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_20092_3.vcf.gz --id UKB-b:10073 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20092_3.txt.gz --cohort_cases 1099 --cohort_controls 63850 --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": "2019-09-13T11:44:52.788126",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-10073/ukb-b-10073.vcf.gz; Date=Sun May 10 12:19:40 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-10073/UKB-b-10073_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-10073/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:40:19 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-10073/UKB-b-10073_data.vcf.gz ...
Read summary statistics for 1632081 SNPs.
Dropped 149 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, 421298 SNPs remain.
After merging with regression SNP LD, 421298 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.007 (0.0094)
Lambda GC: 1.005
Mean Chi^2: 0.9986
Intercept: 0.9879 (0.0118)
Ratio: NA (mean chi^2 < 1)
Analysis finished at Thu Oct 17 14:40:44 2019
Total time elapsed: 25.58sQC metrics
Metrics
Metrics
{
"af_correlation": 0.6232,
"inflation_factor": 1,
"mean_EFFECT": -1.0422e-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": 12966,
"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": 421298,
"ldsc_nsnp_merge_regression_ld": 421298,
"ldsc_observed_scale_h2_beta": 0.007,
"ldsc_observed_scale_h2_se": 0.0094,
"ldsc_intercept_beta": 0.9879,
"ldsc_intercept_se": 0.0118,
"ldsc_lambda_gc": 1.005,
"ldsc_mean_chisq": 0.9986,
"ldsc_ratio": 8.6429
}
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 | 1631934 | 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 | 1632081 | 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 | 8.661626e+00 | 5.766016e+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 | 7.857403e+07 | 5.655529e+07 | 1.23330e+04 | 3.175902e+07 | 6.914708e+07 | 1.145144e+08 | 2.491722e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -1.000000e-06 | 7.358000e-04 | -3.75450e-03 | -4.965000e-04 | -1.700000e-06 | 4.960000e-04 | 3.861300e-03 | ▁▂▇▂▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.361000e-04 | 2.170000e-05 | 6.90900e-04 | 7.210000e-04 | 7.312000e-04 | 7.476000e-04 | 1.409200e-03 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 5.002213e-01 | 2.888926e-01 | 3.00000e-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 | 5.002230e-01 | 2.888660e-01 | 3.00000e-07 | 2.484224e-01 | 5.000173e-01 | 7.508474e-01 | 9.999991e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.790657e-01 | 1.042166e-01 | 3.18472e-01 | 3.876070e-01 | 4.686310e-01 | 5.651370e-01 | 6.815280e-01 | ▇▇▆▅▅ |
| numeric | AF_reference | 12966 | 0.9920555 | NA | NA | NA | NA | NA | NA | NA | 4.578409e-01 | 1.491092e-01 | 1.99700e-04 | 3.454470e-01 | 4.510780e-01 | 5.646960e-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 | -0.0006346 | 0.0012733 | 0.6200004 | 0.6181887 | 0.623812 | 0.782149 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0013331 | 0.0012696 | 0.2900000 | 0.2936850 | 0.399144 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0007996 | 0.0012487 | 0.5199996 | 0.5219380 | 0.455916 | 0.420727 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0000969 | 0.0008802 | 0.9100000 | 0.9122991 | 0.513304 | 0.275160 | NA |
| 1 | 814495 | rs74461805 | C | A | 0.0002919 | 0.0012054 | 0.8100000 | 0.8086730 | 0.340108 | NA | NA |
| 1 | 840753 | rs4970382 | T | C | 0.0008381 | 0.0007310 | 0.2500000 | 0.2515991 | 0.400406 | 0.468850 | NA |
| 1 | 843405 | rs11516185 | A | G | 0.0003675 | 0.0009095 | 0.6899999 | 0.6861632 | 0.362367 | 0.375399 | NA |
| 1 | 850218 | rs6664536 | T | A | -0.0004636 | 0.0007270 | 0.5199996 | 0.5236637 | 0.589315 | 0.345248 | NA |
| 1 | 850371 | rs6679046 | G | T | -0.0004934 | 0.0007305 | 0.5000000 | 0.4994185 | 0.603035 | 0.508786 | NA |
| 1 | 850780 | rs6657440 | C | T | -0.0005144 | 0.0007305 | 0.4799997 | 0.4813524 | 0.603381 | 0.560304 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51158499 | rs8136930 | T | G | -0.0000872 | 0.0007173 | 0.9000000 | 0.9032669 | 0.459892 | 0.544728 | NA |
| 22 | 51161019 | rs5770994 | C | T | -0.0002734 | 0.0007147 | 0.6999999 | 0.7020872 | 0.483487 | 0.425719 | NA |
| 22 | 51163039 | rs715584 | G | T | -0.0001141 | 0.0007254 | 0.8800001 | 0.8750519 | 0.425020 | 0.473642 | NA |
| 22 | 51164109 | rs5770995 | G | C | -0.0002991 | 0.0007219 | 0.6800001 | 0.6786767 | 0.450784 | 0.510982 | NA |
| 22 | 51164115 | rs5770996 | C | T | -0.0002363 | 0.0007215 | 0.7400005 | 0.7433127 | 0.454945 | 0.514776 | NA |
| 22 | 51174048 | rs9628245 | G | C | -0.0007655 | 0.0008178 | 0.3500000 | 0.3492463 | 0.377819 | 0.433107 | NA |
| 22 | 51186143 | rs2879914 | T | C | -0.0008908 | 0.0007707 | 0.2500000 | 0.2477447 | 0.380077 | 0.273363 | NA |
| 22 | 51186228 | rs3865766 | C | T | 0.0001824 | 0.0007507 | 0.8100000 | 0.8080493 | 0.449547 | 0.453275 | NA |
| 22 | 51197266 | rs61290853 | A | G | 0.0002591 | 0.0007736 | 0.7400005 | 0.7376570 | 0.386693 | 0.422923 | NA |
| 22 | 51212875 | rs2238837 | A | C | -0.0006256 | 0.0008273 | 0.4500005 | 0.4495215 | 0.331351 | 0.372404 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623812 ES:SE:LP:AF:ID -0.000634622:0.00127327:0.207608:0.623812:rs10399793
1 54676 rs2462492 C T . PASS AF=0.399144 ES:SE:LP:AF:ID -0.00133314:0.00126957:0.537602:0.399144:rs2462492
1 91536 rs6702460 G T . PASS AF=0.455916 ES:SE:LP:AF:ID -0.000799625:0.00124871:0.283997:0.455916:rs6702460
1 706368 rs12029736 A G . PASS AF=0.513304 ES:SE:LP:AF:ID 9.69497e-05:0.000880248:0.0409586:0.513304:rs12029736
1 814495 rs74461805 C A . PASS AF=0.340108 ES:SE:LP:AF:ID 0.000291876:0.00120541:0.091515:0.340108:rs74461805
1 840753 rs4970382 T C . PASS AF=0.400406 ES:SE:LP:AF:ID 0.000838061:0.00073099:0.60206:0.400406:rs4970382
1 843405 rs11516185 A G . PASS AF=0.362367 ES:SE:LP:AF:ID 0.00036749:0.000909477:0.161151:0.362367:rs11516185
1 850218 rs6664536 T A . PASS AF=0.589315 ES:SE:LP:AF:ID -0.000463631:0.000727027:0.283997:0.589315:rs6664536
1 850371 rs6679046 G T . PASS AF=0.603035 ES:SE:LP:AF:ID -0.000493386:0.000730504:0.30103:0.603035:rs6679046
1 850780 rs6657440 C T . PASS AF=0.603381 ES:SE:LP:AF:ID -0.000514379:0.000730518:0.318759:0.603381:rs6657440
1 852037 rs4970463 G A . PASS AF=0.588673 ES:SE:LP:AF:ID -0.000395927:0.000728064:0.229148:0.588673:rs4970463
1 852063 rs28436996 G A . PASS AF=0.588649 ES:SE:LP:AF:ID -0.000404552:0.000727736:0.236572:0.588649:rs28436996
1 852875 rs13303369 C T . PASS AF=0.606715 ES:SE:LP:AF:ID -0.000482412:0.00073172:0.29243:0.606715:rs13303369
1 853954 rs1806509 C A . PASS AF=0.60688 ES:SE:LP:AF:ID -0.000480876:0.000731796:0.29243:0.60688:rs1806509
1 854777 rs13303019 A G . PASS AF=0.609504 ES:SE:LP:AF:ID -0.000542774:0.000732527:0.337242:0.609504:rs13303019
1 854978 rs13303057 A C . PASS AF=0.602505 ES:SE:LP:AF:ID -0.000489534:0.000730632:0.30103:0.602505:rs13303057
1 855075 rs6673914 C G . PASS AF=0.609473 ES:SE:LP:AF:ID -0.000538342:0.000732431:0.337242:0.609473:rs6673914
1 856099 rs28534711 T G . PASS AF=0.390835 ES:SE:LP:AF:ID 0.000534869:0.000732728:0.327902:0.390835:rs28534711
1 856108 rs28742275 A G . PASS AF=0.390777 ES:SE:LP:AF:ID 0.00054093:0.000732804:0.337242:0.390777:rs28742275
1 856476 rs4040605 A G . PASS AF=0.352504 ES:SE:LP:AF:ID 0.000249563:0.000752202:0.130768:0.352504:rs4040605
1 866893 rs2880024 T C . PASS AF=0.608083 ES:SE:LP:AF:ID -0.000461734:0.000739209:0.275724:0.608083:rs2880024
1 875770 rs4970379 A G . PASS AF=0.598464 ES:SE:LP:AF:ID -0.000103735:0.000744737:0.05061:0.598464:rs4970379
1 881627 rs2272757 G A . PASS AF=0.65048 ES:SE:LP:AF:ID 0.000165249:0.00075232:0.0809219:0.65048:rs2272757
1 891059 rs13303065 C T . PASS AF=0.650626 ES:SE:LP:AF:ID 0.000185847:0.000752247:0.09691:0.650626:rs13303065
1 891945 rs13303106 A G . PASS AF=0.650689 ES:SE:LP:AF:ID 0.000196167:0.000753107:0.102373:0.650689:rs13303106
1 903245 rs28690976 A G . PASS AF=0.565133 ES:SE:LP:AF:ID -0.000125293:0.000747827:0.0604807:0.565133:rs28690976
1 909073 rs3892467 C T . PASS AF=0.386217 ES:SE:LP:AF:ID 0.00049714:0.000746264:0.29243:0.386217:rs3892467
1 909238 rs3829740 G C . PASS AF=0.572164 ES:SE:LP:AF:ID -1.25677e-05:0.00072197:0.00436481:0.572164:rs3829740
1 910394 rs28477686 C T . PASS AF=0.322663 ES:SE:LP:AF:ID 0.00019432:0.000784202:0.09691:0.322663:rs28477686
1 912049 rs7367995 T C . PASS AF=0.585242 ES:SE:LP:AF:ID -2.76299e-05:0.00072897:0.0132283:0.585242:rs7367995
1 913889 rs2340596 G A . PASS AF=0.600025 ES:SE:LP:AF:ID 0.00029424:0.000730419:0.161151:0.600025:rs2340596
1 914333 rs13302979 C G . PASS AF=0.60338 ES:SE:LP:AF:ID 0.000219903:0.000732897:0.119186:0.60338:rs13302979
1 914852 rs13303368 G C . PASS AF=0.60101 ES:SE:LP:AF:ID 0.000245284:0.000731548:0.130768:0.60101:rs13303368
1 914940 rs13303033 T C . PASS AF=0.585327 ES:SE:LP:AF:ID 0.000261724:0.000727908:0.142668:0.585327:rs13303033
1 916834 rs6694632 G A . PASS AF=0.590116 ES:SE:LP:AF:ID 0.000256582:0.000728909:0.142668:0.590116:rs6694632
1 918384 rs13303118 G T . PASS AF=0.585329 ES:SE:LP:AF:ID 0.000286532:0.000727636:0.161151:0.585329:rs13303118
1 918573 rs2341354 A G . PASS AF=0.590348 ES:SE:LP:AF:ID 0.0002632:0.000728397:0.142668:0.590348:rs2341354
1 919501 rs4970414 G T . PASS AF=0.583818 ES:SE:LP:AF:ID -0.000237676:0.000755302:0.124939:0.583818:rs4970414
1 921716 rs13303278 C A . PASS AF=0.569359 ES:SE:LP:AF:ID 0.000106522:0.000729062:0.0555173:0.569359:rs13303278
1 924528 rs34712273 C A . PASS AF=0.579293 ES:SE:LP:AF:ID 8.78522e-05:0.000731884:0.0457575:0.579293:rs34712273
1 930533 rs3128110 C G . PASS AF=0.384463 ES:SE:LP:AF:ID -4.6837e-05:0.000741724:0.0222764:0.384463:rs3128110
1 930567 rs3121574 A G . PASS AF=0.384502 ES:SE:LP:AF:ID -5.11566e-05:0.000741752:0.0222764:0.384502:rs3121574
1 930751 rs3128111 C G . PASS AF=0.383433 ES:SE:LP:AF:ID 2.86093e-05:0.000742416:0.0132283:0.383433:rs3128111
1 931166 rs2710880 A G . PASS AF=0.385119 ES:SE:LP:AF:ID -2.26957e-05:0.000741938:0.00877392:0.385119:rs2710880
1 931362 rs2799060 G A . PASS AF=0.383925 ES:SE:LP:AF:ID 6.10796e-05:0.000742461:0.0315171:0.383925:rs2799060
1 933790 rs9442392 G A . PASS AF=0.579341 ES:SE:LP:AF:ID 5.77228e-05:0.000731211:0.0268721:0.579341:rs9442392
1 936111 rs1936360 C T . PASS AF=0.574447 ES:SE:LP:AF:ID 4.59875e-05:0.00073126:0.0222764:0.574447:rs1936360
1 940005 rs2799056 A G . PASS AF=0.396767 ES:SE:LP:AF:ID 0.000231654:0.00073968:0.124939:0.396767:rs2799056
1 940096 rs4503294 C T . PASS AF=0.56692 ES:SE:LP:AF:ID -8.86717e-05:0.000729051:0.0457575:0.56692:rs4503294
1 941284 rs3128116 C T . PASS AF=0.39488 ES:SE:LP:AF:ID 9.6986e-05:0.00073964:0.0457575:0.39488:rs3128116