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_6143_100.vcf.gz --id UKB-b:394 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_6143_100.txt.gz --cohort_cases 1031 --cohort_controls 244333 --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": "2019-09-13T01:51:45.601522",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-394/ukb-b-394.vcf.gz; Date=Sun May 10 06:19:28 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-394/UKB-b-394_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-394/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:43:52 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-394/UKB-b-394_data.vcf.gz ...
Read summary statistics for 1431712 SNPs.
Dropped 120 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, 370453 SNPs remain.
After merging with regression SNP LD, 370453 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: -0.0005 (0.0026)
Lambda GC: 1.0179
Mean Chi^2: 1.0138
Intercept: 1.0168 (0.0101)
Ratio: 1.2227 (0.7314)
Analysis finished at Thu Oct 17 14:44:16 2019
Total time elapsed: 24.41sQC metrics
Metrics
Metrics
{
"af_correlation": 0.5741,
"inflation_factor": 1,
"mean_EFFECT": -1.1002e-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": 11403,
"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": 370453,
"ldsc_nsnp_merge_regression_ld": 370453,
"ldsc_observed_scale_h2_beta": "NA",
"ldsc_observed_scale_h2_se": "NA",
"ldsc_intercept_beta": 1.0168,
"ldsc_intercept_se": 0.0101,
"ldsc_lambda_gc": 1.0179,
"ldsc_mean_chisq": 1.0138,
"ldsc_ratio": 1.2174
}
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 | 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 | 1431594 | 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 | 1431712 | 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 | 8.677923e+00 | 5.768674e+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 | 7.853079e+07 | 5.655377e+07 | 1.23330e+04 | 3.166488e+07 | 6.900022e+07 | 1.144787e+08 | 2.491722e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -1.100000e-06 | 1.902000e-04 | -9.31300e-04 | -1.292000e-04 | -7.000000e-07 | 1.274000e-04 | 9.646000e-04 | ▁▂▇▂▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.889000e-04 | 4.900000e-06 | 1.78300e-04 | 1.859000e-04 | 1.879000e-04 | 1.912000e-04 | 3.522000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.980793e-01 | 2.888948e-01 | 8.40000e-06 | 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 | 4.980822e-01 | 2.888683e-01 | 8.40000e-06 | 2.485251e-01 | 4.970212e-01 | 7.484439e-01 | 9.999998e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.833891e-01 | 9.218610e-02 | 3.39477e-01 | 4.022420e-01 | 4.751340e-01 | 5.600030e-01 | 6.605230e-01 | ▇▇▆▆▅ |
| numeric | AF_reference | 11403 | 0.9920354 | NA | NA | NA | NA | NA | NA | NA | 4.614058e-01 | 1.433337e-01 | 1.99700e-04 | 3.544330e-01 | 4.558710e-01 | 5.636980e-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 | -0.0001145 | 0.0003282 | 0.7300002 | 0.7270770 | 0.623695 | 0.782149 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0001872 | 0.0003249 | 0.5600000 | 0.5643945 | 0.400681 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0000441 | 0.0003202 | 0.8900000 | 0.8904100 | 0.456668 | 0.420727 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0001365 | 0.0002267 | 0.5500004 | 0.5470779 | 0.515436 | 0.275160 | NA |
| 1 | 814495 | rs74461805 | C | A | -0.0000594 | 0.0003108 | 0.8499999 | 0.8485273 | 0.340751 | NA | NA |
| 1 | 840753 | rs4970382 | T | C | -0.0001397 | 0.0001883 | 0.4600002 | 0.4583253 | 0.400046 | 0.468850 | NA |
| 1 | 843405 | rs11516185 | A | G | 0.0002644 | 0.0002341 | 0.2599998 | 0.2588224 | 0.363135 | 0.375399 | NA |
| 1 | 850218 | rs6664536 | T | A | 0.0001676 | 0.0001877 | 0.3700002 | 0.3718120 | 0.590229 | 0.345248 | NA |
| 1 | 850371 | rs6679046 | G | T | 0.0001471 | 0.0001887 | 0.4400003 | 0.4358025 | 0.603577 | 0.508786 | NA |
| 1 | 850780 | rs6657440 | C | T | 0.0001416 | 0.0001887 | 0.4500005 | 0.4531685 | 0.603891 | 0.560304 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51158017 | rs6010065 | G | C | 0.0001248 | 0.0001852 | 0.5000000 | 0.5002181 | 0.461144 | 0.547524 | NA |
| 22 | 51158499 | rs8136930 | T | G | 0.0001340 | 0.0001854 | 0.4700002 | 0.4698202 | 0.461377 | 0.544728 | NA |
| 22 | 51161019 | rs5770994 | C | T | -0.0001383 | 0.0001846 | 0.4500005 | 0.4538641 | 0.482197 | 0.425719 | NA |
| 22 | 51163039 | rs715584 | G | T | 0.0000650 | 0.0001878 | 0.7300002 | 0.7294265 | 0.426383 | 0.473642 | NA |
| 22 | 51164109 | rs5770995 | G | C | 0.0001291 | 0.0001865 | 0.4899999 | 0.4887666 | 0.452429 | 0.510982 | NA |
| 22 | 51164115 | rs5770996 | C | T | 0.0001414 | 0.0001864 | 0.4500005 | 0.4481702 | 0.456631 | 0.514776 | NA |
| 22 | 51174048 | rs9628245 | G | C | 0.0004423 | 0.0002106 | 0.0359998 | 0.0356942 | 0.380164 | 0.433107 | NA |
| 22 | 51186143 | rs2879914 | T | C | 0.0000221 | 0.0001986 | 0.9100000 | 0.9113508 | 0.381507 | 0.273363 | NA |
| 22 | 51186228 | rs3865766 | C | T | 0.0001988 | 0.0001936 | 0.2999998 | 0.3044722 | 0.450799 | 0.453275 | NA |
| 22 | 51197266 | rs61290853 | A | G | 0.0000658 | 0.0002000 | 0.7400005 | 0.7422169 | 0.385835 | 0.422923 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623695 ES:SE:LP:AF:ID -0.000114536:0.000328168:0.136677:0.623695:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400681 ES:SE:LP:AF:ID -0.000187241:0.000324887:0.251812:0.400681:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456668 ES:SE:LP:AF:ID 4.41237e-05:0.000320235:0.05061:0.456668:rs6702460
1 706368 rs12029736 A G . PASS AF=0.515436 ES:SE:LP:AF:ID 0.000136517:0.000226718:0.259637:0.515436:rs12029736
1 814495 rs74461805 C A . PASS AF=0.340751 ES:SE:LP:AF:ID -5.93648e-05:0.000310814:0.0705811:0.340751:rs74461805
1 840753 rs4970382 T C . PASS AF=0.400046 ES:SE:LP:AF:ID -0.000139681:0.000188349:0.337242:0.400046:rs4970382
1 843405 rs11516185 A G . PASS AF=0.363135 ES:SE:LP:AF:ID 0.000264369:0.000234125:0.585027:0.363135:rs11516185
1 850218 rs6664536 T A . PASS AF=0.590229 ES:SE:LP:AF:ID 0.000167648:0.000187718:0.431798:0.590229:rs6664536
1 850371 rs6679046 G T . PASS AF=0.603577 ES:SE:LP:AF:ID 0.000147079:0.000188732:0.356547:0.603577:rs6679046
1 850780 rs6657440 C T . PASS AF=0.603891 ES:SE:LP:AF:ID 0.000141567:0.00018872:0.346787:0.603891:rs6657440
1 852037 rs4970463 G A . PASS AF=0.589569 ES:SE:LP:AF:ID 0.000175965:0.000188021:0.455932:0.589569:rs4970463
1 852063 rs28436996 G A . PASS AF=0.58953 ES:SE:LP:AF:ID 0.000173621:0.000187935:0.443698:0.58953:rs28436996
1 852875 rs13303369 C T . PASS AF=0.607422 ES:SE:LP:AF:ID 0.000181123:0.000189087:0.468521:0.607422:rs13303369
1 853954 rs1806509 C A . PASS AF=0.607571 ES:SE:LP:AF:ID 0.000174915:0.000189114:0.443698:0.607571:rs1806509
1 854777 rs13303019 A G . PASS AF=0.610112 ES:SE:LP:AF:ID 0.000242037:0.00018931:0.69897:0.610112:rs13303019
1 854978 rs13303057 A C . PASS AF=0.6031 ES:SE:LP:AF:ID 0.000163819:0.000188771:0.408935:0.6031:rs13303057
1 855075 rs6673914 C G . PASS AF=0.610136 ES:SE:LP:AF:ID 0.000240242:0.000189317:0.69897:0.610136:rs6673914
1 856099 rs28534711 T G . PASS AF=0.390122 ES:SE:LP:AF:ID -0.000216575:0.000189341:0.60206:0.390122:rs28534711
1 856108 rs28742275 A G . PASS AF=0.390107 ES:SE:LP:AF:ID -0.000216616:0.00018935:0.60206:0.390107:rs28742275
1 856476 rs4040605 A G . PASS AF=0.350487 ES:SE:LP:AF:ID -0.000240356:0.000194584:0.657577:0.350487:rs4040605
1 866893 rs2880024 T C . PASS AF=0.610594 ES:SE:LP:AF:ID 0.000192856:0.000190541:0.508638:0.610594:rs2880024
1 875770 rs4970379 A G . PASS AF=0.599891 ES:SE:LP:AF:ID 3.57469e-05:0.000192266:0.0705811:0.599891:rs4970379
1 881627 rs2272757 G A . PASS AF=0.652472 ES:SE:LP:AF:ID 0.000105169:0.000194244:0.229148:0.652472:rs2272757
1 891059 rs13303065 C T . PASS AF=0.652523 ES:SE:LP:AF:ID 8.57373e-05:0.000194241:0.180456:0.652523:rs13303065
1 891945 rs13303106 A G . PASS AF=0.652579 ES:SE:LP:AF:ID 9.38835e-05:0.000194451:0.200659:0.652579:rs13303106
1 903245 rs28690976 A G . PASS AF=0.566324 ES:SE:LP:AF:ID 0.000181454:0.000193108:0.455932:0.566324:rs28690976
1 909073 rs3892467 C T . PASS AF=0.386539 ES:SE:LP:AF:ID 0.000226269:0.000192575:0.619789:0.386539:rs3892467
1 909238 rs3829740 G C . PASS AF=0.571453 ES:SE:LP:AF:ID 0.000320274:0.000186522:1.0655:0.571453:rs3829740
1 912049 rs7367995 T C . PASS AF=0.585038 ES:SE:LP:AF:ID 0.000278474:0.000188277:0.853872:0.585038:rs7367995
1 913889 rs2340596 G A . PASS AF=0.598685 ES:SE:LP:AF:ID 0.000235147:0.000188642:0.677781:0.598685:rs2340596
1 914333 rs13302979 C G . PASS AF=0.601897 ES:SE:LP:AF:ID 0.000225451:0.000189224:0.638272:0.601897:rs13302979
1 914852 rs13303368 G C . PASS AF=0.5995 ES:SE:LP:AF:ID 0.000234031:0.000188846:0.657577:0.5995:rs13303368
1 914940 rs13303033 T C . PASS AF=0.583616 ES:SE:LP:AF:ID 0.000243681:0.000187811:0.721246:0.583616:rs13303033
1 916834 rs6694632 G A . PASS AF=0.588358 ES:SE:LP:AF:ID 0.00025704:0.000188112:0.769551:0.588358:rs6694632
1 918384 rs13303118 G T . PASS AF=0.583506 ES:SE:LP:AF:ID 0.000239891:0.000187727:0.69897:0.583506:rs13303118
1 918573 rs2341354 A G . PASS AF=0.588602 ES:SE:LP:AF:ID 0.000256091:0.000187964:0.769551:0.588602:rs2341354
1 919501 rs4970414 G T . PASS AF=0.583489 ES:SE:LP:AF:ID 0.000197542:0.000194388:0.508638:0.583489:rs4970414
1 921716 rs13303278 C A . PASS AF=0.567521 ES:SE:LP:AF:ID 0.00012371:0.000187733:0.29243:0.567521:rs13303278
1 924528 rs34712273 C A . PASS AF=0.578187 ES:SE:LP:AF:ID 0.00022843:0.000188136:0.657577:0.578187:rs34712273
1 930533 rs3128110 C G . PASS AF=0.386281 ES:SE:LP:AF:ID -0.000160305:0.000190496:0.39794:0.386281:rs3128110
1 930567 rs3121574 A G . PASS AF=0.386319 ES:SE:LP:AF:ID -0.000161109:0.0001905:0.39794:0.386319:rs3121574
1 930751 rs3128111 C G . PASS AF=0.385245 ES:SE:LP:AF:ID -0.000161254:0.000190628:0.39794:0.385245:rs3128111
1 931166 rs2710880 A G . PASS AF=0.387008 ES:SE:LP:AF:ID -0.000172629:0.00019054:0.443698:0.387008:rs2710880
1 931362 rs2799060 G A . PASS AF=0.385768 ES:SE:LP:AF:ID -0.000171065:0.000190645:0.431798:0.385768:rs2799060
1 933790 rs9442392 G A . PASS AF=0.578413 ES:SE:LP:AF:ID 0.000241003:0.000188085:0.69897:0.578413:rs9442392
1 936111 rs1936360 C T . PASS AF=0.573435 ES:SE:LP:AF:ID 0.000216884:0.000187991:0.60206:0.573435:rs1936360
1 940005 rs2799056 A G . PASS AF=0.399361 ES:SE:LP:AF:ID -0.000172754:0.000189789:0.443698:0.399361:rs2799056
1 940096 rs4503294 C T . PASS AF=0.565194 ES:SE:LP:AF:ID 0.000234058:0.000187437:0.677781:0.565194:rs4503294
1 941284 rs3128116 C T . PASS AF=0.397448 ES:SE:LP:AF:ID -0.000194406:0.000189825:0.508638:0.397448:rs3128116
1 941334 rs57683598 G A . PASS AF=0.39745 ES:SE:LP:AF:ID -0.000202138:0.000189836:0.537602:0.39745:rs57683598