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_41200_D021.vcf.gz --id UKB-b:11567 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41200_D021.txt.gz --cohort_cases 1189 --cohort_controls 461821 --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": "2019-09-13T11:41:00.714609",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-11567/ukb-b-11567.vcf.gz; Date=Sun May 10 10:06:21 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-11567/UKB-b-11567_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-11567/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:40:53 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-11567/UKB-b-11567_data.vcf.gz ...
Read summary statistics for 1867586 SNPs.
Dropped 180 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, 480072 SNPs remain.
After merging with regression SNP LD, 480072 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0005 (0.0014)
Lambda GC: 0.9988
Mean Chi^2: 0.9983
Intercept: 0.9933 (0.0107)
Ratio: NA (mean chi^2 < 1)
Analysis finished at Thu Oct 17 14:41:20 2019
Total time elapsed: 27.88sQC metrics
Metrics
Metrics
{
"af_correlation": 0.6716,
"inflation_factor": 1,
"mean_EFFECT": -4.5801e-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": 14773,
"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": 480072,
"ldsc_nsnp_merge_regression_ld": 480072,
"ldsc_observed_scale_h2_beta": 0.0005,
"ldsc_observed_scale_h2_se": 0.0014,
"ldsc_intercept_beta": 0.9933,
"ldsc_intercept_se": 0.0107,
"ldsc_lambda_gc": 0.9988,
"ldsc_mean_chisq": 0.9983,
"ldsc_ratio": 3.9412
}
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 | 1867408 | 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 | 1867586 | 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 | 8.652056e+00 | 5.765038e+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 | 7.871196e+07 | 5.663383e+07 | 1.23330e+04 | 3.186066e+07 | 6.929035e+07 | 1.148217e+08 | 2.491722e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -5.000000e-07 | 1.091000e-04 | -5.67000e-04 | -7.390000e-05 | -1.000000e-06 | 7.290000e-05 | 6.020000e-04 | ▁▂▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.090000e-04 | 3.700000e-06 | 1.01500e-04 | 1.061000e-04 | 1.081000e-04 | 1.113000e-04 | 2.082000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 5.004928e-01 | 2.884198e-01 | 1.40000e-06 | 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 | 5.004896e-01 | 2.883941e-01 | 1.40000e-06 | 2.514426e-01 | 5.005270e-01 | 7.494657e-01 | 9.999996e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.730855e-01 | 1.178292e-01 | 2.94366e-01 | 3.695802e-01 | 4.595080e-01 | 5.699890e-01 | 7.056340e-01 | ▇▇▆▅▅ |
| numeric | AF_reference | 14773 | 0.9920898 | NA | NA | NA | NA | NA | NA | NA | 4.523110e-01 | 1.563411e-01 | 1.99700e-04 | 3.326680e-01 | 4.438900e-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 | -0.0000156 | 0.0001868 | 0.9299999 | 0.9336095 | 0.623765 | 0.782149 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0000854 | 0.0001850 | 0.6400000 | 0.6442489 | 0.400401 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0000829 | 0.0001822 | 0.6499995 | 0.6491639 | 0.456846 | 0.420727 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0001507 | 0.0001292 | 0.2399999 | 0.2433945 | 0.515645 | 0.275160 | NA |
| 1 | 814495 | rs74461805 | C | A | -0.0001098 | 0.0001771 | 0.5400003 | 0.5354243 | 0.340396 | NA | NA |
| 1 | 830181 | rs28444699 | A | G | -0.0002261 | 0.0001185 | 0.0560003 | 0.0564336 | 0.697255 | 0.691294 | NA |
| 1 | 831489 | rs4970385 | C | T | -0.0002256 | 0.0001164 | 0.0530005 | 0.0525560 | 0.705397 | 0.649161 | NA |
| 1 | 831909 | rs9697642 | C | T | -0.0002261 | 0.0001164 | 0.0519996 | 0.0519641 | 0.705442 | 0.648562 | NA |
| 1 | 832066 | rs9697380 | G | C | -0.0002223 | 0.0001164 | 0.0560003 | 0.0560521 | 0.705627 | 0.664337 | NA |
| 1 | 832918 | rs28765502 | T | C | 0.0002272 | 0.0001164 | 0.0510000 | 0.0508703 | 0.294377 | 0.345447 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51164115 | rs5770996 | C | T | 0.0002246 | 0.0001062 | 0.0340001 | 0.0343958 | 0.456917 | 0.514776 | NA |
| 22 | 51164287 | rs6009957 | T | C | 0.0002252 | 0.0001142 | 0.0490004 | 0.0486524 | 0.306541 | 0.415535 | NA |
| 22 | 51165664 | rs8137951 | G | A | 0.0002535 | 0.0001145 | 0.0269998 | 0.0268534 | 0.301546 | 0.406350 | NA |
| 22 | 51174048 | rs9628245 | G | C | 0.0002539 | 0.0001200 | 0.0340001 | 0.0343426 | 0.380135 | 0.433107 | NA |
| 22 | 51181919 | rs9616825 | G | C | 0.0001235 | 0.0001210 | 0.3100002 | 0.3073719 | 0.695470 | 0.619409 | NA |
| 22 | 51186143 | rs2879914 | T | C | 0.0001152 | 0.0001131 | 0.3100002 | 0.3081468 | 0.381825 | 0.273363 | NA |
| 22 | 51186228 | rs3865766 | C | T | 0.0002309 | 0.0001102 | 0.0359998 | 0.0361539 | 0.451061 | 0.453275 | NA |
| 22 | 51197266 | rs61290853 | A | G | 0.0002287 | 0.0001138 | 0.0439997 | 0.0444674 | 0.386333 | 0.422923 | NA |
| 22 | 51212875 | rs2238837 | A | C | 0.0001136 | 0.0001214 | 0.3500000 | 0.3494269 | 0.331457 | 0.372404 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0001641 | 0.0001242 | 0.1900002 | 0.1864159 | 0.297974 | 0.205072 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623765 ES:SE:LP:AF:ID -1.55571e-05:0.00018675:0.0315171:0.623765:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID -8.54323e-05:0.000185012:0.19382:0.400401:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456846 ES:SE:LP:AF:ID 8.28717e-05:0.000182166:0.187087:0.456846:rs6702460
1 706368 rs12029736 A G . PASS AF=0.515645 ES:SE:LP:AF:ID -0.000150686:0.000129173:0.619789:0.515645:rs12029736
1 814495 rs74461805 C A . PASS AF=0.340396 ES:SE:LP:AF:ID -0.000109769:0.000177119:0.267606:0.340396:rs74461805
1 830181 rs28444699 A G . PASS AF=0.697255 ES:SE:LP:AF:ID -0.000226063:0.000118502:1.25181:0.697255:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705397 ES:SE:LP:AF:ID -0.000225564:0.000116357:1.27572:0.705397:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705442 ES:SE:LP:AF:ID -0.000226124:0.000116353:1.284:0.705442:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705627 ES:SE:LP:AF:ID -0.000222319:0.000116359:1.25181:0.705627:rs9697380
1 832918 rs28765502 T C . PASS AF=0.294377 ES:SE:LP:AF:ID 0.000227213:0.000116366:1.29243:0.294377:rs28765502
1 840753 rs4970382 T C . PASS AF=0.400124 ES:SE:LP:AF:ID 9.10791e-05:0.00010733:0.39794:0.400124:rs4970382
1 843405 rs11516185 A G . PASS AF=0.362606 ES:SE:LP:AF:ID -4.10042e-05:0.000133235:0.119186:0.362606:rs11516185
1 850218 rs6664536 T A . PASS AF=0.590331 ES:SE:LP:AF:ID -0.000167082:0.000107017:0.920819:0.590331:rs6664536
1 850371 rs6679046 G T . PASS AF=0.603723 ES:SE:LP:AF:ID -0.000155414:0.000107617:0.823909:0.603723:rs6679046
1 850780 rs6657440 C T . PASS AF=0.603942 ES:SE:LP:AF:ID -0.000155137:0.000107602:0.823909:0.603942:rs6657440
1 852037 rs4970463 G A . PASS AF=0.589686 ES:SE:LP:AF:ID -0.000178624:0.000107191:1.01773:0.589686:rs4970463
1 852063 rs28436996 G A . PASS AF=0.589665 ES:SE:LP:AF:ID -0.000170174:0.000107143:0.958607:0.589665:rs28436996
1 852875 rs13303369 C T . PASS AF=0.607671 ES:SE:LP:AF:ID -0.000136274:0.000107843:0.677781:0.607671:rs13303369
1 853954 rs1806509 C A . PASS AF=0.607829 ES:SE:LP:AF:ID -0.000133032:0.000107857:0.657577:0.607829:rs1806509
1 854777 rs13303019 A G . PASS AF=0.610316 ES:SE:LP:AF:ID -0.000148884:0.000107963:0.769551:0.610316:rs13303019
1 854978 rs13303057 A C . PASS AF=0.603283 ES:SE:LP:AF:ID -0.000149308:0.000107644:0.769551:0.603283:rs13303057
1 855075 rs6673914 C G . PASS AF=0.610337 ES:SE:LP:AF:ID -0.000143237:0.000107965:0.744727:0.610337:rs6673914
1 856099 rs28534711 T G . PASS AF=0.389936 ES:SE:LP:AF:ID 0.000144744:0.000107986:0.744727:0.389936:rs28534711
1 856108 rs28742275 A G . PASS AF=0.38992 ES:SE:LP:AF:ID 0.000144961:0.000107991:0.744727:0.38992:rs28742275
1 856476 rs4040605 A G . PASS AF=0.350356 ES:SE:LP:AF:ID 0.000158018:0.000110938:0.823909:0.350356:rs4040605
1 866893 rs2880024 T C . PASS AF=0.610552 ES:SE:LP:AF:ID -0.000113982:0.000108571:0.537602:0.610552:rs2880024
1 868418 rs28546443 C T . PASS AF=0.297867 ES:SE:LP:AF:ID 0.000189562:0.000119288:0.958607:0.297867:rs28546443
1 875770 rs4970379 A G . PASS AF=0.600085 ES:SE:LP:AF:ID -0.000143913:0.000109474:0.721246:0.600085:rs4970379
1 881627 rs2272757 G A . PASS AF=0.652393 ES:SE:LP:AF:ID -0.000127539:0.000110587:0.60206:0.652393:rs2272757
1 891059 rs13303065 C T . PASS AF=0.652432 ES:SE:LP:AF:ID -0.00013306:0.00011057:0.638272:0.652432:rs13303065
1 891945 rs13303106 A G . PASS AF=0.652494 ES:SE:LP:AF:ID -0.000132637:0.000110699:0.638272:0.652494:rs13303106
1 903245 rs28690976 A G . PASS AF=0.566938 ES:SE:LP:AF:ID -0.00013373:0.000109948:0.657577:0.566938:rs28690976
1 909073 rs3892467 C T . PASS AF=0.386681 ES:SE:LP:AF:ID -3.60914e-05:0.000109649:0.130768:0.386681:rs3892467
1 909238 rs3829740 G C . PASS AF=0.571408 ES:SE:LP:AF:ID -0.000105793:0.000106192:0.49485:0.571408:rs3829740
1 910394 rs28477686 C T . PASS AF=0.324458 ES:SE:LP:AF:ID 0.00010146:0.0001151:0.420216:0.324458:rs28477686
1 912049 rs7367995 T C . PASS AF=0.585249 ES:SE:LP:AF:ID -0.000202096:0.000107269:1.22185:0.585249:rs7367995
1 913889 rs2340596 G A . PASS AF=0.59921 ES:SE:LP:AF:ID -0.000212646:0.000107443:1.31876:0.59921:rs2340596
1 914333 rs13302979 C G . PASS AF=0.602516 ES:SE:LP:AF:ID -0.000225376:0.000107769:1.4318:0.602516:rs13302979
1 914852 rs13303368 G C . PASS AF=0.600074 ES:SE:LP:AF:ID -0.000221635:0.000107562:1.40894:0.600074:rs13303368
1 914940 rs13303033 T C . PASS AF=0.584289 ES:SE:LP:AF:ID -0.000210224:0.000106959:1.3098:0.584289:rs13303033
1 916834 rs6694632 G A . PASS AF=0.589102 ES:SE:LP:AF:ID -0.000196188:0.000107122:1.17393:0.589102:rs6694632
1 918384 rs13303118 G T . PASS AF=0.584202 ES:SE:LP:AF:ID -0.000205134:0.000106909:1.25964:0.584202:rs13303118
1 918573 rs2341354 A G . PASS AF=0.589327 ES:SE:LP:AF:ID -0.000189945:0.00010704:1.11919:0.589327:rs2341354
1 919501 rs4970414 G T . PASS AF=0.583926 ES:SE:LP:AF:ID -0.000121073:0.000110697:0.568636:0.583926:rs4970414
1 921716 rs13303278 C A . PASS AF=0.567888 ES:SE:LP:AF:ID -8.99183e-05:0.000106819:0.39794:0.567888:rs13303278
1 924528 rs34712273 C A . PASS AF=0.578491 ES:SE:LP:AF:ID -0.000111964:0.000107126:0.522879:0.578491:rs34712273
1 930533 rs3128110 C G . PASS AF=0.386082 ES:SE:LP:AF:ID 8.75873e-05:0.000108493:0.376751:0.386082:rs3128110
1 930567 rs3121574 A G . PASS AF=0.386124 ES:SE:LP:AF:ID 8.72337e-05:0.000108496:0.376751:0.386124:rs3121574
1 930751 rs3128111 C G . PASS AF=0.385079 ES:SE:LP:AF:ID 9.36004e-05:0.000108562:0.408935:0.385079:rs3128111
1 931166 rs2710880 A G . PASS AF=0.386765 ES:SE:LP:AF:ID 8.08119e-05:0.000108514:0.337242:0.386765:rs2710880