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_41210_E133.vcf.gz --id UKB-b:17962 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41210_E133.txt.gz --cohort_cases 1246 --cohort_controls 461764 --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": "2019-09-12T23:21:43.227078",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-17962/ukb-b-17962.vcf.gz; Date=Sat May 9 22:14:42 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-17962/UKB-b-17962_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-17962/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:40:18 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-17962/UKB-b-17962_data.vcf.gz ...
Read summary statistics for 2000140 SNPs.
Dropped 196 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, 512554 SNPs remain.
After merging with regression SNP LD, 512554 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0006 (0.0013)
Lambda GC: 1.0204
Mean Chi^2: 1.0165
Intercept: 1.0096 (0.0098)
Ratio: 0.5828 (0.591)
Analysis finished at Thu Oct 17 14:40:48 2019
Total time elapsed: 30.52sQC metrics
Metrics
Metrics
{
"af_correlation": 0.6957,
"inflation_factor": 1,
"mean_EFFECT": -1.4769e-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": 15858,
"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": 512554,
"ldsc_nsnp_merge_regression_ld": 512554,
"ldsc_observed_scale_h2_beta": 0.0006,
"ldsc_observed_scale_h2_se": 0.0013,
"ldsc_intercept_beta": 1.0096,
"ldsc_intercept_se": 0.0098,
"ldsc_lambda_gc": 1.0204,
"ldsc_mean_chisq": 1.0165,
"ldsc_ratio": 0.5818
}
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 | 1999946 | 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 | 2000140 | 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 | 8.648808e+00 | 5.763656e+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 | 7.872984e+07 | 5.664699e+07 | 5687.0000000 | 3.183025e+07 | 6.927586e+07 | 1.148324e+08 | 2.491722e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -1.000000e-07 | 1.133000e-04 | -0.0005014 | -7.660000e-05 | -3.000000e-07 | 7.620000e-05 | 6.648000e-04 | ▁▅▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.121000e-04 | 4.200000e-06 | 0.0001039 | 1.088000e-04 | 1.110000e-04 | 1.148000e-04 | 2.131000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.966596e-01 | 2.892906e-01 | 0.0000004 | 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 | 4.966583e-01 | 2.892644e-01 | 0.0000004 | 2.457496e-01 | 4.952595e-01 | 7.472261e-01 | 9.999990e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.691456e-01 | 1.252109e-01 | 0.2808990 | 3.590948e-01 | 4.538310e-01 | 5.716690e-01 | 7.191010e-01 | ▇▆▆▅▅ |
| numeric | AF_reference | 15858 | 0.9920716 | NA | NA | NA | NA | NA | NA | NA | 4.488553e-01 | 1.604208e-01 | 0.0001997 | 3.252800e-01 | 4.392970e-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 | 0.0000747 | 0.0001912 | 0.6999999 | 0.6959163 | 0.623765 | 0.782149 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0001898 | 0.0001894 | 0.3200000 | 0.3164911 | 0.400401 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0001364 | 0.0001865 | 0.4600002 | 0.4644964 | 0.456846 | 0.420727 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0000916 | 0.0001323 | 0.4899999 | 0.4885017 | 0.515645 | 0.275160 | NA |
| 1 | 763394 | rs369924889 | G | A | -0.0000860 | 0.0001551 | 0.5800000 | 0.5790678 | 0.706753 | 0.617612 | NA |
| 1 | 814495 | rs74461805 | C | A | 0.0000820 | 0.0001814 | 0.6499995 | 0.6512392 | 0.340396 | NA | NA |
| 1 | 830181 | rs28444699 | A | G | -0.0001353 | 0.0001213 | 0.2599998 | 0.2648899 | 0.697255 | 0.691294 | NA |
| 1 | 831489 | rs4970385 | C | T | -0.0002480 | 0.0001191 | 0.0369999 | 0.0373457 | 0.705397 | 0.649161 | NA |
| 1 | 831909 | rs9697642 | C | T | -0.0002517 | 0.0001191 | 0.0350002 | 0.0345929 | 0.705442 | 0.648562 | NA |
| 1 | 832066 | rs9697380 | G | C | -0.0002537 | 0.0001191 | 0.0329997 | 0.0332423 | 0.705627 | 0.664337 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51171693 | rs756638 | G | A | -0.0001034 | 0.0001233 | 0.4000000 | 0.4015107 | 0.282284 | 0.304912 | NA |
| 22 | 51174048 | rs9628245 | G | C | 0.0000725 | 0.0001228 | 0.5600000 | 0.5552912 | 0.380135 | 0.433107 | NA |
| 22 | 51180501 | rs5770999 | T | C | 0.0002560 | 0.0001245 | 0.0400000 | 0.0397357 | 0.713656 | 0.636981 | NA |
| 22 | 51181919 | rs9616825 | G | C | 0.0002636 | 0.0001238 | 0.0329997 | 0.0332664 | 0.695470 | 0.619409 | NA |
| 22 | 51182485 | rs6009961 | A | G | 0.0002590 | 0.0001249 | 0.0379997 | 0.0380548 | 0.715502 | 0.638379 | NA |
| 22 | 51186143 | rs2879914 | T | C | 0.0001089 | 0.0001158 | 0.3500000 | 0.3470973 | 0.381825 | 0.273363 | NA |
| 22 | 51186228 | rs3865766 | C | T | 0.0000928 | 0.0001129 | 0.4100001 | 0.4107130 | 0.451061 | 0.453275 | NA |
| 22 | 51197266 | rs61290853 | A | G | 0.0000439 | 0.0001165 | 0.7099994 | 0.7064307 | 0.386333 | 0.422923 | NA |
| 22 | 51212875 | rs2238837 | A | C | 0.0000856 | 0.0001243 | 0.4899999 | 0.4908988 | 0.331457 | 0.372404 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0000716 | 0.0001272 | 0.5700002 | 0.5732529 | 0.297974 | 0.205072 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623765 ES:SE:LP:AF:ID 7.47327e-05:0.000191211:0.154902:0.623765:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID -0.000189752:0.000189431:0.49485:0.400401:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456846 ES:SE:LP:AF:ID 0.000136431:0.000186518:0.337242:0.456846:rs6702460
1 706368 rs12029736 A G . PASS AF=0.515645 ES:SE:LP:AF:ID -9.1615e-05:0.000132259:0.309804:0.515645:rs12029736
1 763394 rs3115847 G A . PASS AF=0.706753 ES:SE:LP:AF:ID -8.60196e-05:0.000155061:0.236572:0.706753:rs3115847
1 814495 rs74461805 C A . PASS AF=0.340396 ES:SE:LP:AF:ID 8.19777e-05:0.00018135:0.187087:0.340396:rs74461805
1 830181 rs28444699 A G . PASS AF=0.697255 ES:SE:LP:AF:ID -0.000135274:0.000121332:0.585027:0.697255:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705397 ES:SE:LP:AF:ID -0.000248039:0.000119137:1.4318:0.705397:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705442 ES:SE:LP:AF:ID -0.000251739:0.000119133:1.45593:0.705442:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705627 ES:SE:LP:AF:ID -0.000253662:0.000119138:1.48149:0.705627:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705655 ES:SE:LP:AF:ID -0.000251699:0.000119151:1.45593:0.705655:rs4500250
1 832918 rs28765502 T C . PASS AF=0.294377 ES:SE:LP:AF:ID 0.000251162:0.000119145:1.45593:0.294377:rs28765502
1 840753 rs4970382 T C . PASS AF=0.400124 ES:SE:LP:AF:ID 0.000173535:0.000109893:0.958607:0.400124:rs4970382
1 843405 rs11516185 A G . PASS AF=0.362606 ES:SE:LP:AF:ID -0.000269468:0.000136417:1.31876:0.362606:rs11516185
1 850218 rs6664536 T A . PASS AF=0.590331 ES:SE:LP:AF:ID -0.000127057:0.000109573:0.60206:0.590331:rs6664536
1 850371 rs6679046 G T . PASS AF=0.603723 ES:SE:LP:AF:ID -0.000147065:0.000110188:0.744727:0.603723:rs6679046
1 850780 rs6657440 C T . PASS AF=0.603942 ES:SE:LP:AF:ID -0.000147811:0.000110172:0.744727:0.603942:rs6657440
1 852037 rs4970463 G A . PASS AF=0.589686 ES:SE:LP:AF:ID -0.000118318:0.000109751:0.552842:0.589686:rs4970463
1 852063 rs28436996 G A . PASS AF=0.589665 ES:SE:LP:AF:ID -0.000116754:0.000109702:0.537602:0.589665:rs28436996
1 852875 rs13303369 C T . PASS AF=0.607671 ES:SE:LP:AF:ID -0.000153055:0.000110418:0.769551:0.607671:rs13303369
1 853954 rs1806509 C A . PASS AF=0.607829 ES:SE:LP:AF:ID -0.000154294:0.000110433:0.79588:0.607829:rs1806509
1 854777 rs13303019 A G . PASS AF=0.610316 ES:SE:LP:AF:ID -0.000164:0.000110542:0.853872:0.610316:rs13303019
1 854978 rs13303057 A C . PASS AF=0.603283 ES:SE:LP:AF:ID -0.000143312:0.000110215:0.721246:0.603283:rs13303057
1 855075 rs6673914 C G . PASS AF=0.610337 ES:SE:LP:AF:ID -0.000163927:0.000110544:0.853872:0.610337:rs6673914
1 856099 rs28534711 T G . PASS AF=0.389936 ES:SE:LP:AF:ID 0.000160645:0.000110565:0.823909:0.389936:rs28534711
1 856108 rs28742275 A G . PASS AF=0.38992 ES:SE:LP:AF:ID 0.000160798:0.000110571:0.823909:0.38992:rs28742275
1 856476 rs4040605 A G . PASS AF=0.350356 ES:SE:LP:AF:ID 0.000219088:0.000113587:1.26761:0.350356:rs4040605
1 866893 rs2880024 T C . PASS AF=0.610552 ES:SE:LP:AF:ID -0.00015661:0.000111164:0.79588:0.610552:rs2880024
1 868418 rs28546443 C T . PASS AF=0.297867 ES:SE:LP:AF:ID 0.000213899:0.000122137:1.09691:0.297867:rs28546443
1 870645 rs28576697 T C . PASS AF=0.291285 ES:SE:LP:AF:ID 0.000195693:0.000121165:0.958607:0.291285:rs28576697
1 873558 rs1110052 G T . PASS AF=0.715251 ES:SE:LP:AF:ID -0.000165183:0.000119458:0.769551:0.715251:rs1110052
1 875770 rs4970379 A G . PASS AF=0.600085 ES:SE:LP:AF:ID -0.000214095:0.000112088:1.25181:0.600085:rs4970379
1 881627 rs2272757 G A . PASS AF=0.652393 ES:SE:LP:AF:ID -0.000172193:0.000113229:0.886057:0.652393:rs2272757
1 891059 rs13303065 C T . PASS AF=0.652432 ES:SE:LP:AF:ID -0.000167935:0.000113211:0.853872:0.652432:rs13303065
1 891945 rs13303106 A G . PASS AF=0.652494 ES:SE:LP:AF:ID -0.000170189:0.000113343:0.886057:0.652494:rs13303106
1 903245 rs28690976 A G . PASS AF=0.566938 ES:SE:LP:AF:ID -0.000157006:0.000112574:0.79588:0.566938:rs28690976
1 909073 rs3892467 C T . PASS AF=0.386681 ES:SE:LP:AF:ID -0.000118712:0.000112268:0.537602:0.386681:rs3892467
1 909238 rs3829740 G C . PASS AF=0.571408 ES:SE:LP:AF:ID -8.06308e-05:0.000108729:0.337242:0.571408:rs3829740
1 910394 rs28477686 C T . PASS AF=0.324458 ES:SE:LP:AF:ID 2.10351e-05:0.00011785:0.0655015:0.324458:rs28477686
1 912049 rs7367995 T C . PASS AF=0.585249 ES:SE:LP:AF:ID -0.000118716:0.000109831:0.552842:0.585249:rs7367995
1 913889 rs2340596 G A . PASS AF=0.59921 ES:SE:LP:AF:ID -7.0609e-05:0.00011001:0.283997:0.59921:rs2340596
1 914333 rs13302979 C G . PASS AF=0.602516 ES:SE:LP:AF:ID -9.17953e-05:0.000110343:0.387216:0.602516:rs13302979
1 914852 rs13303368 G C . PASS AF=0.600074 ES:SE:LP:AF:ID -7.52102e-05:0.000110131:0.309804:0.600074:rs13303368
1 914940 rs13303033 T C . PASS AF=0.584289 ES:SE:LP:AF:ID -7.77758e-05:0.000109514:0.318759:0.584289:rs13303033
1 916834 rs6694632 G A . PASS AF=0.589102 ES:SE:LP:AF:ID -8.626e-05:0.000109681:0.366532:0.589102:rs6694632
1 918384 rs13303118 G T . PASS AF=0.584202 ES:SE:LP:AF:ID -7.03315e-05:0.000109462:0.283997:0.584202:rs13303118
1 918573 rs2341354 A G . PASS AF=0.589327 ES:SE:LP:AF:ID -8.52889e-05:0.000109597:0.356547:0.589327:rs2341354
1 919501 rs4970414 G T . PASS AF=0.583926 ES:SE:LP:AF:ID -0.000110953:0.000113341:0.481486:0.583926:rs4970414
1 921716 rs13303278 C A . PASS AF=0.567888 ES:SE:LP:AF:ID -8.54514e-05:0.00010937:0.366532:0.567888:rs13303278
1 924528 rs34712273 C A . PASS AF=0.578491 ES:SE:LP:AF:ID -5.20146e-05:0.000109684:0.19382:0.578491:rs34712273