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_20003_1140868472.vcf.gz --id UKB-b:8624 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20003_1140868472.txt.gz --cohort_cases 1265 --cohort_controls 461668 --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": "2019-09-13T14:26:17.392420",
"bcftools_viewVersion": "1.9-74-g6af271c+htslib-1.9-64-g226b4a8",
"bcftools_viewCommand": "view -T ^/mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-8624/mac_discard.txt -Oz /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-8624/UKB-b-8624_raw.vcf.gz; Date=Thu Oct 17 12:21:11 2019",
"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-8624/ukb-b-8624.vcf.gz; Date=Sun May 10 09:42:06 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-8624/UKB-b-8624_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-8624/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:42:05 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-8624/UKB-b-8624_data.vcf.gz ...
Read summary statistics for 2042067 SNPs.
Dropped 202 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, 522806 SNPs remain.
After merging with regression SNP LD, 522806 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0021 (0.0014)
Lambda GC: 1.0156
Mean Chi^2: 1.0258
Intercept: 1.0029 (0.011)
Ratio: 0.1128 (0.4262)
Analysis finished at Thu Oct 17 14:42:54 2019
Total time elapsed: 49.2sQC metrics
Metrics
Metrics
{
"af_correlation": 0.7032,
"inflation_factor": 1,
"mean_EFFECT": 1.3388e-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": 16213,
"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": 522806,
"ldsc_nsnp_merge_regression_ld": 522806,
"ldsc_observed_scale_h2_beta": 0.0021,
"ldsc_observed_scale_h2_se": 0.0014,
"ldsc_intercept_beta": 1.0029,
"ldsc_intercept_se": 0.011,
"ldsc_lambda_gc": 1.0156,
"ldsc_mean_chisq": 1.0258,
"ldsc_ratio": 0.1124
}
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 | 2041867 | 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 | 2042067 | 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 | 8.648060e+00 | 5.764462e+00 | 1.0000e+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 | 7.872179e+07 | 5.664216e+07 | 5.6870e+03 | 3.182644e+07 | 6.927753e+07 | 1.148338e+08 | 2.491722e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.300000e-06 | 1.148000e-04 | -5.9000e-04 | -7.530000e-05 | 1.000000e-06 | 7.810000e-05 | 5.244000e-04 | ▁▁▇▃▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.131000e-04 | 4.400000e-06 | 1.0460e-04 | 1.095000e-04 | 1.119000e-04 | 1.159000e-04 | 2.170000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.970267e-01 | 2.912191e-01 | 7.5000e-06 | 2.399999e-01 | 5.000000e-01 | 7.499995e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.970254e-01 | 2.911954e-01 | 7.5000e-06 | 2.434400e-01 | 4.972828e-01 | 7.500741e-01 | 9.999996e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.679361e-01 | 1.275333e-01 | 2.7668e-01 | 3.558715e-01 | 4.519800e-01 | 5.722135e-01 | 7.233200e-01 | ▇▆▆▅▅ |
| numeric | AF_reference | 16213 | 0.9920605 | NA | NA | NA | NA | NA | NA | NA | 4.477998e-01 | 1.618312e-01 | 1.9970e-04 | 3.226840e-01 | 4.378990e-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 | 0.0001073 | 0.0001925 | 0.5800000 | 0.5773303 | 0.623763 | 0.782149 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0002961 | 0.0001907 | 0.1199999 | 0.1204382 | 0.400401 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0002338 | 0.0001878 | 0.2099999 | 0.2131400 | 0.456851 | 0.420727 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0000103 | 0.0001331 | 0.9400001 | 0.9382125 | 0.515650 | 0.275160 | NA |
| 1 | 763394 | rs369924889 | G | A | -0.0000880 | 0.0001561 | 0.5700002 | 0.5729196 | 0.706753 | 0.617612 | NA |
| 1 | 814495 | rs74461805 | C | A | -0.0000400 | 0.0001826 | 0.8300000 | 0.8265235 | 0.340397 | NA | NA |
| 1 | 830181 | rs28444699 | A | G | -0.0000670 | 0.0001221 | 0.5800000 | 0.5834290 | 0.697259 | 0.691294 | NA |
| 1 | 831489 | rs4970385 | C | T | -0.0000710 | 0.0001199 | 0.5500004 | 0.5540727 | 0.705403 | 0.649161 | NA |
| 1 | 831909 | rs9697642 | C | T | -0.0000643 | 0.0001199 | 0.5900000 | 0.5920108 | 0.705448 | 0.648562 | NA |
| 1 | 832066 | rs9697380 | G | C | -0.0000663 | 0.0001199 | 0.5800000 | 0.5801012 | 0.705634 | 0.664337 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51171693 | rs756638 | G | A | -0.0000484 | 0.0001241 | 0.6999999 | 0.6966926 | 0.282282 | 0.304912 | NA |
| 22 | 51174048 | rs9628245 | G | C | -0.0000471 | 0.0001237 | 0.6999999 | 0.7030367 | 0.380130 | 0.433107 | NA |
| 22 | 51180501 | rs5770999 | T | C | 0.0000985 | 0.0001253 | 0.4299995 | 0.4316509 | 0.713658 | 0.636981 | NA |
| 22 | 51181919 | rs9616825 | G | C | 0.0001169 | 0.0001247 | 0.3500000 | 0.3485409 | 0.695471 | 0.619409 | NA |
| 22 | 51182485 | rs6009961 | A | G | 0.0000990 | 0.0001257 | 0.4299995 | 0.4311312 | 0.715505 | 0.638379 | NA |
| 22 | 51186143 | rs2879914 | T | C | -0.0000656 | 0.0001166 | 0.5700002 | 0.5737696 | 0.381826 | 0.273363 | NA |
| 22 | 51186228 | rs3865766 | C | T | -0.0000243 | 0.0001136 | 0.8300000 | 0.8303524 | 0.451063 | 0.453275 | NA |
| 22 | 51197266 | rs61290853 | A | G | -0.0000066 | 0.0001173 | 0.9599999 | 0.9553408 | 0.386333 | 0.422923 | NA |
| 22 | 51212875 | rs2238837 | A | C | -0.0000468 | 0.0001251 | 0.7099994 | 0.7080641 | 0.331455 | 0.372404 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0000458 | 0.0001280 | 0.7199992 | 0.7207224 | 0.297971 | 0.205072 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623763 ES:SE:LP:AF:ID 0.000107268:0.000192482:0.236572:0.623763:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID 0.000296128:0.000190689:0.920819:0.400401:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456851 ES:SE:LP:AF:ID 0.000233757:0.00018776:0.677781:0.456851:rs6702460
1 706368 rs12029736 A G . PASS AF=0.51565 ES:SE:LP:AF:ID 1.03205e-05:0.000133139:0.0268721:0.51565:rs12029736
1 763394 rs3115847 G A . PASS AF=0.706753 ES:SE:LP:AF:ID -8.79985e-05:0.000156093:0.244125:0.706753:rs3115847
1 814495 rs74461805 C A . PASS AF=0.340397 ES:SE:LP:AF:ID -4.00103e-05:0.00018256:0.0809219:0.340397:rs74461805
1 830181 rs28444699 A G . PASS AF=0.697259 ES:SE:LP:AF:ID -6.69784e-05:0.000122138:0.236572:0.697259:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705403 ES:SE:LP:AF:ID -7.09576e-05:0.000119928:0.259637:0.705403:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705448 ES:SE:LP:AF:ID -6.42702e-05:0.000119924:0.229148:0.705448:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705634 ES:SE:LP:AF:ID -6.63497e-05:0.00011993:0.236572:0.705634:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705662 ES:SE:LP:AF:ID -6.68173e-05:0.000119942:0.236572:0.705662:rs4500250
1 832918 rs28765502 T C . PASS AF=0.294371 ES:SE:LP:AF:ID 6.61883e-05:0.000119937:0.236572:0.294371:rs28765502
1 840753 rs4970382 T C . PASS AF=0.400106 ES:SE:LP:AF:ID 3.65705e-05:0.000110624:0.130768:0.400106:rs4970382
1 843405 rs11516185 A G . PASS AF=0.362599 ES:SE:LP:AF:ID -4.02704e-05:0.000137327:0.113509:0.362599:rs11516185
1 850218 rs6664536 T A . PASS AF=0.590333 ES:SE:LP:AF:ID -0.00010466:0.0001103:0.468521:0.590333:rs6664536
1 850371 rs6679046 G T . PASS AF=0.603726 ES:SE:LP:AF:ID -9.48881e-05:0.000110919:0.408935:0.603726:rs6679046
1 850780 rs6657440 C T . PASS AF=0.603944 ES:SE:LP:AF:ID -9.62017e-05:0.000110903:0.408935:0.603944:rs6657440
1 852037 rs4970463 G A . PASS AF=0.589688 ES:SE:LP:AF:ID -0.00011023:0.000110479:0.49485:0.589688:rs4970463
1 852063 rs28436996 G A . PASS AF=0.589667 ES:SE:LP:AF:ID -0.000110118:0.000110429:0.49485:0.589667:rs28436996
1 852875 rs13303369 C T . PASS AF=0.607674 ES:SE:LP:AF:ID -9.56011e-05:0.000111151:0.408935:0.607674:rs13303369
1 853954 rs1806509 C A . PASS AF=0.607833 ES:SE:LP:AF:ID -9.88989e-05:0.000111166:0.431798:0.607833:rs1806509
1 854777 rs13303019 A G . PASS AF=0.610318 ES:SE:LP:AF:ID -8.65668e-05:0.000111275:0.356547:0.610318:rs13303019
1 854978 rs13303057 A C . PASS AF=0.603286 ES:SE:LP:AF:ID -0.000107434:0.000110946:0.481486:0.603286:rs13303057
1 855075 rs6673914 C G . PASS AF=0.610338 ES:SE:LP:AF:ID -8.62404e-05:0.000111278:0.356547:0.610338:rs6673914
1 856099 rs28534711 T G . PASS AF=0.389935 ES:SE:LP:AF:ID 9.15571e-05:0.000111299:0.387216:0.389935:rs28534711
1 856108 rs28742275 A G . PASS AF=0.389918 ES:SE:LP:AF:ID 9.17883e-05:0.000111304:0.387216:0.389918:rs28742275
1 856476 rs4040605 A G . PASS AF=0.350351 ES:SE:LP:AF:ID 9.95925e-05:0.000114341:0.420216:0.350351:rs4040605
1 866893 rs2880024 T C . PASS AF=0.610554 ES:SE:LP:AF:ID -0.000105696:0.000111903:0.468521:0.610554:rs2880024
1 868418 rs28546443 C T . PASS AF=0.297865 ES:SE:LP:AF:ID 0.000186357:0.00012295:0.886057:0.297865:rs28546443
1 870645 rs28576697 T C . PASS AF=0.291287 ES:SE:LP:AF:ID 0.000117028:0.00012197:0.468521:0.291287:rs28576697
1 871334 rs4072383 G T . PASS AF=0.720619 ES:SE:LP:AF:ID -3.84132e-05:0.000121215:0.124939:0.720619:rs4072383
1 873558 rs1110052 G T . PASS AF=0.715253 ES:SE:LP:AF:ID -6.47831e-05:0.000120251:0.229148:0.715253:rs1110052
1 875770 rs4970379 A G . PASS AF=0.600084 ES:SE:LP:AF:ID -7.94308e-05:0.000112833:0.318759:0.600084:rs4970379
1 881627 rs2272757 G A . PASS AF=0.65239 ES:SE:LP:AF:ID -6.2984e-05:0.000113981:0.236572:0.65239:rs2272757
1 891059 rs13303065 C T . PASS AF=0.652428 ES:SE:LP:AF:ID -7.22433e-05:0.000113963:0.275724:0.652428:rs13303065
1 891945 rs13303106 A G . PASS AF=0.65249 ES:SE:LP:AF:ID -6.24025e-05:0.000114095:0.236572:0.65249:rs13303106
1 903245 rs28690976 A G . PASS AF=0.566937 ES:SE:LP:AF:ID -5.51247e-05:0.000113322:0.200659:0.566937:rs28690976
1 909073 rs3892467 C T . PASS AF=0.386683 ES:SE:LP:AF:ID 2.54704e-05:0.000113014:0.0861861:0.386683:rs3892467
1 909238 rs3829740 G C . PASS AF=0.571412 ES:SE:LP:AF:ID 1.48286e-05:0.00010945:0.05061:0.571412:rs3829740
1 910394 rs28477686 C T . PASS AF=0.324456 ES:SE:LP:AF:ID -5.87486e-05:0.000118632:0.207608:0.324456:rs28477686
1 912049 rs7367995 T C . PASS AF=0.58525 ES:SE:LP:AF:ID -2.80017e-05:0.00011056:0.09691:0.58525:rs7367995
1 913889 rs2340596 G A . PASS AF=0.599213 ES:SE:LP:AF:ID -2.56062e-05:0.00011074:0.0861861:0.599213:rs2340596
1 914333 rs13302979 C G . PASS AF=0.602521 ES:SE:LP:AF:ID -1.34295e-05:0.000111076:0.0457575:0.602521:rs13302979
1 914852 rs13303368 G C . PASS AF=0.600078 ES:SE:LP:AF:ID -1.75358e-05:0.000110862:0.0604807:0.600078:rs13303368
1 914940 rs13303033 T C . PASS AF=0.584292 ES:SE:LP:AF:ID -2.24329e-05:0.00011024:0.0757207:0.584292:rs13303033
1 916834 rs6694632 G A . PASS AF=0.589106 ES:SE:LP:AF:ID -2.54626e-05:0.000110409:0.0861861:0.589106:rs6694632
1 918384 rs13303118 G T . PASS AF=0.584205 ES:SE:LP:AF:ID -2.58857e-05:0.000110188:0.091515:0.584205:rs13303118
1 918573 rs2341354 A G . PASS AF=0.589331 ES:SE:LP:AF:ID -2.67455e-05:0.000110324:0.091515:0.589331:rs2341354
1 919501 rs4970414 G T . PASS AF=0.583932 ES:SE:LP:AF:ID -9.98281e-05:0.000114094:0.420216:0.583932:rs4970414
1 921716 rs13303278 C A . PASS AF=0.567888 ES:SE:LP:AF:ID 1.73463e-05:0.000110097:0.0604807:0.567888:rs13303278