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_5529.vcf.gz --id UKB-b:5012 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_5529.txt.gz --cohort_cases 1293 --cohort_controls 32071 --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": "2019-09-12T23:17:36.203256",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-5012/ukb-b-5012.vcf.gz; Date=Sun May 10 08:50:02 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-5012/UKB-b-5012_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5012/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:44:58 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5012/UKB-b-5012_data.vcf.gz ...
Read summary statistics for 2104226 SNPs.
Dropped 218 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, 537831 SNPs remain.
After merging with regression SNP LD, 537831 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0226 (0.0175)
Lambda GC: 1.042
Mean Chi^2: 1.0272
Intercept: 1.0095 (0.0119)
Ratio: 0.3479 (0.4386)
Analysis finished at Thu Oct 17 14:45:31 2019
Total time elapsed: 33.02sQC metrics
Metrics
Metrics
{
"af_correlation": 0.7144,
"inflation_factor": 1.0475,
"mean_EFFECT": -4.1152e-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": 16701,
"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": 537831,
"ldsc_nsnp_merge_regression_ld": 537831,
"ldsc_observed_scale_h2_beta": 0.0226,
"ldsc_observed_scale_h2_se": 0.0175,
"ldsc_intercept_beta": 1.0095,
"ldsc_intercept_se": 0.0119,
"ldsc_lambda_gc": 1.042,
"ldsc_mean_chisq": 1.0272,
"ldsc_ratio": 0.3493
}
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 | 2104010 | 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 | 2104226 | 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 | 8.644407e+00 | 5.764377e+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 | 7.871300e+07 | 5.663803e+07 | 5687.0000000 | 3.181857e+07 | 6.926723e+07 | 1.148628e+08 | 2.491722e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -4.100000e-06 | 1.578900e-03 | -0.0084928 | -1.077200e-03 | -4.600000e-06 | 1.068600e-03 | 8.390200e-03 | ▁▂▇▂▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.559000e-03 | 6.350000e-05 | 0.0014359 | 1.507700e-03 | 1.541900e-03 | 1.600200e-03 | 2.983100e-03 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.945091e-01 | 2.894140e-01 | 0.0000004 | 2.399999e-01 | 4.899999e-01 | 7.499995e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.945096e-01 | 2.893876e-01 | 0.0000005 | 2.434180e-01 | 4.907392e-01 | 7.452543e-01 | 9.999995e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.660103e-01 | 1.308863e-01 | 0.2706890 | 3.510143e-01 | 4.493470e-01 | 5.727348e-01 | 7.293110e-01 | ▇▆▆▅▃ |
| numeric | AF_reference | 16701 | 0.9920631 | NA | NA | NA | NA | NA | NA | NA | 4.461275e-01 | 1.638305e-01 | 0.0001997 | 3.192890e-01 | 4.355030e-01 | 5.650960e-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 | -0.0021399 | 0.0026509 | 0.4199997 | 0.4195279 | 0.623920 | 0.782149 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0005511 | 0.0026374 | 0.8300000 | 0.8344879 | 0.398742 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0015226 | 0.0025894 | 0.5600000 | 0.5565201 | 0.456123 | 0.420727 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0010934 | 0.0018266 | 0.5500004 | 0.5494137 | 0.514239 | 0.275160 | NA |
| 1 | 763394 | rs369924889 | G | A | 0.0005838 | 0.0021478 | 0.7899998 | 0.7857645 | 0.704627 | 0.617612 | NA |
| 1 | 814495 | rs74461805 | C | A | 0.0027911 | 0.0025039 | 0.2599998 | 0.2649641 | 0.340187 | NA | NA |
| 1 | 830181 | rs28444699 | A | G | -0.0002783 | 0.0016892 | 0.8700001 | 0.8691218 | 0.697650 | 0.691294 | NA |
| 1 | 831489 | rs4970385 | C | T | 0.0000578 | 0.0016586 | 0.9699999 | 0.9721916 | 0.705888 | 0.649161 | NA |
| 1 | 831909 | rs9697642 | C | T | 0.0000846 | 0.0016586 | 0.9599999 | 0.9593034 | 0.705944 | 0.648562 | NA |
| 1 | 832066 | rs9697380 | G | C | 0.0000616 | 0.0016586 | 0.9699999 | 0.9703648 | 0.706102 | 0.664337 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51174048 | rs9628245 | G | C | 0.0019463 | 0.0017045 | 0.2500000 | 0.2535224 | 0.376188 | 0.433107 | NA |
| 22 | 51180501 | rs5770999 | T | C | 0.0000318 | 0.0017218 | 0.9900000 | 0.9852695 | 0.713492 | 0.636981 | NA |
| 22 | 51181919 | rs9616825 | G | C | -0.0013167 | 0.0017131 | 0.4400003 | 0.4421237 | 0.694991 | 0.619409 | NA |
| 22 | 51182485 | rs6009961 | A | G | -0.0000062 | 0.0017269 | 1.0000000 | 0.9971533 | 0.715182 | 0.638379 | NA |
| 22 | 51186143 | rs2879914 | T | C | -0.0002938 | 0.0016072 | 0.8499999 | 0.8549410 | 0.381441 | 0.273363 | NA |
| 22 | 51186228 | rs3865766 | C | T | 0.0006235 | 0.0015664 | 0.6899999 | 0.6905781 | 0.449250 | 0.453275 | NA |
| 22 | 51197266 | rs61290853 | A | G | -0.0010690 | 0.0016175 | 0.5099998 | 0.5087038 | 0.385754 | 0.422923 | NA |
| 22 | 51211106 | rs9628250 | T | C | -0.0012976 | 0.0018134 | 0.4700002 | 0.4742449 | 0.272005 | 0.167133 | NA |
| 22 | 51212875 | rs2238837 | A | C | 0.0003741 | 0.0017230 | 0.8300000 | 0.8281008 | 0.331843 | 0.372404 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0007521 | 0.0017654 | 0.6700003 | 0.6700913 | 0.298095 | 0.205072 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.62392 ES:SE:LP:AF:ID -0.00213989:0.00265087:0.376751:0.62392:rs10399793
1 54676 rs2462492 C T . PASS AF=0.398742 ES:SE:LP:AF:ID 0.000551093:0.00263745:0.0809219:0.398742:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456123 ES:SE:LP:AF:ID -0.00152262:0.00258941:0.251812:0.456123:rs6702460
1 706368 rs12029736 A G . PASS AF=0.514239 ES:SE:LP:AF:ID 0.00109345:0.00182656:0.259637:0.514239:rs12029736
1 763394 rs3115847 G A . PASS AF=0.704627 ES:SE:LP:AF:ID 0.000583801:0.00214779:0.102373:0.704627:rs3115847
1 814495 rs74461805 C A . PASS AF=0.340187 ES:SE:LP:AF:ID 0.00279114:0.00250386:0.585027:0.340187:rs74461805
1 830181 rs28444699 A G . PASS AF=0.69765 ES:SE:LP:AF:ID -0.000278333:0.00168918:0.0604807:0.69765:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705888 ES:SE:LP:AF:ID 5.78179e-05:0.00165859:0.0132283:0.705888:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705944 ES:SE:LP:AF:ID 8.46337e-05:0.00165858:0.0177288:0.705944:rs9697642
1 832066 rs9697380 G C . PASS AF=0.706102 ES:SE:LP:AF:ID 6.16183e-05:0.0016586:0.0132283:0.706102:rs9697380
1 832318 rs4500250 C A . PASS AF=0.706141 ES:SE:LP:AF:ID 5.63281e-05:0.00165876:0.0132283:0.706141:rs4500250
1 832918 rs28765502 T C . PASS AF=0.293863 ES:SE:LP:AF:ID -6.22284e-05:0.00165877:0.0132283:0.293863:rs28765502
1 840753 rs4970382 T C . PASS AF=0.400926 ES:SE:LP:AF:ID 0.000164203:0.00152907:0.0409586:0.400926:rs4970382
1 843405 rs11516185 A G . PASS AF=0.363452 ES:SE:LP:AF:ID 0.000639408:0.00189318:0.130768:0.363452:rs11516185
1 850218 rs6664536 T A . PASS AF=0.590826 ES:SE:LP:AF:ID 0.00203579:0.00152692:0.744727:0.590826:rs6664536
1 850371 rs6679046 G T . PASS AF=0.60432 ES:SE:LP:AF:ID 0.00240695:0.0015332:0.920819:0.60432:rs6679046
1 850780 rs6657440 C T . PASS AF=0.604529 ES:SE:LP:AF:ID 0.00235554:0.00153303:0.920819:0.604529:rs6657440
1 852037 rs4970463 G A . PASS AF=0.590069 ES:SE:LP:AF:ID 0.00209317:0.00152851:0.769551:0.590069:rs4970463
1 852063 rs28436996 G A . PASS AF=0.590143 ES:SE:LP:AF:ID 0.00209307:0.00152794:0.769551:0.590143:rs28436996
1 852875 rs13303369 C T . PASS AF=0.608056 ES:SE:LP:AF:ID 0.00241737:0.00153571:0.920819:0.608056:rs13303369
1 853954 rs1806509 C A . PASS AF=0.608249 ES:SE:LP:AF:ID 0.00239686:0.00153593:0.920819:0.608249:rs1806509
1 854777 rs13303019 A G . PASS AF=0.610968 ES:SE:LP:AF:ID 0.0023237:0.00153747:0.886057:0.610968:rs13303019
1 854978 rs13303057 A C . PASS AF=0.603776 ES:SE:LP:AF:ID 0.00247815:0.00153335:0.958607:0.603776:rs13303057
1 855075 rs6673914 C G . PASS AF=0.610985 ES:SE:LP:AF:ID 0.0023946:0.0015374:0.920819:0.610985:rs6673914
1 856099 rs28534711 T G . PASS AF=0.389387 ES:SE:LP:AF:ID -0.00238171:0.0015375:0.920819:0.389387:rs28534711
1 856108 rs28742275 A G . PASS AF=0.389372 ES:SE:LP:AF:ID -0.00238629:0.00153758:0.920819:0.389372:rs28742275
1 856476 rs4040605 A G . PASS AF=0.351481 ES:SE:LP:AF:ID -0.001603:0.00158045:0.508638:0.351481:rs4040605
1 866893 rs2880024 T C . PASS AF=0.609536 ES:SE:LP:AF:ID 0.00211053:0.00155165:0.769551:0.609536:rs2880024
1 868418 rs28546443 C T . PASS AF=0.298439 ES:SE:LP:AF:ID -0.00373884:0.00168916:1.56864:0.298439:rs28546443
1 870645 rs28576697 T C . PASS AF=0.292656 ES:SE:LP:AF:ID -0.00196905:0.00167563:0.619789:0.292656:rs28576697
1 871334 rs4072383 G T . PASS AF=0.718744 ES:SE:LP:AF:ID 0.00105591:0.00166846:0.275724:0.718744:rs4072383
1 873558 rs1110052 G T . PASS AF=0.713134 ES:SE:LP:AF:ID 0.000807488:0.00165467:0.200659:0.713134:rs1110052
1 875770 rs4970379 A G . PASS AF=0.599637 ES:SE:LP:AF:ID 0.0027227:0.00155823:1.09151:0.599637:rs4970379
1 881627 rs2272757 G A . PASS AF=0.650671 ES:SE:LP:AF:ID 0.000402484:0.00157494:0.09691:0.650671:rs2272757
1 891059 rs13303065 C T . PASS AF=0.650686 ES:SE:LP:AF:ID 0.000265331:0.00157407:0.0604807:0.650686:rs13303065
1 891945 rs13303106 A G . PASS AF=0.650799 ES:SE:LP:AF:ID 0.000441022:0.00157635:0.107905:0.650799:rs13303106
1 900505 rs28705211 G C . PASS AF=0.272629 ES:SE:LP:AF:ID 0.000288941:0.00169533:0.0655015:0.272629:rs28705211
1 903245 rs28690976 A G . PASS AF=0.567594 ES:SE:LP:AF:ID 0.000982928:0.00155901:0.275724:0.567594:rs28690976
1 909073 rs3892467 C T . PASS AF=0.386643 ES:SE:LP:AF:ID 0.000405066:0.00155324:0.102373:0.386643:rs3892467
1 909238 rs3829740 G C . PASS AF=0.57054 ES:SE:LP:AF:ID 0.000811996:0.00149987:0.229148:0.57054:rs3829740
1 910394 rs28477686 C T . PASS AF=0.322605 ES:SE:LP:AF:ID -0.0010587:0.00163372:0.283997:0.322605:rs28477686
1 912049 rs7367995 T C . PASS AF=0.585565 ES:SE:LP:AF:ID 0.00146873:0.00152214:0.481486:0.585565:rs7367995
1 913889 rs2340596 G A . PASS AF=0.600483 ES:SE:LP:AF:ID 0.00171274:0.00152364:0.585027:0.600483:rs2340596
1 914333 rs13302979 C G . PASS AF=0.603895 ES:SE:LP:AF:ID 0.00167924:0.00152879:0.568636:0.603895:rs13302979
1 914852 rs13303368 G C . PASS AF=0.601525 ES:SE:LP:AF:ID 0.00173055:0.00152556:0.585027:0.601525:rs13303368
1 914940 rs13303033 T C . PASS AF=0.584758 ES:SE:LP:AF:ID 0.00156044:0.00151651:0.522879:0.584758:rs13303033
1 916834 rs6694632 G A . PASS AF=0.589267 ES:SE:LP:AF:ID 0.00155141:0.00151876:0.508638:0.589267:rs6694632
1 918384 rs13303118 G T . PASS AF=0.584587 ES:SE:LP:AF:ID 0.00151334:0.00151593:0.49485:0.584587:rs13303118
1 918573 rs2341354 A G . PASS AF=0.589483 ES:SE:LP:AF:ID 0.00144824:0.00151707:0.468521:0.589483:rs2341354
1 919501 rs4970414 G T . PASS AF=0.585105 ES:SE:LP:AF:ID 0.000725836:0.00157329:0.19382:0.585105:rs4970414