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_6155_6.vcf.gz --id UKB-b:3563 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_6155_6.txt.gz --cohort_cases 10049 --cohort_controls 450302 --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": "2019-09-13T01:55:28.889191",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-3563/ukb-b-3563.vcf.gz; Date=Sun May 10 07:38:01 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-3563/UKB-b-3563_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-3563/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:43:44 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-3563/UKB-b-3563_data.vcf.gz ...
Read summary statistics for 5957780 SNPs.
Dropped 2676 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, 1211754 SNPs remain.
After merging with regression SNP LD, 1211754 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0028 (0.001)
Lambda GC: 1.0414
Mean Chi^2: 1.0416
Intercept: 1.0158 (0.006)
Ratio: 0.3797 (0.1435)
Analysis finished at Thu Oct 17 14:44:56 2019
Total time elapsed: 1.0m:11.96sQC metrics
Metrics
Metrics
{
"af_correlation": 0.924,
"inflation_factor": 1.0475,
"mean_EFFECT": 3.5343e-06,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 3,
"n_p_sig": 868,
"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": 53756,
"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": 1211754,
"ldsc_nsnp_merge_regression_ld": 1211754,
"ldsc_observed_scale_h2_beta": 0.0028,
"ldsc_observed_scale_h2_se": 0.001,
"ldsc_intercept_beta": 1.0158,
"ldsc_intercept_se": 0.006,
"ldsc_lambda_gc": 1.0414,
"ldsc_mean_chisq": 1.0416,
"ldsc_ratio": 0.3798
}
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 | FALSE |
| 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 | 5955121 | 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 | 5957780 | 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 | 8.672005e+00 | 5.762606e+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 | 7.857604e+07 | 5.652207e+07 | 828.0000000 | 3.198499e+07 | 6.902611e+07 | 1.144909e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.500000e-06 | 4.800000e-04 | -0.0041697 | -2.846000e-04 | 2.900000e-06 | 2.875000e-04 | 5.333800e-03 | ▁▂▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.456000e-04 | 1.472000e-04 | 0.0002937 | 3.243000e-04 | 3.884000e-04 | 5.319000e-04 | 1.513600e-03 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.929708e-01 | 2.904432e-01 | 0.0000000 | 2.399999e-01 | 4.899999e-01 | 7.400005e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.929700e-01 | 2.904171e-01 | 0.0000000 | 2.400811e-01 | 4.903238e-01 | 7.443701e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.133435e-01 | 2.529050e-01 | 0.0348300 | 9.872300e-02 | 2.318760e-01 | 4.787680e-01 | 9.651700e-01 | ▇▃▂▂▁ |
| numeric | AF_reference | 53756 | 0.9909772 | NA | NA | NA | NA | NA | NA | NA | 3.097486e-01 | 2.461453e-01 | 0.0000000 | 1.076280e-01 | 2.394170e-01 | 4.692490e-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 | -0.0005917 | 0.0005405 | 0.2700001 | 0.2736162 | 0.623763 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0000924 | 0.0005355 | 0.8600001 | 0.8630333 | 0.400432 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0011614 | 0.0008562 | 0.1700000 | 0.1749649 | 0.103549 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0002159 | 0.0005273 | 0.6800001 | 0.6821537 | 0.456866 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0012130 | 0.0010395 | 0.2399999 | 0.2432343 | 0.074519 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0004751 | 0.0006023 | 0.4299995 | 0.4302225 | 0.240923 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | -0.0001858 | 0.0007512 | 0.8000000 | 0.8046639 | 0.913452 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | 0.0000630 | 0.0005046 | 0.9000000 | 0.9005718 | 0.116339 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | 0.0003215 | 0.0007395 | 0.6600001 | 0.6637632 | 0.067289 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0006943 | 0.0003739 | 0.0629999 | 0.0633020 | 0.515638 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51218377 | rs2519461 | G | C | 0.0002561 | 0.0005843 | 0.6600001 | 0.6611518 | 0.073612 | 0.0826677 | NA |
| 22 | 51219006 | rs28729663 | G | A | -0.0003048 | 0.0004510 | 0.5000000 | 0.4991178 | 0.137972 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | 0.0002447 | 0.0005855 | 0.6800001 | 0.6759205 | 0.073734 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | -0.0000018 | 0.0007846 | 1.0000000 | 0.9981620 | 0.041941 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | -0.0002027 | 0.0007832 | 0.8000000 | 0.7958328 | 0.049723 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | 0.0002613 | 0.0005858 | 0.6600001 | 0.6555605 | 0.073224 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | -0.0008802 | 0.0006894 | 0.2000000 | 0.2016766 | 0.054493 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | -0.0008848 | 0.0006927 | 0.2000000 | 0.2015257 | 0.054122 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0002553 | 0.0005880 | 0.6600001 | 0.6641239 | 0.073060 | 0.0730831 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0002216 | 0.0003596 | 0.5400003 | 0.5377644 | 0.297978 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623763 ES:SE:LP:AF:ID -0.000591729:0.000540504:0.568636:0.623763:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400432 ES:SE:LP:AF:ID 9.23743e-05:0.000535459:0.0655015:0.400432:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103549 ES:SE:LP:AF:ID 0.00116137:0.000856201:0.769551:0.103549:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456866 ES:SE:LP:AF:ID 0.000215932:0.000527273:0.167491:0.456866:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074519 ES:SE:LP:AF:ID 0.001213:0.00103947:0.619789:0.074519:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240923 ES:SE:LP:AF:ID -0.000475115:0.000602318:0.366532:0.240923:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913452 ES:SE:LP:AF:ID -0.000185792:0.000751234:0.09691:0.913452:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116339 ES:SE:LP:AF:ID 6.30489e-05:0.000504637:0.0457575:0.116339:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067289 ES:SE:LP:AF:ID 0.000321494:0.000739537:0.180456:0.067289:rs72631875
1 706368 rs12029736 A G . PASS AF=0.515638 ES:SE:LP:AF:ID -0.00069431:0.000373875:1.20066:0.515638:rs12029736
1 715265 rs12184267 C T . PASS AF=0.036615 ES:SE:LP:AF:ID 0.000996628:0.000856164:0.619789:0.036615:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036732 ES:SE:LP:AF:ID 0.00105569:0.00085293:0.657577:0.036732:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036432 ES:SE:LP:AF:ID 0.00105656:0.000859055:0.657577:0.036432:rs12184279
1 720381 rs116801199 G T . PASS AF=0.036971 ES:SE:LP:AF:ID 0.00109249:0.000849552:0.69897:0.036971:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037068 ES:SE:LP:AF:ID 0.00100902:0.00084663:0.638272:0.037068:rs12565286
1 722670 rs116030099 T C . PASS AF=0.10121 ES:SE:LP:AF:ID 0.000332455:0.000616726:0.229148:0.10121:rs116030099
1 723891 rs2977670 G C . PASS AF=0.959108 ES:SE:LP:AF:ID -0.00116515:0.000816619:0.823909:0.959108:rs2977670
1 725060 rs865924913 A T . PASS AF=0.053259 ES:SE:LP:AF:ID 0.000571961:0.00117904:0.200659:0.053259:rs865924913
1 726794 rs28454925 C G . PASS AF=0.036587 ES:SE:LP:AF:ID 0.00104856:0.000852093:0.657577:0.036587:rs28454925
1 729632 rs116720794 C T . PASS AF=0.036902 ES:SE:LP:AF:ID 0.00112242:0.000844337:0.744727:0.036902:rs116720794
1 729679 rs4951859 C G . PASS AF=0.843209 ES:SE:LP:AF:ID -0.000429078:0.000437361:0.481486:0.843209:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055938 ES:SE:LP:AF:ID 0.000617301:0.000708012:0.420216:0.055938:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122328 ES:SE:LP:AF:ID 0.000100767:0.000478679:0.0809219:0.122328:rs58276399
1 734349 rs141242758 T C . PASS AF=0.121572 ES:SE:LP:AF:ID 0.000101425:0.000478879:0.0809219:0.121572:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132346 ES:SE:LP:AF:ID 0.000804119:0.000472012:1.05552:0.132346:rs79010578
1 752478 rs146277091 G A . PASS AF=0.036817 ES:SE:LP:AF:ID 0.000955213:0.000835819:0.60206:0.036817:rs146277091
1 752566 rs3094315 G A . PASS AF=0.838925 ES:SE:LP:AF:ID -0.000272069:0.000423545:0.283997:0.838925:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838557 ES:SE:LP:AF:ID -0.00028213:0.000423093:0.30103:0.838557:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869744 ES:SE:LP:AF:ID -4.77301e-05:0.000453975:0.0362122:0.869744:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129904 ES:SE:LP:AF:ID 9.11826e-05:0.000454902:0.0757207:0.129904:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037328 ES:SE:LP:AF:ID 0.00115141:0.000821654:0.79588:0.037328:rs12184312
1 754105 rs12184325 C T . PASS AF=0.037572 ES:SE:LP:AF:ID 0.00115999:0.000816452:0.79588:0.037572:rs12184325
1 754182 rs3131969 A G . PASS AF=0.869089 ES:SE:LP:AF:ID -8.48037e-05:0.000453092:0.0705811:0.869089:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869185 ES:SE:LP:AF:ID -5.48023e-05:0.000453271:0.0457575:0.869185:rs3131968
1 754211 rs12184313 G A . PASS AF=0.03753 ES:SE:LP:AF:ID 0.00112752:0.000819989:0.769551:0.03753:rs12184313
1 754334 rs3131967 T C . PASS AF=0.869092 ES:SE:LP:AF:ID -9.75194e-05:0.000453083:0.0809219:0.869092:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838009 ES:SE:LP:AF:ID -0.000322458:0.000421914:0.356547:0.838009:rs3115859
1 754629 rs10454459 A G . PASS AF=0.037543 ES:SE:LP:AF:ID 0.00117441:0.000821146:0.823909:0.037543:rs10454459
1 754964 rs3131966 C T . PASS AF=0.83864 ES:SE:LP:AF:ID -0.000304909:0.000423102:0.327902:0.83864:rs3131966
1 755775 rs3131965 A G . PASS AF=0.83975 ES:SE:LP:AF:ID -0.000229196:0.000428821:0.229148:0.83975:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869368 ES:SE:LP:AF:ID -7.0973e-05:0.000452552:0.0555173:0.869368:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868914 ES:SE:LP:AF:ID -8.70704e-05:0.000451412:0.0705811:0.868914:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867871 ES:SE:LP:AF:ID -7.28868e-05:0.000450552:0.0604807:0.867871:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869058 ES:SE:LP:AF:ID -7.23362e-05:0.000451783:0.0604807:0.869058:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869067 ES:SE:LP:AF:ID -7.19822e-05:0.000451817:0.0604807:0.869067:rs4951862
1 758144 rs3131956 A G . PASS AF=0.869075 ES:SE:LP:AF:ID -7.3061e-05:0.000451828:0.0604807:0.869075:rs3131956
1 758626 rs3131954 C T . PASS AF=0.869552 ES:SE:LP:AF:ID -8.05198e-05:0.00045307:0.0655015:0.869552:rs3131954
1 759036 rs114525117 G A . PASS AF=0.037594 ES:SE:LP:AF:ID 0.0010621:0.000816312:0.721246:0.037594:rs114525117
1 760912 rs1048488 C T . PASS AF=0.838286 ES:SE:LP:AF:ID -0.000313498:0.000421112:0.337242:0.838286:rs1048488
1 760998 rs148828841 C A . PASS AF=0.03703 ES:SE:LP:AF:ID 0.00107551:0.000831609:0.69897:0.03703:rs148828841