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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/ukb-d-import/processed/ukb-d-22617_9219/ukb-d-22617_9219.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/ukb-d-import/processed/ukb-d-22617_9219/ldsc.txt \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/
Beginning analysis at Mon Nov 25 14:56:32 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-22617_9219/ukb-d-22617_9219.vcf.gz ...
Read summary statistics for 11336023 SNPs.
Dropped 9547 SNPs with duplicated rs numbers.
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, 1282422 SNPs remain.
After merging with regression SNP LD, 1282422 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0113 (0.0048)
Lambda GC: 1.0204
Mean Chi^2: 1.0181
Intercept: 0.9984 (0.0057)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Mon Nov 25 14:59:07 2019
Total time elapsed: 2.0m:34.3s
{
"af_correlation": 0.9471,
"inflation_factor": 1.0133,
"mean_EFFECT": 1.7838e-06,
"n": 91149,
"n_snps": 11336023,
"n_clumped_hits": 0,
"n_p_sig": 3,
"n_mono": 0,
"n_ns": 1154242,
"n_mac": 0,
"is_snpid_unique": false,
"n_miss_EFFECT": 0,
"n_miss_SE": 0,
"n_miss_PVAL": 0,
"n_miss_AF": 0,
"n_miss_AF_reference": 234597,
"n_est": 91318.7947,
"ratio_se_n": 1.0009,
"mean_diff": 7.4034e-06,
"ratio_diff": 24.8292,
"sd_y_est1": 0.1879,
"sd_y_est2": 0.1881,
"r2_sum1": 0,
"r2_sum2": 0,
"r2_sum3": 0,
"r2_sum4": 0,
"ldsc_nsnp_merge_refpanel_ld": 1282422,
"ldsc_nsnp_merge_regression_ld": 1282422,
"ldsc_observed_scale_h2_beta": 0.0113,
"ldsc_observed_scale_h2_se": 0.0048,
"ldsc_intercept_beta": 0.9984,
"ldsc_intercept_se": 0.0057,
"ldsc_lambda_gc": 1.0204,
"ldsc_mean_chisq": 1.0181,
"ldsc_ratio": -0.0884
}
name | value |
---|---|
af_correlation | FALSE |
inflation_factor | FALSE |
n | FALSE |
is_snpid_non_unique | TRUE |
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 |
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 | 3 | 94 | 0 | 11327073 | 0 | NA | NA | NA | NA | NA | NA | NA | NA |
character | REF | 0 | 1.0000000 | 1 | 100 | 0 | 51903 | 0 | NA | NA | NA | NA | NA | NA | NA | NA |
character | ALT | 0 | 1.0000000 | 1 | 342 | 0 | 31588 | 0 | NA | NA | NA | NA | NA | NA | NA | NA |
numeric | CHROM | 0 | 1.0000000 | NA | NA | NA | NA | NA | 9.073595e+00 | 6.182354e+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 | 7.883449e+07 | 5.600588e+07 | 3.02000e+02 | 3.262235e+07 | 6.983177e+07 | 1.147310e+08 | 2.492309e+08 | ▇▆▅▂▁ |
numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | 1.800000e-06 | 2.934800e-03 | -2.93639e-02 | -1.116800e-03 | -1.310000e-05 | 1.094000e-03 | 3.744410e-02 | ▁▁▇▁▁ |
numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | 2.331400e-03 | 1.770300e-03 | 7.27200e-04 | 9.966000e-04 | 1.495800e-03 | 3.154200e-03 | 1.056870e-02 | ▇▂▁▁▁ |
numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | 4.982239e-01 | 2.891619e-01 | 0.00000e+00 | 2.469813e-01 | 4.971724e-01 | 7.489279e-01 | 1.000000e+00 | ▇▇▇▇▇ |
numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | 4.982221e-01 | 2.891629e-01 | 0.00000e+00 | 2.469781e-01 | 4.971701e-01 | 7.489269e-01 | 9.999999e-01 | ▇▇▇▇▇ |
numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | 2.265530e-01 | 2.610869e-01 | 3.74250e-03 | 2.085140e-02 | 1.093060e-01 | 3.622280e-01 | 9.962570e-01 | ▇▂▁▁▁ |
numeric | AF_reference | 234597 | 0.9793052 | NA | NA | NA | NA | NA | 2.270684e-01 | 2.522842e-01 | 0.00000e+00 | 1.857030e-02 | 1.275960e-01 | 3.616210e-01 | 1.000000e+00 | ▇▂▂▁▁ |
numeric | N | 0 | 1.0000000 | NA | NA | NA | NA | NA | 9.114900e+04 | 0.000000e+00 | 9.11490e+04 | 9.114900e+04 | 9.114900e+04 | 9.114900e+04 | 9.114900e+04 | ▁▁▇▁▁ |
CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
---|---|---|---|---|---|---|---|---|---|---|---|
1 | 692794 | rs530212009 | CA | C | -0.0004673 | 0.0015382 | 0.7612723 | 0.7612719 | 0.1114870 | 0.1894970 | 91149 |
1 | 693731 | rs12238997 | A | G | 0.0004170 | 0.0014568 | 0.7746812 | 0.7746812 | 0.1164590 | 0.1417730 | 91149 |
1 | 707522 | rs371890604 | G | C | 0.0002572 | 0.0016372 | 0.8751510 | 0.8751512 | 0.0978765 | 0.1293930 | 91149 |
1 | 717587 | rs144155419 | G | A | -0.0004283 | 0.0039026 | 0.9126200 | 0.9126197 | 0.0157837 | 0.0045926 | 91149 |
1 | 730087 | rs148120343 | T | C | 0.0008030 | 0.0020193 | 0.6908917 | 0.6908910 | 0.0569973 | 0.0127796 | 91149 |
1 | 731718 | rs142557973 | T | C | 0.0000770 | 0.0013824 | 0.9555970 | 0.9555972 | 0.1223590 | 0.1543530 | 91149 |
1 | 732032 | rs61770163 | A | C | 0.0000187 | 0.0014740 | 0.9898510 | 0.9898510 | 0.1220470 | 0.1555510 | 91149 |
1 | 734349 | rs141242758 | T | C | 0.0000589 | 0.0013835 | 0.9660411 | 0.9660411 | 0.1215880 | 0.1525560 | 91149 |
1 | 740284 | rs61770167 | C | T | -0.0083695 | 0.0063525 | 0.1876662 | 0.1876624 | 0.0057444 | 0.0023962 | 91149 |
1 | 749963 | rs529266287 | T | TAA | 0.0000972 | 0.0013609 | 0.9430641 | 0.9430638 | 0.8689170 | 0.7641770 | 91149 |
CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
---|---|---|---|---|---|---|---|---|---|---|---|
23 | 154923311 | rs141127553 | C | T | 0.0019006 | 0.0016183 | 0.2402238 | 0.2402224 | 0.0552869 | 0.0309934 | 91149 |
23 | 154923374 | rs111332691 | T | A | -0.0000176 | 0.0017612 | 0.9920450 | 0.9920453 | 0.0448387 | 0.0116556 | 91149 |
23 | 154925045 | rs509981 | C | T | 0.0013015 | 0.0008487 | 0.1251549 | 0.1251524 | 0.2444670 | 0.3634440 | 91149 |
23 | 154925895 | rs538470 | C | T | 0.0016400 | 0.0008677 | 0.0587435 | 0.0587413 | 0.2409490 | 0.3634440 | 91149 |
23 | 154927581 | rs644138 | G | A | 0.0016542 | 0.0007983 | 0.0382437 | 0.0382411 | 0.3001640 | 0.4635760 | 91149 |
23 | 154929412 | rs557132 | C | T | 0.0013357 | 0.0008490 | 0.1156520 | 0.1156476 | 0.2443160 | 0.3568210 | 91149 |
23 | 154929637 | rs35185538 | CT | C | 0.0012236 | 0.0008865 | 0.1674931 | 0.1674900 | 0.2286640 | 0.3011920 | 91149 |
23 | 154929952 | rs4012982 | CAA | C | 0.0013072 | 0.0008935 | 0.1434891 | 0.1434846 | 0.2382530 | 0.3165560 | 91149 |
23 | 154930230 | rs781880 | A | G | 0.0014374 | 0.0008487 | 0.0903441 | 0.0903417 | 0.2447660 | 0.3618540 | 91149 |
23 | 154930487 | rs781879 | T | A | 0.0003561 | 0.0028784 | 0.9015311 | 0.9015310 | 0.0196632 | 0.1263580 | 91149 |
1 692794 rs530212009 CA C . PASS AF=0.111487 ES:SE:LP:AF:SS:ID -0.000467322:0.0015382:0.11846:0.111487:91149:1_692794_CA_C
1 693731 rs12238997 A G . PASS AF=0.116459 ES:SE:LP:AF:SS:ID 0.000417028:0.00145683:0.110877:0.116459:91149:rs12238997
1 707522 rs371890604 G C . PASS AF=0.0978765 ES:SE:LP:AF:SS:ID 0.000257227:0.00163715:0.057917:0.0978765:91149:rs371890604
1 717587 rs144155419 G A . PASS AF=0.0157837 ES:SE:LP:AF:SS:ID -0.000428252:0.00390261:0.03971:0.0157837:91149:rs144155419
1 730087 rs148120343 T C . PASS AF=0.0569973 ES:SE:LP:AF:SS:ID 0.000802955:0.00201927:0.16059:0.0569973:91149:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122359 ES:SE:LP:AF:SS:ID 7.6974e-05:0.00138245:0.0197252:0.122359:91149:rs58276399
1 732032 rs61770163 A C . PASS AF=0.122047 ES:SE:LP:AF:SS:ID 1.87497e-05:0.00147401:0.00443017:0.122047:91149:rs61770163
1 734349 rs141242758 T C . PASS AF=0.121588 ES:SE:LP:AF:SS:ID 5.89007e-05:0.00138349:0.0150044:0.121588:91149:rs141242758
1 740284 rs61770167 C T . PASS AF=0.00574438 ES:SE:LP:AF:SS:ID -0.00836953:0.00635246:0.726614:0.00574438:91149:rs61770167
1 749963 rs529266287 T TAA . PASS AF=0.868917 ES:SE:LP:AF:SS:ID 9.7195e-05:0.0013609:0.0254588:0.868917:91149:rs529266287
1 751343 rs28544273 T A . PASS AF=0.123699 ES:SE:LP:AF:SS:ID -0.000223436:0.00134923:0.0612452:0.123699:91149:rs28544273
1 751488 rs200141114 G GA . PASS AF=0.143202 ES:SE:LP:AF:SS:ID -0.000739935:0.00133574:0.236861:0.143202:91149:rs200141114
1 751756 rs28527770 T C . PASS AF=0.123834 ES:SE:LP:AF:SS:ID -0.000238859:0.00134727:0.0658648:0.123834:91149:rs28527770
1 753405 rs3115860 C A . PASS AF=0.869892 ES:SE:LP:AF:SS:ID 9.79325e-06:0.00131279:0.00259264:0.869892:91149:rs3115860
1 753425 rs3131970 T C . PASS AF=0.874553 ES:SE:LP:AF:SS:ID -0.000151465:0.00133408:0.0411467:0.874553:91149:rs3131970
1 753541 rs2073813 G A . PASS AF=0.129529 ES:SE:LP:AF:SS:ID -0.00037775:0.00131691:0.111129:0.129529:91149:rs2073813
1 754105 rs12184325 C T . PASS AF=0.0358205 ES:SE:LP:AF:SS:ID -0.000996024:0.00240585:0.16821:0.0358205:91149:rs12184325
1 754182 rs3131969 A G . PASS AF=0.869539 ES:SE:LP:AF:SS:ID 6.93637e-05:0.00131169:0.0187129:0.869539:91149:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869616 ES:SE:LP:AF:SS:ID 5.20944e-05:0.00131226:0.0139748:0.869616:91149:rs3131968
1 754334 rs3131967 T C . PASS AF=0.869535 ES:SE:LP:AF:SS:ID 7.20283e-05:0.00131164:0.0194485:0.869535:91149:rs3131967
1 754433 rs150578204 G A . PASS AF=0.00531588 ES:SE:LP:AF:SS:ID -0.00896676:0.0065522:0.766608:0.00531588:91149:rs150578204
1 754458 rs142682604 G T . PASS AF=0.00528125 ES:SE:LP:AF:SS:ID -0.0088712:0.00656849:0.752429:0.00528125:91149:rs142682604
1 755435 rs184270342 T G . PASS AF=0.0056599 ES:SE:LP:AF:SS:ID -0.0100115:0.00645716:0.917075:0.0056599:91149:rs184270342
1 755890 rs3115858 A T . PASS AF=0.86952 ES:SE:LP:AF:SS:ID 7.49022e-06:0.00130894:0.00198753:0.86952:91149:rs3115858
1 756434 rs61768170 G C . PASS AF=0.126911 ES:SE:LP:AF:SS:ID -0.000302945:0.00133619:0.0858473:0.126911:91149:rs61768170
1 756604 rs3131962 A G . PASS AF=0.86905 ES:SE:LP:AF:SS:ID 2.37775e-05:0.00130555:0.00635703:0.86905:91149:rs3131962
1 757640 rs3115853 G A . PASS AF=0.868436 ES:SE:LP:AF:SS:ID 8.30767e-05:0.00130472:0.0226285:0.868436:91149:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869199 ES:SE:LP:AF:SS:ID 4.54559e-05:0.00130673:0.012222:0.869199:91149:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869205 ES:SE:LP:AF:SS:ID 4.55765e-05:0.00130682:0.0122538:0.869205:91149:rs4951862
1 758144 rs3131956 A G . PASS AF=0.869207 ES:SE:LP:AF:SS:ID 4.48834e-05:0.00130683:0.0120648:0.869207:91149:rs3131956
1 758626 rs3131954 C T . PASS AF=0.869687 ES:SE:LP:AF:SS:ID 9.16353e-05:0.00131032:0.0249144:0.869687:91149:rs3131954
1 759293 rs10157329 T A . PASS AF=0.0991787 ES:SE:LP:AF:SS:ID -6.4451e-05:0.0015249:0.0148939:0.0991787:91149:rs10157329
1 759600 rs545998451 AGT A . PASS AF=0.00649624 ES:SE:LP:AF:SS:ID -0.0075465:0.00588287:0.699909:0.00649624:91149:1_759600_AGT_A
1 759837 rs3115851 T A . PASS AF=0.873682 ES:SE:LP:AF:SS:ID -0.000114721:0.00132973:0.0309342:0.873682:91149:rs3115851
1 761732 rs2286139 C T . PASS AF=0.863058 ES:SE:LP:AF:SS:ID 0.000458451:0.00130436:0.139523:0.863058:91149:rs2286139
1 761752 rs1057213 C T . PASS AF=0.868472 ES:SE:LP:AF:SS:ID 7.70733e-06:0.0013173:0.00203203:0.868472:91149:rs1057213
1 762273 rs3115849 G A . PASS AF=0.865369 ES:SE:LP:AF:SS:ID -6.32254e-06:0.00131755:0.00166611:0.865369:91149:rs3115849
1 762485 rs12095200 C A . PASS AF=0.0995228 ES:SE:LP:AF:SS:ID -0.000884026:0.00157685:0.240292:0.0995228:91149:rs12095200
1 762589 rs3115848 G C . PASS AF=0.870657 ES:SE:LP:AF:SS:ID -8.10473e-05:0.00133256:0.0215903:0.870657:91149:rs3115848
1 762592 rs3131950 C G . PASS AF=0.870657 ES:SE:LP:AF:SS:ID -8.10128e-05:0.00133256:0.0215807:0.870657:91149:rs3131950
1 762601 rs3131949 T C . PASS AF=0.870654 ES:SE:LP:AF:SS:ID -8.16422e-05:0.00133256:0.0217524:0.870654:91149:rs3131949
1 762632 rs3131948 T A . PASS AF=0.871019 ES:SE:LP:AF:SS:ID -6.53947e-05:0.00133318:0.0173317:0.871019:91149:rs3131948
1 764191 rs7515915 T G . PASS AF=0.126515 ES:SE:LP:AF:SS:ID -0.000236231:0.00133466:0.0657486:0.126515:91149:rs7515915
1 766007 rs61768174 A C . PASS AF=0.106037 ES:SE:LP:AF:SS:ID -0.00045606:0.00145079:0.123059:0.106037:91149:rs61768174
1 766105 rs2519015 T A . PASS AF=0.85438 ES:SE:LP:AF:SS:ID -0.000156263:0.00130386:0.043541:0.85438:91149:rs2519015
1 768116 rs376645387 A AGTTTT . PASS AF=0.839153 ES:SE:LP:AF:SS:ID -0.000516515:0.00128827:0.162117:0.839153:91149:rs376645387
1 768253 rs2977608 A C . PASS AF=0.763195 ES:SE:LP:AF:SS:ID 5.12855e-05:0.00103584:0.0174972:0.763195:91149:rs2977608
1 768448 rs12562034 G A . PASS AF=0.105092 ES:SE:LP:AF:SS:ID 0.000373336:0.00142739:0.100361:0.105092:91149:rs12562034
1 768819 rs12562811 C T . PASS AF=0.00772317 ES:SE:LP:AF:SS:ID -0.000788423:0.00528898:0.0547782:0.00772317:91149:rs12562811
1 769138 rs59306077 CAT C . PASS AF=0.129904 ES:SE:LP:AF:SS:ID -0.000490218:0.00131602:0.149035:0.129904:91149:rs762168062