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_102.vcf.gz --id UKB-b:18103 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_102.txt.gz --cohort_controls 436424 --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": "2019-09-13T15:21:45.248520",
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"bcftools_viewCommand": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-18103/ukb-b-18103.vcf.gz; Date=Sat May 9 16:32:54 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-18103/UKB-b-18103_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-18103/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:40:18 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-18103/UKB-b-18103_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: 0.1455 (0.0085)
Lambda GC: 1.7517
Mean Chi^2: 2.3841
Intercept: 1.1099 (0.014)
Ratio: 0.0794 (0.0101)
Analysis finished at Thu Oct 17 14:42:07 2019
Total time elapsed: 1.0m:48.91sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9499,
"inflation_factor": 1.4921,
"mean_EFFECT": -0,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 293,
"n_p_sig": 34467,
"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": 0.1455,
"ldsc_observed_scale_h2_se": 0.0085,
"ldsc_intercept_beta": 1.1099,
"ldsc_intercept_se": 0.014,
"ldsc_lambda_gc": 1.7517,
"ldsc_mean_chisq": 2.3841,
"ldsc_ratio": 0.0794
}
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 | 1.000000 | 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 | 828.000000 | 3.259061e+07 | 6.948835e+07 | 1.145912e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -1.080000e-05 | 1.084850e-02 | -0.320592 | -3.803900e-03 | -1.030000e-05 | 3.759400e-03 | 2.079740e-01 | ▁▁▃▇▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.220700e-03 | 6.826700e-03 | 0.002021 | 2.475100e-03 | 4.150000e-03 | 9.574600e-03 | 1.061500e-01 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.346411e-01 | 3.056193e-01 | 0.000000 | 1.499999e-01 | 4.100001e-01 | 6.999999e-01 | 1.000000e+00 | ▇▅▅▅▅ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.346427e-01 | 3.055965e-01 | 0.000000 | 1.536321e-01 | 4.103642e-01 | 6.987660e-01 | 9.999993e-01 | ▇▅▅▅▅ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.035090e-01 | 2.568554e-01 | 0.000940 | 1.317300e-02 | 7.792100e-02 | 3.164490e-01 | 9.990220e-01 | ▇▂▁▁▁ |
| numeric | AF_reference | 184849 | 0.9812372 | NA | NA | NA | NA | NA | NA | NA | 2.068392e-01 | 2.482924e-01 | 0.000000 | 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 | -0.0030967 | 0.0037178 | 0.4000000 | 0.4048818 | 0.623964 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0046473 | 0.0036812 | 0.2099999 | 0.2067821 | 0.400339 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0027811 | 0.0058939 | 0.6400000 | 0.6370248 | 0.103469 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0066391 | 0.0036252 | 0.0669993 | 0.0670426 | 0.456860 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0004945 | 0.0071463 | 0.9400001 | 0.9448275 | 0.074567 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0019561 | 0.0041426 | 0.6400000 | 0.6367852 | 0.240920 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | -0.0008603 | 0.0051608 | 0.8700001 | 0.8676117 | 0.913351 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | 0.0012805 | 0.0034651 | 0.7099994 | 0.7117224 | 0.116609 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | 0.0061448 | 0.0050820 | 0.2300001 | 0.2266187 | 0.067340 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0050467 | 0.0025702 | 0.0500000 | 0.0495848 | 0.515645 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219704 | rs147475742 | G | A | 0.0038477 | 0.0054190 | 0.4799997 | 0.4776739 | 0.041823 | 0.0473243 | NA |
| 22 | 51219766 | rs182321900 | C | T | 0.0119233 | 0.0251140 | 0.6300007 | 0.6349530 | 0.001950 | NA | NA |
| 22 | 51220146 | rs868950473 | C | T | 0.0144439 | 0.0248753 | 0.5600000 | 0.5614748 | 0.001999 | NA | NA |
| 22 | 51221190 | rs369304721 | G | A | -0.0017599 | 0.0054127 | 0.7499995 | 0.7450782 | 0.049531 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | -0.0002616 | 0.0040467 | 0.9500000 | 0.9484488 | 0.072972 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | 0.0022642 | 0.0047494 | 0.6300007 | 0.6335523 | 0.054572 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | 0.0025802 | 0.0047723 | 0.5900000 | 0.5887363 | 0.054202 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0004199 | 0.0040610 | 0.9199999 | 0.9176403 | 0.072823 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | 0.0006610 | 0.0095394 | 0.9400001 | 0.9447588 | 0.020095 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0010143 | 0.0024795 | 0.6800001 | 0.6824816 | 0.297841 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623964 ES:SE:LP:AF:ID -0.0030967:0.00371782:0.39794:0.623964:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400339 ES:SE:LP:AF:ID 0.00464733:0.00368116:0.677781:0.400339:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103469 ES:SE:LP:AF:ID 0.00278113:0.00589394:0.19382:0.103469:rs114608975
1 91536 rs6702460 G T . PASS AF=0.45686 ES:SE:LP:AF:ID 0.00663913:0.00362519:1.17393:0.45686:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074567 ES:SE:LP:AF:ID 0.000494547:0.00714626:0.0268721:0.074567:rs8179466
1 534192 rs6680723 C T . PASS AF=0.24092 ES:SE:LP:AF:ID 0.00195611:0.00414256:0.19382:0.24092:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913351 ES:SE:LP:AF:ID -0.000860267:0.00516079:0.0604807:0.913351:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116609 ES:SE:LP:AF:ID 0.00128051:0.00346511:0.148742:0.116609:rs12238997
1 705882 rs72631875 G A . PASS AF=0.06734 ES:SE:LP:AF:ID 0.00614477:0.00508204:0.638272:0.06734:rs72631875
1 706368 rs12029736 A G . PASS AF=0.515645 ES:SE:LP:AF:ID 0.0050467:0.00257022:1.30103:0.515645:rs12029736
1 714596 rs149887893 T C . PASS AF=0.033052 ES:SE:LP:AF:ID -0.00139968:0.00647468:0.0809219:0.033052:rs149887893
1 715265 rs12184267 C T . PASS AF=0.036674 ES:SE:LP:AF:ID -0.00275709:0.00588077:0.19382:0.036674:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036796 ES:SE:LP:AF:ID -0.00317294:0.00585772:0.229148:0.036796:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036492 ES:SE:LP:AF:ID -0.00295591:0.00590024:0.207608:0.036492:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016423 ES:SE:LP:AF:ID -0.00386739:0.00908939:0.173925:0.016423:rs144155419
1 720381 rs116801199 G T . PASS AF=0.037034 ES:SE:LP:AF:ID -0.00352854:0.00583488:0.259637:0.037034:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037127 ES:SE:LP:AF:ID -0.00271225:0.0058153:0.19382:0.037127:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101398 ES:SE:LP:AF:ID -0.0169285:0.0042358:4.19382:0.101398:rs116030099
1 723891 rs2977670 G C . PASS AF=0.958985 ES:SE:LP:AF:ID 0.00487314:0.00560537:0.420216:0.958985:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031554 ES:SE:LP:AF:ID -0.00119171:0.0101579:0.0409586:0.031554:rs12126395
1 725060 rs865924913 A T . PASS AF=0.053101 ES:SE:LP:AF:ID 0.000119397:0.00812893:0.00436481:0.053101:rs865924913
1 726794 rs28454925 C G . PASS AF=0.036653 ES:SE:LP:AF:ID -0.00250936:0.00585189:0.173925:0.036653:rs28454925
1 729632 rs116720794 C T . PASS AF=0.036969 ES:SE:LP:AF:ID -0.00273691:0.005799:0.19382:0.036969:rs116720794
1 729679 rs4951859 C G . PASS AF=0.842837 ES:SE:LP:AF:ID 0.00144218:0.00300349:0.200659:0.842837:rs4951859
1 730087 rs148120343 T C . PASS AF=0.056026 ES:SE:LP:AF:ID 0.00493355:0.00486433:0.508638:0.056026:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122593 ES:SE:LP:AF:ID 0.000526412:0.00328729:0.0604807:0.122593:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025653 ES:SE:LP:AF:ID 0.00933259:0.00810274:0.60206:0.025653:rs369030935
1 734349 rs141242758 T C . PASS AF=0.121836 ES:SE:LP:AF:ID 0.000615722:0.00328869:0.0705811:0.121836:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132628 ES:SE:LP:AF:ID -1.38118e-06:0.00324218:-0:0.132628:rs79010578
1 736689 rs181876450 T C . PASS AF=0.011113 ES:SE:LP:AF:ID 0.0123724:0.0118127:0.537602:0.011113:rs181876450
1 740284 rs61770167 C T . PASS AF=0.005767 ES:SE:LP:AF:ID -0.00188795:0.0151298:0.0457575:0.005767:rs61770167
1 742813 rs112573343 C T . PASS AF=0.00229 ES:SE:LP:AF:ID -0.0317136:0.0254871:0.677781:0.00229:rs112573343
1 746189 rs139221807 A G . PASS AF=0.001038 ES:SE:LP:AF:ID -0.0802566:0.0417599:1.25964:0.001038:rs139221807
1 752478 rs146277091 G A . PASS AF=0.036891 ES:SE:LP:AF:ID -0.00219866:0.00573979:0.154902:0.036891:rs146277091
1 752566 rs3094315 G A . PASS AF=0.83857 ES:SE:LP:AF:ID 0.00158866:0.00290881:0.236572:0.83857:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838187 ES:SE:LP:AF:ID 0.00170181:0.0029056:0.251812:0.838187:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869441 ES:SE:LP:AF:ID 0.000864801:0.00311711:0.107905:0.869441:rs3115860
1 753541 rs2073813 G A . PASS AF=0.13021 ES:SE:LP:AF:ID -0.00104859:0.00312351:0.130768:0.13021:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037398 ES:SE:LP:AF:ID -0.00199248:0.00564257:0.142668:0.037398:rs12184312
1 754105 rs12184325 C T . PASS AF=0.037643 ES:SE:LP:AF:ID -0.00182781:0.00560695:0.130768:0.037643:rs12184325
1 754182 rs3131969 A G . PASS AF=0.868765 ES:SE:LP:AF:ID 0.0011552:0.00311084:0.148742:0.868765:rs3131969
1 754192 rs3131968 A G . PASS AF=0.868866 ES:SE:LP:AF:ID 0.000976703:0.00311209:0.124939:0.868866:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037601 ES:SE:LP:AF:ID -0.00179786:0.00563125:0.124939:0.037601:rs12184313
1 754334 rs3131967 T C . PASS AF=0.868767 ES:SE:LP:AF:ID 0.00111643:0.00311074:0.142668:0.868767:rs3131967
1 754433 rs150578204 G A . PASS AF=0.005156 ES:SE:LP:AF:ID -0.035527:0.0159388:1.58503:0.005156:rs150578204
1 754458 rs142682604 G T . PASS AF=0.005122 ES:SE:LP:AF:ID -0.0358207:0.0159801:1.60206:0.005122:rs142682604
1 754503 rs3115859 G A . PASS AF=0.837632 ES:SE:LP:AF:ID 0.00185931:0.0028974:0.283997:0.837632:rs3115859
1 754629 rs10454459 A G . PASS AF=0.037613 ES:SE:LP:AF:ID -0.00196321:0.00563936:0.136677:0.037613:rs10454459
1 754964 rs3131966 C T . PASS AF=0.838264 ES:SE:LP:AF:ID 0.00186675:0.00290554:0.283997:0.838264:rs3131966
1 755240 rs181660517 T G . PASS AF=0.013667 ES:SE:LP:AF:ID -0.00752421:0.0101963:0.337242:0.013667:rs181660517