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_20004_1482.vcf.gz --id UKB-b:7564 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20004_1482.txt.gz --cohort_cases 1125 --cohort_controls 461808 --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": "2019-09-13T01:19:54.538949",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-7564/ukb-b-7564.vcf.gz; Date=Sun May 10 08:49:49 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-7564/UKB-b-7564_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-7564/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:40:17 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-7564/UKB-b-7564_data.vcf.gz ...
Read summary statistics for 1703109 SNPs.
Dropped 163 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, 439263 SNPs remain.
After merging with regression SNP LD, 439263 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0048 (0.0014)
Lambda GC: 1.0339
Mean Chi^2: 1.0364
Intercept: 0.9813 (0.0117)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Thu Oct 17 14:40:55 2019
Total time elapsed: 38.47sQC metrics
Metrics
Metrics
{
"af_correlation": 0.6382,
"inflation_factor": 1.0475,
"mean_EFFECT": -1.8813e-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": 13504,
"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": 439263,
"ldsc_nsnp_merge_regression_ld": 439263,
"ldsc_observed_scale_h2_beta": 0.0048,
"ldsc_observed_scale_h2_se": 0.0014,
"ldsc_intercept_beta": 0.9813,
"ldsc_intercept_se": 0.0117,
"ldsc_lambda_gc": 1.0339,
"ldsc_mean_chisq": 1.0364,
"ldsc_ratio": -0.5137
}
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 | 1.000000 | 4 | 58 | 0 | 1702948 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | REF | 0 | 1.000000 | 1 | 1 | 0 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | ALT | 0 | 1.000000 | 1 | 1 | 0 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | N | 1703109 | 0.000000 | NA | NA | NA | NA | NA | NaN | : | NA | NA | NA | NA | NA | NA | NA | NA |
| numeric | CHROM | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 8.659349e+00 | 5.764525e+00 | 1.00000e+00 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.200000e+01 | ▇▅▅▂▂ |
| numeric | POS | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 7.862158e+07 | 5.656974e+07 | 1.23330e+04 | 3.176571e+07 | 6.923238e+07 | 1.146621e+08 | 2.491722e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | -2.000000e-07 | 1.080000e-04 | -5.00500e-04 | -7.290000e-05 | -2.000000e-07 | 7.260000e-05 | 5.015000e-04 | ▁▂▇▂▁ |
| numeric | SE | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 1.055000e-04 | 3.200000e-06 | 9.87000e-05 | 1.032000e-04 | 1.048000e-04 | 1.073000e-04 | 2.006000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 4.929955e-01 | 2.915574e-01 | 1.30000e-06 | 2.399999e-01 | 4.899999e-01 | 7.499995e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 4.929951e-01 | 2.915320e-01 | 1.30000e-06 | 2.373029e-01 | 4.903025e-01 | 7.468777e-01 | 9.999998e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 4.775251e-01 | 1.084122e-01 | 3.11112e-01 | 3.824370e-01 | 4.660590e-01 | 5.669640e-01 | 6.888880e-01 | ▇▇▆▅▅ |
| numeric | AF_reference | 13504 | 0.992071 | NA | NA | NA | NA | NA | NA | NA | 4.563657e-01 | 1.512749e-01 | 1.99700e-04 | 3.418530e-01 | 4.492810e-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 | -0.0001051 | 0.0001817 | 0.5600000 | 0.5630111 | 0.623763 | 0.782149 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0000282 | 0.0001800 | 0.8800001 | 0.8754288 | 0.400401 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0001533 | 0.0001773 | 0.3900004 | 0.3872501 | 0.456851 | 0.420727 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0001771 | 0.0001257 | 0.1600000 | 0.1588518 | 0.515650 | 0.275160 | NA |
| 1 | 814495 | rs74461805 | C | A | 0.0001487 | 0.0001724 | 0.3900004 | 0.3881892 | 0.340397 | NA | NA |
| 1 | 840753 | rs4970382 | T | C | -0.0001517 | 0.0001044 | 0.1499999 | 0.1464226 | 0.400106 | 0.468850 | NA |
| 1 | 843405 | rs11516185 | A | G | -0.0001889 | 0.0001297 | 0.1499999 | 0.1450290 | 0.362599 | 0.375399 | NA |
| 1 | 850218 | rs6664536 | T | A | 0.0000695 | 0.0001041 | 0.5000000 | 0.5043824 | 0.590333 | 0.345248 | NA |
| 1 | 850371 | rs6679046 | G | T | 0.0001004 | 0.0001047 | 0.3400001 | 0.3376527 | 0.603726 | 0.508786 | NA |
| 1 | 850780 | rs6657440 | C | T | 0.0001124 | 0.0001047 | 0.2800000 | 0.2831614 | 0.603944 | 0.560304 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51158499 | rs8136930 | T | G | -0.0000305 | 0.0001028 | 0.7700005 | 0.7665433 | 0.461648 | 0.544728 | NA |
| 22 | 51161019 | rs5770994 | C | T | -0.0001029 | 0.0001024 | 0.3100002 | 0.3149399 | 0.482193 | 0.425719 | NA |
| 22 | 51163039 | rs715584 | G | T | 0.0000140 | 0.0001040 | 0.8900000 | 0.8930267 | 0.426984 | 0.473642 | NA |
| 22 | 51164109 | rs5770995 | G | C | -0.0000139 | 0.0001034 | 0.8900000 | 0.8930589 | 0.452716 | 0.510982 | NA |
| 22 | 51164115 | rs5770996 | C | T | -0.0000044 | 0.0001033 | 0.9699999 | 0.9657207 | 0.456928 | 0.514776 | NA |
| 22 | 51174048 | rs9628245 | G | C | -0.0001015 | 0.0001168 | 0.3800004 | 0.3845853 | 0.380130 | 0.433107 | NA |
| 22 | 51186143 | rs2879914 | T | C | -0.0000419 | 0.0001100 | 0.6999999 | 0.7036691 | 0.381826 | 0.273363 | NA |
| 22 | 51186228 | rs3865766 | C | T | -0.0001768 | 0.0001073 | 0.0990011 | 0.0991779 | 0.451063 | 0.453275 | NA |
| 22 | 51197266 | rs61290853 | A | G | -0.0001603 | 0.0001107 | 0.1499999 | 0.1477151 | 0.386333 | 0.422923 | NA |
| 22 | 51212875 | rs2238837 | A | C | -0.0000533 | 0.0001181 | 0.6499995 | 0.6517146 | 0.331455 | 0.372404 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623763 ES:SE:LP:AF:ID -0.000105104:0.000181723:0.251812:0.623763:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID -2.82227e-05:0.00018003:0.0555173:0.400401:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456851 ES:SE:LP:AF:ID 0.000153266:0.000177265:0.408935:0.456851:rs6702460
1 706368 rs12029736 A G . PASS AF=0.51565 ES:SE:LP:AF:ID 0.0001771:0.000125697:0.79588:0.51565:rs12029736
1 814495 rs74461805 C A . PASS AF=0.340397 ES:SE:LP:AF:ID 0.000148727:0.000172356:0.408935:0.340397:rs74461805
1 840753 rs4970382 T C . PASS AF=0.400106 ES:SE:LP:AF:ID -0.000151678:0.000104441:0.823909:0.400106:rs4970382
1 843405 rs11516185 A G . PASS AF=0.362599 ES:SE:LP:AF:ID -0.000188944:0.000129652:0.823909:0.362599:rs11516185
1 850218 rs6664536 T A . PASS AF=0.590333 ES:SE:LP:AF:ID 6.95216e-05:0.000104135:0.30103:0.590333:rs6664536
1 850371 rs6679046 G T . PASS AF=0.603726 ES:SE:LP:AF:ID 0.000100406:0.000104719:0.468521:0.603726:rs6679046
1 850780 rs6657440 C T . PASS AF=0.603944 ES:SE:LP:AF:ID 0.000112373:0.000104704:0.552842:0.603944:rs6657440
1 852037 rs4970463 G A . PASS AF=0.589688 ES:SE:LP:AF:ID 7.21139e-05:0.000104304:0.309804:0.589688:rs4970463
1 852063 rs28436996 G A . PASS AF=0.589667 ES:SE:LP:AF:ID 7.10837e-05:0.000104257:0.30103:0.589667:rs28436996
1 852875 rs13303369 C T . PASS AF=0.607674 ES:SE:LP:AF:ID 0.000105071:0.000104938:0.49485:0.607674:rs13303369
1 853954 rs1806509 C A . PASS AF=0.607833 ES:SE:LP:AF:ID 0.000109281:0.000104953:0.522879:0.607833:rs1806509
1 854777 rs13303019 A G . PASS AF=0.610318 ES:SE:LP:AF:ID 0.000121237:0.000105056:0.60206:0.610318:rs13303019
1 854978 rs13303057 A C . PASS AF=0.603286 ES:SE:LP:AF:ID 0.000109112:0.000104745:0.522879:0.603286:rs13303057
1 855075 rs6673914 C G . PASS AF=0.610338 ES:SE:LP:AF:ID 0.000122285:0.000105058:0.619789:0.610338:rs6673914
1 856099 rs28534711 T G . PASS AF=0.389935 ES:SE:LP:AF:ID -0.000119492:0.000105078:0.585027:0.389935:rs28534711
1 856108 rs28742275 A G . PASS AF=0.389918 ES:SE:LP:AF:ID -0.000119287:0.000105083:0.585027:0.389918:rs28742275
1 856476 rs4040605 A G . PASS AF=0.350351 ES:SE:LP:AF:ID -0.000211042:0.00010795:1.29243:0.350351:rs4040605
1 866893 rs2880024 T C . PASS AF=0.610554 ES:SE:LP:AF:ID 0.000270565:0.000105648:2:0.610554:rs2880024
1 875770 rs4970379 A G . PASS AF=0.600084 ES:SE:LP:AF:ID 0.000227231:0.000106526:1.48149:0.600084:rs4970379
1 881627 rs2272757 G A . PASS AF=0.65239 ES:SE:LP:AF:ID 0.000315741:0.00010761:2.48149:0.65239:rs2272757
1 891059 rs13303065 C T . PASS AF=0.652428 ES:SE:LP:AF:ID 0.000315096:0.000107593:2.46852:0.652428:rs13303065
1 891945 rs13303106 A G . PASS AF=0.65249 ES:SE:LP:AF:ID 0.000313914:0.000107718:2.4437:0.65249:rs13303106
1 903245 rs28690976 A G . PASS AF=0.566937 ES:SE:LP:AF:ID 0.000223266:0.000106988:1.4318:0.566937:rs28690976
1 909073 rs3892467 C T . PASS AF=0.386683 ES:SE:LP:AF:ID 0.000359415:0.000106697:3.11919:0.386683:rs3892467
1 909238 rs3829740 G C . PASS AF=0.571412 ES:SE:LP:AF:ID 9.44917e-05:0.000103332:0.443698:0.571412:rs3829740
1 910394 rs28477686 C T . PASS AF=0.324456 ES:SE:LP:AF:ID -0.000156149:0.000112002:0.79588:0.324456:rs28477686
1 912049 rs7367995 T C . PASS AF=0.58525 ES:SE:LP:AF:ID 0.000272012:0.00010438:2.03621:0.58525:rs7367995
1 913889 rs2340596 G A . PASS AF=0.599213 ES:SE:LP:AF:ID 0.000340503:0.00010455:2.95861:0.599213:rs2340596
1 914333 rs13302979 C G . PASS AF=0.602521 ES:SE:LP:AF:ID 0.000352557:0.000104867:3.11351:0.602521:rs13302979
1 914852 rs13303368 G C . PASS AF=0.600078 ES:SE:LP:AF:ID 0.000341787:0.000104665:2.95861:0.600078:rs13303368
1 914940 rs13303033 T C . PASS AF=0.584292 ES:SE:LP:AF:ID 0.000337593:0.000104078:2.92082:0.584292:rs13303033
1 916834 rs6694632 G A . PASS AF=0.589106 ES:SE:LP:AF:ID 0.000336876:0.000104237:2.92082:0.589106:rs6694632
1 918384 rs13303118 G T . PASS AF=0.584205 ES:SE:LP:AF:ID 0.000331488:0.00010403:2.85387:0.584205:rs13303118
1 918573 rs2341354 A G . PASS AF=0.589331 ES:SE:LP:AF:ID 0.000334539:0.000104157:2.88606:0.589331:rs2341354
1 919501 rs4970414 G T . PASS AF=0.583932 ES:SE:LP:AF:ID 0.000349334:0.000107717:2.92082:0.583932:rs4970414
1 921716 rs13303278 C A . PASS AF=0.567888 ES:SE:LP:AF:ID 0.000280573:0.000103943:2.16115:0.567888:rs13303278
1 924528 rs34712273 C A . PASS AF=0.578492 ES:SE:LP:AF:ID 0.000279657:0.000104242:2.13668:0.578492:rs34712273
1 930533 rs3128110 C G . PASS AF=0.386078 ES:SE:LP:AF:ID -0.000211228:0.000105574:1.34679:0.386078:rs3128110
1 930567 rs3121574 A G . PASS AF=0.38612 ES:SE:LP:AF:ID -0.000211724:0.000105576:1.34679:0.38612:rs3121574
1 930751 rs3128111 C G . PASS AF=0.385075 ES:SE:LP:AF:ID -0.000210143:0.00010564:1.3279:0.385075:rs3128111
1 931166 rs2710880 A G . PASS AF=0.386762 ES:SE:LP:AF:ID -0.000205709:0.000105593:1.29243:0.386762:rs2710880
1 931362 rs2799060 G A . PASS AF=0.385568 ES:SE:LP:AF:ID -0.000203938:0.000105645:1.26761:0.385568:rs2799060
1 933790 rs9442392 G A . PASS AF=0.578607 ES:SE:LP:AF:ID 0.000253817:0.000104207:1.82391:0.578607:rs9442392
1 936111 rs1936360 C T . PASS AF=0.573546 ES:SE:LP:AF:ID 0.000242302:0.000104181:1.69897:0.573546:rs1936360
1 940005 rs2799056 A G . PASS AF=0.399259 ES:SE:LP:AF:ID -0.000212917:0.000105208:1.36653:0.399259:rs2799056
1 940096 rs4503294 C T . PASS AF=0.565311 ES:SE:LP:AF:ID 0.000261854:0.000103883:1.92082:0.565311:rs4503294
1 941284 rs3128116 C T . PASS AF=0.397393 ES:SE:LP:AF:ID -0.000211046:0.00010523:1.34679:0.397393:rs3128116