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_20088_339.vcf.gz --id UKB-b:260 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20088_339.txt.gz --cohort_cases 2690 --cohort_controls 62259 --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": "2019-09-13T13:39:35.371613",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-260/ukb-b-260.vcf.gz; Date=Sun May 10 13:41:27 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-260/UKB-b-260_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-260/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:42:50 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-260/UKB-b-260_data.vcf.gz ...
Read summary statistics for 3809602 SNPs.
Dropped 666 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, 916659 SNPs remain.
After merging with regression SNP LD, 916659 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.009 (0.008)
Lambda GC: 1.0215
Mean Chi^2: 1.0163
Intercept: 1.0031 (0.0088)
Ratio: 0.19 (0.5391)
Analysis finished at Thu Oct 17 14:43:38 2019
Total time elapsed: 47.89sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8673,
"inflation_factor": 1,
"mean_EFFECT": -2.4565e-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": 30886,
"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": 916659,
"ldsc_nsnp_merge_regression_ld": 916659,
"ldsc_observed_scale_h2_beta": 0.009,
"ldsc_observed_scale_h2_se": 0.008,
"ldsc_intercept_beta": 1.0031,
"ldsc_intercept_se": 0.0088,
"ldsc_lambda_gc": 1.0215,
"ldsc_mean_chisq": 1.0163,
"ldsc_ratio": 0.1902
}
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 | 3808939 | 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 | 3809602 | 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 | 8.661667e+00 | 5.768761e+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 | 7.856424e+07 | 5.676369e+07 | 828.0000000 | 3.160665e+07 | 6.890020e+07 | 1.147143e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -2.500000e-06 | 1.290900e-03 | -0.0076412 | -8.619000e-04 | -3.700000e-06 | 8.551000e-04 | 7.754000e-03 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.273800e-03 | 1.578000e-04 | 0.0010672 | 1.136700e-03 | 1.222800e-03 | 1.383200e-03 | 3.922000e-03 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.972855e-01 | 2.893904e-01 | 0.0000010 | 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 | 4.972853e-01 | 2.893633e-01 | 0.0000010 | 2.455303e-01 | 4.961241e-01 | 7.482896e-01 | 9.999999e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.021161e-01 | 2.065107e-01 | 0.1301120 | 2.218280e-01 | 3.576130e-01 | 5.566750e-01 | 8.698870e-01 | ▇▅▃▃▂ |
| numeric | AF_reference | 30886 | 0.9918926 | NA | NA | NA | NA | NA | NA | NA | 3.901866e-01 | 2.125069e-01 | 0.0000000 | 2.156550e-01 | 3.530350e-01 | 5.443290e-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 | 0.0015885 | 0.0019668 | 0.4199997 | 0.4192645 | 0.623812 | 0.782149 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0003314 | 0.0019610 | 0.8700001 | 0.8657947 | 0.399144 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0004276 | 0.0019288 | 0.8200001 | 0.8245677 | 0.455916 | 0.420727 | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0037634 | 0.0021968 | 0.0870001 | 0.0866904 | 0.242057 | NA | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0014277 | 0.0013597 | 0.2900000 | 0.2937159 | 0.513304 | 0.275160 | NA |
| 1 | 729679 | rs4951859 | C | G | 0.0009855 | 0.0015841 | 0.5300002 | 0.5338830 | 0.841441 | 0.639976 | NA |
| 1 | 736289 | rs79010578 | T | A | -0.0004357 | 0.0017086 | 0.8000000 | 0.7987111 | 0.134139 | 0.139577 | NA |
| 1 | 752566 | rs3094315 | G | A | 0.0009981 | 0.0015325 | 0.5099998 | 0.5148571 | 0.837029 | 0.718251 | NA |
| 1 | 752721 | rs3131972 | A | G | 0.0010134 | 0.0015313 | 0.5099998 | 0.5081039 | 0.836733 | 0.653355 | NA |
| 1 | 753405 | rs3115860 | C | A | 0.0018072 | 0.0016455 | 0.2700001 | 0.2720928 | 0.868562 | 0.751797 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51186228 | rs3865766 | C | T | 0.0008060 | 0.0011596 | 0.4899999 | 0.4869835 | 0.449547 | 0.4532750 | NA |
| 22 | 51192586 | rs5771006 | G | A | 0.0000856 | 0.0015633 | 0.9599999 | 0.9563137 | 0.167396 | 0.0848642 | NA |
| 22 | 51193227 | rs34608236 | T | G | 0.0009430 | 0.0015942 | 0.5500004 | 0.5542001 | 0.168560 | 0.0692891 | NA |
| 22 | 51197266 | rs61290853 | A | G | 0.0009971 | 0.0011950 | 0.4000000 | 0.4040408 | 0.386693 | 0.4229230 | NA |
| 22 | 51198027 | rs34939255 | A | G | -0.0006547 | 0.0013552 | 0.6300007 | 0.6290149 | 0.254586 | 0.0984425 | NA |
| 22 | 51211106 | rs9628250 | T | C | -0.0006863 | 0.0013444 | 0.6100002 | 0.6097058 | 0.271468 | 0.1671330 | NA |
| 22 | 51211392 | rs3888396 | T | C | 0.0018212 | 0.0017901 | 0.3100002 | 0.3089759 | 0.132383 | 0.1641370 | NA |
| 22 | 51212875 | rs2238837 | A | C | 0.0001210 | 0.0012779 | 0.9199999 | 0.9245475 | 0.331351 | 0.3724040 | NA |
| 22 | 51219006 | rs28729663 | G | A | -0.0012837 | 0.0016537 | 0.4400003 | 0.4375935 | 0.136315 | 0.2052720 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0005470 | 0.0013056 | 0.6800001 | 0.6752505 | 0.298393 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623812 ES:SE:LP:AF:ID 0.00158854:0.00196675:0.376751:0.623812:rs10399793
1 54676 rs2462492 C T . PASS AF=0.399144 ES:SE:LP:AF:ID -0.000331419:0.00196103:0.0604807:0.399144:rs2462492
1 91536 rs6702460 G T . PASS AF=0.455916 ES:SE:LP:AF:ID -0.000427567:0.00192881:0.0861861:0.455916:rs6702460
1 534192 rs6680723 C T . PASS AF=0.242057 ES:SE:LP:AF:ID -0.0037634:0.00219681:1.06048:0.242057:rs6680723
1 706368 rs12029736 A G . PASS AF=0.513304 ES:SE:LP:AF:ID 0.00142766:0.00135967:0.537602:0.513304:rs12029736
1 729679 rs4951859 C G . PASS AF=0.841441 ES:SE:LP:AF:ID 0.000985477:0.00158414:0.275724:0.841441:rs4951859
1 736289 rs79010578 T A . PASS AF=0.134139 ES:SE:LP:AF:ID -0.000435722:0.00170861:0.09691:0.134139:rs79010578
1 752566 rs3094315 G A . PASS AF=0.837029 ES:SE:LP:AF:ID 0.000998081:0.00153246:0.29243:0.837029:rs3094315
1 752721 rs3131972 A G . PASS AF=0.836733 ES:SE:LP:AF:ID 0.00101343:0.00153134:0.29243:0.836733:rs3131972
1 753405 rs3115860 C A . PASS AF=0.868562 ES:SE:LP:AF:ID 0.00180718:0.0016455:0.568636:0.868562:rs3115860
1 753541 rs2073813 G A . PASS AF=0.131004 ES:SE:LP:AF:ID -0.00188345:0.00164973:0.60206:0.131004:rs2073813
1 754182 rs3131969 A G . PASS AF=0.867976 ES:SE:LP:AF:ID 0.00182102:0.00164286:0.568636:0.867976:rs3131969
1 754192 rs3131968 A G . PASS AF=0.86805 ES:SE:LP:AF:ID 0.00179622:0.00164353:0.568636:0.86805:rs3131968
1 754334 rs3131967 T C . PASS AF=0.867987 ES:SE:LP:AF:ID 0.00182424:0.00164282:0.568636:0.867987:rs3131967
1 754503 rs3115859 G A . PASS AF=0.836159 ES:SE:LP:AF:ID 0.00109706:0.00152686:0.327902:0.836159:rs3115859
1 754964 rs3131966 C T . PASS AF=0.836793 ES:SE:LP:AF:ID 0.00105471:0.00153104:0.309804:0.836793:rs3131966
1 755775 rs3131965 A G . PASS AF=0.838109 ES:SE:LP:AF:ID 0.000952563:0.0015526:0.267606:0.838109:rs3131965
1 755890 rs3115858 A T . PASS AF=0.868228 ES:SE:LP:AF:ID 0.00173364:0.0016407:0.537602:0.868228:rs3115858
1 756604 rs3131962 A G . PASS AF=0.867742 ES:SE:LP:AF:ID 0.00179623:0.00163636:0.568636:0.867742:rs3131962
1 757640 rs3115853 G A . PASS AF=0.866644 ES:SE:LP:AF:ID 0.00176033:0.00163336:0.552842:0.866644:rs3115853
1 757734 rs4951929 C T . PASS AF=0.867911 ES:SE:LP:AF:ID 0.00173834:0.00163802:0.537602:0.867911:rs4951929
1 757936 rs4951862 C A . PASS AF=0.867923 ES:SE:LP:AF:ID 0.0017369:0.00163813:0.537602:0.867923:rs4951862
1 758144 rs3131956 A G . PASS AF=0.867932 ES:SE:LP:AF:ID 0.00173337:0.00163819:0.537602:0.867932:rs3131956
1 758626 rs3131954 C T . PASS AF=0.868398 ES:SE:LP:AF:ID 0.00170276:0.0016425:0.522879:0.868398:rs3131954
1 760912 rs1048488 C T . PASS AF=0.836369 ES:SE:LP:AF:ID 0.00106458:0.00152305:0.318759:0.836369:rs1048488
1 761147 rs3115850 T C . PASS AF=0.836495 ES:SE:LP:AF:ID 0.00110203:0.00152409:0.327902:0.836495:rs3115850
1 761732 rs2286139 C T . PASS AF=0.860851 ES:SE:LP:AF:ID 0.00182798:0.00163238:0.585027:0.860851:rs2286139
1 763394 rs3115847 G A . PASS AF=0.705804 ES:SE:LP:AF:ID 0.000522386:0.00159292:0.130768:0.705804:rs3115847
1 768253 rs2977608 A C . PASS AF=0.758252 ES:SE:LP:AF:ID 0.000441853:0.00129107:0.136677:0.758252:rs2977608
1 769223 rs60320384 C G . PASS AF=0.13066 ES:SE:LP:AF:ID -0.00184946:0.0016491:0.585027:0.13066:rs60320384
1 771823 rs2977605 T C . PASS AF=0.867852 ES:SE:LP:AF:ID 0.00171759:0.00164009:0.537602:0.867852:rs2977605
1 771967 rs59066358 G A . PASS AF=0.130756 ES:SE:LP:AF:ID -0.00177686:0.001648:0.552842:0.130756:rs59066358
1 772755 rs2905039 A C . PASS AF=0.867878 ES:SE:LP:AF:ID 0.00170931:0.00164019:0.522879:0.867878:rs2905039
1 776546 rs12124819 A G . PASS AF=0.263729 ES:SE:LP:AF:ID -7.22229e-05:0.00145886:0.0177288:0.263729:rs12124819
1 777122 rs2980319 A T . PASS AF=0.869067 ES:SE:LP:AF:ID 0.00193619:0.00164451:0.619789:0.869067:rs2980319
1 780785 rs2977612 T A . PASS AF=0.867843 ES:SE:LP:AF:ID 0.00199852:0.00164042:0.657577:0.867843:rs2977612
1 782981 rs6594026 C T . PASS AF=0.130424 ES:SE:LP:AF:ID -0.00212601:0.00164828:0.69897:0.130424:rs6594026
1 785050 rs2905062 G A . PASS AF=0.867493 ES:SE:LP:AF:ID 0.0019773:0.00163994:0.638272:0.867493:rs2905062
1 785989 rs2980300 T C . PASS AF=0.867448 ES:SE:LP:AF:ID 0.00201116:0.00164108:0.657577:0.867448:rs2980300
1 787399 rs2905055 G T . PASS AF=0.859749 ES:SE:LP:AF:ID 0.00189558:0.00164057:0.60206:0.859749:rs2905055
1 787685 rs2905054 G T . PASS AF=0.860334 ES:SE:LP:AF:ID 0.00203554:0.00164149:0.677781:0.860334:rs2905054
1 787844 rs2905053 C T . PASS AF=0.868172 ES:SE:LP:AF:ID 0.00190914:0.00164663:0.60206:0.868172:rs2905053
1 795988 rs59380221 C T . PASS AF=0.144405 ES:SE:LP:AF:ID -0.0025081:0.00169686:0.853872:0.144405:rs59380221
1 798400 rs10900604 A G . PASS AF=0.209824 ES:SE:LP:AF:ID -0.00148437:0.00137644:0.552842:0.209824:rs10900604
1 798959 rs11240777 G A . PASS AF=0.209642 ES:SE:LP:AF:ID -0.00147489:0.00137717:0.552842:0.209642:rs11240777
1 808631 rs11240779 G A . PASS AF=0.76828 ES:SE:LP:AF:ID 0.000993908:0.00130888:0.346787:0.76828:rs11240779
1 808928 rs11240780 C T . PASS AF=0.768417 ES:SE:LP:AF:ID 0.000959939:0.00131056:0.337242:0.768417:rs11240780
1 814495 rs74461805 C A . PASS AF=0.340108 ES:SE:LP:AF:ID -0.00258791:0.00186192:0.79588:0.340108:rs74461805
1 824398 rs7538305 A C . PASS AF=0.14012 ES:SE:LP:AF:ID -0.00287387:0.00164337:1.09691:0.14012:rs7538305
1 830181 rs28444699 A G . PASS AF=0.696612 ES:SE:LP:AF:ID -0.00037233:0.00124567:0.113509:0.696612:rs28444699