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_41246_1150.vcf.gz --id UKB-b:6351 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41246_1150.txt.gz --cohort_cases 1277 --cohort_controls 442701 --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": "2019-09-13T13:16:32.023178",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-6351/ukb-b-6351.vcf.gz; Date=Sun May 10 04:51:31 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-6351/UKB-b-6351_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-6351/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:40:19 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-6351/UKB-b-6351_data.vcf.gz ...
Read summary statistics for 2068578 SNPs.
Dropped 211 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, 529242 SNPs remain.
After merging with regression SNP LD, 529242 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0035 (0.0013)
Lambda GC: 1.0592
Mean Chi^2: 1.0553
Intercept: 1.0189 (0.0108)
Ratio: 0.3412 (0.1959)
Analysis finished at Thu Oct 17 14:40:48 2019
Total time elapsed: 29.76sQC metrics
Metrics
Metrics
{
"af_correlation": 0.7078,
"inflation_factor": 1.0475,
"mean_EFFECT": -2.2418e-07,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 1,
"n_p_sig": 54,
"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": 16436,
"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": 529242,
"ldsc_nsnp_merge_regression_ld": 529242,
"ldsc_observed_scale_h2_beta": 0.0035,
"ldsc_observed_scale_h2_se": 0.0013,
"ldsc_intercept_beta": 1.0189,
"ldsc_intercept_se": 0.0108,
"ldsc_lambda_gc": 1.0592,
"ldsc_mean_chisq": 1.0553,
"ldsc_ratio": 0.3418
}
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 | 4 | 58 | 0 | 2068369 | 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 | 2068578 | 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 | 8.645272e+00 | 5.765224e+00 | 1.0000e+00 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.200000e+01 | ▇▅▅▂▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.875364e+07 | 5.666956e+07 | 5.6870e+03 | 3.184053e+07 | 6.930991e+07 | 1.148866e+08 | 2.491722e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -2.000000e-07 | 1.215000e-04 | -6.3130e-04 | -8.230000e-05 | -6.000000e-07 | 8.130000e-05 | 7.508000e-04 | ▁▃▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.186000e-04 | 4.700000e-06 | 1.0960e-04 | 1.148000e-04 | 1.174000e-04 | 1.216000e-04 | 2.274000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.931719e-01 | 2.907327e-01 | 0.0000e+00 | 2.399999e-01 | 4.899999e-01 | 7.499995e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.931724e-01 | 2.907062e-01 | 0.0000e+00 | 2.401652e-01 | 4.896879e-01 | 7.458156e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.670370e-01 | 1.289618e-01 | 2.7408e-01 | 3.537750e-01 | 4.507930e-01 | 5.723530e-01 | 7.259200e-01 | ▇▆▆▅▃ |
| numeric | AF_reference | 16436 | 0.9920544 | NA | NA | NA | NA | NA | NA | NA | 4.469612e-01 | 1.627411e-01 | 1.9970e-04 | 3.210860e-01 | 4.367010e-01 | 5.650960e-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 | -0.0002755 | 0.0002017 | 0.1700000 | 0.1720329 | 0.623751 | 0.782149 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0000914 | 0.0001998 | 0.6499995 | 0.6473711 | 0.400423 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0000839 | 0.0001967 | 0.6700003 | 0.6697230 | 0.456856 | 0.420727 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0001280 | 0.0001395 | 0.3599996 | 0.3590516 | 0.515726 | 0.275160 | NA |
| 1 | 763394 | rs369924889 | G | A | 0.0001783 | 0.0001636 | 0.2800000 | 0.2757044 | 0.706662 | 0.617612 | NA |
| 1 | 814495 | rs74461805 | C | A | -0.0000803 | 0.0001914 | 0.6700003 | 0.6745679 | 0.340409 | NA | NA |
| 1 | 830181 | rs28444699 | A | G | -0.0001325 | 0.0001280 | 0.2999998 | 0.3004404 | 0.697226 | 0.691294 | NA |
| 1 | 831489 | rs4970385 | C | T | -0.0001093 | 0.0001257 | 0.3800004 | 0.3842773 | 0.705392 | 0.649161 | NA |
| 1 | 831909 | rs9697642 | C | T | -0.0001148 | 0.0001257 | 0.3599996 | 0.3609775 | 0.705436 | 0.648562 | NA |
| 1 | 832066 | rs9697380 | G | C | -0.0001103 | 0.0001257 | 0.3800004 | 0.3802245 | 0.705623 | 0.664337 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51171693 | rs756638 | G | A | 0.0000310 | 0.0001301 | 0.8100000 | 0.8118815 | 0.282296 | 0.304912 | NA |
| 22 | 51174048 | rs9628245 | G | C | 0.0001567 | 0.0001296 | 0.2300001 | 0.2265342 | 0.380238 | 0.433107 | NA |
| 22 | 51180501 | rs5770999 | T | C | -0.0000513 | 0.0001313 | 0.6999999 | 0.6960673 | 0.713712 | 0.636981 | NA |
| 22 | 51181919 | rs9616825 | G | C | -0.0000363 | 0.0001307 | 0.7800007 | 0.7812431 | 0.695500 | 0.619409 | NA |
| 22 | 51182485 | rs6009961 | A | G | -0.0000434 | 0.0001317 | 0.7400005 | 0.7418724 | 0.715546 | 0.638379 | NA |
| 22 | 51186143 | rs2879914 | T | C | 0.0000160 | 0.0001222 | 0.9000000 | 0.8955440 | 0.382027 | 0.273363 | NA |
| 22 | 51186228 | rs3865766 | C | T | 0.0000244 | 0.0001191 | 0.8400000 | 0.8374025 | 0.451295 | 0.453275 | NA |
| 22 | 51197266 | rs61290853 | A | G | -0.0001100 | 0.0001229 | 0.3700002 | 0.3707733 | 0.386525 | 0.422923 | NA |
| 22 | 51212875 | rs2238837 | A | C | 0.0000772 | 0.0001311 | 0.5600000 | 0.5560946 | 0.331619 | 0.372404 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0000486 | 0.0001342 | 0.7199992 | 0.7170587 | 0.298160 | 0.205072 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623751 ES:SE:LP:AF:ID -0.000275455:0.000201695:0.769551:0.623751:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400423 ES:SE:LP:AF:ID -9.1408e-05:0.000199835:0.187087:0.400423:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456856 ES:SE:LP:AF:ID -8.39109e-05:0.00019673:0.173925:0.456856:rs6702460
1 706368 rs12029736 A G . PASS AF=0.515726 ES:SE:LP:AF:ID -0.000127987:0.000139545:0.443698:0.515726:rs12029736
1 763394 rs3115847 G A . PASS AF=0.706662 ES:SE:LP:AF:ID 0.000178312:0.000163586:0.552842:0.706662:rs3115847
1 814495 rs74461805 C A . PASS AF=0.340409 ES:SE:LP:AF:ID -8.03496e-05:0.00019136:0.173925:0.340409:rs74461805
1 830181 rs28444699 A G . PASS AF=0.697226 ES:SE:LP:AF:ID -0.000132524:0.000127982:0.522879:0.697226:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705392 ES:SE:LP:AF:ID -0.000109333:0.000125664:0.420216:0.705392:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705436 ES:SE:LP:AF:ID -0.00011479:0.000125659:0.443698:0.705436:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705623 ES:SE:LP:AF:ID -0.000110268:0.000125664:0.420216:0.705623:rs9697380
1 832318 rs4500250 C A . PASS AF=0.70565 ES:SE:LP:AF:ID -0.000110282:0.000125677:0.420216:0.70565:rs4500250
1 832918 rs28765502 T C . PASS AF=0.294383 ES:SE:LP:AF:ID 0.000109709:0.000125671:0.420216:0.294383:rs28765502
1 840753 rs4970382 T C . PASS AF=0.400296 ES:SE:LP:AF:ID 2.9076e-05:0.000115929:0.09691:0.400296:rs4970382
1 843405 rs11516185 A G . PASS AF=0.36261 ES:SE:LP:AF:ID -8.31084e-05:0.000143918:0.251812:0.36261:rs11516185
1 850218 rs6664536 T A . PASS AF=0.590337 ES:SE:LP:AF:ID -0.000152147:0.000115607:0.721246:0.590337:rs6664536
1 850371 rs6679046 G T . PASS AF=0.603698 ES:SE:LP:AF:ID -0.000159361:0.00011626:0.769551:0.603698:rs6679046
1 850780 rs6657440 C T . PASS AF=0.60392 ES:SE:LP:AF:ID -0.000153495:0.000116244:0.721246:0.60392:rs6657440
1 852037 rs4970463 G A . PASS AF=0.589699 ES:SE:LP:AF:ID -0.000160106:0.000115794:0.769551:0.589699:rs4970463
1 852063 rs28436996 G A . PASS AF=0.589684 ES:SE:LP:AF:ID -0.000159219:0.000115743:0.769551:0.589684:rs28436996
1 852875 rs13303369 C T . PASS AF=0.60767 ES:SE:LP:AF:ID -0.000190986:0.000116509:1:0.60767:rs13303369
1 853954 rs1806509 C A . PASS AF=0.607829 ES:SE:LP:AF:ID -0.000186475:0.000116524:0.958607:0.607829:rs1806509
1 854777 rs13303019 A G . PASS AF=0.610313 ES:SE:LP:AF:ID -0.000188497:0.000116639:0.958607:0.610313:rs13303019
1 854978 rs13303057 A C . PASS AF=0.603265 ES:SE:LP:AF:ID -0.000163588:0.000116288:0.79588:0.603265:rs13303057
1 855075 rs6673914 C G . PASS AF=0.610332 ES:SE:LP:AF:ID -0.000189169:0.000116641:1:0.610332:rs6673914
1 856099 rs28534711 T G . PASS AF=0.389943 ES:SE:LP:AF:ID 0.000188481:0.000116665:0.958607:0.389943:rs28534711
1 856108 rs28742275 A G . PASS AF=0.389926 ES:SE:LP:AF:ID 0.000188668:0.000116671:0.958607:0.389926:rs28742275
1 856476 rs4040605 A G . PASS AF=0.350343 ES:SE:LP:AF:ID 0.000180063:0.000119838:0.886057:0.350343:rs4040605
1 866893 rs2880024 T C . PASS AF=0.610567 ES:SE:LP:AF:ID -5.07814e-05:0.000117263:0.180456:0.610567:rs2880024
1 868418 rs28546443 C T . PASS AF=0.297804 ES:SE:LP:AF:ID 0.000122619:0.00012889:0.468521:0.297804:rs28546443
1 870645 rs28576697 T C . PASS AF=0.291186 ES:SE:LP:AF:ID 0.000113273:0.000127821:0.420216:0.291186:rs28576697
1 871334 rs4072383 G T . PASS AF=0.72069 ES:SE:LP:AF:ID -0.000113316:0.000127032:0.431798:0.72069:rs4072383
1 873558 rs1110052 G T . PASS AF=0.715324 ES:SE:LP:AF:ID -0.000112739:0.000126033:0.431798:0.715324:rs1110052
1 875770 rs4970379 A G . PASS AF=0.599969 ES:SE:LP:AF:ID 4.24652e-06:0.000118237:0.0132283:0.599969:rs4970379
1 881627 rs2272757 G A . PASS AF=0.652304 ES:SE:LP:AF:ID -2.0119e-05:0.00011944:0.0604807:0.652304:rs2272757
1 891059 rs13303065 C T . PASS AF=0.652357 ES:SE:LP:AF:ID -3.10845e-05:0.000119423:0.102373:0.652357:rs13303065
1 891945 rs13303106 A G . PASS AF=0.652415 ES:SE:LP:AF:ID -2.88675e-05:0.000119562:0.091515:0.652415:rs13303106
1 903245 rs28690976 A G . PASS AF=0.566905 ES:SE:LP:AF:ID 4.58573e-05:0.000118741:0.154902:0.566905:rs28690976
1 909073 rs3892467 C T . PASS AF=0.386738 ES:SE:LP:AF:ID -6.99876e-05:0.000118435:0.259637:0.386738:rs3892467
1 909238 rs3829740 G C . PASS AF=0.5714 ES:SE:LP:AF:ID 2.78962e-05:0.000114709:0.091515:0.5714:rs3829740
1 910394 rs28477686 C T . PASS AF=0.324416 ES:SE:LP:AF:ID -1.05671e-05:0.000124369:0.0315171:0.324416:rs28477686
1 912049 rs7367995 T C . PASS AF=0.585275 ES:SE:LP:AF:ID -3.16924e-05:0.000115876:0.107905:0.585275:rs7367995
1 913889 rs2340596 G A . PASS AF=0.59924 ES:SE:LP:AF:ID -1.30132e-05:0.000116067:0.0409586:0.59924:rs2340596
1 914333 rs13302979 C G . PASS AF=0.602544 ES:SE:LP:AF:ID -2.84366e-05:0.000116415:0.091515:0.602544:rs13302979
1 914852 rs13303368 G C . PASS AF=0.600088 ES:SE:LP:AF:ID -8.8964e-06:0.000116194:0.0268721:0.600088:rs13303368
1 914940 rs13303033 T C . PASS AF=0.584327 ES:SE:LP:AF:ID -2.55388e-05:0.000115551:0.0809219:0.584327:rs13303033
1 916834 rs6694632 G A . PASS AF=0.589156 ES:SE:LP:AF:ID -4.17603e-05:0.000115726:0.142668:0.589156:rs6694632
1 918384 rs13303118 G T . PASS AF=0.584242 ES:SE:LP:AF:ID -1.33771e-05:0.000115497:0.0409586:0.584242:rs13303118
1 918573 rs2341354 A G . PASS AF=0.58938 ES:SE:LP:AF:ID -3.85053e-05:0.000115638:0.130768:0.58938:rs2341354
1 919501 rs4970414 G T . PASS AF=0.583967 ES:SE:LP:AF:ID 6.81444e-05:0.000119601:0.244125:0.583967:rs4970414
1 921716 rs13303278 C A . PASS AF=0.56799 ES:SE:LP:AF:ID 0.000147225:0.000115417:0.69897:0.56799:rs13303278