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_41246_1220.vcf.gz --id UKB-b:12396 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41246_1220.txt.gz --cohort_cases 3484 --cohort_controls 440727 --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": "2019-09-13T13:17:23.661761",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-12396/ukb-b-12396.vcf.gz; Date=Sun May 10 04:03:54 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-12396/UKB-b-12396_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-12396/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:41:54 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-12396/UKB-b-12396_data.vcf.gz ...
Read summary statistics for 4299340 SNPs.
Dropped 911 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, 1006594 SNPs remain.
After merging with regression SNP LD, 1006594 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0032 (0.0011)
Lambda GC: 1.0285
Mean Chi^2: 1.0233
Intercept: 0.9926 (0.0083)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Thu Oct 17 14:42:49 2019
Total time elapsed: 54.76sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8865,
"inflation_factor": 1.0475,
"mean_EFFECT": -5.7251e-07,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 1,
"n_p_sig": 49,
"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": 35466,
"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": 1006594,
"ldsc_nsnp_merge_regression_ld": 1006594,
"ldsc_observed_scale_h2_beta": 0.0032,
"ldsc_observed_scale_h2_se": 0.0011,
"ldsc_intercept_beta": 0.9926,
"ldsc_intercept_se": 0.0083,
"ldsc_lambda_gc": 1.0285,
"ldsc_mean_chisq": 1.0233,
"ldsc_ratio": -0.3176
}
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 | 4298434 | 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 | 4299340 | 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 | 8.654941e+00 | 5.766001e+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 | 7.858711e+07 | 5.671768e+07 | 828.0000000 | 3.170646e+07 | 6.895824e+07 | 1.146829e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -6.000000e-07 | 2.319000e-04 | -0.0012259 | -1.526000e-04 | -1.000000e-06 | 1.504000e-04 | 2.868700e-03 | ▁▇▁▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.249000e-04 | 3.600000e-05 | 0.0001806 | 1.938000e-04 | 2.127000e-04 | 2.491000e-04 | 6.604000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.947385e-01 | 2.905840e-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 | 4.947383e-01 | 2.905575e-01 | 0.0000000 | 2.419276e-01 | 4.922448e-01 | 7.464639e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.814526e-01 | 2.216389e-01 | 0.1004600 | 1.888730e-01 | 3.281780e-01 | 5.430760e-01 | 8.995400e-01 | ▇▅▃▂▂ |
| numeric | AF_reference | 35466 | 0.9917508 | NA | NA | NA | NA | NA | NA | NA | 3.718748e-01 | 2.230251e-01 | 0.0000000 | 1.880990e-01 | 3.264780e-01 | 5.311500e-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 | -0.0006039 | 0.0003323 | 0.0690001 | 0.0691680 | 0.623754 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0000417 | 0.0003292 | 0.9000000 | 0.8993141 | 0.400408 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0011666 | 0.0005263 | 0.0269998 | 0.0266566 | 0.103548 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0002298 | 0.0003241 | 0.4799997 | 0.4784128 | 0.456852 | 0.4207270 | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0003116 | 0.0003703 | 0.4000000 | 0.4001216 | 0.240900 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0003857 | 0.0003104 | 0.2099999 | 0.2140395 | 0.116358 | 0.1417730 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0001053 | 0.0002299 | 0.6499995 | 0.6470096 | 0.515710 | 0.2751600 | NA |
| 1 | 722670 | rs116030099 | T | C | 0.0001687 | 0.0003793 | 0.6600001 | 0.6563683 | 0.101251 | 0.0413339 | NA |
| 1 | 729679 | rs4951859 | C | G | 0.0004353 | 0.0002690 | 0.1100001 | 0.1056048 | 0.843130 | 0.6399760 | NA |
| 1 | 731718 | rs142557973 | T | C | -0.0003484 | 0.0002944 | 0.2399999 | 0.2366206 | 0.122349 | 0.1543530 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51198027 | rs34939255 | A | G | 0.0001206 | 0.0002293 | 0.5999997 | 0.5988104 | 0.254388 | 0.0984425 | NA |
| 22 | 51208537 | rs72619593 | G | A | 0.0002600 | 0.0003064 | 0.4000000 | 0.3961413 | 0.120818 | 0.1142170 | NA |
| 22 | 51210289 | rs112565862 | C | T | -0.0001904 | 0.0003053 | 0.5300002 | 0.5327690 | 0.129933 | 0.1018370 | NA |
| 22 | 51211106 | rs9628250 | T | C | 0.0001120 | 0.0002273 | 0.6200004 | 0.6220880 | 0.271377 | 0.1671330 | NA |
| 22 | 51211392 | rs3888396 | T | C | -0.0001560 | 0.0003025 | 0.6100002 | 0.6060383 | 0.132620 | 0.1641370 | NA |
| 22 | 51212875 | rs2238837 | A | C | -0.0000852 | 0.0002160 | 0.6899999 | 0.6931195 | 0.331635 | 0.3724040 | NA |
| 22 | 51213613 | rs34726907 | C | T | 0.0000339 | 0.0002844 | 0.9100000 | 0.9051703 | 0.127909 | 0.1727240 | NA |
| 22 | 51216564 | rs9616970 | T | C | 0.0000380 | 0.0002832 | 0.8900000 | 0.8933038 | 0.128430 | 0.1563500 | NA |
| 22 | 51219006 | rs28729663 | G | A | -0.0000089 | 0.0002772 | 0.9699999 | 0.9742559 | 0.138032 | 0.2052720 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0000532 | 0.0002211 | 0.8100000 | 0.8097078 | 0.298157 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623754 ES:SE:LP:AF:ID -0.000603932:0.00033232:1.16115:0.623754:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400408 ES:SE:LP:AF:ID 4.16593e-05:0.00032925:0.0457575:0.400408:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103548 ES:SE:LP:AF:ID 0.00116664:0.000526341:1.56864:0.103548:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456852 ES:SE:LP:AF:ID -0.000229767:0.000324137:0.318759:0.456852:rs6702460
1 534192 rs6680723 C T . PASS AF=0.2409 ES:SE:LP:AF:ID -0.000311614:0.00037035:0.39794:0.2409:rs6680723
1 693731 rs12238997 A G . PASS AF=0.116358 ES:SE:LP:AF:ID -0.000385664:0.000310385:0.677781:0.116358:rs12238997
1 706368 rs12029736 A G . PASS AF=0.51571 ES:SE:LP:AF:ID 0.000105275:0.000229898:0.187087:0.51571:rs12029736
1 722670 rs116030099 T C . PASS AF=0.101251 ES:SE:LP:AF:ID 0.000168747:0.000379264:0.180456:0.101251:rs116030099
1 729679 rs4951859 C G . PASS AF=0.84313 ES:SE:LP:AF:ID 0.000435264:0.000268969:0.958607:0.84313:rs4951859
1 731718 rs58276399 T C . PASS AF=0.122349 ES:SE:LP:AF:ID -0.000348407:0.000294393:0.619789:0.122349:rs58276399
1 734349 rs141242758 T C . PASS AF=0.121591 ES:SE:LP:AF:ID -0.000351396:0.000294514:0.638272:0.121591:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132408 ES:SE:LP:AF:ID -0.000615498:0.000290246:1.46852:0.132408:rs79010578
1 752566 rs3094315 G A . PASS AF=0.838819 ES:SE:LP:AF:ID 0.000626853:0.000260413:1.79588:0.838819:rs3094315
1 752721 rs3131972 A G . PASS AF=0.83844 ES:SE:LP:AF:ID 0.000620874:0.000260122:1.76955:0.83844:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869698 ES:SE:LP:AF:ID 0.000575087:0.000279135:1.40894:0.869698:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129966 ES:SE:LP:AF:ID -0.000567979:0.00027969:1.37675:0.129966:rs2073813
1 754182 rs3131969 A G . PASS AF=0.869038 ES:SE:LP:AF:ID 0.000573907:0.00027858:1.40894:0.869038:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869138 ES:SE:LP:AF:ID 0.000597136:0.000278693:1.49485:0.869138:rs3131968
1 754334 rs3131967 T C . PASS AF=0.869041 ES:SE:LP:AF:ID 0.000573027:0.000278575:1.39794:0.869041:rs3131967
1 754503 rs3115859 G A . PASS AF=0.83789 ES:SE:LP:AF:ID 0.000630976:0.000259399:1.82391:0.83789:rs3115859
1 754964 rs3131966 C T . PASS AF=0.838522 ES:SE:LP:AF:ID 0.000624524:0.000260128:1.79588:0.838522:rs3131966
1 755775 rs3131965 A G . PASS AF=0.83964 ES:SE:LP:AF:ID 0.000628104:0.000263661:1.76955:0.83964:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869327 ES:SE:LP:AF:ID 0.000587247:0.000278268:1.45593:0.869327:rs3115858
1 756604 rs3131962 A G . PASS AF=0.86888 ES:SE:LP:AF:ID 0.000578077:0.000277581:1.4318:0.86888:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867823 ES:SE:LP:AF:ID 0.000560177:0.000277031:1.36653:0.867823:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869019 ES:SE:LP:AF:ID 0.000576905:0.000277802:1.42022:0.869019:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869027 ES:SE:LP:AF:ID 0.000576534:0.000277824:1.42022:0.869027:rs4951862
1 758144 rs3131956 A G . PASS AF=0.869035 ES:SE:LP:AF:ID 0.000576016:0.00027783:1.42022:0.869035:rs3131956
1 758626 rs3131954 C T . PASS AF=0.869511 ES:SE:LP:AF:ID 0.000591045:0.000278584:1.46852:0.869511:rs3131954
1 760912 rs1048488 C T . PASS AF=0.838176 ES:SE:LP:AF:ID 0.000625629:0.00025892:1.79588:0.838176:rs1048488
1 761147 rs3115850 T C . PASS AF=0.838296 ES:SE:LP:AF:ID 0.000624146:0.000259103:1.79588:0.838296:rs3115850
1 761732 rs2286139 C T . PASS AF=0.862179 ES:SE:LP:AF:ID 0.000623056:0.000276836:1.61979:0.862179:rs2286139
1 763394 rs3115847 G A . PASS AF=0.706668 ES:SE:LP:AF:ID 0.000628568:0.000269517:1.69897:0.706668:rs3115847
1 766007 rs61768174 A C . PASS AF=0.105168 ES:SE:LP:AF:ID -0.00050434:0.000310498:1:0.105168:rs61768174
1 768253 rs2977608 A C . PASS AF=0.761323 ES:SE:LP:AF:ID 0.000269863:0.000220071:0.657577:0.761323:rs2977608
1 768448 rs12562034 G A . PASS AF=0.106403 ES:SE:LP:AF:ID 0.000157174:0.000303381:0.221849:0.106403:rs12562034
1 769223 rs60320384 C G . PASS AF=0.129653 ES:SE:LP:AF:ID -0.000525873:0.000279557:1.22185:0.129653:rs60320384
1 771823 rs2977605 T C . PASS AF=0.868842 ES:SE:LP:AF:ID 0.000552421:0.000278066:1.3279:0.868842:rs2977605
1 771967 rs59066358 G A . PASS AF=0.129753 ES:SE:LP:AF:ID -0.00052439:0.000279375:1.21467:0.129753:rs59066358
1 772755 rs2905039 A C . PASS AF=0.868852 ES:SE:LP:AF:ID 0.000550012:0.000278072:1.31876:0.868852:rs2905039
1 776546 rs12124819 A G . PASS AF=0.265279 ES:SE:LP:AF:ID 0.00024698:0.00024578:0.508638:0.265279:rs12124819
1 777122 rs2980319 A T . PASS AF=0.870001 ES:SE:LP:AF:ID 0.00056304:0.000278659:1.36653:0.870001:rs2980319
1 778745 rs1055606 A G . PASS AF=0.12862 ES:SE:LP:AF:ID -0.000523973:0.000279759:1.21467:0.12862:rs1055606
1 779322 rs4040617 A G . PASS AF=0.128915 ES:SE:LP:AF:ID -0.000520751:0.000279289:1.20761:0.128915:rs4040617
1 780785 rs2977612 T A . PASS AF=0.868739 ES:SE:LP:AF:ID 0.000550693:0.000277917:1.31876:0.868739:rs2977612
1 781845 rs61768199 A G . PASS AF=0.10187 ES:SE:LP:AF:ID -0.000468046:0.000314959:0.853872:0.10187:rs61768199
1 782981 rs6594026 C T . PASS AF=0.129565 ES:SE:LP:AF:ID -0.000527742:0.000279196:1.22915:0.129565:rs6594026
1 785050 rs2905062 G A . PASS AF=0.868493 ES:SE:LP:AF:ID 0.000551602:0.000277853:1.3279:0.868493:rs2905062
1 785989 rs2980300 T C . PASS AF=0.868435 ES:SE:LP:AF:ID 0.000546605:0.000278027:1.3098:0.868435:rs2980300
1 787399 rs2905055 G T . PASS AF=0.860726 ES:SE:LP:AF:ID 0.000554681:0.00027782:1.33724:0.860726:rs2905055