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_20002_1459.vcf.gz --id UKB-b:3044 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20002_1459.txt.gz --cohort_cases 1193 --cohort_controls 461740 --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": "2019-09-12T22:40:15.697712",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-3044/ukb-b-3044.vcf.gz; Date=Sun May 10 04:03:14 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-3044/UKB-b-3044_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-3044/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:43:15 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-3044/UKB-b-3044_data.vcf.gz ...
Read summary statistics for 1877501 SNPs.
Dropped 181 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, 482475 SNPs remain.
After merging with regression SNP LD, 482475 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0008 (0.0014)
Lambda GC: 1.0038
Mean Chi^2: 1.0205
Intercept: 1.0118 (0.0122)
Ratio: 0.573 (0.5928)
Analysis finished at Thu Oct 17 14:43:45 2019
Total time elapsed: 29.51sQC metrics
Metrics
Metrics
{
"af_correlation": 0.6735,
"inflation_factor": 1,
"mean_EFFECT": 2.1409e-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": 14860,
"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": 482475,
"ldsc_nsnp_merge_regression_ld": 482475,
"ldsc_observed_scale_h2_beta": 0.0008,
"ldsc_observed_scale_h2_se": 0.0014,
"ldsc_intercept_beta": 1.0118,
"ldsc_intercept_se": 0.0122,
"ldsc_lambda_gc": 1.0038,
"ldsc_mean_chisq": 1.0205,
"ldsc_ratio": 0.5756
}
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 | 1877322 | 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 | 1877501 | 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 | 8.651708e+00 | 5.764403e+00 | 1.00000e+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 | 7.871034e+07 | 5.663078e+07 | 1.23330e+04 | 3.186509e+07 | 6.927857e+07 | 1.148172e+08 | 2.491722e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.000000e-07 | 1.108000e-04 | -6.10800e-04 | -7.310000e-05 | 6.000000e-07 | 7.440000e-05 | 6.469000e-04 | ▁▂▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.093000e-04 | 3.800000e-06 | 1.01700e-04 | 1.064000e-04 | 1.083000e-04 | 1.116000e-04 | 2.086000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.987048e-01 | 2.901713e-01 | 2.00000e-07 | 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 | 4.987045e-01 | 2.901521e-01 | 2.00000e-07 | 2.458854e-01 | 4.992393e-01 | 7.506117e-01 | 9.999994e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.727883e-01 | 1.183843e-01 | 2.93379e-01 | 3.687770e-01 | 4.591060e-01 | 5.701070e-01 | 7.066210e-01 | ▇▆▆▅▅ |
| numeric | AF_reference | 14860 | 0.9920852 | NA | NA | NA | NA | NA | NA | NA | 4.520492e-01 | 1.566302e-01 | 1.99700e-04 | 3.322680e-01 | 4.436900e-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 | 0.0000556 | 0.0001871 | 0.7700005 | 0.7664750 | 0.623763 | 0.782149 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0001685 | 0.0001854 | 0.3599996 | 0.3634807 | 0.400401 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0001492 | 0.0001826 | 0.4100001 | 0.4136979 | 0.456851 | 0.420727 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0000978 | 0.0001294 | 0.4500005 | 0.4498837 | 0.515650 | 0.275160 | NA |
| 1 | 814495 | rs74461805 | C | A | 0.0000029 | 0.0001775 | 0.9900000 | 0.9868180 | 0.340397 | NA | NA |
| 1 | 830181 | rs28444699 | A | G | -0.0000036 | 0.0001187 | 0.9800000 | 0.9758692 | 0.697259 | 0.691294 | NA |
| 1 | 831489 | rs4970385 | C | T | -0.0000202 | 0.0001166 | 0.8600001 | 0.8625530 | 0.705403 | 0.649161 | NA |
| 1 | 831909 | rs9697642 | C | T | -0.0000213 | 0.0001166 | 0.8499999 | 0.8547191 | 0.705448 | 0.648562 | NA |
| 1 | 832066 | rs9697380 | G | C | -0.0000227 | 0.0001166 | 0.8499999 | 0.8454546 | 0.705634 | 0.664337 | NA |
| 1 | 832318 | rs4500250 | C | A | -0.0000232 | 0.0001166 | 0.8400000 | 0.8424431 | 0.705662 | 0.664537 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51164115 | rs5770996 | C | T | 0.0000313 | 0.0001064 | 0.7700005 | 0.7689770 | 0.456928 | 0.514776 | NA |
| 22 | 51164287 | rs6009957 | T | C | 0.0000695 | 0.0001145 | 0.5400003 | 0.5436933 | 0.306554 | 0.415535 | NA |
| 22 | 51165664 | rs8137951 | G | A | 0.0000833 | 0.0001148 | 0.4700002 | 0.4680528 | 0.301558 | 0.406350 | NA |
| 22 | 51174048 | rs9628245 | G | C | -0.0001973 | 0.0001202 | 0.1000000 | 0.1007715 | 0.380130 | 0.433107 | NA |
| 22 | 51181919 | rs9616825 | G | C | -0.0000961 | 0.0001212 | 0.4299995 | 0.4279849 | 0.695471 | 0.619409 | NA |
| 22 | 51186143 | rs2879914 | T | C | -0.0000510 | 0.0001133 | 0.6499995 | 0.6523828 | 0.381826 | 0.273363 | NA |
| 22 | 51186228 | rs3865766 | C | T | -0.0001312 | 0.0001104 | 0.2399999 | 0.2350282 | 0.451063 | 0.453275 | NA |
| 22 | 51197266 | rs61290853 | A | G | -0.0002137 | 0.0001140 | 0.0610000 | 0.0609869 | 0.386333 | 0.422923 | NA |
| 22 | 51212875 | rs2238837 | A | C | -0.0000475 | 0.0001216 | 0.6999999 | 0.6962040 | 0.331455 | 0.372404 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0000153 | 0.0001245 | 0.9000000 | 0.9018677 | 0.297971 | 0.205072 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623763 ES:SE:LP:AF:ID 5.55788e-05:0.000187141:0.113509:0.623763:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID 0.00016848:0.000185397:0.443698:0.400401:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456851 ES:SE:LP:AF:ID 0.000149217:0.00018255:0.387216:0.456851:rs6702460
1 706368 rs12029736 A G . PASS AF=0.51565 ES:SE:LP:AF:ID 9.78098e-05:0.000129445:0.346787:0.51565:rs12029736
1 814495 rs74461805 C A . PASS AF=0.340397 ES:SE:LP:AF:ID 2.93254e-06:0.000177494:0.00436481:0.340397:rs74461805
1 830181 rs28444699 A G . PASS AF=0.697259 ES:SE:LP:AF:ID -3.59193e-06:0.000118749:0.00877392:0.697259:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705403 ES:SE:LP:AF:ID -2.01864e-05:0.0001166:0.0655015:0.705403:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705448 ES:SE:LP:AF:ID -2.13488e-05:0.000116596:0.0705811:0.705448:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705634 ES:SE:LP:AF:ID -2.27282e-05:0.000116602:0.0705811:0.705634:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705662 ES:SE:LP:AF:ID -2.31793e-05:0.000116614:0.0757207:0.705662:rs4500250
1 832918 rs28765502 T C . PASS AF=0.294371 ES:SE:LP:AF:ID 2.26016e-05:0.000116609:0.0705811:0.294371:rs28765502
1 840753 rs4970382 T C . PASS AF=0.400106 ES:SE:LP:AF:ID 4.62991e-05:0.000107554:0.173925:0.400106:rs4970382
1 843405 rs11516185 A G . PASS AF=0.362599 ES:SE:LP:AF:ID -2.84199e-05:0.000133517:0.0809219:0.362599:rs11516185
1 850218 rs6664536 T A . PASS AF=0.590333 ES:SE:LP:AF:ID 0.000106746:0.000107239:0.49485:0.590333:rs6664536
1 850371 rs6679046 G T . PASS AF=0.603726 ES:SE:LP:AF:ID 5.84481e-05:0.000107841:0.229148:0.603726:rs6679046
1 850780 rs6657440 C T . PASS AF=0.603944 ES:SE:LP:AF:ID 6.5133e-05:0.000107826:0.259637:0.603944:rs6657440
1 852037 rs4970463 G A . PASS AF=0.589688 ES:SE:LP:AF:ID 0.000106956:0.000107413:0.49485:0.589688:rs4970463
1 852063 rs28436996 G A . PASS AF=0.589667 ES:SE:LP:AF:ID 0.000108241:0.000107365:0.508638:0.589667:rs28436996
1 852875 rs13303369 C T . PASS AF=0.607674 ES:SE:LP:AF:ID 3.20135e-05:0.000108067:0.113509:0.607674:rs13303369
1 853954 rs1806509 C A . PASS AF=0.607833 ES:SE:LP:AF:ID 2.78006e-05:0.000108082:0.09691:0.607833:rs1806509
1 854777 rs13303019 A G . PASS AF=0.610318 ES:SE:LP:AF:ID 5.50551e-05:0.000108188:0.21467:0.610318:rs13303019
1 854978 rs13303057 A C . PASS AF=0.603286 ES:SE:LP:AF:ID 6.21039e-05:0.000107867:0.251812:0.603286:rs13303057
1 855075 rs6673914 C G . PASS AF=0.610338 ES:SE:LP:AF:ID 5.43672e-05:0.00010819:0.207608:0.610338:rs6673914
1 856099 rs28534711 T G . PASS AF=0.389935 ES:SE:LP:AF:ID -5.81991e-05:0.00010821:0.229148:0.389935:rs28534711
1 856108 rs28742275 A G . PASS AF=0.389918 ES:SE:LP:AF:ID -5.81131e-05:0.000108216:0.229148:0.389918:rs28742275
1 856476 rs4040605 A G . PASS AF=0.350351 ES:SE:LP:AF:ID -4.94509e-05:0.000111169:0.180456:0.350351:rs4040605
1 866893 rs2880024 T C . PASS AF=0.610554 ES:SE:LP:AF:ID 5.16409e-05:0.000108797:0.19382:0.610554:rs2880024
1 868418 rs28546443 C T . PASS AF=0.297865 ES:SE:LP:AF:ID 8.62049e-05:0.000119538:0.327902:0.297865:rs28546443
1 875770 rs4970379 A G . PASS AF=0.600084 ES:SE:LP:AF:ID 0.000120552:0.000109702:0.568636:0.600084:rs4970379
1 881627 rs2272757 G A . PASS AF=0.65239 ES:SE:LP:AF:ID 0.000124641:0.000110818:0.585027:0.65239:rs2272757
1 891059 rs13303065 C T . PASS AF=0.652428 ES:SE:LP:AF:ID 0.000109815:0.0001108:0.49485:0.652428:rs13303065
1 891945 rs13303106 A G . PASS AF=0.65249 ES:SE:LP:AF:ID 0.000117727:0.000110929:0.537602:0.65249:rs13303106
1 903245 rs28690976 A G . PASS AF=0.566937 ES:SE:LP:AF:ID 0.000220939:0.000110177:1.34679:0.566937:rs28690976
1 909073 rs3892467 C T . PASS AF=0.386683 ES:SE:LP:AF:ID -6.66848e-06:0.000109878:0.0222764:0.386683:rs3892467
1 909238 rs3829740 G C . PASS AF=0.571412 ES:SE:LP:AF:ID 3.26011e-05:0.000106413:0.119186:0.571412:rs3829740
1 910394 rs28477686 C T . PASS AF=0.324456 ES:SE:LP:AF:ID -0.000118004:0.000115341:0.508638:0.324456:rs28477686
1 912049 rs7367995 T C . PASS AF=0.58525 ES:SE:LP:AF:ID 0.000141017:0.000107492:0.721246:0.58525:rs7367995
1 913889 rs2340596 G A . PASS AF=0.599213 ES:SE:LP:AF:ID 0.000162063:0.000107667:0.886057:0.599213:rs2340596
1 914333 rs13302979 C G . PASS AF=0.602521 ES:SE:LP:AF:ID 0.000141325:0.000107993:0.721246:0.602521:rs13302979
1 914852 rs13303368 G C . PASS AF=0.600078 ES:SE:LP:AF:ID 0.000156594:0.000107786:0.823909:0.600078:rs13303368
1 914940 rs13303033 T C . PASS AF=0.584292 ES:SE:LP:AF:ID 0.000163529:0.000107181:0.886057:0.584292:rs13303033
1 916834 rs6694632 G A . PASS AF=0.589106 ES:SE:LP:AF:ID 0.000136697:0.000107345:0.69897:0.589106:rs6694632
1 918384 rs13303118 G T . PASS AF=0.584205 ES:SE:LP:AF:ID 0.000159015:0.000107131:0.853872:0.584205:rs13303118
1 918573 rs2341354 A G . PASS AF=0.589331 ES:SE:LP:AF:ID 0.000132388:0.000107263:0.657577:0.589331:rs2341354
1 919501 rs4970414 G T . PASS AF=0.583932 ES:SE:LP:AF:ID 5.05002e-05:0.000110928:0.187087:0.583932:rs4970414
1 921716 rs13303278 C A . PASS AF=0.567888 ES:SE:LP:AF:ID 8.1256e-05:0.000107042:0.346787:0.567888:rs13303278
1 924528 rs34712273 C A . PASS AF=0.578492 ES:SE:LP:AF:ID 6.82504e-05:0.000107349:0.283997:0.578492:rs34712273
1 930533 rs3128110 C G . PASS AF=0.386078 ES:SE:LP:AF:ID -0.000121324:0.000108721:0.585027:0.386078:rs3128110
1 930567 rs3121574 A G . PASS AF=0.38612 ES:SE:LP:AF:ID -0.00012199:0.000108724:0.585027:0.38612:rs3121574
1 930751 rs3128111 C G . PASS AF=0.385075 ES:SE:LP:AF:ID -0.000124777:0.00010879:0.60206:0.385075:rs3128111