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_20003_1141171948.vcf.gz --id UKB-b:2415 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20003_1141171948.txt.gz --cohort_cases 1358 --cohort_controls 461575 --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": "2019-09-13T11:04:21.819374",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-2415/ukb-b-2415.vcf.gz; Date=Sun May 10 03:10:09 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-2415/UKB-b-2415_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-2415/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:42:45 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-2415/UKB-b-2415_data.vcf.gz ...
Read summary statistics for 2238970 SNPs.
Dropped 240 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, 570365 SNPs remain.
After merging with regression SNP LD, 570365 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0011 (0.0012)
Lambda GC: 1.0043
Mean Chi^2: 1.0123
Intercept: 1.0001 (0.0099)
Ratio: 0.0051 (0.8016)
Analysis finished at Thu Oct 17 14:43:18 2019
Total time elapsed: 32.94sQC metrics
Metrics
Metrics
{
"af_correlation": 0.7342,
"inflation_factor": 1,
"mean_EFFECT": 2.0144e-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": 17727,
"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": 570365,
"ldsc_nsnp_merge_regression_ld": 570365,
"ldsc_observed_scale_h2_beta": 0.0011,
"ldsc_observed_scale_h2_se": 0.0012,
"ldsc_intercept_beta": 1.0001,
"ldsc_intercept_se": 0.0099,
"ldsc_lambda_gc": 1.0043,
"ldsc_mean_chisq": 1.0123,
"ldsc_ratio": 0.0081
}
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 | 2238732 | 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 | 2238970 | 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 | 8.648916e+00 | 5.766775e+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 | 7.865628e+07 | 5.665625e+07 | 5687.0000000 | 3.177196e+07 | 6.914753e+07 | 1.147729e+08 | 2.491722e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.000000e-07 | 1.189000e-04 | -0.0006927 | -7.960000e-05 | 4.000000e-07 | 7.990000e-05 | 6.498000e-04 | ▁▁▇▂▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.181000e-04 | 5.300000e-06 | 0.0001084 | 1.137000e-04 | 1.166000e-04 | 1.216000e-04 | 2.284000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.987748e-01 | 2.894180e-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 | 4.987749e-01 | 2.893924e-01 | 0.0000001 | 2.474302e-01 | 4.988508e-01 | 7.490341e-01 | 9.999997e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.619231e-01 | 1.381703e-01 | 0.2577320 | 3.405750e-01 | 4.434290e-01 | 5.740500e-01 | 7.422680e-01 | ▇▆▅▅▃ |
| numeric | AF_reference | 17727 | 0.9920825 | NA | NA | NA | NA | NA | NA | NA | 4.425109e-01 | 1.682202e-01 | 0.0001997 | 3.113020e-01 | 4.303120e-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 | -0.0001266 | 0.0001995 | 0.5300002 | 0.5256285 | 0.623763 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0001227 | 0.0001976 | 0.5300002 | 0.5348015 | 0.400401 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0002910 | 0.0001946 | 0.1299999 | 0.1348151 | 0.456851 | 0.4207270 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0001068 | 0.0001380 | 0.4400003 | 0.4390646 | 0.515650 | 0.2751600 | NA |
| 1 | 763394 | rs369924889 | G | A | 0.0000253 | 0.0001618 | 0.8800001 | 0.8756666 | 0.706753 | 0.6176120 | NA |
| 1 | 776546 | rs12124819 | A | G | 0.0000875 | 0.0001475 | 0.5500004 | 0.5529352 | 0.265390 | 0.0756789 | NA |
| 1 | 814495 | rs74461805 | C | A | -0.0004312 | 0.0001892 | 0.0230001 | 0.0226823 | 0.340397 | NA | NA |
| 1 | 830181 | rs28444699 | A | G | 0.0001107 | 0.0001266 | 0.3800004 | 0.3819883 | 0.697259 | 0.6912940 | NA |
| 1 | 831489 | rs4970385 | C | T | 0.0000964 | 0.0001243 | 0.4400003 | 0.4380994 | 0.705403 | 0.6491610 | NA |
| 1 | 831909 | rs9697642 | C | T | 0.0000930 | 0.0001243 | 0.4500005 | 0.4543262 | 0.705448 | 0.6485620 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51174662 | rs1858748 | T | C | 0.0000965 | 0.0001334 | 0.4700002 | 0.4694457 | 0.737431 | 0.732827 | NA |
| 22 | 51180501 | rs5770999 | T | C | 0.0001230 | 0.0001299 | 0.3400001 | 0.3437978 | 0.713658 | 0.636981 | NA |
| 22 | 51181919 | rs9616825 | G | C | 0.0001217 | 0.0001292 | 0.3500000 | 0.3461593 | 0.695471 | 0.619409 | NA |
| 22 | 51182485 | rs6009961 | A | G | 0.0001230 | 0.0001303 | 0.3500000 | 0.3452206 | 0.715505 | 0.638379 | NA |
| 22 | 51186143 | rs2879914 | T | C | -0.0000305 | 0.0001208 | 0.8000000 | 0.8009334 | 0.381826 | 0.273363 | NA |
| 22 | 51186228 | rs3865766 | C | T | -0.0001396 | 0.0001178 | 0.2399999 | 0.2358595 | 0.451063 | 0.453275 | NA |
| 22 | 51197266 | rs61290853 | A | G | -0.0001295 | 0.0001216 | 0.2900000 | 0.2867759 | 0.386333 | 0.422923 | NA |
| 22 | 51211106 | rs9628250 | T | C | 0.0002660 | 0.0001364 | 0.0510000 | 0.0512153 | 0.271547 | 0.167133 | NA |
| 22 | 51212875 | rs2238837 | A | C | 0.0000570 | 0.0001296 | 0.6600001 | 0.6600691 | 0.331455 | 0.372404 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0000698 | 0.0001327 | 0.5999997 | 0.5987379 | 0.297971 | 0.205072 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623763 ES:SE:LP:AF:ID -0.000126627:0.000199509:0.275724:0.623763:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID -0.00012268:0.00019765:0.275724:0.400401:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456851 ES:SE:LP:AF:ID 0.000291022:0.000194614:0.886057:0.456851:rs6702460
1 706368 rs12029736 A G . PASS AF=0.51565 ES:SE:LP:AF:ID -0.00010678:0.000137999:0.356547:0.51565:rs12029736
1 763394 rs3115847 G A . PASS AF=0.706753 ES:SE:LP:AF:ID 2.53146e-05:0.000161791:0.0555173:0.706753:rs3115847
1 776546 rs12124819 A G . PASS AF=0.26539 ES:SE:LP:AF:ID 8.75229e-05:0.000147502:0.259637:0.26539:rs12124819
1 814495 rs74461805 C A . PASS AF=0.340397 ES:SE:LP:AF:ID -0.000431193:0.000189224:1.63827:0.340397:rs74461805
1 830181 rs28444699 A G . PASS AF=0.697259 ES:SE:LP:AF:ID 0.000110676:0.000126597:0.420216:0.697259:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705403 ES:SE:LP:AF:ID 9.63877e-05:0.000124306:0.356547:0.705403:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705448 ES:SE:LP:AF:ID 9.30055e-05:0.000124302:0.346787:0.705448:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705634 ES:SE:LP:AF:ID 9.18517e-05:0.000124308:0.337242:0.705634:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705662 ES:SE:LP:AF:ID 9.36836e-05:0.000124321:0.346787:0.705662:rs4500250
1 832398 rs4553118 T C . PASS AF=0.730134 ES:SE:LP:AF:ID 9.3286e-05:0.000127708:0.327902:0.730134:rs4553118
1 832918 rs28765502 T C . PASS AF=0.294371 ES:SE:LP:AF:ID -9.45274e-05:0.000124315:0.346787:0.294371:rs28765502
1 836896 rs28705752 T C . PASS AF=0.269503 ES:SE:LP:AF:ID 9.51184e-05:0.000126722:0.346787:0.269503:rs28705752
1 839103 rs28562941 A G . PASS AF=0.27001 ES:SE:LP:AF:ID 8.06305e-05:0.000126812:0.283997:0.27001:rs28562941
1 840753 rs4970382 T C . PASS AF=0.400106 ES:SE:LP:AF:ID 0.000122286:0.000114662:0.537602:0.400106:rs4970382
1 843405 rs11516185 A G . PASS AF=0.362599 ES:SE:LP:AF:ID -0.000160417:0.000142341:0.585027:0.362599:rs11516185
1 850218 rs6664536 T A . PASS AF=0.590333 ES:SE:LP:AF:ID 8.12459e-05:0.000114326:0.318759:0.590333:rs6664536
1 850371 rs6679046 G T . PASS AF=0.603726 ES:SE:LP:AF:ID 7.51412e-05:0.000114968:0.29243:0.603726:rs6679046
1 850780 rs6657440 C T . PASS AF=0.603944 ES:SE:LP:AF:ID 8.15805e-05:0.000114952:0.318759:0.603944:rs6657440
1 852037 rs4970463 G A . PASS AF=0.589688 ES:SE:LP:AF:ID 8.62783e-05:0.000114512:0.346787:0.589688:rs4970463
1 852063 rs28436996 G A . PASS AF=0.589667 ES:SE:LP:AF:ID 8.71541e-05:0.000114461:0.346787:0.589667:rs28436996
1 852875 rs13303369 C T . PASS AF=0.607674 ES:SE:LP:AF:ID 9.94602e-05:0.000115209:0.408935:0.607674:rs13303369
1 853954 rs1806509 C A . PASS AF=0.607833 ES:SE:LP:AF:ID 9.54877e-05:0.000115224:0.387216:0.607833:rs1806509
1 854777 rs13303019 A G . PASS AF=0.610318 ES:SE:LP:AF:ID 0.000117018:0.000115338:0.508638:0.610318:rs13303019
1 854978 rs13303057 A C . PASS AF=0.603286 ES:SE:LP:AF:ID 7.6576e-05:0.000114996:0.29243:0.603286:rs13303057
1 855075 rs6673914 C G . PASS AF=0.610338 ES:SE:LP:AF:ID 0.000116744:0.00011534:0.508638:0.610338:rs6673914
1 856099 rs28534711 T G . PASS AF=0.389935 ES:SE:LP:AF:ID -0.000110884:0.000115362:0.468521:0.389935:rs28534711
1 856108 rs28742275 A G . PASS AF=0.389918 ES:SE:LP:AF:ID -0.00011129:0.000115368:0.481486:0.389918:rs28742275
1 856476 rs4040605 A G . PASS AF=0.350351 ES:SE:LP:AF:ID -3.49586e-05:0.000118515:0.113509:0.350351:rs4040605
1 866893 rs2880024 T C . PASS AF=0.610554 ES:SE:LP:AF:ID 0.000129746:0.000115988:0.585027:0.610554:rs2880024
1 868418 rs28546443 C T . PASS AF=0.297865 ES:SE:LP:AF:ID -0.000100169:0.000127438:0.366532:0.297865:rs28546443
1 870645 rs28576697 T C . PASS AF=0.291287 ES:SE:LP:AF:ID -0.000109541:0.000126422:0.408935:0.291287:rs28576697
1 871334 rs4072383 G T . PASS AF=0.720619 ES:SE:LP:AF:ID 9.36594e-05:0.00012564:0.337242:0.720619:rs4072383
1 872352 rs1806780 G C . PASS AF=0.267526 ES:SE:LP:AF:ID -7.68857e-05:0.000127343:0.259637:0.267526:rs1806780
1 873558 rs1110052 G T . PASS AF=0.715253 ES:SE:LP:AF:ID 3.88773e-05:0.000124641:0.119186:0.715253:rs1110052
1 875770 rs4970379 A G . PASS AF=0.600084 ES:SE:LP:AF:ID 8.33285e-05:0.000116952:0.318759:0.600084:rs4970379
1 881627 rs2272757 G A . PASS AF=0.65239 ES:SE:LP:AF:ID 0.000162667:0.000118141:0.769551:0.65239:rs2272757
1 891059 rs13303065 C T . PASS AF=0.652428 ES:SE:LP:AF:ID 0.000167042:0.000118123:0.79588:0.652428:rs13303065
1 891945 rs13303106 A G . PASS AF=0.65249 ES:SE:LP:AF:ID 0.000165994:0.00011826:0.79588:0.65249:rs13303106
1 900505 rs28705211 G C . PASS AF=0.271766 ES:SE:LP:AF:ID -0.000226859:0.000127849:1.11919:0.271766:rs28705211
1 903245 rs28690976 A G . PASS AF=0.566937 ES:SE:LP:AF:ID 0.000162085:0.000117459:0.769551:0.566937:rs28690976
1 909073 rs3892467 C T . PASS AF=0.386683 ES:SE:LP:AF:ID -0.000196811:0.000117139:1.03152:0.386683:rs3892467
1 909238 rs3829740 G C . PASS AF=0.571412 ES:SE:LP:AF:ID -0.000176105:0.000113446:0.920819:0.571412:rs3829740
1 910394 rs28477686 C T . PASS AF=0.324456 ES:SE:LP:AF:ID 7.75777e-05:0.000122963:0.275724:0.324456:rs28477686
1 912049 rs7367995 T C . PASS AF=0.58525 ES:SE:LP:AF:ID -0.000113139:0.000114596:0.49485:0.58525:rs7367995
1 913889 rs2340596 G A . PASS AF=0.599213 ES:SE:LP:AF:ID -6.37461e-05:0.000114782:0.236572:0.599213:rs2340596
1 914333 rs13302979 C G . PASS AF=0.602521 ES:SE:LP:AF:ID -4.81448e-05:0.00011513:0.167491:0.602521:rs13302979
1 914852 rs13303368 G C . PASS AF=0.600078 ES:SE:LP:AF:ID -6.40794e-05:0.000114909:0.236572:0.600078:rs13303368