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_2510.vcf.gz --id UKB-b:14801 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41246_2510.txt.gz --cohort_cases 3553 --cohort_controls 440646 --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": "2019-09-13T13:22:14.349782",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-14801/ukb-b-14801.vcf.gz; Date=Sat May 9 17:57:48 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-14801/UKB-b-14801_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-14801/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:43:46 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-14801/UKB-b-14801_data.vcf.gz ...
Read summary statistics for 4334096 SNPs.
Dropped 929 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, 1012638 SNPs remain.
After merging with regression SNP LD, 1012638 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0006 (0.0011)
Lambda GC: 1.0225
Mean Chi^2: 1.0169
Intercept: 1.0113 (0.0086)
Ratio: 0.6687 (0.5096)
Analysis finished at Thu Oct 17 14:44:36 2019
Total time elapsed: 50.37sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8877,
"inflation_factor": 1.0475,
"mean_EFFECT": 2.0519e-06,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 3,
"n_p_sig": 963,
"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": 35805,
"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": 1012638,
"ldsc_nsnp_merge_regression_ld": 1012638,
"ldsc_observed_scale_h2_beta": 0.0006,
"ldsc_observed_scale_h2_se": 0.0011,
"ldsc_intercept_beta": 1.0113,
"ldsc_intercept_se": 0.0086,
"ldsc_lambda_gc": 1.0225,
"ldsc_mean_chisq": 1.0169,
"ldsc_ratio": 0.6686
}
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 | TRUE |
| 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 | 4333172 | 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 | 4334096 | 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 | 8.655384e+00 | 5.765877e+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 | 7.859603e+07 | 5.671333e+07 | 828.0000000 | 3.172532e+07 | 6.897225e+07 | 1.146835e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.100000e-06 | 2.359000e-04 | -0.0017189 | -1.521000e-04 | 1.600000e-06 | 1.536000e-04 | 2.856500e-03 | ▁▇▃▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.278000e-04 | 3.710000e-05 | 0.0001823 | 1.958000e-04 | 2.152000e-04 | 2.527000e-04 | 6.668000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.964302e-01 | 2.901646e-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 | 4.964295e-01 | 2.901377e-01 | 0.0000000 | 2.441642e-01 | 4.943762e-01 | 7.482428e-01 | 9.999996e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.799947e-01 | 2.226103e-01 | 0.0985090 | 1.866160e-01 | 3.260645e-01 | 5.419400e-01 | 9.014910e-01 | ▇▅▃▂▂ |
| numeric | AF_reference | 35805 | 0.9917388 | NA | NA | NA | NA | NA | NA | NA | 3.705775e-01 | 2.237083e-01 | 0.0000000 | 1.861020e-01 | 3.246810e-01 | 5.301520e-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 | -0.0003002 | 0.0003355 | 0.3700002 | 0.3709223 | 0.623744 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0001941 | 0.0003324 | 0.5600000 | 0.5592315 | 0.400430 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0005162 | 0.0005315 | 0.3300000 | 0.3314478 | 0.103533 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0003081 | 0.0003272 | 0.3500000 | 0.3464235 | 0.456867 | 0.4207270 | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0000022 | 0.0003739 | 1.0000000 | 0.9953006 | 0.240920 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | 0.0001388 | 0.0003134 | 0.6600001 | 0.6577324 | 0.116358 | 0.1417730 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0003172 | 0.0002321 | 0.1700000 | 0.1717348 | 0.515712 | 0.2751600 | NA |
| 1 | 722670 | rs116030099 | T | C | 0.0002983 | 0.0003829 | 0.4400003 | 0.4360162 | 0.101257 | 0.0413339 | NA |
| 1 | 729679 | rs4951859 | C | G | -0.0002335 | 0.0002716 | 0.3900004 | 0.3899834 | 0.843129 | 0.6399760 | NA |
| 1 | 731718 | rs142557973 | T | C | 0.0001042 | 0.0002972 | 0.7300002 | 0.7258918 | 0.122347 | 0.1543530 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51198027 | rs34939255 | A | G | -0.0002953 | 0.0002315 | 0.2000000 | 0.2021217 | 0.254388 | 0.0984425 | NA |
| 22 | 51208537 | rs72619593 | G | A | 0.0003635 | 0.0003094 | 0.2399999 | 0.2399565 | 0.120822 | 0.1142170 | NA |
| 22 | 51210289 | rs112565862 | C | T | 0.0004491 | 0.0003082 | 0.1499999 | 0.1451286 | 0.129936 | 0.1018370 | NA |
| 22 | 51211106 | rs9628250 | T | C | -0.0002977 | 0.0002295 | 0.1900002 | 0.1945560 | 0.271375 | 0.1671330 | NA |
| 22 | 51211392 | rs3888396 | T | C | 0.0004714 | 0.0003055 | 0.1199999 | 0.1227498 | 0.132625 | 0.1641370 | NA |
| 22 | 51212875 | rs2238837 | A | C | 0.0003374 | 0.0002181 | 0.1199999 | 0.1218340 | 0.331617 | 0.3724040 | NA |
| 22 | 51213613 | rs34726907 | C | T | -0.0003069 | 0.0002872 | 0.2900000 | 0.2852914 | 0.127910 | 0.1727240 | NA |
| 22 | 51216564 | rs9616970 | T | C | -0.0003015 | 0.0002859 | 0.2900000 | 0.2917533 | 0.128432 | 0.1563500 | NA |
| 22 | 51219006 | rs28729663 | G | A | -0.0002953 | 0.0002799 | 0.2900000 | 0.2914882 | 0.138036 | 0.2052720 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0004874 | 0.0002232 | 0.0290001 | 0.0289910 | 0.298151 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623744 ES:SE:LP:AF:ID -0.000300193:0.000335506:0.431798:0.623744:rs10399793
1 54676 rs2462492 C T . PASS AF=0.40043 ES:SE:LP:AF:ID 0.000194126:0.000332417:0.251812:0.40043:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103533 ES:SE:LP:AF:ID 0.000516187:0.000531493:0.481486:0.103533:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456867 ES:SE:LP:AF:ID 0.000308122:0.00032725:0.455932:0.456867:rs6702460
1 534192 rs6680723 C T . PASS AF=0.24092 ES:SE:LP:AF:ID -2.20218e-06:0.000373891:-0:0.24092:rs6680723
1 693731 rs12238997 A G . PASS AF=0.116358 ES:SE:LP:AF:ID 0.000138844:0.000313385:0.180456:0.116358:rs12238997
1 706368 rs12029736 A G . PASS AF=0.515712 ES:SE:LP:AF:ID -0.000317242:0.000232131:0.769551:0.515712:rs12029736
1 722670 rs116030099 T C . PASS AF=0.101257 ES:SE:LP:AF:ID 0.000298278:0.000382929:0.356547:0.101257:rs116030099
1 729679 rs4951859 C G . PASS AF=0.843129 ES:SE:LP:AF:ID -0.000233451:0.000271566:0.408935:0.843129:rs4951859
1 731718 rs58276399 T C . PASS AF=0.122347 ES:SE:LP:AF:ID 0.000104211:0.00029724:0.136677:0.122347:rs58276399
1 734349 rs141242758 T C . PASS AF=0.121589 ES:SE:LP:AF:ID 8.98642e-05:0.000297362:0.119186:0.121589:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132409 ES:SE:LP:AF:ID 0.000327208:0.00029305:0.585027:0.132409:rs79010578
1 752566 rs3094315 G A . PASS AF=0.838824 ES:SE:LP:AF:ID -0.000168948:0.000262934:0.283997:0.838824:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838446 ES:SE:LP:AF:ID -0.000161312:0.000262641:0.267606:0.838446:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869704 ES:SE:LP:AF:ID -0.000127686:0.000281839:0.187087:0.869704:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129962 ES:SE:LP:AF:ID 0.000125367:0.000282399:0.180456:0.129962:rs2073813
1 754182 rs3131969 A G . PASS AF=0.869043 ES:SE:LP:AF:ID -0.000132667:0.000281278:0.19382:0.869043:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869143 ES:SE:LP:AF:ID -0.000120472:0.000281393:0.173925:0.869143:rs3131968
1 754334 rs3131967 T C . PASS AF=0.869046 ES:SE:LP:AF:ID -0.000133777:0.000281273:0.200659:0.869046:rs3131967
1 754503 rs3115859 G A . PASS AF=0.837893 ES:SE:LP:AF:ID -0.000177606:0.000261908:0.30103:0.837893:rs3115859
1 754964 rs3131966 C T . PASS AF=0.838524 ES:SE:LP:AF:ID -0.00018962:0.000262643:0.327902:0.838524:rs3131966
1 755775 rs3131965 A G . PASS AF=0.839643 ES:SE:LP:AF:ID -0.000182027:0.000266209:0.309804:0.839643:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869332 ES:SE:LP:AF:ID -0.000128917:0.000280963:0.187087:0.869332:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868884 ES:SE:LP:AF:ID -0.000122481:0.00028027:0.180456:0.868884:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867825 ES:SE:LP:AF:ID -0.000157411:0.000279714:0.244125:0.867825:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869023 ES:SE:LP:AF:ID -0.000123525:0.000280493:0.180456:0.869023:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869032 ES:SE:LP:AF:ID -0.000123711:0.000280515:0.180456:0.869032:rs4951862
1 758144 rs3131956 A G . PASS AF=0.869039 ES:SE:LP:AF:ID -0.000123306:0.000280521:0.180456:0.869039:rs3131956
1 758626 rs3131954 C T . PASS AF=0.869515 ES:SE:LP:AF:ID -0.000133776:0.000281281:0.200659:0.869515:rs3131954
1 760912 rs1048488 C T . PASS AF=0.838178 ES:SE:LP:AF:ID -0.000167883:0.000261419:0.283997:0.838178:rs1048488
1 761147 rs3115850 T C . PASS AF=0.838297 ES:SE:LP:AF:ID -0.000167628:0.000261604:0.283997:0.838297:rs3115850
1 761732 rs2286139 C T . PASS AF=0.862182 ES:SE:LP:AF:ID -0.000176426:0.000279516:0.275724:0.862182:rs2286139
1 763394 rs3115847 G A . PASS AF=0.70666 ES:SE:LP:AF:ID -0.000124407:0.000272122:0.187087:0.70666:rs3115847
1 766007 rs61768174 A C . PASS AF=0.105166 ES:SE:LP:AF:ID 0.000235224:0.000313502:0.346787:0.105166:rs61768174
1 768253 rs2977608 A C . PASS AF=0.761325 ES:SE:LP:AF:ID -0.000258235:0.000222194:0.60206:0.761325:rs2977608
1 768448 rs12562034 G A . PASS AF=0.106404 ES:SE:LP:AF:ID 0.000239237:0.000306299:0.366532:0.106404:rs12562034
1 769223 rs60320384 C G . PASS AF=0.12965 ES:SE:LP:AF:ID 0.000139562:0.000282264:0.207608:0.12965:rs60320384
1 771823 rs2977605 T C . PASS AF=0.868846 ES:SE:LP:AF:ID -0.000144675:0.000280759:0.21467:0.868846:rs2977605
1 771967 rs59066358 G A . PASS AF=0.12975 ES:SE:LP:AF:ID 0.000143515:0.000282081:0.21467:0.12975:rs59066358
1 772755 rs2905039 A C . PASS AF=0.868856 ES:SE:LP:AF:ID -0.000145969:0.000280765:0.221849:0.868856:rs2905039
1 776546 rs12124819 A G . PASS AF=0.265283 ES:SE:LP:AF:ID 0.000128872:0.000248155:0.221849:0.265283:rs12124819
1 777122 rs2980319 A T . PASS AF=0.870003 ES:SE:LP:AF:ID -0.000162466:0.000281357:0.251812:0.870003:rs2980319
1 778745 rs1055606 A G . PASS AF=0.128619 ES:SE:LP:AF:ID 0.000182234:0.000282467:0.283997:0.128619:rs1055606
1 779322 rs4040617 A G . PASS AF=0.128914 ES:SE:LP:AF:ID 0.000191653:0.000281993:0.30103:0.128914:rs4040617
1 780785 rs2977612 T A . PASS AF=0.868741 ES:SE:LP:AF:ID -0.000202746:0.000280608:0.327902:0.868741:rs2977612
1 781845 rs61768199 A G . PASS AF=0.10187 ES:SE:LP:AF:ID 0.000289383:0.000318005:0.443698:0.10187:rs61768199
1 782981 rs6594026 C T . PASS AF=0.129563 ES:SE:LP:AF:ID 0.000182157:0.000281898:0.283997:0.129563:rs6594026
1 785050 rs2905062 G A . PASS AF=0.868494 ES:SE:LP:AF:ID -0.000208115:0.000280542:0.337242:0.868494:rs2905062
1 785989 rs2980300 T C . PASS AF=0.868436 ES:SE:LP:AF:ID -0.000215219:0.000280717:0.356547:0.868436:rs2980300
1 787399 rs2905055 G T . PASS AF=0.860724 ES:SE:LP:AF:ID -0.000237543:0.000280505:0.39794:0.860724:rs2905055