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_41230_5JY.vcf.gz --id UKB-b:14654 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41230_5JY.txt.gz --cohort_cases 1325 --cohort_controls 461685 --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": "2019-09-12T22:13:58.601328",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-14654/ukb-b-14654.vcf.gz; Date=Sun May 10 04:00:36 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-14654/UKB-b-14654_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-14654/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:43:45 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-14654/UKB-b-14654_data.vcf.gz ...
Read summary statistics for 2172369 SNPs.
Dropped 230 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, 554311 SNPs remain.
After merging with regression SNP LD, 554311 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0008 (0.0016)
Lambda GC: 1.1693
Mean Chi^2: 1.1642
Intercept: 1.1553 (0.013)
Ratio: 0.946 (0.0791)
Analysis finished at Thu Oct 17 14:44:18 2019
Total time elapsed: 32.39sQC metrics
Metrics
Metrics
{
"af_correlation": 0.7244,
"inflation_factor": 1.1999,
"mean_EFFECT": -1.9599e-06,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 0,
"n_p_sig": 0,
"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": 17226,
"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": 554311,
"ldsc_nsnp_merge_regression_ld": 554311,
"ldsc_observed_scale_h2_beta": 0.0008,
"ldsc_observed_scale_h2_se": 0.0016,
"ldsc_intercept_beta": 1.1553,
"ldsc_intercept_se": 0.013,
"ldsc_lambda_gc": 1.1693,
"ldsc_mean_chisq": 1.1642,
"ldsc_ratio": 0.9458
}
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 | TRUE |
| 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 | 2172141 | 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 | 2172369 | 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 | 8.645843e+00 | 5.765007e+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 | 7.868908e+07 | 5.664640e+07 | 5687.0000000 | 3.179543e+07 | 6.923238e+07 | 1.148013e+08 | 2.491722e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -2.000000e-06 | 1.257000e-04 | -0.0006687 | -8.730000e-05 | -1.900000e-06 | 8.260000e-05 | 6.490000e-04 | ▁▂▇▂▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.161000e-04 | 5.000000e-06 | 0.0001069 | 1.120000e-04 | 1.147000e-04 | 1.194000e-04 | 2.250000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.744409e-01 | 2.945993e-01 | 0.0000002 | 2.099999e-01 | 4.600002e-01 | 7.300002e-01 | 1.000000e+00 | ▇▇▆▆▆ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.744434e-01 | 2.945758e-01 | 0.0000002 | 2.139748e-01 | 4.632683e-01 | 7.294161e-01 | 9.999998e-01 | ▇▇▆▆▆ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.639354e-01 | 1.346042e-01 | 0.2641510 | 3.457960e-01 | 4.464580e-01 | 5.734640e-01 | 7.358490e-01 | ▇▆▅▅▃ |
| numeric | AF_reference | 17226 | 0.9920704 | NA | NA | NA | NA | NA | NA | NA | 4.442543e-01 | 1.660857e-01 | 0.0001997 | 3.152960e-01 | 4.329070e-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 | -0.0002722 | 0.0001966 | 0.1700000 | 0.1661372 | 0.623765 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0000200 | 0.0001948 | 0.9199999 | 0.9181753 | 0.400401 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0000422 | 0.0001918 | 0.8300000 | 0.8256632 | 0.456846 | 0.4207270 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0003398 | 0.0001360 | 0.0120000 | 0.0124485 | 0.515645 | 0.2751600 | NA |
| 1 | 763394 | rs369924889 | G | A | 0.0000162 | 0.0001594 | 0.9199999 | 0.9190975 | 0.706753 | 0.6176120 | NA |
| 1 | 776546 | rs12124819 | A | G | -0.0000243 | 0.0001454 | 0.8700001 | 0.8671971 | 0.265385 | 0.0756789 | NA |
| 1 | 814495 | rs74461805 | C | A | -0.0000597 | 0.0001865 | 0.7499995 | 0.7488282 | 0.340396 | NA | NA |
| 1 | 830181 | rs28444699 | A | G | -0.0001561 | 0.0001247 | 0.2099999 | 0.2108760 | 0.697255 | 0.6912940 | NA |
| 1 | 831489 | rs4970385 | C | T | -0.0002243 | 0.0001225 | 0.0669993 | 0.0670297 | 0.705397 | 0.6491610 | NA |
| 1 | 831909 | rs9697642 | C | T | -0.0002250 | 0.0001225 | 0.0659994 | 0.0662129 | 0.705442 | 0.6485620 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51174048 | rs9628245 | G | C | -0.0000992 | 0.0001264 | 0.4299995 | 0.4322815 | 0.380135 | 0.433107 | NA |
| 22 | 51180501 | rs5770999 | T | C | -0.0001135 | 0.0001280 | 0.3800004 | 0.3752832 | 0.713656 | 0.636981 | NA |
| 22 | 51181919 | rs9616825 | G | C | -0.0000736 | 0.0001274 | 0.5600000 | 0.5634339 | 0.695470 | 0.619409 | NA |
| 22 | 51182485 | rs6009961 | A | G | -0.0001446 | 0.0001284 | 0.2599998 | 0.2602799 | 0.715502 | 0.638379 | NA |
| 22 | 51186143 | rs2879914 | T | C | -0.0000117 | 0.0001191 | 0.9199999 | 0.9216475 | 0.381825 | 0.273363 | NA |
| 22 | 51186228 | rs3865766 | C | T | -0.0001008 | 0.0001161 | 0.3900004 | 0.3851303 | 0.451061 | 0.453275 | NA |
| 22 | 51197266 | rs61290853 | A | G | -0.0001106 | 0.0001199 | 0.3599996 | 0.3562766 | 0.386333 | 0.422923 | NA |
| 22 | 51211106 | rs9628250 | T | C | 0.0000692 | 0.0001345 | 0.6100002 | 0.6069733 | 0.271547 | 0.167133 | NA |
| 22 | 51212875 | rs2238837 | A | C | 0.0000090 | 0.0001278 | 0.9400001 | 0.9441376 | 0.331457 | 0.372404 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0000159 | 0.0001308 | 0.9000000 | 0.9032179 | 0.297974 | 0.205072 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623765 ES:SE:LP:AF:ID -0.000272231:0.000196596:0.769551:0.623765:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID 2.00088e-05:0.000194766:0.0362122:0.400401:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456846 ES:SE:LP:AF:ID -4.22406e-05:0.00019177:0.0809219:0.456846:rs6702460
1 706368 rs12029736 A G . PASS AF=0.515645 ES:SE:LP:AF:ID -0.000339847:0.000135984:1.92082:0.515645:rs12029736
1 763394 rs3115847 G A . PASS AF=0.706753 ES:SE:LP:AF:ID 1.61932e-05:0.000159428:0.0362122:0.706753:rs3115847
1 776546 rs12124819 A G . PASS AF=0.265385 ES:SE:LP:AF:ID -2.43054e-05:0.00014535:0.0604807:0.265385:rs12124819
1 814495 rs74461805 C A . PASS AF=0.340396 ES:SE:LP:AF:ID -5.97007e-05:0.000186457:0.124939:0.340396:rs74461805
1 830181 rs28444699 A G . PASS AF=0.697255 ES:SE:LP:AF:ID -0.000156081:0.000124749:0.677781:0.697255:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705397 ES:SE:LP:AF:ID -0.000224341:0.000122492:1.17393:0.705397:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705442 ES:SE:LP:AF:ID -0.000225008:0.000122488:1.18046:0.705442:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705627 ES:SE:LP:AF:ID -0.000226741:0.000122493:1.19382:0.705627:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705655 ES:SE:LP:AF:ID -0.000227435:0.000122506:1.20066:0.705655:rs4500250
1 832398 rs4553118 T C . PASS AF=0.730129 ES:SE:LP:AF:ID -0.000180428:0.000125844:0.823909:0.730129:rs4553118
1 832918 rs28765502 T C . PASS AF=0.294377 ES:SE:LP:AF:ID 0.000227137:0.000122501:1.19382:0.294377:rs28765502
1 836896 rs28705752 T C . PASS AF=0.269511 ES:SE:LP:AF:ID 0.000136761:0.000124872:0.568636:0.269511:rs28705752
1 839103 rs28562941 A G . PASS AF=0.270019 ES:SE:LP:AF:ID 0.000121896:0.00012496:0.481486:0.270019:rs28562941
1 840753 rs4970382 T C . PASS AF=0.400124 ES:SE:LP:AF:ID 0.000237007:0.000112988:1.4437:0.400124:rs4970382
1 843405 rs11516185 A G . PASS AF=0.362606 ES:SE:LP:AF:ID 8.13752e-06:0.000140259:0.0222764:0.362606:rs11516185
1 850218 rs6664536 T A . PASS AF=0.590331 ES:SE:LP:AF:ID -0.000359389:0.000112659:2.85387:0.590331:rs6664536
1 850371 rs6679046 G T . PASS AF=0.603723 ES:SE:LP:AF:ID -0.000367744:0.000113291:2.92082:0.603723:rs6679046
1 850780 rs6657440 C T . PASS AF=0.603942 ES:SE:LP:AF:ID -0.000364164:0.000113275:2.88606:0.603942:rs6657440
1 852037 rs4970463 G A . PASS AF=0.589686 ES:SE:LP:AF:ID -0.000350984:0.000112842:2.72125:0.589686:rs4970463
1 852063 rs28436996 G A . PASS AF=0.589665 ES:SE:LP:AF:ID -0.000350045:0.000112791:2.72125:0.589665:rs28436996
1 852875 rs13303369 C T . PASS AF=0.607671 ES:SE:LP:AF:ID -0.000324054:0.000113528:2.36653:0.607671:rs13303369
1 853954 rs1806509 C A . PASS AF=0.607829 ES:SE:LP:AF:ID -0.000323582:0.000113543:2.35655:0.607829:rs1806509
1 854777 rs13303019 A G . PASS AF=0.610316 ES:SE:LP:AF:ID -0.000319944:0.000113655:2.3098:0.610316:rs13303019
1 854978 rs13303057 A C . PASS AF=0.603283 ES:SE:LP:AF:ID -0.000361529:0.000113319:2.85387:0.603283:rs13303057
1 855075 rs6673914 C G . PASS AF=0.610337 ES:SE:LP:AF:ID -0.000320188:0.000113657:2.31876:0.610337:rs6673914
1 856099 rs28534711 T G . PASS AF=0.389936 ES:SE:LP:AF:ID 0.000320846:0.000113679:2.31876:0.389936:rs28534711
1 856108 rs28742275 A G . PASS AF=0.38992 ES:SE:LP:AF:ID 0.000321427:0.000113685:2.3279:0.38992:rs28742275
1 856476 rs4040605 A G . PASS AF=0.350356 ES:SE:LP:AF:ID 0.000235684:0.000116786:1.35655:0.350356:rs4040605
1 866893 rs2880024 T C . PASS AF=0.610552 ES:SE:LP:AF:ID -0.000162949:0.000114295:0.823909:0.610552:rs2880024
1 868418 rs28546443 C T . PASS AF=0.297867 ES:SE:LP:AF:ID -2.71618e-05:0.000125577:0.0809219:0.297867:rs28546443
1 870645 rs28576697 T C . PASS AF=0.291285 ES:SE:LP:AF:ID 0.000167736:0.000124578:0.744727:0.291285:rs28576697
1 871334 rs4072383 G T . PASS AF=0.72062 ES:SE:LP:AF:ID -0.000224892:0.000123806:1.16115:0.72062:rs4072383
1 872352 rs1806780 G C . PASS AF=0.267527 ES:SE:LP:AF:ID 0.000268086:0.000125484:1.48149:0.267527:rs1806780
1 873558 rs1110052 G T . PASS AF=0.715251 ES:SE:LP:AF:ID -0.000187318:0.000122822:0.886057:0.715251:rs1110052
1 875770 rs4970379 A G . PASS AF=0.600085 ES:SE:LP:AF:ID -7.32269e-05:0.000115245:0.275724:0.600085:rs4970379
1 881627 rs2272757 G A . PASS AF=0.652393 ES:SE:LP:AF:ID -0.000138706:0.000116417:0.638272:0.652393:rs2272757
1 891059 rs13303065 C T . PASS AF=0.652432 ES:SE:LP:AF:ID -0.000124155:0.0001164:0.537602:0.652432:rs13303065
1 891945 rs13303106 A G . PASS AF=0.652494 ES:SE:LP:AF:ID -0.00012467:0.000116535:0.552842:0.652494:rs13303106
1 900505 rs28705211 G C . PASS AF=0.271763 ES:SE:LP:AF:ID 8.3611e-05:0.000125983:0.29243:0.271763:rs28705211
1 903245 rs28690976 A G . PASS AF=0.566938 ES:SE:LP:AF:ID -5.62336e-05:0.000115745:0.200659:0.566938:rs28690976
1 909073 rs3892467 C T . PASS AF=0.386681 ES:SE:LP:AF:ID -4.85701e-05:0.00011543:0.173925:0.386681:rs3892467
1 909238 rs3829740 G C . PASS AF=0.571408 ES:SE:LP:AF:ID 2.08933e-05:0.000111791:0.0705811:0.571408:rs3829740
1 910394 rs28477686 C T . PASS AF=0.324458 ES:SE:LP:AF:ID 0.000247301:0.000121169:1.38722:0.324458:rs28477686
1 912049 rs7367995 T C . PASS AF=0.585249 ES:SE:LP:AF:ID -0.000244631:0.000112924:1.52288:0.585249:rs7367995
1 913889 rs2340596 G A . PASS AF=0.59921 ES:SE:LP:AF:ID -0.000225251:0.000113108:1.33724:0.59921:rs2340596
1 914333 rs13302979 C G . PASS AF=0.602516 ES:SE:LP:AF:ID -0.000205258:0.00011345:1.1549:0.602516:rs13302979
1 914852 rs13303368 G C . PASS AF=0.600074 ES:SE:LP:AF:ID -0.000233187:0.000113232:1.40894:0.600074:rs13303368