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_1140875408.vcf.gz --id UKB-b:15895 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20003_1140875408.txt.gz --cohort_cases 5243 --cohort_controls 457690 --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": "2019-09-12T16:37:12.645694",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-15895/ukb-b-15895.vcf.gz; Date=Sun May 10 11:07:25 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-15895/UKB-b-15895_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-15895/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:44:33 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-15895/UKB-b-15895_data.vcf.gz ...
Read summary statistics for 4979668 SNPs.
Dropped 1411 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, 1112788 SNPs remain.
After merging with regression SNP LD, 1112788 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0163 (0.0038)
Lambda GC: 1.1099
Mean Chi^2: 1.1915
Intercept: 1.0241 (0.0087)
Ratio: 0.1259 (0.0453)
Analysis finished at Thu Oct 17 14:45:37 2019
Total time elapsed: 1.0m:3.96sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9059,
"inflation_factor": 1.0966,
"mean_EFFECT": -8.5389e-08,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 16,
"n_p_sig": 2592,
"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": 42471,
"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": 1112788,
"ldsc_nsnp_merge_regression_ld": 1112788,
"ldsc_observed_scale_h2_beta": 0.0163,
"ldsc_observed_scale_h2_se": 0.0038,
"ldsc_intercept_beta": 1.0241,
"ldsc_intercept_se": 0.0087,
"ldsc_lambda_gc": 1.1099,
"ldsc_mean_chisq": 1.1915,
"ldsc_ratio": 0.1258
}
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 | TRUE |
| 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 | 3 | 58 | 0 | 4978267 | 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 | 4979668 | 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 | 8.667230e+00 | 5.766071e+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 | 7.857924e+07 | 5.665029e+07 | 828.0000000 | 3.188450e+07 | 6.896688e+07 | 1.146210e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -1.000000e-07 | 3.129000e-04 | -0.0106862 | -1.953000e-04 | -1.800000e-06 | 1.939000e-04 | 1.073590e-02 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.819000e-04 | 6.240000e-05 | 0.0002112 | 2.290000e-04 | 2.597000e-04 | 3.219000e-04 | 8.019000e-04 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.820407e-01 | 2.939355e-01 | 0.0000000 | 2.200002e-01 | 4.799997e-01 | 7.400005e-01 | 1.000000e+00 | ▇▇▇▆▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.820437e-01 | 2.939091e-01 | 0.0000000 | 2.237331e-01 | 4.757892e-01 | 7.367557e-01 | 9.999995e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.526240e-01 | 2.379262e-01 | 0.0667560 | 1.476260e-01 | 2.875115e-01 | 5.190420e-01 | 9.332440e-01 | ▇▅▃▂▂ |
| numeric | AF_reference | 42471 | 0.9914711 | NA | NA | NA | NA | NA | NA | NA | 3.459086e-01 | 2.348061e-01 | 0.0000000 | 1.521570e-01 | 2.895370e-01 | 5.083870e-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 | -0.0001331 | 0.0003886 | 0.7300002 | 0.7320004 | 0.623763 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0001467 | 0.0003850 | 0.6999999 | 0.7030777 | 0.400401 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | -0.0004369 | 0.0006155 | 0.4799997 | 0.4778201 | 0.103556 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0002655 | 0.0003791 | 0.4799997 | 0.4837282 | 0.456851 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | -0.0001874 | 0.0007474 | 0.8000000 | 0.8019907 | 0.074508 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0001086 | 0.0004330 | 0.8000000 | 0.8020489 | 0.240960 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | 0.0002490 | 0.0005402 | 0.6400000 | 0.6447744 | 0.913473 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | 0.0001584 | 0.0003629 | 0.6600001 | 0.6625431 | 0.116325 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | -0.0006790 | 0.0005318 | 0.2000000 | 0.2016227 | 0.067285 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0000108 | 0.0002688 | 0.9699999 | 0.9678244 | 0.515650 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51213613 | rs34726907 | C | T | -0.0001518 | 0.0003327 | 0.6499995 | 0.6481926 | 0.127816 | 0.1727240 | NA |
| 22 | 51216564 | rs9616970 | T | C | -0.0001249 | 0.0003313 | 0.7099994 | 0.7062022 | 0.128330 | 0.1563500 | NA |
| 22 | 51217954 | rs9616974 | G | A | -0.0003240 | 0.0004204 | 0.4400003 | 0.4409318 | 0.073314 | 0.0621006 | NA |
| 22 | 51218224 | rs9616975 | C | A | -0.0003258 | 0.0004206 | 0.4400003 | 0.4384801 | 0.073336 | 0.0619010 | NA |
| 22 | 51218377 | rs2519461 | G | C | -0.0003364 | 0.0004201 | 0.4199997 | 0.4232315 | 0.073625 | 0.0826677 | NA |
| 22 | 51219006 | rs28729663 | G | A | -0.0001517 | 0.0003243 | 0.6400000 | 0.6398557 | 0.137953 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | -0.0003306 | 0.0004209 | 0.4299995 | 0.4321821 | 0.073747 | 0.0654952 | NA |
| 22 | 51221731 | rs115055839 | T | C | -0.0003192 | 0.0004212 | 0.4500005 | 0.4485422 | 0.073238 | 0.0625000 | NA |
| 22 | 51229805 | rs9616985 | T | C | -0.0003494 | 0.0004227 | 0.4100001 | 0.4085035 | 0.073073 | 0.0730831 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0005467 | 0.0002585 | 0.0340001 | 0.0344776 | 0.297971 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623763 ES:SE:LP:AF:ID -0.000133088:0.000388617:0.136677:0.623763:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID -0.000146749:0.000384997:0.154902:0.400401:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103556 ES:SE:LP:AF:ID -0.000436914:0.000615534:0.318759:0.103556:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456851 ES:SE:LP:AF:ID -0.000265479:0.000379083:0.318759:0.456851:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074508 ES:SE:LP:AF:ID -0.000187438:0.000747445:0.09691:0.074508:rs8179466
1 534192 rs6680723 C T . PASS AF=0.24096 ES:SE:LP:AF:ID -0.000108553:0.000433006:0.09691:0.24096:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913473 ES:SE:LP:AF:ID 0.00024905:0.000540199:0.19382:0.913473:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116325 ES:SE:LP:AF:ID 0.000158363:0.000362881:0.180456:0.116325:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067285 ES:SE:LP:AF:ID -0.000679028:0.000531761:0.69897:0.067285:rs72631875
1 706368 rs12029736 A G . PASS AF=0.51565 ES:SE:LP:AF:ID 1.08428e-05:0.000268805:0.0132283:0.51565:rs12029736
1 722670 rs116030099 T C . PASS AF=0.101199 ES:SE:LP:AF:ID 2.78782e-05:0.000443511:0.0222764:0.101199:rs116030099
1 729679 rs4951859 C G . PASS AF=0.843212 ES:SE:LP:AF:ID -0.000216329:0.000314483:0.309804:0.843212:rs4951859
1 731718 rs58276399 T C . PASS AF=0.122307 ES:SE:LP:AF:ID 0.000135276:0.000344229:0.161151:0.122307:rs58276399
1 734349 rs141242758 T C . PASS AF=0.121549 ES:SE:LP:AF:ID 0.000137882:0.000344374:0.161151:0.121549:rs141242758
1 736289 rs79010578 T A . PASS AF=0.13233 ES:SE:LP:AF:ID 0.000218792:0.000339414:0.283997:0.13233:rs79010578
1 752566 rs3094315 G A . PASS AF=0.838951 ES:SE:LP:AF:ID -0.000231739:0.000304554:0.346787:0.838951:rs3094315
1 752721 rs3131972 A G . PASS AF=0.83858 ES:SE:LP:AF:ID -0.000258329:0.000304227:0.39794:0.83858:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869781 ES:SE:LP:AF:ID -0.000186754:0.000326447:0.244125:0.869781:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129871 ES:SE:LP:AF:ID 0.000248871:0.000327116:0.346787:0.129871:rs2073813
1 754182 rs3131969 A G . PASS AF=0.869123 ES:SE:LP:AF:ID -0.000208626:0.000325808:0.283997:0.869123:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869221 ES:SE:LP:AF:ID -0.000192095:0.000325937:0.251812:0.869221:rs3131968
1 754334 rs3131967 T C . PASS AF=0.869126 ES:SE:LP:AF:ID -0.000208411:0.000325801:0.283997:0.869126:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838033 ES:SE:LP:AF:ID -0.00027006:0.000303382:0.431798:0.838033:rs3115859
1 754964 rs3131966 C T . PASS AF=0.838664 ES:SE:LP:AF:ID -0.000257972:0.000304235:0.39794:0.838664:rs3131966
1 755775 rs3131965 A G . PASS AF=0.839777 ES:SE:LP:AF:ID -0.000209242:0.00030835:0.30103:0.839777:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869405 ES:SE:LP:AF:ID -0.000173821:0.000325425:0.229148:0.869405:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868952 ES:SE:LP:AF:ID -0.000174086:0.000324606:0.229148:0.868952:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867905 ES:SE:LP:AF:ID -0.000219545:0.000323984:0.30103:0.867905:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869095 ES:SE:LP:AF:ID -0.00018065:0.000324872:0.236572:0.869095:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869104 ES:SE:LP:AF:ID -0.000180377:0.000324897:0.236572:0.869104:rs4951862
1 758144 rs3131956 A G . PASS AF=0.869112 ES:SE:LP:AF:ID -0.000181478:0.000324904:0.236572:0.869112:rs3131956
1 758626 rs3131954 C T . PASS AF=0.869589 ES:SE:LP:AF:ID -0.000183196:0.000325796:0.244125:0.869589:rs3131954
1 760912 rs1048488 C T . PASS AF=0.838313 ES:SE:LP:AF:ID -0.000270171:0.000302807:0.431798:0.838313:rs1048488
1 761147 rs3115850 T C . PASS AF=0.838434 ES:SE:LP:AF:ID -0.000270423:0.000303021:0.431798:0.838434:rs3115850
1 761732 rs2286139 C T . PASS AF=0.862261 ES:SE:LP:AF:ID -0.000192418:0.000323729:0.259637:0.862261:rs2286139
1 763394 rs3115847 G A . PASS AF=0.706753 ES:SE:LP:AF:ID -0.000334056:0.000315148:0.537602:0.706753:rs3115847
1 766007 rs61768174 A C . PASS AF=0.105142 ES:SE:LP:AF:ID 6.93241e-05:0.000363048:0.0705811:0.105142:rs61768174
1 768253 rs2977608 A C . PASS AF=0.761304 ES:SE:LP:AF:ID -0.000122669:0.000257217:0.200659:0.761304:rs2977608
1 768448 rs12562034 G A . PASS AF=0.106488 ES:SE:LP:AF:ID 3.58253e-05:0.000354523:0.0362122:0.106488:rs12562034
1 769223 rs60320384 C G . PASS AF=0.129576 ES:SE:LP:AF:ID 0.00021723:0.00032692:0.29243:0.129576:rs60320384
1 771823 rs2977605 T C . PASS AF=0.868911 ES:SE:LP:AF:ID -0.000159179:0.000325174:0.207608:0.868911:rs2977605
1 771967 rs59066358 G A . PASS AF=0.129677 ES:SE:LP:AF:ID 0.00020865:0.000326709:0.283997:0.129677:rs59066358
1 772755 rs2905039 A C . PASS AF=0.868921 ES:SE:LP:AF:ID -0.000160824:0.00032518:0.207608:0.868921:rs2905039
1 776546 rs12124819 A G . PASS AF=0.26539 ES:SE:LP:AF:ID -4.37288e-06:0.000287316:0.00436481:0.26539:rs12124819
1 777122 rs2980319 A T . PASS AF=0.870044 ES:SE:LP:AF:ID -0.000236318:0.000325844:0.327902:0.870044:rs2980319
1 777232 rs112618790 C T . PASS AF=0.095139 ES:SE:LP:AF:ID 0.000231899:0.00037764:0.267606:0.095139:rs112618790
1 778745 rs1055606 A G . PASS AF=0.128576 ES:SE:LP:AF:ID 0.000270839:0.00032713:0.387216:0.128576:rs1055606
1 779322 rs4040617 A G . PASS AF=0.128873 ES:SE:LP:AF:ID 0.00025597:0.000326575:0.366532:0.128873:rs4040617
1 780785 rs2977612 T A . PASS AF=0.868786 ES:SE:LP:AF:ID -0.000260942:0.000324975:0.376751:0.868786:rs2977612
1 781845 rs61768199 A G . PASS AF=0.10187 ES:SE:LP:AF:ID 0.000177739:0.000368228:0.200659:0.10187:rs61768199