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_41200_X298.vcf.gz --id UKB-b:19166 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41200_X298.txt.gz --cohort_cases 3902 --cohort_controls 459108 --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": "2019-09-12T18:50:11.160119",
"bcftools_viewVersion": "1.9-74-g6af271c+htslib-1.9-64-g226b4a8",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-19166/ukb-b-19166.vcf.gz; Date=Sun May 10 00:45:48 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-19166/UKB-b-19166_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19166/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:41:01 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19166/UKB-b-19166_data.vcf.gz ...
Read summary statistics for 4495373 SNPs.
Dropped 1031 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, 1040000 SNPs remain.
After merging with regression SNP LD, 1040000 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0023 (0.0012)
Lambda GC: 1.0198
Mean Chi^2: 1.0197
Intercept: 0.9963 (0.0076)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Thu Oct 17 14:41:59 2019
Total time elapsed: 58.39sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8928,
"inflation_factor": 1.0475,
"mean_EFFECT": -6.5413e-08,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 1,
"n_p_sig": 9,
"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": 37437,
"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": 1040000,
"ldsc_nsnp_merge_regression_ld": 1040000,
"ldsc_observed_scale_h2_beta": 0.0023,
"ldsc_observed_scale_h2_se": 0.0012,
"ldsc_intercept_beta": 0.9963,
"ldsc_intercept_se": 0.0076,
"ldsc_lambda_gc": 1.0198,
"ldsc_mean_chisq": 1.0197,
"ldsc_ratio": -0.1878
}
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 | 3 | 58 | 0 | 4494349 | 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 | 4495373 | 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 | 8.658454e+00 | 5.765509e+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 | 7.858396e+07 | 5.672218e+07 | 828.0000000 | 3.172866e+07 | 6.892944e+07 | 1.147211e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -1.000000e-07 | 2.394000e-04 | -0.0015390 | -1.559000e-04 | 0.000000e+00 | 1.554000e-04 | 1.432100e-03 | ▁▁▇▂▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.325000e-04 | 4.100000e-05 | 0.0001833 | 1.973000e-04 | 2.185000e-04 | 2.597000e-04 | 6.705000e-04 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.957452e-01 | 2.894664e-01 | 0.0000000 | 2.399999e-01 | 4.899999e-01 | 7.499995e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.957457e-01 | 2.894424e-01 | 0.0000000 | 2.447331e-01 | 4.933389e-01 | 7.464382e-01 | 9.999999e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.731246e-01 | 2.268794e-01 | 0.0896980 | 1.764330e-01 | 3.164510e-01 | 5.366320e-01 | 9.103020e-01 | ▇▅▃▂▂ |
| numeric | AF_reference | 37437 | 0.9916721 | NA | NA | NA | NA | NA | NA | NA | 3.644407e-01 | 2.267308e-01 | 0.0000000 | 1.773160e-01 | 3.158950e-01 | 5.249600e-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 | 0.0001278 | 0.0003374 | 0.6999999 | 0.7048753 | 0.623765 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0007489 | 0.0003342 | 0.0250000 | 0.0250547 | 0.400401 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0008330 | 0.0005344 | 0.1199999 | 0.1190279 | 0.103556 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0005822 | 0.0003291 | 0.0769999 | 0.0769007 | 0.456846 | 0.4207270 | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0002581 | 0.0003759 | 0.4899999 | 0.4923902 | 0.240959 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | 0.0002717 | 0.0003150 | 0.3900004 | 0.3884994 | 0.116329 | 0.1417730 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0000686 | 0.0002334 | 0.7700005 | 0.7689295 | 0.515645 | 0.2751600 | NA |
| 1 | 722670 | rs116030099 | T | C | -0.0001764 | 0.0003850 | 0.6499995 | 0.6469196 | 0.101200 | 0.0413339 | NA |
| 1 | 729679 | rs4951859 | C | G | -0.0002056 | 0.0002730 | 0.4500005 | 0.4515172 | 0.843204 | 0.6399760 | NA |
| 1 | 731718 | rs142557973 | T | C | 0.0001508 | 0.0002988 | 0.6100002 | 0.6137368 | 0.122312 | 0.1543530 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51198027 | rs34939255 | A | G | -0.0000645 | 0.0002327 | 0.7800007 | 0.7815252 | 0.254562 | 0.0984425 | NA |
| 22 | 51208537 | rs72619593 | G | A | -0.0002952 | 0.0003111 | 0.3400001 | 0.3426274 | 0.120735 | 0.1142170 | NA |
| 22 | 51210289 | rs112565862 | C | T | -0.0001603 | 0.0003098 | 0.5999997 | 0.6049476 | 0.129958 | 0.1018370 | NA |
| 22 | 51211106 | rs9628250 | T | C | -0.0000876 | 0.0002307 | 0.6999999 | 0.7040507 | 0.271547 | 0.1671330 | NA |
| 22 | 51211392 | rs3888396 | T | C | -0.0001910 | 0.0003070 | 0.5300002 | 0.5338246 | 0.132638 | 0.1641370 | NA |
| 22 | 51212875 | rs2238837 | A | C | 0.0000028 | 0.0002192 | 0.9900000 | 0.9898768 | 0.331457 | 0.3724040 | NA |
| 22 | 51213613 | rs34726907 | C | T | 0.0004483 | 0.0002888 | 0.1199999 | 0.1206525 | 0.127814 | 0.1727240 | NA |
| 22 | 51216564 | rs9616970 | T | C | 0.0004054 | 0.0002876 | 0.1600000 | 0.1586906 | 0.128328 | 0.1563500 | NA |
| 22 | 51219006 | rs28729663 | G | A | 0.0003305 | 0.0002815 | 0.2399999 | 0.2403370 | 0.137950 | 0.2052720 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0000799 | 0.0002244 | 0.7199992 | 0.7217388 | 0.297974 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623765 ES:SE:LP:AF:ID 0.000127783:0.000337383:0.154902:0.623765:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID 0.000748891:0.000334243:1.60206:0.400401:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103556 ES:SE:LP:AF:ID 0.000833035:0.000534385:0.920819:0.103556:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456846 ES:SE:LP:AF:ID 0.000582168:0.000329102:1.11351:0.456846:rs6702460
1 534192 rs6680723 C T . PASS AF=0.240959 ES:SE:LP:AF:ID 0.000258075:0.000375922:0.309804:0.240959:rs6680723
1 693731 rs12238997 A G . PASS AF=0.116329 ES:SE:LP:AF:ID 0.000271667:0.000315034:0.408935:0.116329:rs12238997
1 706368 rs12029736 A G . PASS AF=0.515645 ES:SE:LP:AF:ID 6.85569e-05:0.000233365:0.113509:0.515645:rs12029736
1 722670 rs116030099 T C . PASS AF=0.1012 ES:SE:LP:AF:ID -0.000176365:0.000385038:0.187087:0.1012:rs116030099
1 729679 rs4951859 C G . PASS AF=0.843204 ES:SE:LP:AF:ID -0.000205552:0.000273018:0.346787:0.843204:rs4951859
1 731718 rs58276399 T C . PASS AF=0.122312 ES:SE:LP:AF:ID 0.000150839:0.000298841:0.21467:0.122312:rs58276399
1 734349 rs141242758 T C . PASS AF=0.121554 ES:SE:LP:AF:ID 0.000157785:0.000298967:0.221849:0.121554:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132335 ES:SE:LP:AF:ID 0.00031311:0.000294662:0.537602:0.132335:rs79010578
1 752566 rs3094315 G A . PASS AF=0.838945 ES:SE:LP:AF:ID -0.000119999:0.000264399:0.187087:0.838945:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838573 ES:SE:LP:AF:ID -0.000132553:0.000264115:0.207608:0.838573:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869776 ES:SE:LP:AF:ID -9.21899e-05:0.000283405:0.130768:0.869776:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129876 ES:SE:LP:AF:ID 9.21638e-05:0.000283985:0.124939:0.129876:rs2073813
1 754182 rs3131969 A G . PASS AF=0.869117 ES:SE:LP:AF:ID -9.36172e-05:0.000282849:0.130768:0.869117:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869215 ES:SE:LP:AF:ID -9.0935e-05:0.000282961:0.124939:0.869215:rs3131968
1 754334 rs3131967 T C . PASS AF=0.869121 ES:SE:LP:AF:ID -9.33731e-05:0.000282844:0.130768:0.869121:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838026 ES:SE:LP:AF:ID -0.000117799:0.000263382:0.187087:0.838026:rs3115859
1 754964 rs3131966 C T . PASS AF=0.838657 ES:SE:LP:AF:ID -0.000116725:0.000264122:0.180456:0.838657:rs3131966
1 755775 rs3131965 A G . PASS AF=0.83977 ES:SE:LP:AF:ID -5.40505e-05:0.000267694:0.0757207:0.83977:rs3131965
1 755890 rs3115858 A T . PASS AF=0.8694 ES:SE:LP:AF:ID -8.49951e-05:0.000282517:0.119186:0.8694:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868947 ES:SE:LP:AF:ID -7.80801e-05:0.000281806:0.107905:0.868947:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867898 ES:SE:LP:AF:ID -7.66203e-05:0.000281265:0.102373:0.867898:rs3115853
1 757734 rs4951929 C T . PASS AF=0.86909 ES:SE:LP:AF:ID -7.32845e-05:0.000282037:0.102373:0.86909:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869098 ES:SE:LP:AF:ID -7.36128e-05:0.000282058:0.102373:0.869098:rs4951862
1 758144 rs3131956 A G . PASS AF=0.869106 ES:SE:LP:AF:ID -7.38509e-05:0.000282065:0.102373:0.869106:rs3131956
1 758626 rs3131954 C T . PASS AF=0.869584 ES:SE:LP:AF:ID -7.29758e-05:0.000282839:0.09691:0.869584:rs3131954
1 760912 rs1048488 C T . PASS AF=0.838306 ES:SE:LP:AF:ID -0.000116699:0.000262882:0.180456:0.838306:rs1048488
1 761147 rs3115850 T C . PASS AF=0.838427 ES:SE:LP:AF:ID -0.000117803:0.000263068:0.187087:0.838427:rs3115850
1 761732 rs2286139 C T . PASS AF=0.862254 ES:SE:LP:AF:ID -6.91682e-05:0.000281045:0.091515:0.862254:rs2286139
1 763394 rs3115847 G A . PASS AF=0.706753 ES:SE:LP:AF:ID 5.4795e-05:0.000273598:0.0757207:0.706753:rs3115847
1 766007 rs61768174 A C . PASS AF=0.105145 ES:SE:LP:AF:ID 0.000301514:0.00031518:0.468521:0.105145:rs61768174
1 768253 rs2977608 A C . PASS AF=0.761297 ES:SE:LP:AF:ID -1.45259e-05:0.000223304:0.0222764:0.761297:rs2977608
1 768448 rs12562034 G A . PASS AF=0.10649 ES:SE:LP:AF:ID -1.23181e-05:0.000307782:0.0132283:0.10649:rs12562034
1 769223 rs60320384 C G . PASS AF=0.129581 ES:SE:LP:AF:ID 8.16362e-05:0.000283815:0.113509:0.129581:rs60320384
1 771823 rs2977605 T C . PASS AF=0.868906 ES:SE:LP:AF:ID -6.74799e-05:0.000282299:0.091515:0.868906:rs2977605
1 771967 rs59066358 G A . PASS AF=0.129682 ES:SE:LP:AF:ID 7.65487e-05:0.000283632:0.102373:0.129682:rs59066358
1 772755 rs2905039 A C . PASS AF=0.868916 ES:SE:LP:AF:ID -6.90965e-05:0.000282304:0.091515:0.868916:rs2905039
1 776546 rs12124819 A G . PASS AF=0.265385 ES:SE:LP:AF:ID -0.000277508:0.000249439:0.568636:0.265385:rs12124819
1 777122 rs2980319 A T . PASS AF=0.870039 ES:SE:LP:AF:ID -8.07897e-05:0.00028288:0.107905:0.870039:rs2980319
1 777232 rs112618790 C T . PASS AF=0.095141 ES:SE:LP:AF:ID -0.000314651:0.000327853:0.468521:0.095141:rs112618790
1 778745 rs1055606 A G . PASS AF=0.12858 ES:SE:LP:AF:ID 8.36471e-05:0.000283997:0.113509:0.12858:rs1055606
1 779322 rs4040617 A G . PASS AF=0.128877 ES:SE:LP:AF:ID 7.92765e-05:0.000283516:0.107905:0.128877:rs4040617
1 780785 rs2977612 T A . PASS AF=0.86878 ES:SE:LP:AF:ID -8.29351e-05:0.000282126:0.113509:0.86878:rs2977612
1 781845 rs61768199 A G . PASS AF=0.101873 ES:SE:LP:AF:ID 0.000327538:0.000319676:0.508638:0.101873:rs61768199
1 782981 rs6594026 C T . PASS AF=0.129518 ES:SE:LP:AF:ID 8.98287e-05:0.000283424:0.124939:0.129518:rs6594026
1 785050 rs2905062 G A . PASS AF=0.868533 ES:SE:LP:AF:ID -6.54182e-05:0.00028206:0.0861861:0.868533:rs2905062
1 785989 rs2980300 T C . PASS AF=0.868474 ES:SE:LP:AF:ID -7.49529e-05:0.000282236:0.102373:0.868474:rs2980300