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_41245_1820.vcf.gz --id UKB-b:7763 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41245_1820.txt.gz --cohort_cases 3899 --cohort_controls 457255 --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": "2019-09-13T13:15:58.403838",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-7763/ukb-b-7763.vcf.gz; Date=Sun May 10 09:20:25 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-7763/UKB-b-7763_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-7763/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:40:54 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-7763/UKB-b-7763_data.vcf.gz ...
Read summary statistics for 4494064 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, 1039779 SNPs remain.
After merging with regression SNP LD, 1039779 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0026 (0.0011)
Lambda GC: 1.0324
Mean Chi^2: 1.0355
Intercept: 1.0102 (0.0089)
Ratio: 0.2863 (0.252)
Analysis finished at Thu Oct 17 14:41:49 2019
Total time elapsed: 55.55sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8928,
"inflation_factor": 1.0475,
"mean_EFFECT": 2.2543e-06,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 2,
"n_p_sig": 465,
"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": 37434,
"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": 1039779,
"ldsc_nsnp_merge_regression_ld": 1039779,
"ldsc_observed_scale_h2_beta": 0.0026,
"ldsc_observed_scale_h2_se": 0.0011,
"ldsc_intercept_beta": 1.0102,
"ldsc_intercept_se": 0.0089,
"ldsc_lambda_gc": 1.0324,
"ldsc_mean_chisq": 1.0355,
"ldsc_ratio": 0.2873
}
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 | 4493040 | 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 | 4494064 | 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 | 8.658230e+00 | 5.765396e+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 | 7.858519e+07 | 5.672277e+07 | 828.0000000 | 3.172913e+07 | 6.893219e+07 | 1.147222e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.300000e-06 | 2.423000e-04 | -0.0014727 | -1.558000e-04 | 1.100000e-06 | 1.580000e-04 | 2.429700e-03 | ▁▇▅▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.333000e-04 | 4.110000e-05 | 0.0001840 | 1.980000e-04 | 2.193000e-04 | 2.606000e-04 | 6.730000e-04 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.938873e-01 | 2.902861e-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 | 4.938888e-01 | 2.902580e-01 | 0.0000000 | 2.407969e-01 | 4.918465e-01 | 7.455313e-01 | 9.999998e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.731806e-01 | 2.268461e-01 | 0.0897670 | 1.765130e-01 | 3.165300e-01 | 5.366770e-01 | 9.102330e-01 | ▇▅▃▂▂ |
| numeric | AF_reference | 37434 | 0.9916703 | NA | NA | NA | NA | NA | NA | NA | 3.644913e-01 | 2.267071e-01 | 0.0000000 | 1.773160e-01 | 3.158950e-01 | 5.251600e-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 | -0.0002992 | 0.0003386 | 0.3800004 | 0.3768677 | 0.623775 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0004713 | 0.0003355 | 0.1600000 | 0.1600893 | 0.400405 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0004818 | 0.0005364 | 0.3700002 | 0.3690423 | 0.103552 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0004792 | 0.0003303 | 0.1499999 | 0.1467803 | 0.456862 | 0.4207270 | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0000235 | 0.0003773 | 0.9500000 | 0.9503737 | 0.240948 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | 0.0001990 | 0.0003162 | 0.5300002 | 0.5291753 | 0.116323 | 0.1417730 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0001764 | 0.0002342 | 0.4500005 | 0.4512689 | 0.515676 | 0.2751600 | NA |
| 1 | 722670 | rs116030099 | T | C | -0.0000185 | 0.0003864 | 0.9599999 | 0.9617610 | 0.101207 | 0.0413339 | NA |
| 1 | 729679 | rs4951859 | C | G | -0.0002838 | 0.0002740 | 0.2999998 | 0.3003468 | 0.843199 | 0.6399760 | NA |
| 1 | 731718 | rs142557973 | T | C | 0.0001279 | 0.0003000 | 0.6700003 | 0.6697972 | 0.122311 | 0.1543530 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51198027 | rs34939255 | A | G | -0.0003768 | 0.0002336 | 0.1100001 | 0.1067068 | 0.254582 | 0.0984425 | NA |
| 22 | 51208537 | rs72619593 | G | A | 0.0001382 | 0.0003122 | 0.6600001 | 0.6580964 | 0.120740 | 0.1142170 | NA |
| 22 | 51210289 | rs112565862 | C | T | 0.0004229 | 0.0003110 | 0.1700000 | 0.1739239 | 0.129931 | 0.1018370 | NA |
| 22 | 51211106 | rs9628250 | T | C | -0.0003597 | 0.0002316 | 0.1199999 | 0.1204034 | 0.271574 | 0.1671330 | NA |
| 22 | 51211392 | rs3888396 | T | C | 0.0004609 | 0.0003082 | 0.1299999 | 0.1348403 | 0.132613 | 0.1641370 | NA |
| 22 | 51212875 | rs2238837 | A | C | 0.0002910 | 0.0002200 | 0.1900002 | 0.1860037 | 0.331442 | 0.3724040 | NA |
| 22 | 51213613 | rs34726907 | C | T | 0.0000190 | 0.0002899 | 0.9500000 | 0.9477938 | 0.127811 | 0.1727240 | NA |
| 22 | 51216564 | rs9616970 | T | C | 0.0000053 | 0.0002887 | 0.9900000 | 0.9853664 | 0.128322 | 0.1563500 | NA |
| 22 | 51219006 | rs28729663 | G | A | -0.0000058 | 0.0002825 | 0.9800000 | 0.9835586 | 0.137946 | 0.2052720 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0003396 | 0.0002253 | 0.1299999 | 0.1316871 | 0.297972 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623775 ES:SE:LP:AF:ID -0.000299191:0.000338573:0.420216:0.623775:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400405 ES:SE:LP:AF:ID 0.000471305:0.000335503:0.79588:0.400405:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103552 ES:SE:LP:AF:ID 0.000481808:0.000536374:0.431798:0.103552:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456862 ES:SE:LP:AF:ID 0.000479243:0.000330285:0.823909:0.456862:rs6702460
1 534192 rs6680723 C T . PASS AF=0.240948 ES:SE:LP:AF:ID 2.34817e-05:0.000377292:0.0222764:0.240948:rs6680723
1 693731 rs12238997 A G . PASS AF=0.116323 ES:SE:LP:AF:ID 0.00019898:0.00031621:0.275724:0.116323:rs12238997
1 706368 rs12029736 A G . PASS AF=0.515676 ES:SE:LP:AF:ID -0.000176448:0.000234233:0.346787:0.515676:rs12029736
1 722670 rs116030099 T C . PASS AF=0.101207 ES:SE:LP:AF:ID -1.85273e-05:0.000386438:0.0177288:0.101207:rs116030099
1 729679 rs4951859 C G . PASS AF=0.843199 ES:SE:LP:AF:ID -0.000283806:0.000274026:0.522879:0.843199:rs4951859
1 731718 rs58276399 T C . PASS AF=0.122311 ES:SE:LP:AF:ID 0.000127907:0.000299951:0.173925:0.122311:rs58276399
1 734349 rs141242758 T C . PASS AF=0.121554 ES:SE:LP:AF:ID 0.000115971:0.000300077:0.154902:0.121554:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132333 ES:SE:LP:AF:ID 0.00034899:0.00029576:0.619789:0.132333:rs79010578
1 752566 rs3094315 G A . PASS AF=0.838942 ES:SE:LP:AF:ID -0.000241549:0.000265375:0.443698:0.838942:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838569 ES:SE:LP:AF:ID -0.00022259:0.000265089:0.39794:0.838569:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869777 ES:SE:LP:AF:ID -0.000179773:0.000284455:0.275724:0.869777:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129876 ES:SE:LP:AF:ID 0.000157809:0.000285032:0.236572:0.129876:rs2073813
1 754182 rs3131969 A G . PASS AF=0.869117 ES:SE:LP:AF:ID -0.000170117:0.000283896:0.259637:0.869117:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869215 ES:SE:LP:AF:ID -0.000158317:0.000284008:0.236572:0.869215:rs3131968
1 754334 rs3131967 T C . PASS AF=0.869121 ES:SE:LP:AF:ID -0.000171007:0.000283891:0.259637:0.869121:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838022 ES:SE:LP:AF:ID -0.000229851:0.000264353:0.420216:0.838022:rs3115859
1 754964 rs3131966 C T . PASS AF=0.838652 ES:SE:LP:AF:ID -0.000241909:0.000265095:0.443698:0.838652:rs3131966
1 755775 rs3131965 A G . PASS AF=0.839768 ES:SE:LP:AF:ID -0.000227917:0.000268683:0.39794:0.839768:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869402 ES:SE:LP:AF:ID -0.000160873:0.000283565:0.244125:0.869402:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868952 ES:SE:LP:AF:ID -0.000147299:0.000282854:0.221849:0.868952:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867901 ES:SE:LP:AF:ID -0.000175001:0.000282308:0.267606:0.867901:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869094 ES:SE:LP:AF:ID -0.000151493:0.000283084:0.229148:0.869094:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869102 ES:SE:LP:AF:ID -0.000151765:0.000283106:0.229148:0.869102:rs4951862
1 758144 rs3131956 A G . PASS AF=0.86911 ES:SE:LP:AF:ID -0.000151427:0.000283113:0.229148:0.86911:rs3131956
1 758626 rs3131954 C T . PASS AF=0.869586 ES:SE:LP:AF:ID -0.000166898:0.000283888:0.251812:0.869586:rs3131954
1 760912 rs1048488 C T . PASS AF=0.838302 ES:SE:LP:AF:ID -0.000243908:0.000263849:0.443698:0.838302:rs1048488
1 761147 rs3115850 T C . PASS AF=0.838423 ES:SE:LP:AF:ID -0.000244177:0.000264036:0.443698:0.838423:rs3115850
1 761732 rs2286139 C T . PASS AF=0.862257 ES:SE:LP:AF:ID -0.000204676:0.000282085:0.327902:0.862257:rs2286139
1 763394 rs3115847 G A . PASS AF=0.706761 ES:SE:LP:AF:ID -0.000178284:0.000274614:0.283997:0.706761:rs3115847
1 766007 rs61768174 A C . PASS AF=0.105127 ES:SE:LP:AF:ID 0.000269991:0.00031637:0.408935:0.105127:rs61768174
1 768253 rs2977608 A C . PASS AF=0.76134 ES:SE:LP:AF:ID -0.000283181:0.000224148:0.677781:0.76134:rs2977608
1 768448 rs12562034 G A . PASS AF=0.106455 ES:SE:LP:AF:ID 0.000202195:0.000308957:0.29243:0.106455:rs12562034
1 769223 rs60320384 C G . PASS AF=0.129573 ES:SE:LP:AF:ID 0.000174135:0.00028487:0.267606:0.129573:rs60320384
1 771823 rs2977605 T C . PASS AF=0.868913 ES:SE:LP:AF:ID -0.000202453:0.00028335:0.327902:0.868913:rs2977605
1 771967 rs59066358 G A . PASS AF=0.129674 ES:SE:LP:AF:ID 0.00017762:0.000284685:0.275724:0.129674:rs59066358
1 772755 rs2905039 A C . PASS AF=0.868924 ES:SE:LP:AF:ID -0.000204461:0.000283356:0.327902:0.868924:rs2905039
1 776546 rs12124819 A G . PASS AF=0.265371 ES:SE:LP:AF:ID 0.000208166:0.000250354:0.387216:0.265371:rs12124819
1 777122 rs2980319 A T . PASS AF=0.870042 ES:SE:LP:AF:ID -0.000203853:0.000283932:0.327902:0.870042:rs2980319
1 777232 rs112618790 C T . PASS AF=0.095116 ES:SE:LP:AF:ID 0.000156194:0.000329095:0.19382:0.095116:rs112618790
1 778745 rs1055606 A G . PASS AF=0.128575 ES:SE:LP:AF:ID 0.000183412:0.00028505:0.283997:0.128575:rs1055606
1 779322 rs4040617 A G . PASS AF=0.128873 ES:SE:LP:AF:ID 0.000189801:0.000284566:0.30103:0.128873:rs4040617
1 780785 rs2977612 T A . PASS AF=0.868783 ES:SE:LP:AF:ID -0.000234021:0.000283173:0.387216:0.868783:rs2977612
1 781845 rs61768199 A G . PASS AF=0.10186 ES:SE:LP:AF:ID 0.000278601:0.000320883:0.408935:0.10186:rs61768199
1 782981 rs6594026 C T . PASS AF=0.129515 ES:SE:LP:AF:ID 0.00018875:0.000284473:0.29243:0.129515:rs6594026
1 785050 rs2905062 G A . PASS AF=0.868537 ES:SE:LP:AF:ID -0.000239576:0.000283111:0.39794:0.868537:rs2905062
1 785989 rs2980300 T C . PASS AF=0.868478 ES:SE:LP:AF:ID -0.000245958:0.000283287:0.408935:0.868478:rs2980300