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_1488.vcf.gz --id UKB-b:6066 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_1488.txt.gz --cohort_controls 447485 --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": "2019-09-13T15:34:57.530897",
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"bcftools_viewCommand": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-6066/ukb-b-6066.vcf.gz; Date=Sat May 9 20:54:14 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-6066/UKB-b-6066_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-6066/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:45:46 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-6066/UKB-b-6066_data.vcf.gz ...
Read summary statistics for 9851866 SNPs.
Dropped 14738 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, 1289166 SNPs remain.
After merging with regression SNP LD, 1289166 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0568 (0.0027)
Lambda GC: 1.433
Mean Chi^2: 1.5568
Intercept: 1.0488 (0.0092)
Ratio: 0.0876 (0.0164)
Analysis finished at Thu Oct 17 14:47:14 2019
Total time elapsed: 1.0m:28.84sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9499,
"inflation_factor": 1.3107,
"mean_EFFECT": -0.0002,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 41,
"n_p_sig": 4031,
"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": 184849,
"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": 1289166,
"ldsc_nsnp_merge_regression_ld": 1289166,
"ldsc_observed_scale_h2_beta": 0.0568,
"ldsc_observed_scale_h2_se": 0.0027,
"ldsc_intercept_beta": 1.0488,
"ldsc_intercept_se": 0.0092,
"ldsc_lambda_gc": 1.433,
"ldsc_mean_chisq": 1.5568,
"ldsc_ratio": 0.0876
}
Flags
| name | value |
|---|---|
| af_correlation | FALSE |
| inflation_factor | TRUE |
| n | TRUE |
| 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 | 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 | 1.0000000 | 3 | 58 | 0 | 9837196 | 0 | NA | NA | 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 | NA | NA |
| character | ALT | 0 | 1.0000000 | 1 | 1 | 0 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | N | 9851866 | 0.0000000 | NA | NA | NA | NA | NA | NaN | : | NA | NA | NA | NA | NA | NA | NA | NA |
| numeric | CHROM | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 8.622825e+00 | 5.748290e+00 | 1.0000000 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.200000e+01 | ▇▅▃▂▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.886027e+07 | 5.628334e+07 | 828.0000000 | 3.259061e+07 | 6.948835e+07 | 1.145912e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -1.904000e-04 | 1.063760e-02 | -0.1961480 | -3.530100e-03 | -1.790000e-05 | 3.441200e-03 | 1.911290e-01 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.303400e-03 | 6.917700e-03 | 0.0020433 | 2.501000e-03 | 4.193900e-03 | 9.677300e-03 | 1.072930e-01 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.581184e-01 | 2.998433e-01 | 0.0000000 | 1.900002e-01 | 4.400003e-01 | 7.199992e-01 | 1.000000e+00 | ▇▆▆▅▆ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.581199e-01 | 2.998182e-01 | 0.0000000 | 1.874305e-01 | 4.437841e-01 | 7.176093e-01 | 9.999999e-01 | ▇▆▆▆▆ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.035073e-01 | 2.568650e-01 | 0.0009550 | 1.316800e-02 | 7.791300e-02 | 3.164400e-01 | 9.990270e-01 | ▇▂▁▁▁ |
| numeric | AF_reference | 184849 | 0.9812372 | NA | NA | NA | NA | NA | NA | NA | 2.068392e-01 | 2.482924e-01 | 0.0000000 | 1.198080e-02 | 9.984030e-02 | 3.202880e-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 | -0.0054260 | 0.0037584 | 0.1499999 | 0.1488198 | 0.623759 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0038300 | 0.0037238 | 0.2999998 | 0.3037057 | 0.400462 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0065409 | 0.0059514 | 0.2700001 | 0.2717463 | 0.103569 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0023717 | 0.0036667 | 0.5199996 | 0.5177528 | 0.456868 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0066774 | 0.0072272 | 0.3599996 | 0.3555289 | 0.074509 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0014978 | 0.0041879 | 0.7199992 | 0.7206088 | 0.240935 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | 0.0029884 | 0.0052229 | 0.5700002 | 0.5672029 | 0.913474 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | 0.0013496 | 0.0035098 | 0.6999999 | 0.7005822 | 0.116301 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | -0.0044459 | 0.0051482 | 0.3900004 | 0.3878116 | 0.067194 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0028148 | 0.0026001 | 0.2800000 | 0.2789990 | 0.515691 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219704 | rs147475742 | G | A | 0.0066919 | 0.0054568 | 0.2200002 | 0.2200706 | 0.041997 | 0.0473243 | NA |
| 22 | 51219766 | rs182321900 | C | T | 0.0145596 | 0.0254389 | 0.5700002 | 0.5670943 | 0.001936 | NA | NA |
| 22 | 51220146 | rs868950473 | C | T | 0.0084558 | 0.0252046 | 0.7400005 | 0.7372585 | 0.001985 | NA | NA |
| 22 | 51221190 | rs369304721 | G | A | 0.0132558 | 0.0054486 | 0.0150000 | 0.0149796 | 0.049761 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | 0.0082504 | 0.0040757 | 0.0430002 | 0.0429375 | 0.073276 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | 0.0097512 | 0.0047946 | 0.0420001 | 0.0419715 | 0.054553 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | 0.0093786 | 0.0048179 | 0.0519996 | 0.0515813 | 0.054180 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0082828 | 0.0040905 | 0.0430002 | 0.0428780 | 0.073111 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | 0.0080804 | 0.0096356 | 0.4000000 | 0.4016947 | 0.020054 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0029892 | 0.0025026 | 0.2300001 | 0.2323089 | 0.297918 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623759 ES:SE:LP:AF:ID -0.00542605:0.0037584:0.823909:0.623759:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400462 ES:SE:LP:AF:ID -0.00383001:0.00372381:0.522879:0.400462:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103569 ES:SE:LP:AF:ID 0.00654093:0.00595144:0.568636:0.103569:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456868 ES:SE:LP:AF:ID -0.00237167:0.0036667:0.283997:0.456868:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074509 ES:SE:LP:AF:ID 0.00667736:0.00722722:0.443698:0.074509:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240935 ES:SE:LP:AF:ID 0.00149779:0.00418792:0.142668:0.240935:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913474 ES:SE:LP:AF:ID 0.00298844:0.00522294:0.244125:0.913474:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116301 ES:SE:LP:AF:ID 0.00134964:0.0035098:0.154902:0.116301:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067194 ES:SE:LP:AF:ID -0.00444594:0.00514819:0.408935:0.067194:rs72631875
1 706368 rs12029736 A G . PASS AF=0.515691 ES:SE:LP:AF:ID -0.00281476:0.00260007:0.552842:0.515691:rs12029736
1 714596 rs149887893 T C . PASS AF=0.03291 ES:SE:LP:AF:ID 0.00793769:0.00656231:0.638272:0.03291:rs149887893
1 715265 rs12184267 C T . PASS AF=0.036521 ES:SE:LP:AF:ID 0.00690226:0.0059601:0.60206:0.036521:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036638 ES:SE:LP:AF:ID 0.00720321:0.0059374:0.638272:0.036638:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036338 ES:SE:LP:AF:ID 0.0075556:0.00598014:0.677781:0.036338:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016366 ES:SE:LP:AF:ID -0.00353851:0.00921563:0.154902:0.016366:rs144155419
1 720381 rs116801199 G T . PASS AF=0.036874 ES:SE:LP:AF:ID 0.00728437:0.005914:0.657577:0.036874:rs116801199
1 721290 rs12565286 G C . PASS AF=0.036971 ES:SE:LP:AF:ID 0.00627224:0.00589374:0.537602:0.036971:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101176 ES:SE:LP:AF:ID -0.000847356:0.00428958:0.0757207:0.101176:rs116030099
1 723891 rs2977670 G C . PASS AF=0.959215 ES:SE:LP:AF:ID -0.00530901:0.00568476:0.455932:0.959215:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031479 ES:SE:LP:AF:ID -0.00521547:0.0102995:0.21467:0.031479:rs12126395
1 725060 rs865924913 A T . PASS AF=0.053223 ES:SE:LP:AF:ID 0.00278143:0.00820418:0.136677:0.053223:rs865924913
1 726794 rs28454925 C G . PASS AF=0.036493 ES:SE:LP:AF:ID 0.00760561:0.00593175:0.69897:0.036493:rs28454925
1 729632 rs116720794 C T . PASS AF=0.036808 ES:SE:LP:AF:ID 0.00737367:0.00587779:0.677781:0.036808:rs116720794
1 729679 rs4951859 C G . PASS AF=0.843362 ES:SE:LP:AF:ID -0.00262001:0.00304299:0.408935:0.843362:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055883 ES:SE:LP:AF:ID -0.00122605:0.004925:0.09691:0.055883:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122251 ES:SE:LP:AF:ID 0.00243393:0.00332977:0.337242:0.122251:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025765 ES:SE:LP:AF:ID -0.00458567:0.00818206:0.236572:0.025765:rs369030935
1 734349 rs141242758 T C . PASS AF=0.121497 ES:SE:LP:AF:ID 0.00207946:0.00333118:0.275724:0.121497:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132239 ES:SE:LP:AF:ID 0.00441186:0.00328307:0.744727:0.132239:rs79010578
1 736689 rs181876450 T C . PASS AF=0.011115 ES:SE:LP:AF:ID 0.00468445:0.0119447:0.161151:0.011115:rs181876450
1 740284 rs61770167 C T . PASS AF=0.005706 ES:SE:LP:AF:ID 0.014144:0.0153942:0.443698:0.005706:rs61770167
1 742813 rs112573343 C T . PASS AF=0.002267 ES:SE:LP:AF:ID -0.0238781:0.0258998:0.443698:0.002267:rs112573343
1 746189 rs139221807 A G . PASS AF=0.001018 ES:SE:LP:AF:ID 0.0506112:0.0427798:0.619789:0.001018:rs139221807
1 752478 rs146277091 G A . PASS AF=0.036717 ES:SE:LP:AF:ID 0.00708786:0.0058188:0.657577:0.036717:rs146277091
1 752566 rs3094315 G A . PASS AF=0.839093 ES:SE:LP:AF:ID -0.00335473:0.00294667:0.60206:0.839093:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838732 ES:SE:LP:AF:ID -0.00309807:0.0029436:0.537602:0.838732:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869844 ES:SE:LP:AF:ID -0.00172571:0.00315794:0.236572:0.869844:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129803 ES:SE:LP:AF:ID 0.001414:0.00316436:0.187087:0.129803:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037229 ES:SE:LP:AF:ID 0.00648563:0.00571965:0.585027:0.037229:rs12184312
1 754105 rs12184325 C T . PASS AF=0.037471 ES:SE:LP:AF:ID 0.00677613:0.00568364:0.638272:0.037471:rs12184325
1 754182 rs3131969 A G . PASS AF=0.869197 ES:SE:LP:AF:ID -0.00148194:0.00315181:0.19382:0.869197:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869295 ES:SE:LP:AF:ID -0.00140969:0.00315309:0.187087:0.869295:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037432 ES:SE:LP:AF:ID 0.00658613:0.00570789:0.60206:0.037432:rs12184313
1 754334 rs3131967 T C . PASS AF=0.869199 ES:SE:LP:AF:ID -0.00151436:0.00315175:0.200659:0.869199:rs3131967
1 754433 rs150578204 G A . PASS AF=0.005117 ES:SE:LP:AF:ID -0.00510248:0.0161894:0.124939:0.005117:rs150578204
1 754458 rs142682604 G T . PASS AF=0.005083 ES:SE:LP:AF:ID -0.00438705:0.0162319:0.102373:0.005083:rs142682604
1 754503 rs3115859 G A . PASS AF=0.838188 ES:SE:LP:AF:ID -0.00273193:0.00293547:0.455932:0.838188:rs3115859
1 754629 rs10454459 A G . PASS AF=0.037445 ES:SE:LP:AF:ID 0.00644254:0.00571595:0.585027:0.037445:rs10454459
1 754964 rs3131966 C T . PASS AF=0.838815 ES:SE:LP:AF:ID -0.00270506:0.0029437:0.443698:0.838815:rs3131966
1 755240 rs181660517 T G . PASS AF=0.013771 ES:SE:LP:AF:ID -0.00270842:0.0102752:0.102373:0.013771:rs181660517