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_D151.vcf.gz --id UKB-b:9484 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41200_D151.txt.gz --cohort_cases 1267 --cohort_controls 461743 --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": "2019-09-12T20:28:47.451101",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-9484/ukb-b-9484.vcf.gz; Date=Sat May 9 18:39:24 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-9484/UKB-b-9484_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-9484/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:42:45 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-9484/UKB-b-9484_data.vcf.gz ...
Read summary statistics for 2046314 SNPs.
Dropped 203 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, 523857 SNPs remain.
After merging with regression SNP LD, 523857 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: -0.0005 (0.0012)
Lambda GC: 0.9994
Mean Chi^2: 1.0037
Intercept: 1.0097 (0.0104)
Ratio: 2.6362 (2.8418)
Analysis finished at Thu Oct 17 14:43:15 2019
Total time elapsed: 29.76sQC metrics
Metrics
Metrics
{
"af_correlation": 0.7039,
"inflation_factor": 1,
"mean_EFFECT": -2.0359e-07,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 1,
"n_p_sig": 11,
"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": 16248,
"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": 523857,
"ldsc_nsnp_merge_regression_ld": 523857,
"ldsc_observed_scale_h2_beta": "NA",
"ldsc_observed_scale_h2_se": "NA",
"ldsc_intercept_beta": 1.0097,
"ldsc_intercept_se": 0.0104,
"ldsc_lambda_gc": 0.9994,
"ldsc_mean_chisq": 1.0037,
"ldsc_ratio": 2.6216
}
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 | 2046113 | 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 | 2046314 | 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 | 8.648071e+00 | 5.764804e+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 | 7.872419e+07 | 5.664172e+07 | 5687.0000000 | 3.183026e+07 | 6.928379e+07 | 1.148383e+08 | 2.491722e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -2.000000e-07 | 1.135000e-04 | -0.0007763 | -7.670000e-05 | -7.000000e-07 | 7.610000e-05 | 6.748000e-04 | ▁▁▇▂▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.133000e-04 | 4.400000e-06 | 0.0001048 | 1.097000e-04 | 1.121000e-04 | 1.161000e-04 | 2.174000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.994489e-01 | 2.890811e-01 | 0.0000000 | 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 | 4.994503e-01 | 2.890531e-01 | 0.0000000 | 2.482206e-01 | 4.995963e-01 | 7.498477e-01 | 9.999993e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.678061e-01 | 1.277662e-01 | 0.2762440 | 3.555332e-01 | 4.518070e-01 | 5.722590e-01 | 7.237550e-01 | ▇▆▆▅▅ |
| numeric | AF_reference | 16248 | 0.9920599 | NA | NA | NA | NA | NA | NA | NA | 4.476881e-01 | 1.619768e-01 | 0.0001997 | 3.224840e-01 | 4.377000e-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 | 0.0000803 | 0.0001928 | 0.6800001 | 0.6769634 | 0.623765 | 0.782149 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0001414 | 0.0001910 | 0.4600002 | 0.4592008 | 0.400401 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0000107 | 0.0001881 | 0.9500000 | 0.9547371 | 0.456846 | 0.420727 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0000157 | 0.0001334 | 0.9100000 | 0.9060438 | 0.515645 | 0.275160 | NA |
| 1 | 763394 | rs369924889 | G | A | -0.0001183 | 0.0001564 | 0.4500005 | 0.4492426 | 0.706753 | 0.617612 | NA |
| 1 | 814495 | rs74461805 | C | A | 0.0000272 | 0.0001829 | 0.8800001 | 0.8818366 | 0.340396 | NA | NA |
| 1 | 830181 | rs28444699 | A | G | 0.0001636 | 0.0001224 | 0.1800002 | 0.1811238 | 0.697255 | 0.691294 | NA |
| 1 | 831489 | rs4970385 | C | T | 0.0001605 | 0.0001201 | 0.1800002 | 0.1816374 | 0.705397 | 0.649161 | NA |
| 1 | 831909 | rs9697642 | C | T | 0.0001638 | 0.0001201 | 0.1700000 | 0.1727347 | 0.705442 | 0.648562 | NA |
| 1 | 832066 | rs9697380 | G | C | 0.0001636 | 0.0001201 | 0.1700000 | 0.1732522 | 0.705627 | 0.664337 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51171693 | rs756638 | G | A | -0.0001322 | 0.0001243 | 0.2900000 | 0.2876830 | 0.282284 | 0.304912 | NA |
| 22 | 51174048 | rs9628245 | G | C | 0.0001213 | 0.0001239 | 0.3300000 | 0.3274784 | 0.380135 | 0.433107 | NA |
| 22 | 51180501 | rs5770999 | T | C | 0.0000362 | 0.0001255 | 0.7700005 | 0.7732890 | 0.713656 | 0.636981 | NA |
| 22 | 51181919 | rs9616825 | G | C | -0.0001376 | 0.0001249 | 0.2700001 | 0.2706179 | 0.695470 | 0.619409 | NA |
| 22 | 51182485 | rs6009961 | A | G | 0.0000157 | 0.0001259 | 0.9000000 | 0.9007142 | 0.715502 | 0.638379 | NA |
| 22 | 51186143 | rs2879914 | T | C | 0.0000335 | 0.0001168 | 0.7700005 | 0.7739944 | 0.381825 | 0.273363 | NA |
| 22 | 51186228 | rs3865766 | C | T | 0.0000802 | 0.0001138 | 0.4799997 | 0.4807135 | 0.451061 | 0.453275 | NA |
| 22 | 51197266 | rs61290853 | A | G | 0.0000248 | 0.0001175 | 0.8300000 | 0.8329598 | 0.386333 | 0.422923 | NA |
| 22 | 51212875 | rs2238837 | A | C | -0.0000672 | 0.0001253 | 0.5900000 | 0.5915078 | 0.331457 | 0.372404 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0001141 | 0.0001283 | 0.3700002 | 0.3735564 | 0.297974 | 0.205072 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623765 ES:SE:LP:AF:ID 8.03304e-05:0.000192819:0.167491:0.623765:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID -0.000141389:0.000191024:0.337242:0.400401:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456846 ES:SE:LP:AF:ID -1.06756e-05:0.000188086:0.0222764:0.456846:rs6702460
1 706368 rs12029736 A G . PASS AF=0.515645 ES:SE:LP:AF:ID 1.57418e-05:0.000133371:0.0409586:0.515645:rs12029736
1 763394 rs3115847 G A . PASS AF=0.706753 ES:SE:LP:AF:ID -0.000118318:0.000156365:0.346787:0.706753:rs3115847
1 814495 rs74461805 C A . PASS AF=0.340396 ES:SE:LP:AF:ID 2.71828e-05:0.000182875:0.0555173:0.340396:rs74461805
1 830181 rs28444699 A G . PASS AF=0.697255 ES:SE:LP:AF:ID 0.000163623:0.000122353:0.744727:0.697255:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705397 ES:SE:LP:AF:ID 0.000160472:0.000120138:0.744727:0.705397:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705442 ES:SE:LP:AF:ID 0.000163799:0.000120134:0.769551:0.705442:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705627 ES:SE:LP:AF:ID 0.00016361:0.00012014:0.769551:0.705627:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705655 ES:SE:LP:AF:ID 0.000163251:0.000120153:0.769551:0.705655:rs4500250
1 832918 rs28765502 T C . PASS AF=0.294377 ES:SE:LP:AF:ID -0.000163763:0.000120147:0.769551:0.294377:rs28765502
1 840753 rs4970382 T C . PASS AF=0.400124 ES:SE:LP:AF:ID -0.000231906:0.000110818:1.4437:0.400124:rs4970382
1 843405 rs11516185 A G . PASS AF=0.362606 ES:SE:LP:AF:ID -4.68044e-05:0.000137564:0.136677:0.362606:rs11516185
1 850218 rs6664536 T A . PASS AF=0.590331 ES:SE:LP:AF:ID 0.000159729:0.000110494:0.823909:0.590331:rs6664536
1 850371 rs6679046 G T . PASS AF=0.603723 ES:SE:LP:AF:ID 0.000140561:0.000111114:0.677781:0.603723:rs6679046
1 850780 rs6657440 C T . PASS AF=0.603942 ES:SE:LP:AF:ID 0.000142464:0.000111099:0.69897:0.603942:rs6657440
1 852037 rs4970463 G A . PASS AF=0.589686 ES:SE:LP:AF:ID 0.000154549:0.000110674:0.79588:0.589686:rs4970463
1 852063 rs28436996 G A . PASS AF=0.589665 ES:SE:LP:AF:ID 0.000156607:0.000110624:0.79588:0.589665:rs28436996
1 852875 rs13303369 C T . PASS AF=0.607671 ES:SE:LP:AF:ID 0.000129829:0.000111347:0.619789:0.607671:rs13303369
1 853954 rs1806509 C A . PASS AF=0.607829 ES:SE:LP:AF:ID 0.000134234:0.000111362:0.638272:0.607829:rs1806509
1 854777 rs13303019 A G . PASS AF=0.610316 ES:SE:LP:AF:ID 0.000148401:0.000111472:0.744727:0.610316:rs13303019
1 854978 rs13303057 A C . PASS AF=0.603283 ES:SE:LP:AF:ID 0.0001437:0.000111142:0.69897:0.603283:rs13303057
1 855075 rs6673914 C G . PASS AF=0.610337 ES:SE:LP:AF:ID 0.000147992:0.000111474:0.744727:0.610337:rs6673914
1 856099 rs28534711 T G . PASS AF=0.389936 ES:SE:LP:AF:ID -0.000129292:0.000111495:0.60206:0.389936:rs28534711
1 856108 rs28742275 A G . PASS AF=0.38992 ES:SE:LP:AF:ID -0.000129133:0.000111501:0.60206:0.38992:rs28742275
1 856476 rs4040605 A G . PASS AF=0.350356 ES:SE:LP:AF:ID -0.000209041:0.000114543:1.16749:0.350356:rs4040605
1 866893 rs2880024 T C . PASS AF=0.610552 ES:SE:LP:AF:ID 0.000239612:0.000112099:1.48149:0.610552:rs2880024
1 868418 rs28546443 C T . PASS AF=0.297867 ES:SE:LP:AF:ID -0.000244856:0.000123164:1.3279:0.297867:rs28546443
1 870645 rs28576697 T C . PASS AF=0.291285 ES:SE:LP:AF:ID -0.000200061:0.000122184:1:0.291285:rs28576697
1 871334 rs4072383 G T . PASS AF=0.72062 ES:SE:LP:AF:ID 0.000219761:0.000121427:1.1549:0.72062:rs4072383
1 873558 rs1110052 G T . PASS AF=0.715251 ES:SE:LP:AF:ID 0.000214554:0.000120462:1.12494:0.715251:rs1110052
1 875770 rs4970379 A G . PASS AF=0.600085 ES:SE:LP:AF:ID 0.000263931:0.000113031:1.69897:0.600085:rs4970379
1 881627 rs2272757 G A . PASS AF=0.652393 ES:SE:LP:AF:ID 0.000260641:0.000114181:1.65758:0.652393:rs2272757
1 891059 rs13303065 C T . PASS AF=0.652432 ES:SE:LP:AF:ID 0.000255168:0.000114163:1.60206:0.652432:rs13303065
1 891945 rs13303106 A G . PASS AF=0.652494 ES:SE:LP:AF:ID 0.000258063:0.000114296:1.61979:0.652494:rs13303106
1 903245 rs28690976 A G . PASS AF=0.566938 ES:SE:LP:AF:ID 0.000244335:0.000113521:1.50864:0.566938:rs28690976
1 909073 rs3892467 C T . PASS AF=0.386681 ES:SE:LP:AF:ID -5.81019e-05:0.000113212:0.21467:0.386681:rs3892467
1 909238 rs3829740 G C . PASS AF=0.571408 ES:SE:LP:AF:ID -8.39417e-05:0.000109643:0.356547:0.571408:rs3829740
1 910394 rs28477686 C T . PASS AF=0.324458 ES:SE:LP:AF:ID -4.37864e-06:0.000118841:0.0132283:0.324458:rs28477686
1 912049 rs7367995 T C . PASS AF=0.585249 ES:SE:LP:AF:ID 0.000140759:0.000110755:0.69897:0.585249:rs7367995
1 913889 rs2340596 G A . PASS AF=0.59921 ES:SE:LP:AF:ID 0.000118919:0.000110935:0.552842:0.59921:rs2340596
1 914333 rs13302979 C G . PASS AF=0.602516 ES:SE:LP:AF:ID 0.000122367:0.000111271:0.568636:0.602516:rs13302979
1 914852 rs13303368 G C . PASS AF=0.600074 ES:SE:LP:AF:ID 0.000126658:0.000111057:0.60206:0.600074:rs13303368
1 914940 rs13303033 T C . PASS AF=0.584289 ES:SE:LP:AF:ID 0.000134665:0.000110435:0.657577:0.584289:rs13303033
1 916834 rs6694632 G A . PASS AF=0.589102 ES:SE:LP:AF:ID 0.000134442:0.000110603:0.657577:0.589102:rs6694632
1 918384 rs13303118 G T . PASS AF=0.584202 ES:SE:LP:AF:ID 0.000142911:0.000110383:0.69897:0.584202:rs13303118
1 918573 rs2341354 A G . PASS AF=0.589327 ES:SE:LP:AF:ID 0.000141407:0.000110518:0.69897:0.589327:rs2341354
1 919501 rs4970414 G T . PASS AF=0.583926 ES:SE:LP:AF:ID 0.000206463:0.000114294:1.14874:0.583926:rs4970414
1 921716 rs13303278 C A . PASS AF=0.567888 ES:SE:LP:AF:ID 0.000105929:0.00011029:0.468521:0.567888:rs13303278