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_41200_W581.vcf.gz --id UKB-b:6725 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41200_W581.txt.gz --cohort_cases 1346 --cohort_controls 461664 --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": "2019-09-12T16:18:35.750693",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-6725/ukb-b-6725.vcf.gz; Date=Sun May 10 00:37:47 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-6725/UKB-b-6725_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-6725/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:40:17 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-6725/UKB-b-6725_data.vcf.gz ...
Read summary statistics for 2215398 SNPs.
Dropped 235 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, 564639 SNPs remain.
After merging with regression SNP LD, 564639 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.001 (0.0014)
Lambda GC: 1.0403
Mean Chi^2: 1.0348
Intercept: 1.0242 (0.0114)
Ratio: 0.6964 (0.3262)
Analysis finished at Thu Oct 17 14:40:43 2019
Total time elapsed: 26.11sQC metrics
Metrics
Metrics
{
"af_correlation": 0.7309,
"inflation_factor": 1.0475,
"mean_EFFECT": -2.1628e-07,
"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": 17543,
"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": 564639,
"ldsc_nsnp_merge_regression_ld": 564639,
"ldsc_observed_scale_h2_beta": 0.001,
"ldsc_observed_scale_h2_se": 0.0014,
"ldsc_intercept_beta": 1.0242,
"ldsc_intercept_se": 0.0114,
"ldsc_lambda_gc": 1.0403,
"ldsc_mean_chisq": 1.0348,
"ldsc_ratio": 0.6954
}
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 | 2215165 | 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 | 2215398 | 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 | 8.647593e+00 | 5.765451e+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 | 7.867545e+07 | 5.666155e+07 | 5.6870e+03 | 3.177722e+07 | 6.918436e+07 | 1.147881e+08 | 2.491722e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -2.000000e-07 | 1.195000e-04 | -5.8990e-04 | -8.110000e-05 | -3.000000e-07 | 8.010000e-05 | 5.763000e-04 | ▁▂▇▂▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.175000e-04 | 5.200000e-06 | 1.0800e-04 | 1.132000e-04 | 1.161000e-04 | 1.210000e-04 | 2.274000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.938074e-01 | 2.896971e-01 | 3.0000e-07 | 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 | 4.938083e-01 | 2.896744e-01 | 3.0000e-07 | 2.418681e-01 | 4.921845e-01 | 7.439278e-01 | 9.999999e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.626704e-01 | 1.369213e-01 | 2.6003e-01 | 3.424650e-01 | 4.445120e-01 | 5.738840e-01 | 7.399700e-01 | ▇▆▅▅▃ |
| numeric | AF_reference | 17543 | 0.9920813 | NA | NA | NA | NA | NA | NA | NA | 4.431444e-01 | 1.674968e-01 | 1.9970e-04 | 3.127000e-01 | 4.313100e-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 | -0.0000947 | 0.0001987 | 0.6300007 | 0.6334613 | 0.623765 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0001383 | 0.0001968 | 0.4799997 | 0.4824092 | 0.400401 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0000133 | 0.0001938 | 0.9500000 | 0.9453799 | 0.456846 | 0.4207270 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0001264 | 0.0001374 | 0.3599996 | 0.3578594 | 0.515645 | 0.2751600 | NA |
| 1 | 763394 | rs369924889 | G | A | -0.0000138 | 0.0001611 | 0.9299999 | 0.9318562 | 0.706753 | 0.6176120 | NA |
| 1 | 776546 | rs12124819 | A | G | 0.0000783 | 0.0001469 | 0.5900000 | 0.5939271 | 0.265385 | 0.0756789 | NA |
| 1 | 814495 | rs74461805 | C | A | -0.0002725 | 0.0001884 | 0.1499999 | 0.1481273 | 0.340396 | NA | NA |
| 1 | 830181 | rs28444699 | A | G | 0.0002625 | 0.0001261 | 0.0369999 | 0.0373327 | 0.697255 | 0.6912940 | NA |
| 1 | 831489 | rs4970385 | C | T | 0.0001986 | 0.0001238 | 0.1100001 | 0.1087212 | 0.705397 | 0.6491610 | NA |
| 1 | 831909 | rs9697642 | C | T | 0.0002006 | 0.0001238 | 0.1100001 | 0.1051478 | 0.705442 | 0.6485620 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51174662 | rs1858748 | T | C | 0.0000521 | 0.0001329 | 0.6899999 | 0.6948013 | 0.737429 | 0.732827 | NA |
| 22 | 51180501 | rs5770999 | T | C | 0.0000562 | 0.0001293 | 0.6600001 | 0.6639432 | 0.713656 | 0.636981 | NA |
| 22 | 51181919 | rs9616825 | G | C | -0.0000230 | 0.0001287 | 0.8600001 | 0.8581198 | 0.695470 | 0.619409 | NA |
| 22 | 51182485 | rs6009961 | A | G | 0.0000658 | 0.0001298 | 0.6100002 | 0.6120994 | 0.715502 | 0.638379 | NA |
| 22 | 51186143 | rs2879914 | T | C | 0.0001145 | 0.0001203 | 0.3400001 | 0.3414167 | 0.381825 | 0.273363 | NA |
| 22 | 51186228 | rs3865766 | C | T | 0.0000697 | 0.0001173 | 0.5500004 | 0.5521551 | 0.451061 | 0.453275 | NA |
| 22 | 51197266 | rs61290853 | A | G | 0.0000047 | 0.0001211 | 0.9699999 | 0.9688873 | 0.386333 | 0.422923 | NA |
| 22 | 51211106 | rs9628250 | T | C | 0.0000751 | 0.0001359 | 0.5800000 | 0.5803785 | 0.271547 | 0.167133 | NA |
| 22 | 51212875 | rs2238837 | A | C | 0.0000532 | 0.0001291 | 0.6800001 | 0.6804670 | 0.331457 | 0.372404 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0000555 | 0.0001322 | 0.6700003 | 0.6743599 | 0.297974 | 0.205072 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623765 ES:SE:LP:AF:ID -9.47484e-05:0.000198692:0.200659:0.623765:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID -0.000138269:0.000196843:0.318759:0.400401:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456846 ES:SE:LP:AF:ID -1.32782e-05:0.000193815:0.0222764:0.456846:rs6702460
1 706368 rs12029736 A G . PASS AF=0.515645 ES:SE:LP:AF:ID -0.000126363:0.000137433:0.443698:0.515645:rs12029736
1 763394 rs3115847 G A . PASS AF=0.706753 ES:SE:LP:AF:ID -1.3778e-05:0.000161128:0.0315171:0.706753:rs3115847
1 776546 rs12124819 A G . PASS AF=0.265385 ES:SE:LP:AF:ID 7.83203e-05:0.0001469:0.229148:0.265385:rs12124819
1 814495 rs74461805 C A . PASS AF=0.340396 ES:SE:LP:AF:ID -0.000272525:0.000188445:0.823909:0.340396:rs74461805
1 830181 rs28444699 A G . PASS AF=0.697255 ES:SE:LP:AF:ID 0.000262512:0.00012608:1.4318:0.697255:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705397 ES:SE:LP:AF:ID 0.000198568:0.000123798:0.958607:0.705397:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705442 ES:SE:LP:AF:ID 0.000200595:0.000123794:0.958607:0.705442:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705627 ES:SE:LP:AF:ID 0.000198659:0.000123799:0.958607:0.705627:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705655 ES:SE:LP:AF:ID 0.000197927:0.000123812:0.958607:0.705655:rs4500250
1 832398 rs4553118 T C . PASS AF=0.730129 ES:SE:LP:AF:ID 0.000241617:0.000127186:1.24413:0.730129:rs4553118
1 832918 rs28765502 T C . PASS AF=0.294377 ES:SE:LP:AF:ID -0.000198532:0.000123807:0.958607:0.294377:rs28765502
1 836896 rs28705752 T C . PASS AF=0.269511 ES:SE:LP:AF:ID -5.19182e-05:0.000126203:0.167491:0.269511:rs28705752
1 839103 rs28562941 A G . PASS AF=0.270019 ES:SE:LP:AF:ID -6.22817e-05:0.000126293:0.207608:0.270019:rs28562941
1 840753 rs4970382 T C . PASS AF=0.400124 ES:SE:LP:AF:ID -9.4491e-05:0.000114193:0.387216:0.400124:rs4970382
1 843405 rs11516185 A G . PASS AF=0.362606 ES:SE:LP:AF:ID 5.47738e-05:0.000141754:0.154902:0.362606:rs11516185
1 850218 rs6664536 T A . PASS AF=0.590331 ES:SE:LP:AF:ID 2.31573e-05:0.00011386:0.0757207:0.590331:rs6664536
1 850371 rs6679046 G T . PASS AF=0.603723 ES:SE:LP:AF:ID 2.45148e-05:0.000114499:0.0809219:0.603723:rs6679046
1 850780 rs6657440 C T . PASS AF=0.603942 ES:SE:LP:AF:ID 2.973e-05:0.000114483:0.09691:0.603942:rs6657440
1 852037 rs4970463 G A . PASS AF=0.589686 ES:SE:LP:AF:ID 2.90797e-05:0.000114045:0.09691:0.589686:rs4970463
1 852063 rs28436996 G A . PASS AF=0.589665 ES:SE:LP:AF:ID 3.07467e-05:0.000113994:0.102373:0.589665:rs28436996
1 852875 rs13303369 C T . PASS AF=0.607671 ES:SE:LP:AF:ID 4.33615e-05:0.000114739:0.148742:0.607671:rs13303369
1 853954 rs1806509 C A . PASS AF=0.607829 ES:SE:LP:AF:ID 4.58164e-05:0.000114754:0.161151:0.607829:rs1806509
1 854777 rs13303019 A G . PASS AF=0.610316 ES:SE:LP:AF:ID 3.95469e-05:0.000114867:0.136677:0.610316:rs13303019
1 854978 rs13303057 A C . PASS AF=0.603283 ES:SE:LP:AF:ID 3.77388e-05:0.000114527:0.130768:0.603283:rs13303057
1 855075 rs6673914 C G . PASS AF=0.610337 ES:SE:LP:AF:ID 3.90379e-05:0.000114869:0.136677:0.610337:rs6673914
1 856099 rs28534711 T G . PASS AF=0.389936 ES:SE:LP:AF:ID -3.35952e-05:0.000114891:0.113509:0.389936:rs28534711
1 856108 rs28742275 A G . PASS AF=0.38992 ES:SE:LP:AF:ID -3.35104e-05:0.000114897:0.113509:0.38992:rs28742275
1 856476 rs4040605 A G . PASS AF=0.350356 ES:SE:LP:AF:ID -8.23936e-05:0.000118031:0.309804:0.350356:rs4040605
1 866893 rs2880024 T C . PASS AF=0.610552 ES:SE:LP:AF:ID 0.000111647:0.000115514:0.481486:0.610552:rs2880024
1 868418 rs28546443 C T . PASS AF=0.297867 ES:SE:LP:AF:ID -0.000142198:0.000126916:0.585027:0.297867:rs28546443
1 870645 rs28576697 T C . PASS AF=0.291285 ES:SE:LP:AF:ID -0.000216851:0.000125906:1.07058:0.291285:rs28576697
1 871334 rs4072383 G T . PASS AF=0.72062 ES:SE:LP:AF:ID 0.000266095:0.000125126:1.48149:0.72062:rs4072383
1 872352 rs1806780 G C . PASS AF=0.267527 ES:SE:LP:AF:ID -0.000263589:0.000126822:1.42022:0.267527:rs1806780
1 873558 rs1110052 G T . PASS AF=0.715251 ES:SE:LP:AF:ID 0.000248961:0.000124131:1.34679:0.715251:rs1110052
1 875770 rs4970379 A G . PASS AF=0.600085 ES:SE:LP:AF:ID 0.000162873:0.000116474:0.79588:0.600085:rs4970379
1 881627 rs2272757 G A . PASS AF=0.652393 ES:SE:LP:AF:ID 9.75283e-05:0.000117659:0.387216:0.652393:rs2272757
1 891059 rs13303065 C T . PASS AF=0.652432 ES:SE:LP:AF:ID 0.000111143:0.000117641:0.468521:0.652432:rs13303065
1 891945 rs13303106 A G . PASS AF=0.652494 ES:SE:LP:AF:ID 0.000108608:0.000117778:0.443698:0.652494:rs13303106
1 900505 rs28705211 G C . PASS AF=0.271763 ES:SE:LP:AF:ID -0.000172498:0.000127327:0.744727:0.271763:rs28705211
1 903245 rs28690976 A G . PASS AF=0.566938 ES:SE:LP:AF:ID 0.000175712:0.000116979:0.886057:0.566938:rs28690976
1 909073 rs3892467 C T . PASS AF=0.386681 ES:SE:LP:AF:ID -6.90931e-05:0.000116661:0.259637:0.386681:rs3892467
1 909238 rs3829740 G C . PASS AF=0.571408 ES:SE:LP:AF:ID -0.000245501:0.000112983:1.52288:0.571408:rs3829740
1 910394 rs28477686 C T . PASS AF=0.324458 ES:SE:LP:AF:ID 0.000320202:0.000122461:2.05061:0.324458:rs28477686
1 912049 rs7367995 T C . PASS AF=0.585249 ES:SE:LP:AF:ID -0.000222815:0.000114128:1.29243:0.585249:rs7367995
1 913889 rs2340596 G A . PASS AF=0.59921 ES:SE:LP:AF:ID -0.000232962:0.000114314:1.37675:0.59921:rs2340596
1 914333 rs13302979 C G . PASS AF=0.602516 ES:SE:LP:AF:ID -0.000244663:0.00011466:1.48149:0.602516:rs13302979
1 914852 rs13303368 G C . PASS AF=0.600074 ES:SE:LP:AF:ID -0.000239323:0.00011444:1.4318:0.600074:rs13303368