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_22129.vcf.gz --id UKB-b:1234 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_22129.txt.gz --cohort_cases 1273 --cohort_controls 111310 --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": "2019-09-13T03:59:47.758054",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-1234/ukb-b-1234.vcf.gz; Date=Sun May 10 03:05:16 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-1234/UKB-b-1234_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-1234/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:41:39 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-1234/UKB-b-1234_data.vcf.gz ...
Read summary statistics for 2059473 SNPs.
Dropped 206 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, 527048 SNPs remain.
After merging with regression SNP LD, 527048 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.005 (0.0055)
Lambda GC: 1.0067
Mean Chi^2: 1.0118
Intercept: 0.9986 (0.0102)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Thu Oct 17 14:42:09 2019
Total time elapsed: 29.84sQC metrics
Metrics
Metrics
{
"af_correlation": 0.7058,
"inflation_factor": 1,
"mean_EFFECT": 1.9011e-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": 16365,
"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": 527048,
"ldsc_nsnp_merge_regression_ld": 527048,
"ldsc_observed_scale_h2_beta": 0.005,
"ldsc_observed_scale_h2_se": 0.0055,
"ldsc_intercept_beta": 0.9986,
"ldsc_intercept_se": 0.0102,
"ldsc_lambda_gc": 1.0067,
"ldsc_mean_chisq": 1.0118,
"ldsc_ratio": -0.1186
}
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 | 2059269 | 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 | 2059473 | 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 | 8.645666e+00 | 5.765446e+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 | 7.875431e+07 | 5.666507e+07 | 5687.0000000 | 3.183543e+07 | 6.932598e+07 | 1.148879e+08 | 2.491722e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.900000e-06 | 4.690000e-04 | -0.0028735 | -3.128000e-04 | 1.400000e-06 | 3.172000e-04 | 2.229700e-03 | ▁▁▇▅▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.650000e-04 | 1.830000e-05 | 0.0004299 | 4.502000e-04 | 4.601000e-04 | 4.768000e-04 | 8.908000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.977587e-01 | 2.897153e-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 | 4.977631e-01 | 2.896878e-01 | 0.0000001 | 2.461440e-01 | 4.976742e-01 | 7.488820e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.673658e-01 | 1.285024e-01 | 0.2749420 | 3.543740e-01 | 4.512630e-01 | 5.723260e-01 | 7.250580e-01 | ▇▆▆▅▅ |
| numeric | AF_reference | 16365 | 0.9920538 | NA | NA | NA | NA | NA | NA | NA | 4.472496e-01 | 1.624524e-01 | 0.0001997 | 3.216850e-01 | 4.371010e-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 | 0.0004895 | 0.0007903 | 0.5400003 | 0.5356497 | 0.624290 | 0.782149 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0004826 | 0.0007833 | 0.5400003 | 0.5378014 | 0.400055 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0002205 | 0.0007715 | 0.7700005 | 0.7749880 | 0.456878 | 0.420727 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0004192 | 0.0005469 | 0.4400003 | 0.4434610 | 0.515771 | 0.275160 | NA |
| 1 | 763394 | rs369924889 | G | A | -0.0005848 | 0.0006419 | 0.3599996 | 0.3623295 | 0.706831 | 0.617612 | NA |
| 1 | 814495 | rs74461805 | C | A | -0.0004363 | 0.0007505 | 0.5600000 | 0.5610585 | 0.340813 | NA | NA |
| 1 | 830181 | rs28444699 | A | G | 0.0001395 | 0.0005029 | 0.7800007 | 0.7815079 | 0.696960 | 0.691294 | NA |
| 1 | 831489 | rs4970385 | C | T | -0.0001374 | 0.0004939 | 0.7800007 | 0.7808039 | 0.705749 | 0.649161 | NA |
| 1 | 831909 | rs9697642 | C | T | -0.0001389 | 0.0004938 | 0.7800007 | 0.7785319 | 0.705791 | 0.648562 | NA |
| 1 | 832066 | rs9697380 | G | C | -0.0001390 | 0.0004938 | 0.7800007 | 0.7783817 | 0.705939 | 0.664337 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51171693 | rs756638 | G | A | -0.0003996 | 0.0005099 | 0.4299995 | 0.4332948 | 0.282323 | 0.304912 | NA |
| 22 | 51174048 | rs9628245 | G | C | -0.0000651 | 0.0005084 | 0.9000000 | 0.8981107 | 0.379224 | 0.433107 | NA |
| 22 | 51180501 | rs5770999 | T | C | 0.0003936 | 0.0005141 | 0.4400003 | 0.4439297 | 0.712166 | 0.636981 | NA |
| 22 | 51181919 | rs9616825 | G | C | 0.0002372 | 0.0005119 | 0.6400000 | 0.6430257 | 0.694545 | 0.619409 | NA |
| 22 | 51182485 | rs6009961 | A | G | 0.0003145 | 0.0005157 | 0.5400003 | 0.5420522 | 0.714054 | 0.638379 | NA |
| 22 | 51186143 | rs2879914 | T | C | -0.0000712 | 0.0004786 | 0.8800001 | 0.8816723 | 0.380938 | 0.273363 | NA |
| 22 | 51186228 | rs3865766 | C | T | -0.0000061 | 0.0004666 | 0.9900000 | 0.9896175 | 0.449163 | 0.453275 | NA |
| 22 | 51197266 | rs61290853 | A | G | 0.0005266 | 0.0004818 | 0.2700001 | 0.2743708 | 0.385056 | 0.422923 | NA |
| 22 | 51212875 | rs2238837 | A | C | -0.0001127 | 0.0005132 | 0.8300000 | 0.8261981 | 0.330790 | 0.372404 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0009926 | 0.0005253 | 0.0589997 | 0.0588101 | 0.297871 | 0.205072 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.62429 ES:SE:LP:AF:ID 0.000489489:0.000790257:0.267606:0.62429:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400055 ES:SE:LP:AF:ID -0.000482643:0.000783332:0.267606:0.400055:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456878 ES:SE:LP:AF:ID -0.000220548:0.000771534:0.113509:0.456878:rs6702460
1 706368 rs12029736 A G . PASS AF=0.515771 ES:SE:LP:AF:ID -0.000419154:0.00054694:0.356547:0.515771:rs12029736
1 763394 rs3115847 G A . PASS AF=0.706831 ES:SE:LP:AF:ID -0.000584764:0.000641938:0.443698:0.706831:rs3115847
1 814495 rs74461805 C A . PASS AF=0.340813 ES:SE:LP:AF:ID -0.000436271:0.000750548:0.251812:0.340813:rs74461805
1 830181 rs28444699 A G . PASS AF=0.69696 ES:SE:LP:AF:ID 0.000139483:0.000502905:0.107905:0.69696:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705749 ES:SE:LP:AF:ID -0.000137435:0.000493888:0.107905:0.705749:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705791 ES:SE:LP:AF:ID -0.000138884:0.00049384:0.107905:0.705791:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705939 ES:SE:LP:AF:ID -0.000138977:0.000493827:0.107905:0.705939:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705972 ES:SE:LP:AF:ID -0.000131986:0.000493897:0.102373:0.705972:rs4500250
1 832918 rs28765502 T C . PASS AF=0.294052 ES:SE:LP:AF:ID 0.000131149:0.000493883:0.102373:0.294052:rs28765502
1 840753 rs4970382 T C . PASS AF=0.400563 ES:SE:LP:AF:ID -0.000122442:0.000454902:0.102373:0.400563:rs4970382
1 843405 rs11516185 A G . PASS AF=0.361607 ES:SE:LP:AF:ID 0.000881043:0.000564671:0.920819:0.361607:rs11516185
1 850218 rs6664536 T A . PASS AF=0.589752 ES:SE:LP:AF:ID 0.00042242:0.000452489:0.455932:0.589752:rs6664536
1 850371 rs6679046 G T . PASS AF=0.602907 ES:SE:LP:AF:ID 0.000512252:0.000455427:0.585027:0.602907:rs6679046
1 850780 rs6657440 C T . PASS AF=0.603088 ES:SE:LP:AF:ID 0.000505557:0.000455177:0.568636:0.603088:rs6657440
1 852037 rs4970463 G A . PASS AF=0.589077 ES:SE:LP:AF:ID 0.000467621:0.000453236:0.522879:0.589077:rs4970463
1 852063 rs28436996 G A . PASS AF=0.589032 ES:SE:LP:AF:ID 0.000472108:0.000453025:0.522879:0.589032:rs28436996
1 852875 rs13303369 C T . PASS AF=0.60701 ES:SE:LP:AF:ID 0.000630223:0.000456195:0.769551:0.60701:rs13303369
1 853954 rs1806509 C A . PASS AF=0.607161 ES:SE:LP:AF:ID 0.000628343:0.000456249:0.769551:0.607161:rs1806509
1 854777 rs13303019 A G . PASS AF=0.609576 ES:SE:LP:AF:ID 0.000660604:0.000456577:0.823909:0.609576:rs13303019
1 854978 rs13303057 A C . PASS AF=0.602429 ES:SE:LP:AF:ID 0.000515409:0.000455503:0.585027:0.602429:rs13303057
1 855075 rs6673914 C G . PASS AF=0.609613 ES:SE:LP:AF:ID 0.000657527:0.000456583:0.823909:0.609613:rs6673914
1 856099 rs28534711 T G . PASS AF=0.390622 ES:SE:LP:AF:ID -0.000659592:0.000456711:0.823909:0.390622:rs28534711
1 856108 rs28742275 A G . PASS AF=0.390631 ES:SE:LP:AF:ID -0.000661404:0.00045673:0.823909:0.390631:rs28742275
1 856476 rs4040605 A G . PASS AF=0.351651 ES:SE:LP:AF:ID -0.00042819:0.000468691:0.443698:0.351651:rs4040605
1 866893 rs2880024 T C . PASS AF=0.609188 ES:SE:LP:AF:ID -7.55336e-05:0.000458403:0.0604807:0.609188:rs2880024
1 868418 rs28546443 C T . PASS AF=0.299308 ES:SE:LP:AF:ID -3.29046e-05:0.000503847:0.0222764:0.299308:rs28546443
1 870645 rs28576697 T C . PASS AF=0.293134 ES:SE:LP:AF:ID -0.000198251:0.000500139:0.161151:0.293134:rs28576697
1 871334 rs4072383 G T . PASS AF=0.718942 ES:SE:LP:AF:ID -0.000226968:0.000497156:0.187087:0.718942:rs4072383
1 873558 rs1110052 G T . PASS AF=0.713837 ES:SE:LP:AF:ID -0.00018926:0.000493679:0.154902:0.713837:rs1110052
1 875770 rs4970379 A G . PASS AF=0.598618 ES:SE:LP:AF:ID -0.000375534:0.000463578:0.376751:0.598618:rs4970379
1 881627 rs2272757 G A . PASS AF=0.65041 ES:SE:LP:AF:ID 0.000142519:0.000467949:0.119186:0.65041:rs2272757
1 891059 rs13303065 C T . PASS AF=0.650437 ES:SE:LP:AF:ID 0.000161291:0.000467882:0.136677:0.650437:rs13303065
1 891945 rs13303106 A G . PASS AF=0.650513 ES:SE:LP:AF:ID 0.000145672:0.000468416:0.119186:0.650513:rs13303106
1 903245 rs28690976 A G . PASS AF=0.565741 ES:SE:LP:AF:ID -0.000382185:0.000465776:0.387216:0.565741:rs28690976
1 909073 rs3892467 C T . PASS AF=0.38558 ES:SE:LP:AF:ID -0.000423154:0.000465297:0.443698:0.38558:rs3892467
1 909238 rs3829740 G C . PASS AF=0.571865 ES:SE:LP:AF:ID -0.000598531:0.000450205:0.744727:0.571865:rs3829740
1 910394 rs28477686 C T . PASS AF=0.324606 ES:SE:LP:AF:ID 0.000475631:0.000488508:0.481486:0.324606:rs28477686
1 912049 rs7367995 T C . PASS AF=0.584453 ES:SE:LP:AF:ID -0.000297552:0.000454591:0.29243:0.584453:rs7367995
1 913889 rs2340596 G A . PASS AF=0.59854 ES:SE:LP:AF:ID -0.000187997:0.000455594:0.167491:0.59854:rs2340596
1 914333 rs13302979 C G . PASS AF=0.602083 ES:SE:LP:AF:ID 6.09451e-06:0.000457069:0.00436481:0.602083:rs13302979
1 914852 rs13303368 G C . PASS AF=0.599428 ES:SE:LP:AF:ID -0.000204712:0.000456161:0.187087:0.599428:rs13303368
1 914940 rs13303033 T C . PASS AF=0.583674 ES:SE:LP:AF:ID -0.000553304:0.00045398:0.657577:0.583674:rs13303033
1 916834 rs6694632 G A . PASS AF=0.588692 ES:SE:LP:AF:ID -0.000376829:0.000454632:0.387216:0.588692:rs6694632
1 918384 rs13303118 G T . PASS AF=0.583654 ES:SE:LP:AF:ID -0.000539439:0.000453773:0.638272:0.583654:rs13303118
1 918573 rs2341354 A G . PASS AF=0.588902 ES:SE:LP:AF:ID -0.000362811:0.000454264:0.376751:0.588902:rs2341354
1 919501 rs4970414 G T . PASS AF=0.582835 ES:SE:LP:AF:ID -0.000121204:0.000469236:0.09691:0.582835:rs4970414
1 921716 rs13303278 C A . PASS AF=0.566968 ES:SE:LP:AF:ID -0.000422273:0.00045341:0.455932:0.566968:rs13303278