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_41251_6004.vcf.gz --id UKB-b:16174 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41251_6004.txt.gz --cohort_cases 1222 --cohort_controls 460145 --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": "2019-09-12T23:03:19.400343",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-16174/ukb-b-16174.vcf.gz; Date=Sun May 10 06:15:45 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-16174/UKB-b-16174_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-16174/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:44:56 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-16174/UKB-b-16174_data.vcf.gz ...
Read summary statistics for 1945796 SNPs.
Dropped 187 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, 499250 SNPs remain.
After merging with regression SNP LD, 499250 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0013 (0.0016)
Lambda GC: 1.1335
Mean Chi^2: 1.1375
Intercept: 1.1233 (0.0117)
Ratio: 0.8962 (0.0848)
Analysis finished at Thu Oct 17 14:45:26 2019
Total time elapsed: 30.03sQC metrics
Metrics
Metrics
{
"af_correlation": 0.6864,
"inflation_factor": 1.1474,
"mean_EFFECT": 3.8505e-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": 15423,
"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": 499250,
"ldsc_nsnp_merge_regression_ld": 499250,
"ldsc_observed_scale_h2_beta": 0.0013,
"ldsc_observed_scale_h2_se": 0.0016,
"ldsc_intercept_beta": 1.1233,
"ldsc_intercept_se": 0.0117,
"ldsc_lambda_gc": 1.1335,
"ldsc_mean_chisq": 1.1375,
"ldsc_ratio": 0.8967
}
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 | 1945611 | 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 | 1945796 | 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 | 8.649239e+00 | 5.762231e+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 | 7.871915e+07 | 5.663137e+07 | 5687.0000000 | 3.184219e+07 | 6.927698e+07 | 1.148788e+08 | 2.491722e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.000000e-07 | 1.189000e-04 | -0.0005360 | -7.960000e-05 | 1.000000e-07 | 8.010000e-05 | 5.873000e-04 | ▁▃▇▂▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.110000e-04 | 4.000000e-06 | 0.0001030 | 1.078000e-04 | 1.099000e-04 | 1.135000e-04 | 2.113000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.793660e-01 | 2.941816e-01 | 0.0000019 | 2.200002e-01 | 4.700002e-01 | 7.300002e-01 | 1.000000e+00 | ▇▇▆▆▆ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.793668e-01 | 2.941531e-01 | 0.0000019 | 2.204883e-01 | 4.717133e-01 | 7.334634e-01 | 9.999999e-01 | ▇▇▇▆▆ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.708488e-01 | 1.222181e-01 | 0.2864160 | 3.634180e-01 | 4.562260e-01 | 5.711110e-01 | 7.135840e-01 | ▇▆▆▅▅ |
| numeric | AF_reference | 15423 | 0.9920737 | NA | NA | NA | NA | NA | NA | NA | 4.502898e-01 | 1.587664e-01 | 0.0001997 | 3.282750e-01 | 4.412940e-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 | -0.0002635 | 0.0001896 | 0.1600000 | 0.1645820 | 0.623774 | 0.782149 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0002479 | 0.0001878 | 0.1900002 | 0.1868066 | 0.400403 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0000060 | 0.0001849 | 0.9699999 | 0.9741421 | 0.456841 | 0.420727 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0000468 | 0.0001311 | 0.7199992 | 0.7213064 | 0.515617 | 0.275160 | NA |
| 1 | 763394 | rs369924889 | G | A | 0.0000221 | 0.0001537 | 0.8900000 | 0.8856169 | 0.706737 | 0.617612 | NA |
| 1 | 814495 | rs74461805 | C | A | -0.0001020 | 0.0001798 | 0.5700002 | 0.5705328 | 0.340375 | NA | NA |
| 1 | 830181 | rs28444699 | A | G | -0.0000060 | 0.0001203 | 0.9599999 | 0.9601011 | 0.697204 | 0.691294 | NA |
| 1 | 831489 | rs4970385 | C | T | 0.0000197 | 0.0001181 | 0.8700001 | 0.8674125 | 0.705363 | 0.649161 | NA |
| 1 | 831909 | rs9697642 | C | T | 0.0000186 | 0.0001181 | 0.8700001 | 0.8748465 | 0.705408 | 0.648562 | NA |
| 1 | 832066 | rs9697380 | G | C | 0.0000229 | 0.0001181 | 0.8499999 | 0.8464724 | 0.705593 | 0.664337 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51164115 | rs5770996 | C | T | -0.0001759 | 0.0001078 | 0.1000000 | 0.1028875 | 0.456846 | 0.514776 | NA |
| 22 | 51164287 | rs6009957 | T | C | -0.0000279 | 0.0001160 | 0.8100000 | 0.8095769 | 0.306495 | 0.415535 | NA |
| 22 | 51165664 | rs8137951 | G | A | -0.0000203 | 0.0001163 | 0.8600001 | 0.8615620 | 0.301507 | 0.406350 | NA |
| 22 | 51174048 | rs9628245 | G | C | -0.0000590 | 0.0001218 | 0.6300007 | 0.6278886 | 0.380096 | 0.433107 | NA |
| 22 | 51181919 | rs9616825 | G | C | -0.0001561 | 0.0001228 | 0.2000000 | 0.2037212 | 0.695459 | 0.619409 | NA |
| 22 | 51186143 | rs2879914 | T | C | 0.0002192 | 0.0001148 | 0.0560003 | 0.0562819 | 0.381779 | 0.273363 | NA |
| 22 | 51186228 | rs3865766 | C | T | 0.0000742 | 0.0001119 | 0.5099998 | 0.5076211 | 0.451010 | 0.453275 | NA |
| 22 | 51197266 | rs61290853 | A | G | -0.0000312 | 0.0001156 | 0.7899998 | 0.7871662 | 0.386288 | 0.422923 | NA |
| 22 | 51212875 | rs2238837 | A | C | 0.0001658 | 0.0001232 | 0.1800002 | 0.1785465 | 0.331408 | 0.372404 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0000999 | 0.0001261 | 0.4299995 | 0.4283932 | 0.297935 | 0.205072 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623774 ES:SE:LP:AF:ID -0.00026349:0.000189585:0.79588:0.623774:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400403 ES:SE:LP:AF:ID 0.000247912:0.0001878:0.721246:0.400403:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456841 ES:SE:LP:AF:ID 5.99396e-06:0.00018492:0.0132283:0.456841:rs6702460
1 706368 rs12029736 A G . PASS AF=0.515617 ES:SE:LP:AF:ID -4.67719e-05:0.000131119:0.142668:0.515617:rs12029736
1 763394 rs3115847 G A . PASS AF=0.706737 ES:SE:LP:AF:ID 2.21143e-05:0.000153729:0.05061:0.706737:rs3115847
1 814495 rs74461805 C A . PASS AF=0.340375 ES:SE:LP:AF:ID -0.000101998:0.000179806:0.244125:0.340375:rs74461805
1 830181 rs28444699 A G . PASS AF=0.697204 ES:SE:LP:AF:ID -6.01737e-06:0.000120283:0.0177288:0.697204:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705363 ES:SE:LP:AF:ID 1.9718e-05:0.00011811:0.0604807:0.705363:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705408 ES:SE:LP:AF:ID 1.86025e-05:0.000118107:0.0604807:0.705408:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705593 ES:SE:LP:AF:ID 2.2869e-05:0.000118112:0.0705811:0.705593:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705622 ES:SE:LP:AF:ID 2.30569e-05:0.000118124:0.0705811:0.705622:rs4500250
1 832918 rs28765502 T C . PASS AF=0.294411 ES:SE:LP:AF:ID -1.82212e-05:0.000118119:0.0555173:0.294411:rs28765502
1 840753 rs4970382 T C . PASS AF=0.400158 ES:SE:LP:AF:ID -2.83741e-05:0.000108946:0.102373:0.400158:rs4970382
1 843405 rs11516185 A G . PASS AF=0.362582 ES:SE:LP:AF:ID -0.000282144:0.000135244:1.4318:0.362582:rs11516185
1 850218 rs6664536 T A . PASS AF=0.590305 ES:SE:LP:AF:ID -0.000101285:0.000108635:0.455932:0.590305:rs6664536
1 850371 rs6679046 G T . PASS AF=0.603697 ES:SE:LP:AF:ID -0.000133441:0.000109244:0.657577:0.603697:rs6679046
1 850780 rs6657440 C T . PASS AF=0.60392 ES:SE:LP:AF:ID -0.00012332:0.000109229:0.585027:0.60392:rs6657440
1 852037 rs4970463 G A . PASS AF=0.58966 ES:SE:LP:AF:ID -0.000100712:0.000108812:0.455932:0.58966:rs4970463
1 852063 rs28436996 G A . PASS AF=0.589639 ES:SE:LP:AF:ID -0.000102595:0.000108763:0.455932:0.589639:rs28436996
1 852875 rs13303369 C T . PASS AF=0.607646 ES:SE:LP:AF:ID -0.000145217:0.000109474:0.744727:0.607646:rs13303369
1 853954 rs1806509 C A . PASS AF=0.607806 ES:SE:LP:AF:ID -0.000141563:0.000109489:0.69897:0.607806:rs1806509
1 854777 rs13303019 A G . PASS AF=0.610292 ES:SE:LP:AF:ID -0.000138348:0.000109594:0.677781:0.610292:rs13303019
1 854978 rs13303057 A C . PASS AF=0.603258 ES:SE:LP:AF:ID -0.000133656:0.000109271:0.657577:0.603258:rs13303057
1 855075 rs6673914 C G . PASS AF=0.610313 ES:SE:LP:AF:ID -0.000140737:0.000109596:0.69897:0.610313:rs6673914
1 856099 rs28534711 T G . PASS AF=0.38996 ES:SE:LP:AF:ID 0.000135749:0.000109618:0.657577:0.38996:rs28534711
1 856108 rs28742275 A G . PASS AF=0.389944 ES:SE:LP:AF:ID 0.000135738:0.000109623:0.657577:0.389944:rs28742275
1 856476 rs4040605 A G . PASS AF=0.350361 ES:SE:LP:AF:ID 8.53561e-05:0.000112617:0.346787:0.350361:rs4040605
1 866893 rs2880024 T C . PASS AF=0.610544 ES:SE:LP:AF:ID 1.72402e-05:0.000110211:0.0555173:0.610544:rs2880024
1 868418 rs28546443 C T . PASS AF=0.297853 ES:SE:LP:AF:ID -4.57175e-05:0.00012109:0.148742:0.297853:rs28546443
1 870645 rs28576697 T C . PASS AF=0.291271 ES:SE:LP:AF:ID 8.26693e-05:0.000120131:0.309804:0.291271:rs28576697
1 875770 rs4970379 A G . PASS AF=0.600047 ES:SE:LP:AF:ID -8.17901e-06:0.000111128:0.0268721:0.600047:rs4970379
1 881627 rs2272757 G A . PASS AF=0.652389 ES:SE:LP:AF:ID -0.000118475:0.00011226:0.537602:0.652389:rs2272757
1 891059 rs13303065 C T . PASS AF=0.652427 ES:SE:LP:AF:ID -0.000125287:0.000112243:0.585027:0.652427:rs13303065
1 891945 rs13303106 A G . PASS AF=0.652489 ES:SE:LP:AF:ID -0.000132036:0.000112373:0.619789:0.652489:rs13303106
1 903245 rs28690976 A G . PASS AF=0.566893 ES:SE:LP:AF:ID -0.000145132:0.000111599:0.721246:0.566893:rs28690976
1 909073 rs3892467 C T . PASS AF=0.38667 ES:SE:LP:AF:ID 2.5897e-05:0.000111306:0.0861861:0.38667:rs3892467
1 909238 rs3829740 G C . PASS AF=0.571391 ES:SE:LP:AF:ID 6.0057e-05:0.000107787:0.236572:0.571391:rs3829740
1 910394 rs28477686 C T . PASS AF=0.324411 ES:SE:LP:AF:ID 0.000177218:0.000116854:0.886057:0.324411:rs28477686
1 912049 rs7367995 T C . PASS AF=0.585269 ES:SE:LP:AF:ID -0.000124759:0.000108893:0.60206:0.585269:rs7367995
1 913889 rs2340596 G A . PASS AF=0.599237 ES:SE:LP:AF:ID -0.000154004:0.000109071:0.79588:0.599237:rs2340596
1 914333 rs13302979 C G . PASS AF=0.602538 ES:SE:LP:AF:ID -0.000153431:0.000109399:0.79588:0.602538:rs13302979
1 914852 rs13303368 G C . PASS AF=0.600098 ES:SE:LP:AF:ID -0.000154251:0.00010919:0.79588:0.600098:rs13303368
1 914940 rs13303033 T C . PASS AF=0.584318 ES:SE:LP:AF:ID -0.000133712:0.00010858:0.657577:0.584318:rs13303033
1 916834 rs6694632 G A . PASS AF=0.589133 ES:SE:LP:AF:ID -0.00014589:0.000108745:0.744727:0.589133:rs6694632
1 918384 rs13303118 G T . PASS AF=0.584231 ES:SE:LP:AF:ID -0.000137515:0.000108528:0.677781:0.584231:rs13303118
1 918573 rs2341354 A G . PASS AF=0.589359 ES:SE:LP:AF:ID -0.00015062:0.000108661:0.769551:0.589359:rs2341354
1 919501 rs4970414 G T . PASS AF=0.583961 ES:SE:LP:AF:ID -0.000145176:0.000112378:0.69897:0.583961:rs4970414
1 921716 rs13303278 C A . PASS AF=0.567935 ES:SE:LP:AF:ID -8.35041e-05:0.000108435:0.356547:0.567935:rs13303278
1 924528 rs34712273 C A . PASS AF=0.578538 ES:SE:LP:AF:ID -6.48021e-05:0.000108746:0.259637:0.578538:rs34712273
1 930533 rs3128110 C G . PASS AF=0.386027 ES:SE:LP:AF:ID 3.09285e-05:0.000110142:0.107905:0.386027:rs3128110