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_41221_4.vcf.gz --id UKB-b:19294 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41221_4.txt.gz --cohort_cases 1044 --cohort_controls 9443 --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": "2019-09-13T01:32:30.086366",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-19294/ukb-b-19294.vcf.gz; Date=Sat May 9 18:25:56 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-19294/UKB-b-19294_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19294/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:41:09 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19294/UKB-b-19294_data.vcf.gz ...
Read summary statistics for 1471686 SNPs.
Dropped 126 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, 380692 SNPs remain.
After merging with regression SNP LD, 380692 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: -0.0652 (0.0572)
Lambda GC: 0.9969
Mean Chi^2: 0.9945
Intercept: 1.0109 (0.0108)
Ratio: NA (mean chi^2 < 1)
Analysis finished at Thu Oct 17 14:41:33 2019
Total time elapsed: 24.34sQC metrics
Metrics
Metrics
{
"af_correlation": 0.5853,
"inflation_factor": 1,
"mean_EFFECT": 0,
"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": 11730,
"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": 380692,
"ldsc_nsnp_merge_regression_ld": 380692,
"ldsc_observed_scale_h2_beta": "NA",
"ldsc_observed_scale_h2_se": "NA",
"ldsc_intercept_beta": 1.0109,
"ldsc_intercept_se": 0.0108,
"ldsc_lambda_gc": 0.9969,
"ldsc_mean_chisq": 0.9945,
"ldsc_ratio": -1.9818
}
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 | 1471562 | 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 | 1471686 | 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 | 8.676206e+00 | 5.769191e+00 | 1.00000e+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 | 7.855815e+07 | 5.659285e+07 | 1.23330e+04 | 3.163649e+07 | 6.902480e+07 | 1.145526e+08 | 2.491722e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.190000e-05 | 4.226300e-03 | -2.07816e-02 | -2.824100e-03 | 2.390000e-05 | 2.881000e-03 | 2.200780e-02 | ▁▂▇▂▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.236000e-03 | 1.152000e-04 | 3.96380e-03 | 4.165400e-03 | 4.213500e-03 | 4.289800e-03 | 7.900200e-03 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 5.003622e-01 | 2.873392e-01 | 3.00000e-07 | 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 | 5.003604e-01 | 2.873161e-01 | 3.00000e-07 | 2.529664e-01 | 5.002092e-01 | 7.477996e-01 | 9.999998e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.825266e-01 | 9.455070e-02 | 3.35250e-01 | 3.992222e-01 | 4.739330e-01 | 5.611460e-01 | 6.647500e-01 | ▇▇▆▆▅ |
| numeric | AF_reference | 11730 | 0.9920295 | NA | NA | NA | NA | NA | NA | NA | 4.608918e-01 | 1.441933e-01 | 1.99700e-04 | 3.532350e-01 | 4.550720e-01 | 5.638980e-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 | 0.0104287 | 0.0074188 | 0.1600000 | 0.1598089 | 0.625501 | 0.782149 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0015083 | 0.0073332 | 0.8400000 | 0.8370410 | 0.398443 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0051325 | 0.0072219 | 0.4799997 | 0.4772803 | 0.458861 | 0.420727 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0096599 | 0.0050474 | 0.0560003 | 0.0556395 | 0.513615 | 0.275160 | NA |
| 1 | 814495 | rs74461805 | C | A | 0.0023034 | 0.0070240 | 0.7400005 | 0.7429596 | 0.340789 | NA | NA |
| 1 | 840753 | rs4970382 | T | C | 0.0028856 | 0.0042296 | 0.5000000 | 0.4950911 | 0.400114 | 0.468850 | NA |
| 1 | 843405 | rs11516185 | A | G | 0.0049206 | 0.0052550 | 0.3500000 | 0.3490863 | 0.359026 | 0.375399 | NA |
| 1 | 850218 | rs6664536 | T | A | -0.0083030 | 0.0042183 | 0.0490004 | 0.0490324 | 0.591615 | 0.345248 | NA |
| 1 | 850371 | rs6679046 | G | T | -0.0083524 | 0.0042394 | 0.0490004 | 0.0488164 | 0.604429 | 0.508786 | NA |
| 1 | 850780 | rs6657440 | C | T | -0.0079511 | 0.0042361 | 0.0610000 | 0.0605207 | 0.604939 | 0.560304 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51158017 | rs6010065 | G | C | -0.0084425 | 0.0041408 | 0.0409996 | 0.0414657 | 0.456851 | 0.547524 | NA |
| 22 | 51158499 | rs8136930 | T | G | -0.0083947 | 0.0041419 | 0.0430002 | 0.0426849 | 0.457034 | 0.544728 | NA |
| 22 | 51161019 | rs5770994 | C | T | 0.0044652 | 0.0041440 | 0.2800000 | 0.2812584 | 0.485220 | 0.425719 | NA |
| 22 | 51163039 | rs715584 | G | T | -0.0082520 | 0.0041872 | 0.0490004 | 0.0487499 | 0.421860 | 0.473642 | NA |
| 22 | 51164109 | rs5770995 | G | C | -0.0078540 | 0.0041705 | 0.0599998 | 0.0596680 | 0.447933 | 0.510982 | NA |
| 22 | 51164115 | rs5770996 | C | T | -0.0079244 | 0.0041707 | 0.0569994 | 0.0574312 | 0.452722 | 0.514776 | NA |
| 22 | 51174048 | rs9628245 | G | C | -0.0000430 | 0.0047053 | 0.9900000 | 0.9927007 | 0.381423 | 0.433107 | NA |
| 22 | 51186143 | rs2879914 | T | C | -0.0039848 | 0.0044404 | 0.3700002 | 0.3695110 | 0.378583 | 0.273363 | NA |
| 22 | 51186228 | rs3865766 | C | T | -0.0034441 | 0.0043322 | 0.4299995 | 0.4266128 | 0.450005 | 0.453275 | NA |
| 22 | 51197266 | rs61290853 | A | G | -0.0031551 | 0.0044818 | 0.4799997 | 0.4814484 | 0.384827 | 0.422923 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.625501 ES:SE:LP:AF:ID 0.0104287:0.00741879:0.79588:0.625501:rs10399793
1 54676 rs2462492 C T . PASS AF=0.398443 ES:SE:LP:AF:ID 0.0015083:0.00733324:0.0757207:0.398443:rs2462492
1 91536 rs6702460 G T . PASS AF=0.458861 ES:SE:LP:AF:ID 0.0051325:0.00722192:0.318759:0.458861:rs6702460
1 706368 rs12029736 A G . PASS AF=0.513615 ES:SE:LP:AF:ID -0.00965992:0.00504738:1.25181:0.513615:rs12029736
1 814495 rs74461805 C A . PASS AF=0.340789 ES:SE:LP:AF:ID 0.00230344:0.00702404:0.130768:0.340789:rs74461805
1 840753 rs4970382 T C . PASS AF=0.400114 ES:SE:LP:AF:ID 0.00288559:0.00422962:0.30103:0.400114:rs4970382
1 843405 rs11516185 A G . PASS AF=0.359026 ES:SE:LP:AF:ID 0.00492056:0.00525497:0.455932:0.359026:rs11516185
1 850218 rs6664536 T A . PASS AF=0.591615 ES:SE:LP:AF:ID -0.00830296:0.00421832:1.3098:0.591615:rs6664536
1 850371 rs6679046 G T . PASS AF=0.604429 ES:SE:LP:AF:ID -0.00835237:0.00423937:1.3098:0.604429:rs6679046
1 850780 rs6657440 C T . PASS AF=0.604939 ES:SE:LP:AF:ID -0.00795108:0.0042361:1.21467:0.604939:rs6657440
1 852037 rs4970463 G A . PASS AF=0.59093 ES:SE:LP:AF:ID -0.00860872:0.00422367:1.37675:0.59093:rs4970463
1 852063 rs28436996 G A . PASS AF=0.590923 ES:SE:LP:AF:ID -0.008438:0.00422097:1.33724:0.590923:rs28436996
1 852875 rs13303369 C T . PASS AF=0.607555 ES:SE:LP:AF:ID -0.00943776:0.00424851:1.58503:0.607555:rs13303369
1 853954 rs1806509 C A . PASS AF=0.607748 ES:SE:LP:AF:ID -0.00934137:0.0042478:1.55284:0.607748:rs1806509
1 854777 rs13303019 A G . PASS AF=0.611232 ES:SE:LP:AF:ID -0.00945165:0.0042543:1.58503:0.611232:rs13303019
1 854978 rs13303057 A C . PASS AF=0.603997 ES:SE:LP:AF:ID -0.00891:0.00423671:1.45593:0.603997:rs13303057
1 855075 rs6673914 C G . PASS AF=0.611231 ES:SE:LP:AF:ID -0.00944515:0.00425466:1.58503:0.611231:rs6673914
1 856099 rs28534711 T G . PASS AF=0.388963 ES:SE:LP:AF:ID 0.00964057:0.00425595:1.61979:0.388963:rs28534711
1 856108 rs28742275 A G . PASS AF=0.388954 ES:SE:LP:AF:ID 0.00963719:0.00425597:1.61979:0.388954:rs28742275
1 856476 rs4040605 A G . PASS AF=0.350591 ES:SE:LP:AF:ID 0.00959668:0.00435582:1.55284:0.350591:rs4040605
1 866893 rs2880024 T C . PASS AF=0.611753 ES:SE:LP:AF:ID -0.00554697:0.00425131:0.721246:0.611753:rs2880024
1 875770 rs4970379 A G . PASS AF=0.601132 ES:SE:LP:AF:ID -0.00591437:0.00429638:0.769551:0.601132:rs4970379
1 881627 rs2272757 G A . PASS AF=0.653094 ES:SE:LP:AF:ID -0.00407606:0.00432506:0.455932:0.653094:rs2272757
1 891059 rs13303065 C T . PASS AF=0.65338 ES:SE:LP:AF:ID -0.00441115:0.00432759:0.508638:0.65338:rs13303065
1 891945 rs13303106 A G . PASS AF=0.653334 ES:SE:LP:AF:ID -0.00437463:0.004332:0.508638:0.653334:rs13303106
1 903245 rs28690976 A G . PASS AF=0.565974 ES:SE:LP:AF:ID -0.00510938:0.00429069:0.638272:0.565974:rs28690976
1 909073 rs3892467 C T . PASS AF=0.388136 ES:SE:LP:AF:ID -5.58143e-05:0.00429631:0.00436481:0.388136:rs3892467
1 909238 rs3829740 G C . PASS AF=0.577048 ES:SE:LP:AF:ID -0.00381524:0.00419272:0.443698:0.577048:rs3829740
1 912049 rs7367995 T C . PASS AF=0.583929 ES:SE:LP:AF:ID 0.000444271:0.00421961:0.0362122:0.583929:rs7367995
1 913889 rs2340596 G A . PASS AF=0.597978 ES:SE:LP:AF:ID 0.000401761:0.00422796:0.0362122:0.597978:rs2340596
1 914333 rs13302979 C G . PASS AF=0.600902 ES:SE:LP:AF:ID -0.000923761:0.0042405:0.0809219:0.600902:rs13302979
1 914852 rs13303368 G C . PASS AF=0.598653 ES:SE:LP:AF:ID 0.000251161:0.00423211:0.0222764:0.598653:rs13303368
1 914940 rs13303033 T C . PASS AF=0.582475 ES:SE:LP:AF:ID 0.000425008:0.00421923:0.0362122:0.582475:rs13303033
1 916834 rs6694632 G A . PASS AF=0.586697 ES:SE:LP:AF:ID 2.45686e-05:0.00422664:-0:0.586697:rs6694632
1 918384 rs13303118 G T . PASS AF=0.582367 ES:SE:LP:AF:ID 0.000545462:0.00421757:0.0457575:0.582367:rs13303118
1 918573 rs2341354 A G . PASS AF=0.587156 ES:SE:LP:AF:ID 0.000153486:0.00422332:0.0132283:0.587156:rs2341354
1 919501 rs4970414 G T . PASS AF=0.579369 ES:SE:LP:AF:ID -0.00176942:0.00433798:0.167491:0.579369:rs4970414
1 921716 rs13303278 C A . PASS AF=0.567324 ES:SE:LP:AF:ID -9.49998e-05:0.00418961:0.00877392:0.567324:rs13303278
1 924528 rs34712273 C A . PASS AF=0.579397 ES:SE:LP:AF:ID -0.000387301:0.00421081:0.0315171:0.579397:rs34712273
1 930533 rs3128110 C G . PASS AF=0.383482 ES:SE:LP:AF:ID 0.00182226:0.00425641:0.173925:0.383482:rs3128110
1 930567 rs3121574 A G . PASS AF=0.383512 ES:SE:LP:AF:ID 0.00180385:0.00425597:0.173925:0.383512:rs3121574
1 930751 rs3128111 C G . PASS AF=0.382187 ES:SE:LP:AF:ID 0.00203235:0.00425961:0.200659:0.382187:rs3128111
1 931166 rs2710880 A G . PASS AF=0.384431 ES:SE:LP:AF:ID 0.0017083:0.00425619:0.161151:0.384431:rs2710880
1 931362 rs2799060 G A . PASS AF=0.382829 ES:SE:LP:AF:ID 0.00198513:0.00425993:0.19382:0.382829:rs2799060
1 933790 rs9442392 G A . PASS AF=0.579481 ES:SE:LP:AF:ID -0.00198474:0.00420947:0.19382:0.579481:rs9442392
1 936111 rs1936360 C T . PASS AF=0.575341 ES:SE:LP:AF:ID -0.00168138:0.00421326:0.161151:0.575341:rs1936360
1 940005 rs2799056 A G . PASS AF=0.396972 ES:SE:LP:AF:ID 0.00135329:0.00422985:0.124939:0.396972:rs2799056
1 940096 rs4503294 C T . PASS AF=0.56593 ES:SE:LP:AF:ID -0.00133633:0.00418756:0.124939:0.56593:rs4503294
1 941284 rs3128116 C T . PASS AF=0.394914 ES:SE:LP:AF:ID 0.00195813:0.00422853:0.19382:0.394914:rs3128116
1 941334 rs57683598 G A . PASS AF=0.394894 ES:SE:LP:AF:ID 0.00194886:0.00422888:0.19382:0.394894:rs57683598