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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"gwas_harmonisation_command": "--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/vcf_09_19b/bgzip_vcf/data.batch_20161.vcf.gz --id UKB-b:10831 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20161.txt.gz --cohort_controls 142387 --ref /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/human_g1k_v37.fasta --json /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/ukb_gwas.json; 1.1.1",
"file_date": "2019-09-12T19:35:24.239802",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-10831/ukb-b-10831.vcf.gz; Date=Sun May 10 07:38:36 2020"
}
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/public/UKB-b-10831/UKB-b-10831_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-10831/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:40:19 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-10831/UKB-b-10831_data.vcf.gz ...
Read summary statistics for 9277584 SNPs.
Dropped 10146 SNPs with duplicated rs numbers.
Reading reference panel LD Score from ../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 ../reference/eur_w_ld_chr/[1-22] ...
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 1287885 SNPs remain.
After merging with regression SNP LD, 1287885 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0955 (0.0061)
Lambda GC: 1.254
Mean Chi^2: 1.2964
Intercept: 1.032 (0.0081)
Ratio: 0.1078 (0.0275)
Analysis finished at Thu Oct 17 14:42:00 2019
Total time elapsed: 1.0m:41.25sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9486,
"inflation_factor": 1.1999,
"mean_EFFECT": 0,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 11,
"n_p_sig": 1004,
"n_mono": 0,
"n_ns": 0,
"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": 110116,
"n_est": "NA",
"ratio_se_n": "NA",
"mean_diff": "NaN",
"ratio_diff": "NaN",
"sd_y_est1": "NaN",
"sd_y_est2": "NA",
"r2_sum1": 0,
"r2_sum2": 0,
"r2_sum3": 0,
"r2_sum4": 0,
"ldsc_nsnp_merge_refpanel_ld": 1287885,
"ldsc_nsnp_merge_regression_ld": 1287885,
"ldsc_observed_scale_h2_beta": 0.0955,
"ldsc_observed_scale_h2_se": 0.0061,
"ldsc_intercept_beta": 1.032,
"ldsc_intercept_se": 0.0081,
"ldsc_lambda_gc": 1.254,
"ldsc_mean_chisq": 1.2964,
"ldsc_ratio": 0.108
}
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 | 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 | logical.mean | logical.count | numeric.mean | numeric.sd | numeric.p0 | numeric.p25 | numeric.p50 | numeric.p75 | numeric.p100 | numeric.hist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| character | ID | 0 | 1.000000 | 3 | 58 | 0 | 9267489 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | REF | 0 | 1.000000 | 1 | 1 | 0 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | ALT | 0 | 1.000000 | 1 | 1 | 0 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | N | 9277584 | 0.000000 | NA | NA | NA | NA | NA | NaN | : | NA | NA | NA | NA | NA | NA | NA | NA |
| numeric | CHROM | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 8.635594e+00 | 5.754308e+00 | 1.0000000 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.200000e+01 | ▇▅▃▂▂ |
| numeric | POS | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 7.880595e+07 | 5.630781e+07 | 828.0000000 | 3.249786e+07 | 6.939145e+07 | 1.145356e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 1.350000e-05 | 1.305250e-02 | -0.1628740 | -5.030300e-03 | 1.070000e-05 | 5.024400e-03 | 1.803050e-01 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 9.919200e-03 | 7.774000e-03 | 0.0033491 | 4.031000e-03 | 6.346500e-03 | 1.360000e-02 | 8.499770e-02 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 4.726614e-01 | 2.959227e-01 | 0.0000000 | 2.099999e-01 | 4.600002e-01 | 7.300002e-01 | 1.000000e+00 | ▇▇▇▆▆ |
| numeric | PVAL_ztest | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 4.726627e-01 | 2.958965e-01 | 0.0000000 | 2.097096e-01 | 4.628676e-01 | 7.289169e-01 | 9.999999e-01 | ▇▆▆▆▆ |
| numeric | AF | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 2.143522e-01 | 2.578726e-01 | 0.0024590 | 1.776300e-02 | 9.326400e-02 | 3.368800e-01 | 9.975410e-01 | ▇▂▁▁▁ |
| numeric | AF_reference | 110116 | 0.988131 | NA | NA | NA | NA | NA | NA | NA | 2.152675e-01 | 2.496829e-01 | 0.0000000 | 1.497600e-02 | 1.116210e-01 | 3.364620e-01 | 1.000000e+00 | ▇▂▁▁▁ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 49298 | rs200943160 | T | C | 0.0042427 | 0.0061797 | 0.4899999 | 0.4923616 | 0.624399 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0023521 | 0.0061080 | 0.6999999 | 0.7001775 | 0.400301 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | -0.0291183 | 0.0097707 | 0.0029000 | 0.0028808 | 0.103610 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0050695 | 0.0060108 | 0.4000000 | 0.3990062 | 0.457291 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | -0.0051504 | 0.0118437 | 0.6600001 | 0.6636635 | 0.074584 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0042843 | 0.0068741 | 0.5300002 | 0.5331167 | 0.241523 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | 0.0167411 | 0.0086193 | 0.0519996 | 0.0521031 | 0.913947 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0000882 | 0.0057669 | 0.9900000 | 0.9877979 | 0.115983 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | -0.0086805 | 0.0084952 | 0.3100002 | 0.3068697 | 0.066648 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0056842 | 0.0042750 | 0.1800002 | 0.1836378 | 0.514667 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219006 | rs28729663 | G | A | -0.0040555 | 0.0051662 | 0.4299995 | 0.4324537 | 0.137559 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | -0.0027997 | 0.0067009 | 0.6800001 | 0.6760882 | 0.073374 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | -0.0066770 | 0.0089776 | 0.4600002 | 0.4570338 | 0.041777 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | -0.0033560 | 0.0089789 | 0.7099994 | 0.7085749 | 0.049355 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | -0.0025299 | 0.0067052 | 0.7099994 | 0.7059452 | 0.072865 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | -0.0058715 | 0.0078904 | 0.4600002 | 0.4567981 | 0.054352 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | -0.0064036 | 0.0079278 | 0.4199997 | 0.4192412 | 0.053978 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | -0.0024510 | 0.0067269 | 0.7199992 | 0.7155941 | 0.072740 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | -0.0241597 | 0.0159380 | 0.1299999 | 0.1295560 | 0.019880 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0009328 | 0.0041055 | 0.8200001 | 0.8202558 | 0.298766 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.624399 ES:SE:LP:AF:ID 0.00424274:0.00617973:0.309804:0.624399:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400301 ES:SE:LP:AF:ID 0.00235207:0.00610799:0.154902:0.400301:rs2462492
1 86028 rs114608975 T C . PASS AF=0.10361 ES:SE:LP:AF:ID -0.0291183:0.00977066:2.5376:0.10361:rs114608975
1 91536 rs6702460 G T . PASS AF=0.457291 ES:SE:LP:AF:ID 0.00506946:0.00601076:0.39794:0.457291:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074584 ES:SE:LP:AF:ID -0.00515036:0.0118437:0.180456:0.074584:rs8179466
1 534192 rs6680723 C T . PASS AF=0.241523 ES:SE:LP:AF:ID 0.00428432:0.0068741:0.275724:0.241523:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913947 ES:SE:LP:AF:ID 0.0167411:0.0086193:1.284:0.913947:rs12025928
1 693731 rs12238997 A G . PASS AF=0.115983 ES:SE:LP:AF:ID -8.81963e-05:0.00576686:0.00436481:0.115983:rs12238997
1 705882 rs72631875 G A . PASS AF=0.066648 ES:SE:LP:AF:ID -0.00868046:0.00849516:0.508638:0.066648:rs72631875
1 706368 rs12029736 A G . PASS AF=0.514667 ES:SE:LP:AF:ID -0.00568416:0.00427497:0.744727:0.514667:rs12029736
1 714596 rs149887893 T C . PASS AF=0.03322 ES:SE:LP:AF:ID -0.00893386:0.0107228:0.39794:0.03322:rs149887893
1 715265 rs12184267 C T . PASS AF=0.03685 ES:SE:LP:AF:ID -0.00892705:0.00974274:0.443698:0.03685:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036958 ES:SE:LP:AF:ID -0.0093612:0.00970774:0.481486:0.036958:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036653 ES:SE:LP:AF:ID -0.00914405:0.00977692:0.455932:0.036653:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016524 ES:SE:LP:AF:ID -0.0179439:0.0149886:0.638272:0.016524:rs144155419
1 720381 rs116801199 G T . PASS AF=0.037207 ES:SE:LP:AF:ID -0.00953707:0.00966577:0.49485:0.037207:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037311 ES:SE:LP:AF:ID -0.00875618:0.00963178:0.443698:0.037311:rs12565286
1 722670 rs116030099 T C . PASS AF=0.100835 ES:SE:LP:AF:ID -0.00517404:0.00705375:0.337242:0.100835:rs116030099
1 723891 rs2977670 G C . PASS AF=0.958734 ES:SE:LP:AF:ID 0.011698:0.00927918:0.677781:0.958734:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031399 ES:SE:LP:AF:ID -0.0154516:0.0169176:0.443698:0.031399:rs12126395
1 725060 rs865924913 A T . PASS AF=0.053241 ES:SE:LP:AF:ID 0.0166024:0.0134992:0.657577:0.053241:rs865924913
1 726794 rs28454925 C G . PASS AF=0.036859 ES:SE:LP:AF:ID -0.00981418:0.00969271:0.508638:0.036859:rs28454925
1 729632 rs116720794 C T . PASS AF=0.037096 ES:SE:LP:AF:ID -0.00989333:0.0096156:0.522879:0.037096:rs116720794
1 729679 rs4951859 C G . PASS AF=0.843007 ES:SE:LP:AF:ID 0.00398821:0.00498324:0.376751:0.843007:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055806 ES:SE:LP:AF:ID -0.000510236:0.0081057:0.0222764:0.055806:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122404 ES:SE:LP:AF:ID 0.000183773:0.00545531:0.0132283:0.122404:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025743 ES:SE:LP:AF:ID 0.00770734:0.013449:0.244125:0.025743:rs369030935
1 734349 rs141242758 T C . PASS AF=0.121637 ES:SE:LP:AF:ID 0.000491231:0.00545741:0.0315171:0.121637:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132333 ES:SE:LP:AF:ID -0.00255238:0.00539058:0.19382:0.132333:rs79010578
1 736689 rs181876450 T C . PASS AF=0.011071 ES:SE:LP:AF:ID 0.00161994:0.0197106:0.0315171:0.011071:rs181876450
1 740284 rs61770167 C T . PASS AF=0.005717 ES:SE:LP:AF:ID 3.70877e-05:0.0253392:-0:0.005717:rs61770167
1 752478 rs146277091 G A . PASS AF=0.037106 ES:SE:LP:AF:ID -0.00795304:0.00950414:0.39794:0.037106:rs146277091
1 752566 rs3094315 G A . PASS AF=0.838928 ES:SE:LP:AF:ID 0.00400397:0.00483305:0.387216:0.838928:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838561 ES:SE:LP:AF:ID 0.0042458:0.00482727:0.420216:0.838561:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869728 ES:SE:LP:AF:ID 0.00127408:0.0051783:0.091515:0.869728:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129988 ES:SE:LP:AF:ID -0.00146464:0.00518729:0.107905:0.129988:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037578 ES:SE:LP:AF:ID -0.00918152:0.00934855:0.481486:0.037578:rs12184312
1 754105 rs12184325 C T . PASS AF=0.037841 ES:SE:LP:AF:ID -0.00878255:0.00928692:0.468521:0.037841:rs12184325
1 754182 rs3131969 A G . PASS AF=0.869064 ES:SE:LP:AF:ID 0.00175295:0.00516755:0.136677:0.869064:rs3131969
1 754192 rs3131968 A G . PASS AF=0.86916 ES:SE:LP:AF:ID 0.00169472:0.00516927:0.130768:0.86916:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037791 ES:SE:LP:AF:ID -0.0082468:0.00932729:0.420216:0.037791:rs12184313
1 754334 rs3131967 T C . PASS AF=0.869071 ES:SE:LP:AF:ID 0.00175166:0.00516763:0.136677:0.869071:rs3131967
1 754433 rs150578204 G A . PASS AF=0.004885 ES:SE:LP:AF:ID 0.0261926:0.0272327:0.468521:0.004885:rs150578204
1 754458 rs142682604 G T . PASS AF=0.004847 ES:SE:LP:AF:ID 0.0247651:0.0273025:0.443698:0.004847:rs142682604
1 754503 rs3115859 G A . PASS AF=0.837999 ES:SE:LP:AF:ID 0.00460026:0.00481432:0.468521:0.837999:rs3115859
1 754629 rs10454459 A G . PASS AF=0.037809 ES:SE:LP:AF:ID -0.0080993:0.00934045:0.408935:0.037809:rs10454459
1 754964 rs3131966 C T . PASS AF=0.838613 ES:SE:LP:AF:ID 0.00442045:0.00482763:0.443698:0.838613:rs3131966
1 755240 rs181660517 T G . PASS AF=0.013929 ES:SE:LP:AF:ID -0.0134392:0.0167676:0.376751:0.013929:rs181660517
1 755435 rs184270342 T G . PASS AF=0.00551 ES:SE:LP:AF:ID 0.0308513:0.0261516:0.619789:0.00551:rs184270342
1 755775 rs3131965 A G . PASS AF=0.839774 ES:SE:LP:AF:ID 0.00363035:0.00489296:0.337242:0.839774:rs3131965