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_20002_1226.vcf.gz --id UKB-b:19732 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20002_1226.txt.gz --cohort_cases 22687 --cohort_controls 440246 --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": "2019-09-13T08:03:55.971023",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-19732/ukb-b-19732.vcf.gz; Date=Sat May 9 23:11:40 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-19732/UKB-b-19732_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19732/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:41:34 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19732/UKB-b-19732_data.vcf.gz ...
Read summary statistics for 7112787 SNPs.
Dropped 4472 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, 1266990 SNPs remain.
After merging with regression SNP LD, 1266990 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0454 (0.004)
Lambda GC: 1.258
Mean Chi^2: 1.5151
Intercept: 1.0732 (0.0139)
Ratio: 0.1421 (0.027)
Analysis finished at Thu Oct 17 14:42:52 2019
Total time elapsed: 1.0m:17.98sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9368,
"inflation_factor": 1.2544,
"mean_EFFECT": -1.4194e-07,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 122,
"n_p_sig": 23676,
"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": 65526,
"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": 1266990,
"ldsc_nsnp_merge_regression_ld": 1266990,
"ldsc_observed_scale_h2_beta": 0.0454,
"ldsc_observed_scale_h2_se": 0.004,
"ldsc_intercept_beta": 1.0732,
"ldsc_intercept_se": 0.0139,
"ldsc_lambda_gc": 1.258,
"ldsc_mean_chisq": 1.5151,
"ldsc_ratio": 0.1421
}
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 | 7108337 | 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 | 7112787 | 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 | 8.664471e+00 | 5.764278e+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 | 7.863669e+07 | 5.646297e+07 | 828.0000000 | 3.214419e+07 | 6.906112e+07 | 1.145256e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -1.000000e-07 | 1.024400e-03 | -0.0203664 | -5.161000e-04 | 4.000000e-07 | 5.122000e-04 | 2.096940e-02 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.868000e-04 | 3.788000e-04 | 0.0004272 | 4.836000e-04 | 6.270000e-04 | 9.937000e-04 | 4.632400e-03 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.640929e-01 | 2.994579e-01 | 0.0000000 | 2.000000e-01 | 4.500005e-01 | 7.199992e-01 | 1.000000e+00 | ▇▆▆▆▆ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.640957e-01 | 2.994342e-01 | 0.0000000 | 1.967820e-01 | 4.522085e-01 | 7.235864e-01 | 9.999995e-01 | ▇▆▆▆▆ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.721858e-01 | 2.600924e-01 | 0.0154280 | 5.653200e-02 | 1.743640e-01 | 4.276880e-01 | 9.845720e-01 | ▇▃▂▁▁ |
| numeric | AF_reference | 65526 | 0.9907876 | NA | NA | NA | NA | NA | NA | NA | 2.706098e-01 | 2.520511e-01 | 0.0000000 | 6.369810e-02 | 1.867010e-01 | 4.205270e-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 | 0.0021605 | 0.0007856 | 0.0060000 | 0.0059579 | 0.623763 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0014367 | 0.0007783 | 0.0649995 | 0.0648853 | 0.400401 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | -0.0006515 | 0.0012443 | 0.5999997 | 0.6005970 | 0.103556 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0001848 | 0.0007663 | 0.8100000 | 0.8094490 | 0.456851 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | -0.0001048 | 0.0015110 | 0.9400001 | 0.9447076 | 0.074508 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0003085 | 0.0008753 | 0.7199992 | 0.7244763 | 0.240960 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | 0.0005252 | 0.0010920 | 0.6300007 | 0.6305397 | 0.913473 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0005601 | 0.0007336 | 0.4500005 | 0.4451636 | 0.116325 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | -0.0009734 | 0.0010750 | 0.3700002 | 0.3651771 | 0.067285 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0002626 | 0.0005434 | 0.6300007 | 0.6288812 | 0.515650 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219006 | rs28729663 | G | A | -0.0004514 | 0.0006561 | 0.4899999 | 0.4914670 | 0.137953 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | -0.0002915 | 0.0008517 | 0.7300002 | 0.7321470 | 0.073747 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | 0.0002127 | 0.0011413 | 0.8499999 | 0.8521232 | 0.041955 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | -0.0000556 | 0.0011395 | 0.9599999 | 0.9610860 | 0.049731 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | -0.0001666 | 0.0008522 | 0.8400000 | 0.8449893 | 0.073238 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | -0.0008394 | 0.0010033 | 0.4000000 | 0.4028417 | 0.054459 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | -0.0008968 | 0.0010082 | 0.3700002 | 0.3737141 | 0.054088 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | -0.0002353 | 0.0008553 | 0.7800007 | 0.7832493 | 0.073073 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | -0.0026044 | 0.0020149 | 0.2000000 | 0.1961609 | 0.020043 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0009159 | 0.0005231 | 0.0800000 | 0.0799741 | 0.297971 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623763 ES:SE:LP:AF:ID 0.00216046:0.000785597:2.22185:0.623763:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID -0.00143674:0.000778279:1.18709:0.400401:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103556 ES:SE:LP:AF:ID -0.000651454:0.00124432:0.221849:0.103556:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456851 ES:SE:LP:AF:ID 0.000184789:0.000766324:0.091515:0.456851:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074508 ES:SE:LP:AF:ID -0.000104793:0.00151098:0.0268721:0.074508:rs8179466
1 534192 rs6680723 C T . PASS AF=0.24096 ES:SE:LP:AF:ID -0.000308539:0.000875331:0.142668:0.24096:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913473 ES:SE:LP:AF:ID 0.000525226:0.00109202:0.200659:0.913473:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116325 ES:SE:LP:AF:ID -0.000560085:0.000733573:0.346787:0.116325:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067285 ES:SE:LP:AF:ID -0.000973433:0.00107497:0.431798:0.067285:rs72631875
1 706368 rs12029736 A G . PASS AF=0.51565 ES:SE:LP:AF:ID 0.000262624:0.000543395:0.200659:0.51565:rs12029736
1 714596 rs149887893 T C . PASS AF=0.033004 ES:SE:LP:AF:ID 0.00100173:0.00136994:0.337242:0.033004:rs149887893
1 715265 rs12184267 C T . PASS AF=0.036621 ES:SE:LP:AF:ID 0.00108429:0.00124434:0.420216:0.036621:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036737 ES:SE:LP:AF:ID 0.00101366:0.00123963:0.387216:0.036737:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036437 ES:SE:LP:AF:ID 0.00102123:0.00124857:0.387216:0.036437:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016407 ES:SE:LP:AF:ID -4.65822e-05:0.00192245:0.00877392:0.016407:rs144155419
1 720381 rs116801199 G T . PASS AF=0.036976 ES:SE:LP:AF:ID 0.00104922:0.00123474:0.39794:0.036976:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037072 ES:SE:LP:AF:ID 0.000957781:0.00123051:0.356547:0.037072:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101199 ES:SE:LP:AF:ID 0.000248405:0.000896567:0.107905:0.101199:rs116030099
1 723891 rs2977670 G C . PASS AF=0.959096 ES:SE:LP:AF:ID -0.00113392:0.0011868:0.468521:0.959096:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031451 ES:SE:LP:AF:ID -0.000215723:0.00215439:0.0362122:0.031451:rs12126395
1 725060 rs865924913 A T . PASS AF=0.053254 ES:SE:LP:AF:ID -0.00188305:0.00171391:0.568636:0.053254:rs865924913
1 726794 rs28454925 C G . PASS AF=0.03659 ES:SE:LP:AF:ID 0.000934992:0.00123847:0.346787:0.03659:rs28454925
1 729632 rs116720794 C T . PASS AF=0.036907 ES:SE:LP:AF:ID 0.00105159:0.0012272:0.408935:0.036907:rs116720794
1 729679 rs4951859 C G . PASS AF=0.843212 ES:SE:LP:AF:ID -3.67646e-05:0.000635734:0.0222764:0.843212:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055911 ES:SE:LP:AF:ID -0.000134391:0.00102935:0.0457575:0.055911:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122307 ES:SE:LP:AF:ID -0.000437488:0.000695866:0.275724:0.122307:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025713 ES:SE:LP:AF:ID 0.00536489:0.00171168:2.76955:0.025713:rs369030935
1 734349 rs141242758 T C . PASS AF=0.121549 ES:SE:LP:AF:ID -0.000450088:0.000696159:0.283997:0.121549:rs141242758
1 736289 rs79010578 T A . PASS AF=0.13233 ES:SE:LP:AF:ID -4.1743e-05:0.000686133:0.0222764:0.13233:rs79010578
1 752478 rs146277091 G A . PASS AF=0.036821 ES:SE:LP:AF:ID 0.000824716:0.00121479:0.30103:0.036821:rs146277091
1 752566 rs3094315 G A . PASS AF=0.838951 ES:SE:LP:AF:ID 3.81733e-05:0.000615663:0.0222764:0.838951:rs3094315
1 752721 rs3131972 A G . PASS AF=0.83858 ES:SE:LP:AF:ID 5.63361e-05:0.000615001:0.0315171:0.83858:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869781 ES:SE:LP:AF:ID 0.000399711:0.00065992:0.267606:0.869781:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129871 ES:SE:LP:AF:ID -0.000433741:0.000661271:0.29243:0.129871:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037332 ES:SE:LP:AF:ID 0.000921818:0.00119419:0.356547:0.037332:rs12184312
1 754105 rs12184325 C T . PASS AF=0.037576 ES:SE:LP:AF:ID 0.000990626:0.00118664:0.39794:0.037576:rs12184325
1 754182 rs3131969 A G . PASS AF=0.869123 ES:SE:LP:AF:ID 0.000416739:0.000658628:0.275724:0.869123:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869221 ES:SE:LP:AF:ID 0.000422315:0.000658889:0.283997:0.869221:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037534 ES:SE:LP:AF:ID 0.000992944:0.00119178:0.39794:0.037534:rs12184313
1 754334 rs3131967 T C . PASS AF=0.869126 ES:SE:LP:AF:ID 0.000413642:0.000658615:0.275724:0.869126:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838033 ES:SE:LP:AF:ID -2.31685e-06:0.000613294:-0:0.838033:rs3115859
1 754629 rs10454459 A G . PASS AF=0.037547 ES:SE:LP:AF:ID 0.000992619:0.00119346:0.387216:0.037547:rs10454459
1 754964 rs3131966 C T . PASS AF=0.838664 ES:SE:LP:AF:ID 2.24166e-05:0.000615017:0.0132283:0.838664:rs3131966
1 755775 rs3131965 A G . PASS AF=0.839777 ES:SE:LP:AF:ID 6.79431e-05:0.000623335:0.0409586:0.839777:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869405 ES:SE:LP:AF:ID 0.000321745:0.000657853:0.207608:0.869405:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868952 ES:SE:LP:AF:ID 0.000278609:0.000656198:0.173925:0.868952:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867905 ES:SE:LP:AF:ID 0.000386064:0.000654941:0.251812:0.867905:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869095 ES:SE:LP:AF:ID 0.000332216:0.000656735:0.21467:0.869095:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869104 ES:SE:LP:AF:ID 0.000331253:0.000656786:0.21467:0.869104:rs4951862
1 758144 rs3131956 A G . PASS AF=0.869112 ES:SE:LP:AF:ID 0.000329625:0.000656801:0.207608:0.869112:rs3131956