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_20003_1195.vcf.gz --id UKB-b:12466 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20003_1195.txt.gz --cohort_cases 1222 --cohort_controls 461711 --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": "2019-09-13T01:11:54.537714",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-12466/ukb-b-12466.vcf.gz; Date=Sun May 10 07:06:40 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-12466/UKB-b-12466_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-12466/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:41:56 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-12466/UKB-b-12466_data.vcf.gz ...
Read summary statistics for 1945815 SNPs.
Dropped 187 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, 499249 SNPs remain.
After merging with regression SNP LD, 499249 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0017 (0.0013)
Lambda GC: 1.0138
Mean Chi^2: 1.0077
Intercept: 0.9889 (0.0108)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Thu Oct 17 14:42:26 2019
Total time elapsed: 30.71sQC metrics
Metrics
Metrics
{
"af_correlation": 0.6864,
"inflation_factor": 1,
"mean_EFFECT": -6.0422e-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": 15423,
"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": 499249,
"ldsc_nsnp_merge_regression_ld": 499249,
"ldsc_observed_scale_h2_beta": 0.0017,
"ldsc_observed_scale_h2_se": 0.0013,
"ldsc_intercept_beta": 0.9889,
"ldsc_intercept_se": 0.0108,
"ldsc_lambda_gc": 1.0138,
"ldsc_mean_chisq": 1.0077,
"ldsc_ratio": -1.4416
}
Flags
| name | value |
|---|---|
| af_correlation | TRUE |
| 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 | 1945630 | 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 | 1945815 | 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 | 8.649196e+00 | 5.762248e+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 | 7.871973e+07 | 5.663206e+07 | 5687.0000000 | 3.184246e+07 | 6.927702e+07 | 1.148791e+08 | 2.491722e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -6.000000e-07 | 1.115000e-04 | -0.0006262 | -7.580000e-05 | -3.000000e-07 | 7.450000e-05 | 6.350000e-04 | ▁▂▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.109000e-04 | 4.000000e-06 | 0.0001029 | 1.077000e-04 | 1.098000e-04 | 1.134000e-04 | 2.111000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.985545e-01 | 2.890610e-01 | 0.0000001 | 2.500000e-01 | 5.000000e-01 | 7.499995e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.985496e-01 | 2.890333e-01 | 0.0000001 | 2.486029e-01 | 4.977459e-01 | 7.485245e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.708450e-01 | 1.222185e-01 | 0.2864160 | 3.634170e-01 | 4.562270e-01 | 5.711030e-01 | 7.135840e-01 | ▇▆▆▅▅ |
| numeric | AF_reference | 15423 | 0.9920738 | NA | NA | NA | NA | NA | NA | NA | 4.502871e-01 | 1.587661e-01 | 0.0001997 | 3.282750e-01 | 4.412940e-01 | 5.652960e-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 | 0.0001496 | 0.0001894 | 0.4299995 | 0.4296070 | 0.623763 | 0.782149 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0001080 | 0.0001876 | 0.5600000 | 0.5648736 | 0.400401 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0001121 | 0.0001847 | 0.5400003 | 0.5440047 | 0.456851 | 0.420727 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0002709 | 0.0001310 | 0.0389996 | 0.0386186 | 0.515650 | 0.275160 | NA |
| 1 | 763394 | rs369924889 | G | A | 0.0000748 | 0.0001536 | 0.6300007 | 0.6263532 | 0.706753 | 0.617612 | NA |
| 1 | 814495 | rs74461805 | C | A | -0.0003001 | 0.0001796 | 0.0949992 | 0.0947415 | 0.340397 | NA | NA |
| 1 | 830181 | rs28444699 | A | G | -0.0001336 | 0.0001202 | 0.2700001 | 0.2661375 | 0.697259 | 0.691294 | NA |
| 1 | 831489 | rs4970385 | C | T | -0.0001771 | 0.0001180 | 0.1299999 | 0.1333260 | 0.705403 | 0.649161 | NA |
| 1 | 831909 | rs9697642 | C | T | -0.0001741 | 0.0001180 | 0.1400000 | 0.1399860 | 0.705448 | 0.648562 | NA |
| 1 | 832066 | rs9697380 | G | C | -0.0001754 | 0.0001180 | 0.1400000 | 0.1372106 | 0.705634 | 0.664337 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51164115 | rs5770996 | C | T | 0.0000354 | 0.0001077 | 0.7400005 | 0.7426225 | 0.456928 | 0.514776 | NA |
| 22 | 51164287 | rs6009957 | T | C | -0.0000305 | 0.0001158 | 0.7899998 | 0.7921186 | 0.306554 | 0.415535 | NA |
| 22 | 51165664 | rs8137951 | G | A | -0.0000345 | 0.0001161 | 0.7700005 | 0.7665401 | 0.301558 | 0.406350 | NA |
| 22 | 51174048 | rs9628245 | G | C | 0.0001053 | 0.0001217 | 0.3900004 | 0.3869444 | 0.380130 | 0.433107 | NA |
| 22 | 51181919 | rs9616825 | G | C | -0.0000405 | 0.0001227 | 0.7400005 | 0.7411102 | 0.695471 | 0.619409 | NA |
| 22 | 51186143 | rs2879914 | T | C | 0.0001681 | 0.0001147 | 0.1400000 | 0.1427331 | 0.381826 | 0.273363 | NA |
| 22 | 51186228 | rs3865766 | C | T | 0.0001480 | 0.0001118 | 0.1900002 | 0.1855349 | 0.451063 | 0.453275 | NA |
| 22 | 51197266 | rs61290853 | A | G | 0.0002803 | 0.0001154 | 0.0150000 | 0.0151623 | 0.386333 | 0.422923 | NA |
| 22 | 51212875 | rs2238837 | A | C | 0.0002099 | 0.0001231 | 0.0879995 | 0.0881133 | 0.331455 | 0.372404 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0002801 | 0.0001260 | 0.0259998 | 0.0262150 | 0.297971 | 0.205072 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623763 ES:SE:LP:AF:ID 0.000149594:0.000189392:0.366532:0.623763:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID -0.000108002:0.000187628:0.251812:0.400401:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456851 ES:SE:LP:AF:ID 0.000112098:0.000184746:0.267606:0.456851:rs6702460
1 706368 rs12029736 A G . PASS AF=0.51565 ES:SE:LP:AF:ID -0.000270942:0.000131002:1.40894:0.51565:rs12029736
1 763394 rs3115847 G A . PASS AF=0.706753 ES:SE:LP:AF:ID 7.47763e-05:0.000153587:0.200659:0.706753:rs3115847
1 814495 rs74461805 C A . PASS AF=0.340397 ES:SE:LP:AF:ID -0.000300142:0.000179629:1.02228:0.340397:rs74461805
1 830181 rs28444699 A G . PASS AF=0.697259 ES:SE:LP:AF:ID -0.000133637:0.000120177:0.568636:0.697259:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705403 ES:SE:LP:AF:ID -0.000177136:0.000118003:0.886057:0.705403:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705448 ES:SE:LP:AF:ID -0.000174148:0.000117999:0.853872:0.705448:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705634 ES:SE:LP:AF:ID -0.000175386:0.000118005:0.853872:0.705634:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705662 ES:SE:LP:AF:ID -0.000176111:0.000118017:0.853872:0.705662:rs4500250
1 832918 rs28765502 T C . PASS AF=0.294371 ES:SE:LP:AF:ID 0.000175462:0.000118012:0.853872:0.294371:rs28765502
1 840753 rs4970382 T C . PASS AF=0.400106 ES:SE:LP:AF:ID 3.60489e-05:0.000108848:0.130768:0.400106:rs4970382
1 843405 rs11516185 A G . PASS AF=0.362599 ES:SE:LP:AF:ID -2.91676e-05:0.000135123:0.0809219:0.362599:rs11516185
1 850218 rs6664536 T A . PASS AF=0.590333 ES:SE:LP:AF:ID -5.84217e-05:0.000108529:0.229148:0.590333:rs6664536
1 850371 rs6679046 G T . PASS AF=0.603726 ES:SE:LP:AF:ID -5.04709e-05:0.000109139:0.19382:0.603726:rs6679046
1 850780 rs6657440 C T . PASS AF=0.603944 ES:SE:LP:AF:ID -4.80086e-05:0.000109123:0.180456:0.603944:rs6657440
1 852037 rs4970463 G A . PASS AF=0.589688 ES:SE:LP:AF:ID -7.45662e-05:0.000108706:0.309804:0.589688:rs4970463
1 852063 rs28436996 G A . PASS AF=0.589667 ES:SE:LP:AF:ID -7.04e-05:0.000108657:0.283997:0.589667:rs28436996
1 852875 rs13303369 C T . PASS AF=0.607674 ES:SE:LP:AF:ID -6.20264e-05:0.000109367:0.244125:0.607674:rs13303369
1 853954 rs1806509 C A . PASS AF=0.607833 ES:SE:LP:AF:ID -5.82576e-05:0.000109382:0.229148:0.607833:rs1806509
1 854777 rs13303019 A G . PASS AF=0.610318 ES:SE:LP:AF:ID -5.69405e-05:0.000109489:0.221849:0.610318:rs13303019
1 854978 rs13303057 A C . PASS AF=0.603286 ES:SE:LP:AF:ID -5.58464e-05:0.000109165:0.21467:0.603286:rs13303057
1 855075 rs6673914 C G . PASS AF=0.610338 ES:SE:LP:AF:ID -5.94621e-05:0.000109491:0.229148:0.610338:rs6673914
1 856099 rs28534711 T G . PASS AF=0.389935 ES:SE:LP:AF:ID 6.39287e-05:0.000109512:0.251812:0.389935:rs28534711
1 856108 rs28742275 A G . PASS AF=0.389918 ES:SE:LP:AF:ID 6.41066e-05:0.000109518:0.251812:0.389918:rs28742275
1 856476 rs4040605 A G . PASS AF=0.350351 ES:SE:LP:AF:ID 9.78799e-05:0.000112506:0.420216:0.350351:rs4040605
1 866893 rs2880024 T C . PASS AF=0.610554 ES:SE:LP:AF:ID -3.00529e-05:0.000110106:0.107905:0.610554:rs2880024
1 868418 rs28546443 C T . PASS AF=0.297865 ES:SE:LP:AF:ID 0.000122838:0.000120976:0.508638:0.297865:rs28546443
1 870645 rs28576697 T C . PASS AF=0.291287 ES:SE:LP:AF:ID -5.81152e-05:0.000120012:0.200659:0.291287:rs28576697
1 875770 rs4970379 A G . PASS AF=0.600084 ES:SE:LP:AF:ID -0.000163626:0.000111022:0.853872:0.600084:rs4970379
1 881627 rs2272757 G A . PASS AF=0.65239 ES:SE:LP:AF:ID -6.64853e-05:0.000112151:0.259637:0.65239:rs2272757
1 891059 rs13303065 C T . PASS AF=0.652428 ES:SE:LP:AF:ID -6.19903e-05:0.000112133:0.236572:0.652428:rs13303065
1 891945 rs13303106 A G . PASS AF=0.65249 ES:SE:LP:AF:ID -6.34732e-05:0.000112264:0.244125:0.65249:rs13303106
1 903245 rs28690976 A G . PASS AF=0.566937 ES:SE:LP:AF:ID -0.000165801:0.000111503:0.853872:0.566937:rs28690976
1 909073 rs3892467 C T . PASS AF=0.386683 ES:SE:LP:AF:ID -0.000307852:0.0001112:2.25181:0.386683:rs3892467
1 909238 rs3829740 G C . PASS AF=0.571412 ES:SE:LP:AF:ID -0.000182423:0.000107693:1.04576:0.571412:rs3829740
1 910394 rs28477686 C T . PASS AF=0.324456 ES:SE:LP:AF:ID 0.000236425:0.000116728:1.36653:0.324456:rs28477686
1 912049 rs7367995 T C . PASS AF=0.58525 ES:SE:LP:AF:ID -0.000172753:0.000108785:0.958607:0.58525:rs7367995
1 913889 rs2340596 G A . PASS AF=0.599213 ES:SE:LP:AF:ID -0.000135826:0.000108962:0.677781:0.599213:rs2340596
1 914333 rs13302979 C G . PASS AF=0.602521 ES:SE:LP:AF:ID -0.000141409:0.000109293:0.69897:0.602521:rs13302979
1 914852 rs13303368 G C . PASS AF=0.600078 ES:SE:LP:AF:ID -0.000148257:0.000109083:0.769551:0.600078:rs13303368
1 914940 rs13303033 T C . PASS AF=0.584292 ES:SE:LP:AF:ID -0.000159003:0.000108471:0.853872:0.584292:rs13303033
1 916834 rs6694632 G A . PASS AF=0.589106 ES:SE:LP:AF:ID -0.000144749:0.000108636:0.744727:0.589106:rs6694632
1 918384 rs13303118 G T . PASS AF=0.584205 ES:SE:LP:AF:ID -0.000136188:0.00010842:0.677781:0.584205:rs13303118
1 918573 rs2341354 A G . PASS AF=0.589331 ES:SE:LP:AF:ID -0.00014764:0.000108553:0.769551:0.589331:rs2341354
1 919501 rs4970414 G T . PASS AF=0.583932 ES:SE:LP:AF:ID -0.000155806:0.000112263:0.769551:0.583932:rs4970414
1 921716 rs13303278 C A . PASS AF=0.567888 ES:SE:LP:AF:ID -0.000269066:0.00010833:1.88606:0.567888:rs13303278
1 924528 rs34712273 C A . PASS AF=0.578492 ES:SE:LP:AF:ID -0.00025713:0.000108641:1.74473:0.578492:rs34712273
1 930533 rs3128110 C G . PASS AF=0.386078 ES:SE:LP:AF:ID 0.000350184:0.000110029:2.82391:0.386078:rs3128110