | rs11260562 | G | A | 0.0914881 | 0.0210530 | 0.0000139 | 0.0000139 | 0.05560 | 0.101837 | 65642 |
1 | 1173611 | rs6697886 | G | A | 0.0912882 | 0.0143832 | 0.0000000 | 0.0000000 | 0.13730 | 0.220647 | 65642 |
1 | 1186502 | rs6603785 | A | T | 0.0943327 | 0.0152899 | 0.0000000 | 0.0000000 | 0.11950 | 0.246206 | 65642 |
1 | 1194804 | rs11804831 | T | C | 0.0767614 | 0.0129065 | 0.0000000 | 0.0000000 | 0.18560 | 0.685903 | 65642 |
1 | 1218086 | rs6603788 | C | T | 0.0511661 | 0.0186037 | 0.0059537 | 0.0059537 | 0.07697 | 0.469449 | 65642 |
CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
---|---|---|---|---|---|---|---|---|---|---|---|
22 | 50966914 | rs470119 | T | C | -0.0521197 | 0.0101490 | 0.0000003 | 0.0000003 | 0.609800 | 0.5547120 | 65642 |
22 | 50971752 | rs131794 | A | C | -0.0577899 | 0.0121158 | 0.0000018 | 0.0000018 | 0.791900 | 0.8326680 | 65642 |
22 | 50988193 | rs131779 | A | G | -0.0261271 | 0.0109060 | 0.0165901 | 0.0165903 | 0.657900 | 0.5802720 | 65642 |
22 | 50999182 | rs140518 | C | T | -0.0316416 | 0.0109425 | 0.0038327 | 0.0038326 | 0.696600 | 0.7655750 | 65642 |
22 | 51078251 | rs4040041 | C | T | -0.0017874 | 0.0104564 | 0.8642739 | 0.8642737 | 0.373100 | 0.4666530 | 65642 |
22 | 51094926 | rs9616810 | C | T | 0.0162246 | 0.0121480 | 0.1816896 | 0.1816873 | 0.218600 | 0.2224440 | 65642 |
22 | 51105556 | rs9616812 | C | T | -0.0077821 | 0.0102872 | 0.4493616 | 0.4493592 | 0.483200 | 0.3628190 | 65642 |
22 | 51109992 | rs9628185 | T | C | -0.0075342 | 0.0103902 | 0.4683721 | 0.4683738 | 0.484300 | 0.4053510 | 65642 |
22 | 51134186 | rs8135777 | A | G | -0.3165570 | 0.2543200 | 0.2132348 | 0.2132350 | 0.000446 | 0.0273562 | 65642 |
22 | 51156666 | rs9628187 | C | T | -0.0037510 | 0.0130736 | 0.7741765 | 0.7741758 | 0.203200 | 0.1299920 | 65642 |
1 1118275 rs61733845 C T . PASS AF=0.04416 ES:SE:LP:AF:SS:ID -0.00500201:0.0255474:0.0732607:0.04416:65642:rs61733845
1 1120431 rs1320571 G A . PASS AF=0.04653 ES:SE:LP:AF:SS:ID -0.00727888:0.0251118:0.112425:0.04653:65642:rs1320571
1 1135242 rs9729550 A C . PASS AF=0.2587 ES:SE:LP:AF:SS:ID 0.0474646:0.0117498:4.27133:0.2587:65642:rs9729550
1 1140435 rs1815606 G T . PASS AF=0.3179 ES:SE:LP:AF:SS:ID 0.0417639:0.011396:3.60637:0.3179:65642:rs1815606
1 1163804 rs7515488 C T . PASS AF=0.1486 ES:SE:LP:AF:SS:ID 0.0874308:0.0138634:9.54471:0.1486:65642:rs7515488
1 1165310 rs11260562 G A . PASS AF=0.0556 ES:SE:LP:AF:SS:ID 0.0914881:0.021053:4.85734:0.0556:65642:rs11260562
1 1173611 rs6697886 G A . PASS AF=0.1373 ES:SE:LP:AF:SS:ID 0.0912882:0.0143832:9.65802:0.1373:65642:rs6697886
1 1186502 rs6603785 A T . PASS AF=0.1195 ES:SE:LP:AF:SS:ID 0.0943327:0.0152899:9.16453:0.1195:65642:rs6603785
1 1194804 rs11804831 T C . PASS AF=0.1856 ES:SE:LP:AF:SS:ID 0.0767614:0.0129065:8.56496:0.1856:65642:rs11804831
1 1218086 rs6603788 C T . PASS AF=0.07697 ES:SE:LP:AF:SS:ID 0.0511661:0.0186037:2.22521:0.07697:65642:rs6603788
1 1227897 rs3737721 A G . PASS AF=0.002436 ES:SE:LP:AF:SS:ID 0.0629665:0.0734371:0.407588:0.002436:65642:rs3737721
1 1231656 rs1749951 G A . PASS AF=0.04028 ES:SE:LP:AF:SS:ID 0.0371784:0.0264753:0.795229:0.04028:65642:rs1749951
1 1233941 rs1739855 T C . PASS AF=0.07333 ES:SE:LP:AF:SS:ID 0.0483781:0.0190326:1.95756:0.07333:65642:rs1739855
1 1241529 rs1536168 A G . PASS AF=0.95394 ES:SE:LP:AF:SS:ID -0.0405752:0.024294:1.0228:0.95394:65642:rs1536168
1 1242468 rs2274264 G A . PASS AF=0.002467 ES:SE:LP:AF:SS:ID 0.0542622:0.0782352:0.311628:0.002467:65642:rs2274264
1 1247494 rs12103 T C . PASS AF=0.8166 ES:SE:LP:AF:SS:ID -0.0867354:0.0130755:10.4841:0.8166:65642:rs12103
1 1249187 rs12142199 G A . PASS AF=0.8026 ES:SE:LP:AF:SS:ID -0.0789884:0.0128081:9.15753:0.8026:65642:rs12142199
1 1254255 rs62623580 G A . PASS AF=0.007606 ES:SE:LP:AF:SS:ID 0.0695751:0.0562643:0.665054:0.007606:65642:rs62623580
1 1335790 rs1240708 A G . PASS AF=0.1737 ES:SE:LP:AF:SS:ID 0.0814097:0.0132196:9.13345:0.1737:65642:rs1240708
1 1493727 rs880051 G A . PASS AF=0.232 ES:SE:LP:AF:SS:ID 0.0571914:0.0117629:5.93482:0.232:65642:rs880051
1 1497824 rs2296716 C T . PASS AF=0.1208 ES:SE:LP:AF:SS:ID 0.0378595:0.0155007:1.836:0.1208:65642:rs2296716
1 1611995 rs4074196 A G . PASS AF=0.4414 ES:SE:LP:AF:SS:ID -0.00916736:0.0116551:0.364977:0.4414:65642:rs4074196
1 1706160 rs7531583 A G . PASS AF=0.77 ES:SE:LP:AF:SS:ID -0.0357758:0.011644:2.67306:0.77:65642:rs7531583
1 1721479 rs2272908 C T . PASS AF=0.4974 ES:SE:LP:AF:SS:ID -0.0506909:0.00995343:6.45246:0.4974:65642:rs2272908
1 1723031 rs9660180 G A . PASS AF=0.4974 ES:SE:LP:AF:SS:ID -0.0516131:0.00997657:6.63858:0.4974:65642:rs9660180
1 1781220 rs6681938 T C . PASS AF=0.2996 ES:SE:LP:AF:SS:ID -0.0249012:0.011141:1.59499:0.2996:65642:rs6681938
1 1838516 rs2377037 C A . PASS AF=0.2747 ES:SE:LP:AF:SS:ID 0.0362376:0.0113843:2.83656:0.2747:65642:rs2377037
1 1840038 rs2474461 T C . PASS AF=0.95331 ES:SE:LP:AF:SS:ID -0.0692045:0.0237532:2.44681:0.95331:65642:rs2474461
1 1853288 rs1884454 G T . PASS AF=0.2694 ES:SE:LP:AF:SS:ID 0.0342632:0.0114288:2.56576:0.2694:65642:rs1884454
1 1855319 rs2295362 C T . PASS AF=0.0452 ES:SE:LP:AF:SS:ID 0.0452788:0.0244794:1.19138:0.0452:65642:rs2295362
1 1871337 rs16824543 G A . PASS AF=0.04241 ES:SE:LP:AF:SS:ID 0.0357672:0.0253662:0.79989:0.04241:65642:rs16824543
1 1873625 rs12758705 G A . PASS AF=0.2651 ES:SE:LP:AF:SS:ID 0.0340289:0.0115991:2.47511:0.2651:65642:rs12758705
1 1881070 rs4648596 G A . PASS AF=0.04073 ES:SE:LP:AF:SS:ID 0.0456525:0.0266091:1.06438:0.04073:65642:rs4648596
1 1888193 rs3820011 C A . PASS AF=0.2698 ES:SE:LP:AF:SS:ID 0.0329573:0.0116991:2.31458:0.2698:65642:rs3820011
1 2024064 rs2459994 C T . PASS AF=0.1824 ES:SE:LP:AF:SS:ID 0.0284659:0.0135275:1.45157:0.1824:65642:rs2459994
1 2146966 rs7512482 T C . PASS AF=0.1674 ES:SE:LP:AF:SS:ID -0.0246049:0.0143806:1.06006:0.1674:65642:rs7512482
1 2202774 rs6673129 C T . PASS AF=0.1651 ES:SE:LP:AF:SS:ID 0.0166108:0.0144719:0.600235:0.1651:65642:rs6673129
1 2229478 rs12562937 C T . PASS AF=0.1998 ES:SE:LP:AF:SS:ID 0.0220428:0.0132813:1.01332:0.1998:65642:rs12562937
1 2283896 rs2840528 A G . PASS AF=0.4158 ES:SE:LP:AF:SS:ID 0.000250701:0.0104941:0.0083573:0.4158:65642:rs2840528
1 2290143 rs34587196 G A . PASS AF=0.01202 ES:SE:LP:AF:SS:ID 0.060328:0.0475885:0.68845:0.01202:65642:rs34587196
1 2404256 rs2494626 C T . PASS AF=0.2884 ES:SE:LP:AF:SS:ID -0.00409066:0.0123399:0.130611:0.2884:65642:rs2494626
1 2407781 rs78504402 C T . PASS AF=0.06862 ES:SE:LP:AF:SS:ID 0.00566139:0.0213685:0.101793:0.06862:65642:rs78504402
1 2408471 rs115996655 G A . PASS AF=0.007814 ES:SE:LP:AF:SS:ID -0.037422:0.0554724:0.301094:0.007814:65642:rs115996655
1 2408834 rs11588930 G A . PASS AF=0.101 ES:SE:LP:AF:SS:ID 0.0134586:0.018543:0.329793:0.101:65642:rs11588930
1 2409892 rs12727342 A G . PASS AF=0.6229 ES:SE:LP:AF:SS:ID 0.0200619:0.0110419:1.15969:0.6229:65642:rs12727342
1 2410789 rs11799501 C T . PASS AF=0.5834 ES:SE:LP:AF:SS:ID 0.0320413:0.0109856:2.45125:0.5834:65642:rs11799501
1 2412279 rs4648638 A T . PASS AF=0.388 ES:SE:LP:AF:SS:ID -0.0243631:0.0110051:1.57118:0.388:65642:rs4648638
1 2412293 rs12731186 C T . PASS AF=0.1211 ES:SE:LP:AF:SS:ID 0.0168713:0.0157379:0.547123:0.1211:65642:rs12731186
1 2413166 rs115810747 A G . PASS AF=0.04094 ES:SE:LP:AF:SS:ID 0.0408929:0.0284886:0.820537:0.04094:65642:rs115810747
1 2413464 rs114424485 G C . PASS AF=0.008958 ES:SE:LP:AF:SS:ID 0.00992199:0.0545778:0.0676565:0.008958:65642:rs114424485