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_41229_5K8.vcf.gz --id UKB-b:5644 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41229_5K8.txt.gz --cohort_cases 1011 --cohort_controls 461999 --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": "2019-09-13T07:01:51.357927",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-5644/ukb-b-5644.vcf.gz; Date=Sat May 9 14:59:42 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-5644/UKB-b-5644_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5644/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:45:32 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5644/UKB-b-5644_data.vcf.gz ...
Read summary statistics for 1370023 SNPs.
Dropped 112 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, 354867 SNPs remain.
After merging with regression SNP LD, 354867 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0061 (0.0018)
Lambda GC: 1.0698
Mean Chi^2: 1.0794
Intercept: 1.0104 (0.0126)
Ratio: 0.131 (0.1592)
Analysis finished at Thu Oct 17 14:45:56 2019
Total time elapsed: 24.22sQC metrics
Metrics
Metrics
{
"af_correlation": 0.5584,
"inflation_factor": 1.0475,
"mean_EFFECT": -4.7211e-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": 10938,
"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": 354867,
"ldsc_nsnp_merge_regression_ld": 354867,
"ldsc_observed_scale_h2_beta": 0.0061,
"ldsc_observed_scale_h2_se": 0.0018,
"ldsc_intercept_beta": 1.0104,
"ldsc_intercept_se": 0.0126,
"ldsc_lambda_gc": 1.0698,
"ldsc_mean_chisq": 1.0794,
"ldsc_ratio": 0.131
}
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 | 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 | 1369913 | 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 | 1370023 | 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 | 8.681283e+00 | 5.768365e+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 | 7.847044e+07 | 5.650797e+07 | 1.23330e+04 | 3.162723e+07 | 6.894256e+07 | 1.143796e+08 | 2.491722e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -5.000000e-07 | 1.028000e-04 | -4.64800e-04 | -6.930000e-05 | 0.000000e+00 | 6.870000e-05 | 5.518000e-04 | ▁▃▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 9.910000e-05 | 2.500000e-06 | 9.36000e-05 | 9.760000e-05 | 9.860000e-05 | 1.001000e-04 | 1.850000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.891902e-01 | 2.922925e-01 | 8.00000e-07 | 2.300001e-01 | 4.899999e-01 | 7.400005e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.891921e-01 | 2.922683e-01 | 8.00000e-07 | 2.328476e-01 | 4.857515e-01 | 7.434895e-01 | 9.999965e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.847793e-01 | 8.846410e-02 | 3.46192e-01 | 4.069080e-01 | 4.771060e-01 | 5.583715e-01 | 6.538080e-01 | ▇▇▆▆▅ |
| numeric | AF_reference | 10938 | 0.9920162 | NA | NA | NA | NA | NA | NA | NA | 4.624996e-01 | 1.417192e-01 | 1.99700e-04 | 3.572280e-01 | 4.570690e-01 | 5.634980e-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 | 0.0000175 | 0.0001722 | 0.9199999 | 0.9190531 | 0.623765 | 0.782149 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0000881 | 0.0001706 | 0.6100002 | 0.6054039 | 0.400401 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0003554 | 0.0001680 | 0.0340001 | 0.0343554 | 0.456846 | 0.420727 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0000033 | 0.0001191 | 0.9800000 | 0.9778342 | 0.515645 | 0.275160 | NA |
| 1 | 840753 | rs4970382 | T | C | 0.0001613 | 0.0000990 | 0.1000000 | 0.1031966 | 0.400124 | 0.468850 | NA |
| 1 | 843405 | rs11516185 | A | G | 0.0000086 | 0.0001229 | 0.9400001 | 0.9441541 | 0.362606 | 0.375399 | NA |
| 1 | 850218 | rs6664536 | T | A | -0.0000535 | 0.0000987 | 0.5900000 | 0.5878597 | 0.590331 | 0.345248 | NA |
| 1 | 850371 | rs6679046 | G | T | -0.0001082 | 0.0000992 | 0.2800000 | 0.2755853 | 0.603723 | 0.508786 | NA |
| 1 | 850780 | rs6657440 | C | T | -0.0001041 | 0.0000992 | 0.2900000 | 0.2941281 | 0.603942 | 0.560304 | NA |
| 1 | 852037 | rs4970463 | G | A | -0.0000554 | 0.0000988 | 0.5700002 | 0.5748595 | 0.589686 | 0.345048 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51158017 | rs6010065 | G | C | 6.41e-05 | 0.0000973 | 0.5099998 | 0.5097317 | 0.461411 | 0.547524 | NA |
| 22 | 51158499 | rs8136930 | T | G | 5.50e-05 | 0.0000974 | 0.5700002 | 0.5725414 | 0.461636 | 0.544728 | NA |
| 22 | 51161019 | rs5770994 | C | T | -7.01e-05 | 0.0000971 | 0.4700002 | 0.4703297 | 0.482204 | 0.425719 | NA |
| 22 | 51163039 | rs715584 | G | T | 5.71e-05 | 0.0000986 | 0.5600000 | 0.5625466 | 0.426975 | 0.473642 | NA |
| 22 | 51164109 | rs5770995 | G | C | 4.73e-05 | 0.0000980 | 0.6300007 | 0.6293541 | 0.452705 | 0.510982 | NA |
| 22 | 51164115 | rs5770996 | C | T | 3.92e-05 | 0.0000979 | 0.6899999 | 0.6888769 | 0.456917 | 0.514776 | NA |
| 22 | 51174048 | rs9628245 | G | C | 5.51e-05 | 0.0001106 | 0.6200004 | 0.6183224 | 0.380135 | 0.433107 | NA |
| 22 | 51186143 | rs2879914 | T | C | 3.45e-05 | 0.0001043 | 0.7400005 | 0.7406914 | 0.381825 | 0.273363 | NA |
| 22 | 51186228 | rs3865766 | C | T | -1.35e-05 | 0.0001016 | 0.8900000 | 0.8946480 | 0.451061 | 0.453275 | NA |
| 22 | 51197266 | rs61290853 | A | G | -1.73e-05 | 0.0001049 | 0.8700001 | 0.8693901 | 0.386333 | 0.422923 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623765 ES:SE:LP:AF:ID 1.75001e-05:0.0001722:0.0362122:0.623765:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID 8.81381e-05:0.000170597:0.21467:0.400401:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456846 ES:SE:LP:AF:ID 0.00035541:0.000167973:1.46852:0.456846:rs6702460
1 706368 rs12029736 A G . PASS AF=0.515645 ES:SE:LP:AF:ID -3.30936e-06:0.000119109:0.00877392:0.515645:rs12029736
1 840753 rs4970382 T C . PASS AF=0.400124 ES:SE:LP:AF:ID 0.000161272:9.89672e-05:1:0.400124:rs4970382
1 843405 rs11516185 A G . PASS AF=0.362606 ES:SE:LP:AF:ID 8.60588e-06:0.000122854:0.0268721:0.362606:rs11516185
1 850218 rs6664536 T A . PASS AF=0.590331 ES:SE:LP:AF:ID -5.34779e-05:9.86786e-05:0.229148:0.590331:rs6664536
1 850371 rs6679046 G T . PASS AF=0.603723 ES:SE:LP:AF:ID -0.000108192:9.92323e-05:0.552842:0.603723:rs6679046
1 850780 rs6657440 C T . PASS AF=0.603942 ES:SE:LP:AF:ID -0.000104091:9.92185e-05:0.537602:0.603942:rs6657440
1 852037 rs4970463 G A . PASS AF=0.589686 ES:SE:LP:AF:ID -5.54397e-05:9.8839e-05:0.244125:0.589686:rs4970463
1 852063 rs28436996 G A . PASS AF=0.589665 ES:SE:LP:AF:ID -5.24402e-05:9.87947e-05:0.221849:0.589665:rs28436996
1 852875 rs13303369 C T . PASS AF=0.607671 ES:SE:LP:AF:ID -0.000105398:9.94401e-05:0.537602:0.607671:rs13303369
1 853954 rs1806509 C A . PASS AF=0.607829 ES:SE:LP:AF:ID -0.000106184:9.94536e-05:0.537602:0.607829:rs1806509
1 854777 rs13303019 A G . PASS AF=0.610316 ES:SE:LP:AF:ID -0.000107853:9.95515e-05:0.552842:0.610316:rs13303019
1 854978 rs13303057 A C . PASS AF=0.603283 ES:SE:LP:AF:ID -0.000104406:9.92566e-05:0.537602:0.603283:rs13303057
1 855075 rs6673914 C G . PASS AF=0.610337 ES:SE:LP:AF:ID -0.00010617:9.95533e-05:0.537602:0.610337:rs6673914
1 856099 rs28534711 T G . PASS AF=0.389936 ES:SE:LP:AF:ID 0.000101841:9.95722e-05:0.508638:0.389936:rs28534711
1 856108 rs28742275 A G . PASS AF=0.38992 ES:SE:LP:AF:ID 0.000101598:9.95773e-05:0.508638:0.38992:rs28742275
1 856476 rs4040605 A G . PASS AF=0.350356 ES:SE:LP:AF:ID 0.000144578:0.000102294:0.79588:0.350356:rs4040605
1 866893 rs2880024 T C . PASS AF=0.610552 ES:SE:LP:AF:ID -0.000125991:0.000100112:0.677781:0.610552:rs2880024
1 875770 rs4970379 A G . PASS AF=0.600085 ES:SE:LP:AF:ID -0.000106763:0.000100944:0.537602:0.600085:rs4970379
1 881627 rs2272757 G A . PASS AF=0.652393 ES:SE:LP:AF:ID -0.000167641:0.000101971:1:0.652393:rs2272757
1 891059 rs13303065 C T . PASS AF=0.652432 ES:SE:LP:AF:ID -0.000148031:0.000101955:0.823909:0.652432:rs13303065
1 891945 rs13303106 A G . PASS AF=0.652494 ES:SE:LP:AF:ID -0.00015296:0.000102074:0.886057:0.652494:rs13303106
1 903245 rs28690976 A G . PASS AF=0.566938 ES:SE:LP:AF:ID -0.000159789:0.000101382:0.958607:0.566938:rs28690976
1 909073 rs3892467 C T . PASS AF=0.386681 ES:SE:LP:AF:ID 6.65028e-05:0.000101106:0.29243:0.386681:rs3892467
1 909238 rs3829740 G C . PASS AF=0.571408 ES:SE:LP:AF:ID 3.28281e-05:9.79185e-05:0.130768:0.571408:rs3829740
1 912049 rs7367995 T C . PASS AF=0.585249 ES:SE:LP:AF:ID -8.90196e-05:9.89111e-05:0.431798:0.585249:rs7367995
1 913889 rs2340596 G A . PASS AF=0.59921 ES:SE:LP:AF:ID -6.5448e-05:9.90718e-05:0.29243:0.59921:rs2340596
1 914333 rs13302979 C G . PASS AF=0.602516 ES:SE:LP:AF:ID -5.09898e-05:9.93719e-05:0.21467:0.602516:rs13302979
1 914852 rs13303368 G C . PASS AF=0.600074 ES:SE:LP:AF:ID -5.50389e-05:9.91811e-05:0.236572:0.600074:rs13303368
1 914940 rs13303033 T C . PASS AF=0.584289 ES:SE:LP:AF:ID -6.45462e-05:9.86253e-05:0.29243:0.584289:rs13303033
1 916834 rs6694632 G A . PASS AF=0.589102 ES:SE:LP:AF:ID -6.41492e-05:9.87759e-05:0.283997:0.589102:rs6694632
1 918384 rs13303118 G T . PASS AF=0.584202 ES:SE:LP:AF:ID -6.52142e-05:9.8579e-05:0.29243:0.584202:rs13303118
1 918573 rs2341354 A G . PASS AF=0.589327 ES:SE:LP:AF:ID -6.5777e-05:9.87e-05:0.29243:0.589327:rs2341354
1 919501 rs4970414 G T . PASS AF=0.583926 ES:SE:LP:AF:ID -8.87403e-05:0.000102072:0.420216:0.583926:rs4970414
1 921716 rs13303278 C A . PASS AF=0.567888 ES:SE:LP:AF:ID 1.89082e-05:9.84964e-05:0.0705811:0.567888:rs13303278
1 924528 rs34712273 C A . PASS AF=0.578491 ES:SE:LP:AF:ID 1.06491e-05:9.87789e-05:0.0409586:0.578491:rs34712273
1 930533 rs3128110 C G . PASS AF=0.386082 ES:SE:LP:AF:ID -2.09506e-05:0.00010004:0.0809219:0.386082:rs3128110
1 930567 rs3121574 A G . PASS AF=0.386124 ES:SE:LP:AF:ID -2.31351e-05:0.000100043:0.0861861:0.386124:rs3121574
1 930751 rs3128111 C G . PASS AF=0.385079 ES:SE:LP:AF:ID -2.18963e-05:0.000100103:0.0809219:0.385079:rs3128111
1 931166 rs2710880 A G . PASS AF=0.386765 ES:SE:LP:AF:ID -2.69568e-05:0.000100059:0.102373:0.386765:rs2710880
1 931362 rs2799060 G A . PASS AF=0.385571 ES:SE:LP:AF:ID -2.92372e-05:0.000100108:0.113509:0.385571:rs2799060
1 933790 rs9442392 G A . PASS AF=0.578604 ES:SE:LP:AF:ID 1.21198e-05:9.87453e-05:0.0457575:0.578604:rs9442392
1 936111 rs1936360 C T . PASS AF=0.573545 ES:SE:LP:AF:ID 1.76791e-05:9.87216e-05:0.0655015:0.573545:rs1936360
1 940005 rs2799056 A G . PASS AF=0.399264 ES:SE:LP:AF:ID -4.51496e-05:9.96937e-05:0.187087:0.399264:rs2799056
1 940096 rs4503294 C T . PASS AF=0.565308 ES:SE:LP:AF:ID 1.92161e-05:9.84392e-05:0.0705811:0.565308:rs4503294
1 941284 rs3128116 C T . PASS AF=0.397398 ES:SE:LP:AF:ID -3.45412e-05:9.97152e-05:0.136677:0.397398:rs3128116
1 941334 rs57683598 G A . PASS AF=0.397402 ES:SE:LP:AF:ID -3.3831e-05:9.97214e-05:0.136677:0.397402:rs57683598
1 941539 rs9778087 C T . PASS AF=0.399593 ES:SE:LP:AF:ID -4.62444e-05:9.9725e-05:0.19382:0.399593:rs9778087