Summary
Summary {data-width=650}
Manhattan plot
manhattan_plot
QQ plot
qq_plot
AF plot
af_plot
P-Z plot
pz_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/ukb-d-import/processed/ukb-d-1747_6/ukb-d-1747_6.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-1747_6/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 16:55:35 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-1747_6/ukb-d-1747_6.vcf.gz ...
Read summary statistics for 11847797 SNPs.
Dropped 10241 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, 1282914 SNPs remain.
After merging with regression SNP LD, 1282914 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0041 (0.0016)
Lambda GC: 1.0279
Mean Chi^2: 1.0373
Intercept: 1.0094 (0.0057)
Ratio: 0.2507 (0.1535)
Analysis finished at Mon Nov 25 16:57:25 2019
Total time elapsed: 1.0m:49.88sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9486,
"inflation_factor": 1.0236,
"mean_EFFECT": 2.2605e-07,
"n": 360270,
"n_snps": 11847797,
"n_clumped_hits": 3,
"n_p_sig": 1077,
"n_mono": 0,
"n_ns": 1177682,
"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": 269402,
"n_est": 360950.9236,
"ratio_se_n": 1.0009,
"mean_diff": 2.0198e-06,
"ratio_diff": 9.4926,
"sd_y_est1": 0.1111,
"sd_y_est2": 0.1112,
"r2_sum1": 0,
"r2_sum2": 0.0013,
"r2_sum3": 0.0013,
"r2_sum4": 0.0013,
"ldsc_nsnp_merge_refpanel_ld": 1282914,
"ldsc_nsnp_merge_regression_ld": 1282914,
"ldsc_observed_scale_h2_beta": 0.0041,
"ldsc_observed_scale_h2_se": 0.0016,
"ldsc_intercept_beta": 1.0094,
"ldsc_intercept_se": 0.0057,
"ldsc_lambda_gc": 1.0279,
"ldsc_mean_chisq": 1.0373,
"ldsc_ratio": 0.252
}
Flags
| 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 | 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 betweenAFandAF_reference.inflation_factor(lambda): Genomic inflation factor.mean_EFFECT: Mean ofEFFECTsize.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 below5e-8.n_mono: Number of monomorphic (MAF == 1orMAF == 0) SNPs.n_ns: Number of SNPs with nonsense values:- alleles other than
A, C, G or T. - P-values
< 0or> 1. - negative or infinite standard errors (
<= 0or= Infinity). - infinite beta estimates or allele frequencies
< 0or> 1.
- alleles other than
n_mac: Number of cases whereMAC(\(2 \times N \times MAF\)) is less than6.is_snpid_unique:trueif the combination ofIDREFALTis unique and therefore no duplication in snpid.n_miss_<*>: Number ofNAobservations 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 expectratio_se_nto 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 ofdiffand the mean ofdiff2(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<*>:r2statistics under various assumptions1: \(r^2 = \sum_j{\frac{2 \times \beta_j^2 \times {MAF}_j \times (1 - {MAF}_j)}{\texttt{var1}}}\), \(\texttt{var1} = 1\).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: NOTis_snpid_unique.mean_EFFECT_nonfinite:mean(EFFECT)isNA,NaN, orInf.mean_EFFECT_05:abs(mean(EFFECT))> 0.5.mean_EFFECT_01:abs(mean(EFFECT))> 0.1.mean_chisq:ldsc_mean_chisq> 1.3 orldsc_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<*>> 0.5
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 | 94 | 0 | 11838174 | 0 | NA | NA | NA | NA | NA | NA | NA | NA |
| character | REF | 0 | 1.0000000 | 1 | 100 | 0 | 53043 | 0 | NA | NA | NA | NA | NA | NA | NA | NA |
| character | ALT | 0 | 1.0000000 | 1 | 342 | 0 | 32028 | 0 | NA | NA | NA | NA | NA | NA | NA | NA |
| numeric | CHROM | 0 | 1.0000000 | NA | NA | NA | NA | NA | 9.070554e+00 | 6.184215e+00 | 1.0000e+00 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.300000e+01 | ▇▅▅▂▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | 7.887925e+07 | 5.598227e+07 | 3.0200e+02 | 3.268211e+07 | 6.990278e+07 | 1.147444e+08 | 2.492309e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | 2.000000e-07 | 9.959000e-04 | -9.8575e-03 | -3.509000e-04 | -2.200000e-06 | 3.432000e-04 | 1.502690e-02 | ▁▇▆▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | 7.674000e-04 | 6.228000e-04 | 2.1530e-04 | 2.998000e-04 | 4.696000e-04 | 1.053700e-03 | 3.678300e-03 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | 4.964295e-01 | 2.897119e-01 | 0.0000e+00 | 2.445907e-01 | 4.949793e-01 | 7.472553e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | 4.964291e-01 | 2.897122e-01 | 0.0000e+00 | 2.445902e-01 | 4.949784e-01 | 7.472546e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | 2.173058e-01 | 2.598209e-01 | 2.7896e-03 | 1.647150e-02 | 9.617550e-02 | 3.461210e-01 | 9.972100e-01 | ▇▂▁▁▁ |
| numeric | AF_reference | 269402 | 0.9772614 | NA | NA | NA | NA | NA | 2.184304e-01 | 2.512364e-01 | 0.0000e+00 | 1.317890e-02 | 1.154150e-01 | 3.474440e-01 | 1.000000e+00 | ▇▂▁▁▁ |
| numeric | N | 0 | 1.0000000 | NA | NA | NA | NA | NA | 3.602700e+05 | 0.000000e+00 | 3.6027e+05 | 3.602700e+05 | 3.602700e+05 | 3.602700e+05 | 3.602700e+05 | ▁▁▇▁▁ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 692794 | rs530212009 | CA | C | 0.0004849 | 0.0004586 | 0.2903033 | 0.2903023 | 0.1106500 | 0.1894970 | 360270 |
| 1 | 693731 | rs12238997 | A | G | 0.0004477 | 0.0004333 | 0.3014512 | 0.3014502 | 0.1158380 | 0.1417730 | 360270 |
| 1 | 707522 | rs371890604 | G | C | 0.0004680 | 0.0004872 | 0.3367480 | 0.3367477 | 0.0972910 | 0.1293930 | 360270 |
| 1 | 717587 | rs144155419 | G | A | -0.0007755 | 0.0011619 | 0.5044998 | 0.5044998 | 0.0157019 | 0.0045926 | 360270 |
| 1 | 730087 | rs148120343 | T | C | 0.0015537 | 0.0006037 | 0.0100675 | 0.0100670 | 0.0564472 | 0.0127796 | 360270 |
| 1 | 731718 | rs142557973 | T | C | 0.0004724 | 0.0004109 | 0.2502718 | 0.2502714 | 0.1217410 | 0.1543530 | 360270 |
| 1 | 732032 | rs61770163 | A | C | 0.0004512 | 0.0004384 | 0.3032892 | 0.3032870 | 0.1211730 | 0.1555510 | 360270 |
| 1 | 734349 | rs141242758 | T | C | 0.0004815 | 0.0004112 | 0.2416078 | 0.2416071 | 0.1209690 | 0.1525560 | 360270 |
| 1 | 740284 | rs61770167 | C | T | 0.0002252 | 0.0018802 | 0.9046479 | 0.9046477 | 0.0057907 | 0.0023962 | 360270 |
| 1 | 749963 | rs529266287 | T | TAA | -0.0002295 | 0.0004053 | 0.5712984 | 0.5712973 | 0.8697370 | 0.7641770 | 360270 |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 23 | 154923311 | rs141127553 | C | T | 0.0002786 | 0.0004750 | 0.5575246 | 0.5575247 | 0.0561460 | 0.0309934 | 360270 |
| 23 | 154923374 | rs111332691 | T | A | -0.0001880 | 0.0005219 | 0.7187320 | 0.7187322 | 0.0447845 | 0.0116556 | 360270 |
| 23 | 154925045 | rs509981 | C | T | -0.0001609 | 0.0002511 | 0.5215885 | 0.5215867 | 0.2456300 | 0.3634440 | 360270 |
| 23 | 154925895 | rs538470 | C | T | -0.0001367 | 0.0002569 | 0.5945043 | 0.5945025 | 0.2419380 | 0.3634440 | 360270 |
| 23 | 154927581 | rs644138 | G | A | -0.0000269 | 0.0002362 | 0.9094261 | 0.9094262 | 0.3021610 | 0.4635760 | 360270 |
| 23 | 154929412 | rs557132 | C | T | -0.0001549 | 0.0002512 | 0.5373076 | 0.5373082 | 0.2454820 | 0.3568210 | 360270 |
| 23 | 154929637 | rs35185538 | CT | C | -0.0000603 | 0.0002621 | 0.8180631 | 0.8180634 | 0.2297100 | 0.3011920 | 360270 |
| 23 | 154929952 | rs4012982 | CAA | C | -0.0001655 | 0.0002642 | 0.5308979 | 0.5308995 | 0.2394580 | 0.3165560 | 360270 |
| 23 | 154930230 | rs781880 | A | G | -0.0001528 | 0.0002511 | 0.5428289 | 0.5428306 | 0.2458930 | 0.3618540 | 360270 |
| 23 | 154930487 | rs781879 | T | A | -0.0008249 | 0.0008611 | 0.3381123 | 0.3381115 | 0.0195732 | 0.1263580 | 360270 |
bcf preview
1 692794 rs530212009 CA C . PASS AF=0.11065 ES:SE:LP:AF:SS:ID 0.000484903:0.000458555:0.537148:0.11065:360270:1_692794_CA_C
1 693731 rs12238997 A G . PASS AF=0.115838 ES:SE:LP:AF:SS:ID 0.000447733:0.000433292:0.520783:0.115838:360270:rs12238997
1 707522 rs371890604 G C . PASS AF=0.097291 ES:SE:LP:AF:SS:ID 0.000467981:0.00048717:0.472695:0.097291:360270:rs371890604
1 717587 rs144155419 G A . PASS AF=0.0157019 ES:SE:LP:AF:SS:ID -0.000775476:0.00116189:0.297139:0.0157019:360270:rs144155419
1 730087 rs148120343 T C . PASS AF=0.0564472 ES:SE:LP:AF:SS:ID 0.00155366:0.00060371:1.99708:0.0564472:360270:rs148120343
1 731718 rs58276399 T C . PASS AF=0.121741 ES:SE:LP:AF:SS:ID 0.000472448:0.000410935:0.601588:0.121741:360270:rs58276399
1 732032 rs61770163 A C . PASS AF=0.121173 ES:SE:LP:AF:SS:ID 0.000451246:0.000438354:0.518143:0.121173:360270:rs61770163
1 734349 rs141242758 T C . PASS AF=0.120969 ES:SE:LP:AF:SS:ID 0.000481454:0.000411155:0.616889:0.120969:360270:rs141242758
1 740284 rs61770167 C T . PASS AF=0.00579066 ES:SE:LP:AF:SS:ID 0.000225231:0.00188018:0.0435204:0.00579066:360270:rs61770167
1 749963 rs529266287 T TAA . PASS AF=0.869737 ES:SE:LP:AF:SS:ID -0.000229476:0.000405333:0.243137:0.869737:360270:rs529266287
1 751343 rs28544273 T A . PASS AF=0.122922 ES:SE:LP:AF:SS:ID 0.000283438:0.000401435:0.318623:0.122922:360270:rs28544273
1 751488 rs200141114 G GA . PASS AF=0.142717 ES:SE:LP:AF:SS:ID 0.00037145:0.000396791:0.456922:0.142717:360270:rs200141114
1 751756 rs28527770 T C . PASS AF=0.123037 ES:SE:LP:AF:SS:ID 0.000281338:0.000400876:0.316233:0.123037:360270:rs28527770
1 753405 rs3115860 C A . PASS AF=0.87088 ES:SE:LP:AF:SS:ID -0.000238082:0.00039064:0.265828:0.87088:360270:rs3115860
1 753425 rs3131970 T C . PASS AF=0.875466 ES:SE:LP:AF:SS:ID -0.000322332:0.000397121:0.379884:0.875466:360270:rs3131970
1 753541 rs2073813 G A . PASS AF=0.128643 ES:SE:LP:AF:SS:ID 0.000260491:0.000391614:0.295901:0.128643:360270:rs2073813
1 754105 rs12184325 C T . PASS AF=0.0363464 ES:SE:LP:AF:SS:ID 0.00044393:0.000710631:0.27395:0.0363464:360270:rs12184325
1 754182 rs3131969 A G . PASS AF=0.870474 ES:SE:LP:AF:SS:ID -0.000236517:0.000390234:0.264038:0.870474:360270:rs3131969
1 754192 rs3131968 A G . PASS AF=0.870579 ES:SE:LP:AF:SS:ID -0.000249884:0.000390413:0.282213:0.870579:360270:rs3131968
1 754334 rs3131967 T C . PASS AF=0.870471 ES:SE:LP:AF:SS:ID -0.000232481:0.00039022:0.258589:0.870471:360270:rs3131967
1 754433 rs150578204 G A . PASS AF=0.00505041 ES:SE:LP:AF:SS:ID 0.000559158:0.00200196:0.107899:0.00505041:360270:rs150578204
1 754458 rs142682604 G T . PASS AF=0.00501736 ES:SE:LP:AF:SS:ID 0.000595728:0.00200731:0.115411:0.00501736:360270:rs142682604
1 755435 rs184270342 T G . PASS AF=0.00558719 ES:SE:LP:AF:SS:ID -0.0029506:0.00194009:0.891787:0.00558719:360270:rs184270342
1 755890 rs3115858 A T . PASS AF=0.870552 ES:SE:LP:AF:SS:ID -0.000239606:0.00038953:0.268831:0.870552:360270:rs3115858
1 756434 rs61768170 G C . PASS AF=0.125993 ES:SE:LP:AF:SS:ID 0.000290128:0.000397465:0.332152:0.125993:360270:rs61768170
1 756604 rs3131962 A G . PASS AF=0.870117 ES:SE:LP:AF:SS:ID -0.000266679:0.000388593:0.307555:0.870117:360270:rs3131962
1 757640 rs3115853 G A . PASS AF=0.869406 ES:SE:LP:AF:SS:ID -0.000247983:0.00038824:0.281502:0.869406:360270:rs3115853
1 757734 rs4951929 C T . PASS AF=0.870272 ES:SE:LP:AF:SS:ID -0.000271249:0.000388928:0.313779:0.870272:360270:rs4951929
1 757936 rs4951862 C A . PASS AF=0.870278 ES:SE:LP:AF:SS:ID -0.000270985:0.000388956:0.313371:0.870278:360270:rs4951862
1 758144 rs3131956 A G . PASS AF=0.870287 ES:SE:LP:AF:SS:ID -0.000270575:0.000388968:0.312769:0.870287:360270:rs3131956
1 758626 rs3131954 C T . PASS AF=0.870728 ES:SE:LP:AF:SS:ID -0.000227456:0.000389958:0.252042:0.870728:360270:rs3131954
1 759293 rs10157329 T A . PASS AF=0.0988323 ES:SE:LP:AF:SS:ID 0.00036068:0.000453187:0.370483:0.0988323:360270:rs10157329
1 759600 rs545998451 AGT A . PASS AF=0.00647755 ES:SE:LP:AF:SS:ID -0.00244158:0.00175267:0.786211:0.00647755:360270:1_759600_AGT_A
1 759837 rs3115851 T A . PASS AF=0.874628 ES:SE:LP:AF:SS:ID -0.000205478:0.000395776:0.219224:0.874628:360270:rs3115851
1 761732 rs2286139 C T . PASS AF=0.864021 ES:SE:LP:AF:SS:ID -0.000215989:0.000388114:0.238175:0.864021:360270:rs2286139
1 761752 rs1057213 C T . PASS AF=0.869415 ES:SE:LP:AF:SS:ID -0.00023248:0.000391881:0.25726:0.869415:360270:rs1057213
1 762273 rs3115849 G A . PASS AF=0.866359 ES:SE:LP:AF:SS:ID -0.00026317:0.000391958:0.299339:0.866359:360270:rs3115849
1 762485 rs12095200 C A . PASS AF=0.0987912 ES:SE:LP:AF:SS:ID 0.00021453:0.000469247:0.188732:0.0987912:360270:rs12095200
1 762589 rs3115848 G C . PASS AF=0.871548 ES:SE:LP:AF:SS:ID -0.00024527:0.00039643:0.270741:0.871548:360270:rs3115848
1 762592 rs3131950 C G . PASS AF=0.871548 ES:SE:LP:AF:SS:ID -0.000245314:0.00039643:0.270801:0.871548:360270:rs3131950
1 762601 rs3131949 T C . PASS AF=0.871547 ES:SE:LP:AF:SS:ID -0.000245458:0.000396437:0.270988:0.871547:360270:rs3131949
1 762632 rs3131948 T A . PASS AF=0.871922 ES:SE:LP:AF:SS:ID -0.000221234:0.000396664:0.238805:0.871922:360270:rs3131948
1 764191 rs7515915 T G . PASS AF=0.12557 ES:SE:LP:AF:SS:ID 0.000242525:0.000396971:0.26661:0.12557:360270:rs7515915
1 766007 rs61768174 A C . PASS AF=0.105413 ES:SE:LP:AF:SS:ID 0.000616841:0.000432191:0.813858:0.105413:360270:rs61768174
1 766105 rs2519015 T A . PASS AF=0.855359 ES:SE:LP:AF:SS:ID -0.00040014:0.000387962:0.519476:0.855359:360270:rs2519015
1 768116 rs376645387 A AGTTTT . PASS AF=0.838382 ES:SE:LP:AF:SS:ID -0.000291057:0.000382294:0.350225:0.838382:360270:rs376645387
1 768253 rs2977608 A C . PASS AF=0.763384 ES:SE:LP:AF:SS:ID -0.000335142:0.000307631:0.559146:0.763384:360270:rs2977608
1 768448 rs12562034 G A . PASS AF=0.105878 ES:SE:LP:AF:SS:ID 0.000301708:0.000423946:0.321781:0.105878:360270:rs12562034
1 768819 rs12562811 C T . PASS AF=0.00768736 ES:SE:LP:AF:SS:ID 0.00147352:0.0015743:0.456824:0.00768736:360270:rs12562811
1 769138 rs59306077 CAT C . PASS AF=0.128946 ES:SE:LP:AF:SS:ID 0.000279066:0.000391415:0.322514:0.128946:360270:rs762168062