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_23106.vcf.gz --id UKB-b:19921 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_23106.txt.gz --cohort_controls 454840 --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": "2019-09-13T04:01:37.278848",
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"bcftools_viewCommand": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-19921/ukb-b-19921.vcf.gz; Date=Sun May 10 13:46:54 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-19921/UKB-b-19921_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19921/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:41:54 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19921/UKB-b-19921_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: 0.2515 (0.008)
Lambda GC: 2.5196
Mean Chi^2: 3.6512
Intercept: 1.2498 (0.0226)
Ratio: 0.0942 (0.0085)
Analysis finished at Thu Oct 17 14:43:37 2019
Total time elapsed: 1.0m:42.76sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9499,
"inflation_factor": 1.92,
"mean_EFFECT": 0,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 528,
"n_p_sig": 92427,
"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": 0.2515,
"ldsc_observed_scale_h2_se": 0.008,
"ldsc_intercept_beta": 1.2498,
"ldsc_intercept_se": 0.0226,
"ldsc_lambda_gc": 2.5196,
"ldsc_mean_chisq": 3.6512,
"ldsc_ratio": 0.0942
}
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 | 1.950000e-05 | 8.349900e-03 | -0.1362860 | -3.213800e-03 | 9.000000e-06 | 3.231100e-03 | 2.277870e-01 | ▁▇▁▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 5.215500e-03 | 4.936300e-03 | 0.0014566 | 1.786800e-03 | 2.996100e-03 | 6.912700e-03 | 7.669420e-02 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.941504e-01 | 3.123281e-01 | 0.0000000 | 9.800090e-02 | 3.500000e-01 | 6.600001e-01 | 1.000000e+00 | ▇▅▃▃▃ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.941540e-01 | 3.123067e-01 | 0.0000000 | 9.777970e-02 | 3.479714e-01 | 6.616943e-01 | 1.000000e+00 | ▇▃▃▃▃ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.035078e-01 | 2.568614e-01 | 0.0009850 | 1.317000e-02 | 7.791400e-02 | 3.164540e-01 | 9.990060e-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 | -0.0013613 | 0.0026758 | 0.6100002 | 0.6109230 | 0.623731 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0023942 | 0.0026514 | 0.3700002 | 0.3665223 | 0.400354 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0011203 | 0.0042384 | 0.7899998 | 0.7915259 | 0.103560 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0048111 | 0.0026100 | 0.0649995 | 0.0652872 | 0.456843 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0113014 | 0.0051486 | 0.0280001 | 0.0281605 | 0.074496 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0035904 | 0.0029818 | 0.2300001 | 0.2285438 | 0.240942 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | -0.0044134 | 0.0037198 | 0.2399999 | 0.2354466 | 0.913463 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | 0.0019897 | 0.0025001 | 0.4299995 | 0.4261152 | 0.116216 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | 0.0052346 | 0.0036609 | 0.1499999 | 0.1527552 | 0.067319 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0031840 | 0.0018512 | 0.0850002 | 0.0854438 | 0.515786 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219704 | rs147475742 | G | A | 0.0050092 | 0.0039139 | 0.2000000 | 0.2005950 | 0.041966 | 0.0473243 | NA |
| 22 | 51219766 | rs182321900 | C | T | 0.0028076 | 0.0182164 | 0.8800001 | 0.8775096 | 0.001942 | NA | NA |
| 22 | 51220146 | rs868950473 | C | T | 0.0029921 | 0.0180429 | 0.8700001 | 0.8682900 | 0.001991 | NA | NA |
| 22 | 51221190 | rs369304721 | G | A | 0.0030371 | 0.0039068 | 0.4400003 | 0.4369357 | 0.049759 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | 0.0008132 | 0.0029224 | 0.7800007 | 0.7808015 | 0.073278 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | 0.0008653 | 0.0034406 | 0.8000000 | 0.8014369 | 0.054488 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | 0.0009151 | 0.0034573 | 0.7899998 | 0.7912412 | 0.054117 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0008565 | 0.0029330 | 0.7700005 | 0.7702625 | 0.073111 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | -0.0005439 | 0.0069083 | 0.9400001 | 0.9372422 | 0.020055 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0030479 | 0.0017948 | 0.0890000 | 0.0894670 | 0.297895 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623731 ES:SE:LP:AF:ID -0.00136132:0.00267578:0.21467:0.623731:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400354 ES:SE:LP:AF:ID -0.00239425:0.00265142:0.431798:0.400354:rs2462492
1 86028 rs114608975 T C . PASS AF=0.10356 ES:SE:LP:AF:ID 0.00112034:0.00423842:0.102373:0.10356:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456843 ES:SE:LP:AF:ID -0.00481107:0.00261005:1.18709:0.456843:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074496 ES:SE:LP:AF:ID 0.0113014:0.0051486:1.55284:0.074496:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240942 ES:SE:LP:AF:ID 0.00359038:0.00298175:0.638272:0.240942:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913463 ES:SE:LP:AF:ID -0.00441335:0.00371981:0.619789:0.913463:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116216 ES:SE:LP:AF:ID 0.00198973:0.00250011:0.366532:0.116216:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067319 ES:SE:LP:AF:ID 0.0052346:0.0036609:0.823909:0.067319:rs72631875
1 706368 rs12029736 A G . PASS AF=0.515786 ES:SE:LP:AF:ID 0.003184:0.00185123:1.07058:0.515786:rs12029736
1 714596 rs149887893 T C . PASS AF=0.032988 ES:SE:LP:AF:ID -0.0057116:0.00466748:0.657577:0.032988:rs149887893
1 715265 rs12184267 C T . PASS AF=0.036599 ES:SE:LP:AF:ID -0.00572902:0.00424001:0.744727:0.036599:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036718 ES:SE:LP:AF:ID -0.0063637:0.00422365:0.886057:0.036718:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036415 ES:SE:LP:AF:ID -0.00559327:0.00425435:0.721246:0.036415:rs12184279
1 717587 rs144155419 G A . PASS AF=0.01639 ES:SE:LP:AF:ID -0.0017574:0.00655278:0.102373:0.01639:rs144155419
1 720381 rs116801199 G T . PASS AF=0.036955 ES:SE:LP:AF:ID -0.00557439:0.00420718:0.721246:0.036955:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037053 ES:SE:LP:AF:ID -0.00556908:0.00419266:0.744727:0.037053:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101237 ES:SE:LP:AF:ID -0.0022452:0.00305375:0.337242:0.101237:rs116030099
1 723891 rs2977670 G C . PASS AF=0.959097 ES:SE:LP:AF:ID 0.00566031:0.00404279:0.79588:0.959097:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031462 ES:SE:LP:AF:ID 0.0109376:0.00733446:0.853872:0.031462:rs12126395
1 725060 rs865924913 A T . PASS AF=0.053256 ES:SE:LP:AF:ID -0.000621344:0.00583407:0.0362122:0.053256:rs865924913
1 726794 rs28454925 C G . PASS AF=0.036574 ES:SE:LP:AF:ID -0.00548131:0.00421959:0.721246:0.036574:rs28454925
1 729632 rs116720794 C T . PASS AF=0.036892 ES:SE:LP:AF:ID -0.00425563:0.00418117:0.508638:0.036892:rs116720794
1 729679 rs4951859 C G . PASS AF=0.843322 ES:SE:LP:AF:ID 0.000506327:0.00216618:0.0861861:0.843322:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055859 ES:SE:LP:AF:ID 0.00917723:0.00350797:2.05061:0.055859:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122192 ES:SE:LP:AF:ID 0.000861762:0.00237156:0.142668:0.122192:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025729 ES:SE:LP:AF:ID -0.0027562:0.00582766:0.19382:0.025729:rs369030935
1 734349 rs141242758 T C . PASS AF=0.121433 ES:SE:LP:AF:ID 0.00072682:0.00237256:0.119186:0.121433:rs141242758
1 736289 rs79010578 T A . PASS AF=0.13222 ES:SE:LP:AF:ID -0.000290671:0.00233802:0.0457575:0.13222:rs79010578
1 736689 rs181876450 T C . PASS AF=0.011123 ES:SE:LP:AF:ID -0.00372967:0.00850581:0.180456:0.011123:rs181876450
1 740284 rs61770167 C T . PASS AF=0.005693 ES:SE:LP:AF:ID -0.0130973:0.0109732:0.638272:0.005693:rs61770167
1 742813 rs112573343 C T . PASS AF=0.002287 ES:SE:LP:AF:ID -0.00396552:0.0183711:0.0809219:0.002287:rs112573343
1 746189 rs139221807 A G . PASS AF=0.001037 ES:SE:LP:AF:ID 0.0106734:0.030051:0.142668:0.001037:rs139221807
1 752478 rs146277091 G A . PASS AF=0.036806 ES:SE:LP:AF:ID -0.00494539:0.0041389:0.638272:0.036806:rs146277091
1 752566 rs3094315 G A . PASS AF=0.83905 ES:SE:LP:AF:ID 0.000471256:0.00209763:0.0861861:0.83905:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838677 ES:SE:LP:AF:ID 0.000559457:0.00209533:0.102373:0.838677:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869873 ES:SE:LP:AF:ID -0.000525051:0.00224867:0.0861861:0.869873:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129767 ES:SE:LP:AF:ID 0.000780567:0.00225337:0.136677:0.129767:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037318 ES:SE:LP:AF:ID -0.00350635:0.00406876:0.408935:0.037318:rs12184312
1 754105 rs12184325 C T . PASS AF=0.037563 ES:SE:LP:AF:ID -0.00349135:0.00404298:0.408935:0.037563:rs12184325
1 754182 rs3131969 A G . PASS AF=0.869217 ES:SE:LP:AF:ID -0.000499416:0.00224424:0.0861861:0.869217:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869314 ES:SE:LP:AF:ID -0.000445764:0.00224514:0.0757207:0.869314:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037516 ES:SE:LP:AF:ID -0.00353447:0.00406071:0.420216:0.037516:rs12184313
1 754334 rs3131967 T C . PASS AF=0.86922 ES:SE:LP:AF:ID -0.00048579:0.0022442:0.0809219:0.86922:rs3131967
1 754433 rs150578204 G A . PASS AF=0.005131 ES:SE:LP:AF:ID 0.000130263:0.0115048:0.00436481:0.005131:rs150578204
1 754458 rs142682604 G T . PASS AF=0.005098 ES:SE:LP:AF:ID 0.000371202:0.0115346:0.0132283:0.005098:rs142682604
1 754503 rs3115859 G A . PASS AF=0.838133 ES:SE:LP:AF:ID 0.000561871:0.00208956:0.102373:0.838133:rs3115859
1 754629 rs10454459 A G . PASS AF=0.037529 ES:SE:LP:AF:ID -0.00363739:0.00406645:0.431798:0.037529:rs10454459
1 754964 rs3131966 C T . PASS AF=0.838766 ES:SE:LP:AF:ID 0.000442604:0.00209546:0.0809219:0.838766:rs3131966
1 755240 rs181660517 T G . PASS AF=0.013775 ES:SE:LP:AF:ID -0.00733174:0.00731136:0.49485:0.013775:rs181660517