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_20084_480.vcf.gz --id UKB-b:14518 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20084_480.txt.gz --cohort_cases 1257 --cohort_controls 63692 --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": "2019-09-13T13:38:42.848215",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-14518/ukb-b-14518.vcf.gz; Date=Sun May 10 05:29:38 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-14518/UKB-b-14518_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-14518/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:43:45 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-14518/UKB-b-14518_data.vcf.gz ...
Read summary statistics for 2025075 SNPs.
Dropped 200 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, 518792 SNPs remain.
After merging with regression SNP LD, 518792 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0197 (0.0098)
Lambda GC: 1.0128
Mean Chi^2: 1.0213
Intercept: 0.9909 (0.0103)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Thu Oct 17 14:44:14 2019
Total time elapsed: 29.8sQC metrics
Metrics
Metrics
{
"af_correlation": 0.7006,
"inflation_factor": 1,
"mean_EFFECT": -3.9077e-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": 16074,
"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": 518792,
"ldsc_nsnp_merge_regression_ld": 518792,
"ldsc_observed_scale_h2_beta": 0.0197,
"ldsc_observed_scale_h2_se": 0.0098,
"ldsc_intercept_beta": 0.9909,
"ldsc_intercept_se": 0.0103,
"ldsc_lambda_gc": 1.0128,
"ldsc_mean_chisq": 1.0213,
"ldsc_ratio": -0.4272
}
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 | 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 | 2024877 | 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 | 2025075 | 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 | 8.648300e+00 | 5.764551e+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 | 7.871564e+07 | 5.663712e+07 | 5687.0000000 | 3.182972e+07 | 6.925997e+07 | 1.147861e+08 | 2.491722e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -3.900000e-06 | 8.081000e-04 | -0.0042633 | -5.479000e-04 | -5.700000e-06 | 5.375000e-04 | 4.122400e-03 | ▁▂▇▂▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.966000e-04 | 3.060000e-05 | 0.0007380 | 7.722000e-04 | 7.885000e-04 | 8.161000e-04 | 1.516800e-03 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.960309e-01 | 2.896301e-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 | 4.960306e-01 | 2.896066e-01 | 0.0000001 | 2.453412e-01 | 4.952463e-01 | 7.464175e-01 | 9.999998e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.684928e-01 | 1.266084e-01 | 0.2784410 | 3.571610e-01 | 4.528160e-01 | 5.721000e-01 | 7.215590e-01 | ▇▆▆▅▅ |
| numeric | AF_reference | 16074 | 0.9920625 | NA | NA | NA | NA | NA | NA | NA | 4.483173e-01 | 1.612483e-01 | 0.0001997 | 3.238820e-01 | 4.384980e-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 | -0.0007312 | 0.0013600 | 0.5900000 | 0.5908190 | 0.623812 | 0.782149 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0026652 | 0.0013561 | 0.0490004 | 0.0493756 | 0.399144 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0008231 | 0.0013338 | 0.5400003 | 0.5371669 | 0.455916 | 0.420727 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0005060 | 0.0009402 | 0.5900000 | 0.5904602 | 0.513304 | 0.275160 | NA |
| 1 | 763394 | rs369924889 | G | A | 0.0016223 | 0.0011015 | 0.1400000 | 0.1408107 | 0.705804 | 0.617612 | NA |
| 1 | 814495 | rs74461805 | C | A | -0.0027981 | 0.0012876 | 0.0299999 | 0.0297655 | 0.340108 | NA | NA |
| 1 | 830181 | rs28444699 | A | G | 0.0002811 | 0.0008614 | 0.7400005 | 0.7441381 | 0.696612 | 0.691294 | NA |
| 1 | 831489 | rs4970385 | C | T | 0.0004614 | 0.0008458 | 0.5900000 | 0.5853771 | 0.705031 | 0.649161 | NA |
| 1 | 831909 | rs9697642 | C | T | 0.0004552 | 0.0008458 | 0.5900000 | 0.5904300 | 0.705083 | 0.648562 | NA |
| 1 | 832066 | rs9697380 | G | C | 0.0004441 | 0.0008458 | 0.5999997 | 0.5994994 | 0.705261 | 0.664337 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51171693 | rs756638 | G | A | -0.0016919 | 0.0008725 | 0.0519996 | 0.0524654 | 0.284143 | 0.304912 | NA |
| 22 | 51174048 | rs9628245 | G | C | 0.0009567 | 0.0008736 | 0.2700001 | 0.2734478 | 0.377819 | 0.433107 | NA |
| 22 | 51180501 | rs5770999 | T | C | -0.0008948 | 0.0008811 | 0.3100002 | 0.3098221 | 0.712571 | 0.636981 | NA |
| 22 | 51181919 | rs9616825 | G | C | -0.0015178 | 0.0008775 | 0.0840001 | 0.0836712 | 0.695031 | 0.619409 | NA |
| 22 | 51182485 | rs6009961 | A | G | -0.0008960 | 0.0008840 | 0.3100002 | 0.3108172 | 0.714237 | 0.638379 | NA |
| 22 | 51186143 | rs2879914 | T | C | 0.0006971 | 0.0008233 | 0.4000000 | 0.3971682 | 0.380077 | 0.273363 | NA |
| 22 | 51186228 | rs3865766 | C | T | 0.0002478 | 0.0008019 | 0.7600007 | 0.7573024 | 0.449547 | 0.453275 | NA |
| 22 | 51197266 | rs61290853 | A | G | -0.0002050 | 0.0008264 | 0.8000000 | 0.8040663 | 0.386693 | 0.422923 | NA |
| 22 | 51212875 | rs2238837 | A | C | 0.0007998 | 0.0008838 | 0.3700002 | 0.3654820 | 0.331351 | 0.372404 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0004163 | 0.0009029 | 0.6400000 | 0.6447443 | 0.298393 | 0.205072 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623812 ES:SE:LP:AF:ID -0.000731225:0.00136004:0.229148:0.623812:rs10399793
1 54676 rs2462492 C T . PASS AF=0.399144 ES:SE:LP:AF:ID 0.00266517:0.00135609:1.3098:0.399144:rs2462492
1 91536 rs6702460 G T . PASS AF=0.455916 ES:SE:LP:AF:ID 0.000823098:0.00133381:0.267606:0.455916:rs6702460
1 706368 rs12029736 A G . PASS AF=0.513304 ES:SE:LP:AF:ID 0.000506005:0.000940234:0.229148:0.513304:rs12029736
1 763394 rs3115847 G A . PASS AF=0.705804 ES:SE:LP:AF:ID 0.00162231:0.00110153:0.853872:0.705804:rs3115847
1 814495 rs74461805 C A . PASS AF=0.340108 ES:SE:LP:AF:ID -0.0027981:0.00128755:1.52288:0.340108:rs74461805
1 830181 rs28444699 A G . PASS AF=0.696612 ES:SE:LP:AF:ID 0.000281144:0.000861405:0.130768:0.696612:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705031 ES:SE:LP:AF:ID 0.000461424:0.0008458:0.229148:0.705031:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705083 ES:SE:LP:AF:ID 0.00045522:0.000845799:0.229148:0.705083:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705261 ES:SE:LP:AF:ID 0.000444126:0.00084576:0.221849:0.705261:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705286 ES:SE:LP:AF:ID 0.00044012:0.000845878:0.221849:0.705286:rs4500250
1 832918 rs28765502 T C . PASS AF=0.294729 ES:SE:LP:AF:ID -0.000446118:0.000845851:0.221849:0.294729:rs28765502
1 840753 rs4970382 T C . PASS AF=0.400406 ES:SE:LP:AF:ID -0.00102003:0.000780805:0.721246:0.400406:rs4970382
1 843405 rs11516185 A G . PASS AF=0.362367 ES:SE:LP:AF:ID 0.000306301:0.000971455:0.124939:0.362367:rs11516185
1 850218 rs6664536 T A . PASS AF=0.589315 ES:SE:LP:AF:ID 0.000866741:0.000776572:0.585027:0.589315:rs6664536
1 850371 rs6679046 G T . PASS AF=0.603035 ES:SE:LP:AF:ID 0.00104027:0.000780286:0.744727:0.603035:rs6679046
1 850780 rs6657440 C T . PASS AF=0.603381 ES:SE:LP:AF:ID 0.00111898:0.000780301:0.823909:0.603381:rs6657440
1 852037 rs4970463 G A . PASS AF=0.588673 ES:SE:LP:AF:ID 0.000947249:0.00077768:0.657577:0.588673:rs4970463
1 852063 rs28436996 G A . PASS AF=0.588649 ES:SE:LP:AF:ID 0.000885586:0.000777329:0.60206:0.588649:rs28436996
1 852875 rs13303369 C T . PASS AF=0.606715 ES:SE:LP:AF:ID 0.00125031:0.000781584:0.958607:0.606715:rs13303369
1 853954 rs1806509 C A . PASS AF=0.60688 ES:SE:LP:AF:ID 0.00124025:0.000781665:0.958607:0.60688:rs1806509
1 854777 rs13303019 A G . PASS AF=0.609504 ES:SE:LP:AF:ID 0.0012806:0.000782447:1:0.609504:rs13303019
1 854978 rs13303057 A C . PASS AF=0.602505 ES:SE:LP:AF:ID 0.00104395:0.000780423:0.744727:0.602505:rs13303057
1 855075 rs6673914 C G . PASS AF=0.609473 ES:SE:LP:AF:ID 0.00127729:0.000782344:1:0.609473:rs6673914
1 856099 rs28534711 T G . PASS AF=0.390835 ES:SE:LP:AF:ID -0.0012884:0.000782661:1:0.390835:rs28534711
1 856108 rs28742275 A G . PASS AF=0.390777 ES:SE:LP:AF:ID -0.001285:0.000782742:1:0.390777:rs28742275
1 856476 rs4040605 A G . PASS AF=0.352504 ES:SE:LP:AF:ID -0.00127678:0.000803462:0.958607:0.352504:rs4040605
1 866893 rs2880024 T C . PASS AF=0.608083 ES:SE:LP:AF:ID 0.000251188:0.000789584:0.124939:0.608083:rs2880024
1 868418 rs28546443 C T . PASS AF=0.301063 ES:SE:LP:AF:ID -0.000242227:0.000864783:0.107905:0.301063:rs28546443
1 870645 rs28576697 T C . PASS AF=0.293389 ES:SE:LP:AF:ID -0.000737117:0.00085533:0.408935:0.293389:rs28576697
1 871334 rs4072383 G T . PASS AF=0.717959 ES:SE:LP:AF:ID 0.000474683:0.000850891:0.236572:0.717959:rs4072383
1 873558 rs1110052 G T . PASS AF=0.71249 ES:SE:LP:AF:ID 0.000416293:0.000844521:0.207608:0.71249:rs1110052
1 875770 rs4970379 A G . PASS AF=0.598464 ES:SE:LP:AF:ID 0.000253267:0.000795488:0.124939:0.598464:rs4970379
1 881627 rs2272757 G A . PASS AF=0.65048 ES:SE:LP:AF:ID 0.000129347:0.000803588:0.0604807:0.65048:rs2272757
1 891059 rs13303065 C T . PASS AF=0.650626 ES:SE:LP:AF:ID 5.15944e-05:0.00080351:0.0222764:0.650626:rs13303065
1 891945 rs13303106 A G . PASS AF=0.650689 ES:SE:LP:AF:ID 8.47346e-05:0.000804429:0.0362122:0.650689:rs13303106
1 903245 rs28690976 A G . PASS AF=0.565133 ES:SE:LP:AF:ID 0.000386411:0.000798789:0.200659:0.565133:rs28690976
1 909073 rs3892467 C T . PASS AF=0.386217 ES:SE:LP:AF:ID 0.000751634:0.000797119:0.455932:0.386217:rs3892467
1 909238 rs3829740 G C . PASS AF=0.572164 ES:SE:LP:AF:ID 0.00115611:0.00077117:0.886057:0.572164:rs3829740
1 910394 rs28477686 C T . PASS AF=0.322663 ES:SE:LP:AF:ID 0.00102984:0.000837643:0.657577:0.322663:rs28477686
1 912049 rs7367995 T C . PASS AF=0.585242 ES:SE:LP:AF:ID -0.000239206:0.000778647:0.119186:0.585242:rs7367995
1 913889 rs2340596 G A . PASS AF=0.600025 ES:SE:LP:AF:ID -0.000177773:0.000780195:0.0861861:0.600025:rs2340596
1 914333 rs13302979 C G . PASS AF=0.60338 ES:SE:LP:AF:ID 2.80254e-05:0.000782841:0.0132283:0.60338:rs13302979
1 914852 rs13303368 G C . PASS AF=0.60101 ES:SE:LP:AF:ID -0.000212085:0.0007814:0.102373:0.60101:rs13303368
1 914940 rs13303033 T C . PASS AF=0.585327 ES:SE:LP:AF:ID -0.000205911:0.000777513:0.102373:0.585327:rs13303033
1 916834 rs6694632 G A . PASS AF=0.590116 ES:SE:LP:AF:ID -1.41148e-05:0.000778582:0.00436481:0.590116:rs6694632
1 918384 rs13303118 G T . PASS AF=0.585329 ES:SE:LP:AF:ID -0.000169497:0.000777222:0.0809219:0.585329:rs13303118
1 918573 rs2341354 A G . PASS AF=0.590348 ES:SE:LP:AF:ID -1.68071e-05:0.000778035:0.00877392:0.590348:rs2341354
1 919501 rs4970414 G T . PASS AF=0.583818 ES:SE:LP:AF:ID -0.000875272:0.000806773:0.552842:0.583818:rs4970414
1 921716 rs13303278 C A . PASS AF=0.569359 ES:SE:LP:AF:ID -0.00130712:0.000778745:1.03152:0.569359:rs13303278