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_1270.vcf.gz --id UKB-b:11765 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41246_1270.txt.gz --cohort_cases 4945 --cohort_controls 439292 --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": "2019-09-13T13:17:02.875389",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-11765/ukb-b-11765.vcf.gz; Date=Sun May 10 09:10:55 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-11765/UKB-b-11765_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-11765/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:41:09 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-11765/UKB-b-11765_data.vcf.gz ...
Read summary statistics for 4887267 SNPs.
Dropped 1322 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, 1100065 SNPs remain.
After merging with regression SNP LD, 1100065 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.004 (0.0012)
Lambda GC: 1.0339
Mean Chi^2: 1.0323
Intercept: 0.9952 (0.008)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Thu Oct 17 14:42:08 2019
Total time elapsed: 59.25sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9037,
"inflation_factor": 1.0475,
"mean_EFFECT": -1.3097e-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": 41491,
"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": 1100065,
"ldsc_nsnp_merge_regression_ld": 1100065,
"ldsc_observed_scale_h2_beta": 0.004,
"ldsc_observed_scale_h2_se": 0.0012,
"ldsc_intercept_beta": 0.9952,
"ldsc_intercept_se": 0.008,
"ldsc_lambda_gc": 1.0339,
"ldsc_mean_chisq": 1.0323,
"ldsc_ratio": -0.1486
}
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 | 4885954 | 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 | 4887267 | 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 | 8.665798e+00 | 5.766189e+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 | 7.857996e+07 | 5.665863e+07 | 828.0000000 | 3.186588e+07 | 6.894763e+07 | 1.146530e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -1.300000e-06 | 2.935000e-04 | -0.0022030 | -1.893000e-04 | -1.700000e-06 | 1.871000e-04 | 2.427800e-03 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.838000e-04 | 6.020000e-05 | 0.0002148 | 2.326000e-04 | 2.624000e-04 | 3.227000e-04 | 8.160000e-04 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.950249e-01 | 2.895471e-01 | 0.0000003 | 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 | 4.950256e-01 | 2.895222e-01 | 0.0000003 | 2.424963e-01 | 4.937225e-01 | 7.457593e-01 | 9.999997e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.565033e-01 | 2.360167e-01 | 0.0707790 | 1.529070e-01 | 2.930120e-01 | 5.226180e-01 | 9.292210e-01 | ▇▅▃▂▂ |
| numeric | AF_reference | 41491 | 0.9915104 | NA | NA | NA | NA | NA | NA | NA | 3.494238e-01 | 2.334033e-01 | 0.0000000 | 1.569490e-01 | 2.945290e-01 | 5.117810e-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 | -0.0002526 | 0.0003952 | 0.5199996 | 0.5226648 | 0.623717 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0006947 | 0.0003915 | 0.0759994 | 0.0760122 | 0.400430 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0005825 | 0.0006260 | 0.3500000 | 0.3520903 | 0.103543 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0005204 | 0.0003855 | 0.1800002 | 0.1769480 | 0.456852 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0002552 | 0.0007607 | 0.7400005 | 0.7372094 | 0.074437 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0001620 | 0.0004404 | 0.7099994 | 0.7129234 | 0.240923 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | 0.0006324 | 0.0005494 | 0.2500000 | 0.2496836 | 0.913485 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | 0.0001067 | 0.0003691 | 0.7700005 | 0.7725537 | 0.116351 | 0.1417730 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0000706 | 0.0002734 | 0.8000000 | 0.7962837 | 0.515718 | 0.2751600 | NA |
| 1 | 722670 | rs116030099 | T | C | -0.0001402 | 0.0004510 | 0.7600007 | 0.7560056 | 0.101244 | 0.0413339 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51213613 | rs34726907 | C | T | -0.0004024 | 0.0003382 | 0.2300001 | 0.2341828 | 0.127900 | 0.1727240 | NA |
| 22 | 51216564 | rs9616970 | T | C | -0.0004326 | 0.0003368 | 0.2000000 | 0.1989879 | 0.128421 | 0.1563500 | NA |
| 22 | 51217954 | rs9616974 | G | A | -0.0003028 | 0.0004273 | 0.4799997 | 0.4785061 | 0.073358 | 0.0621006 | NA |
| 22 | 51218224 | rs9616975 | C | A | -0.0003085 | 0.0004275 | 0.4700002 | 0.4704584 | 0.073380 | 0.0619010 | NA |
| 22 | 51218377 | rs2519461 | G | C | -0.0003245 | 0.0004270 | 0.4500005 | 0.4472319 | 0.073661 | 0.0826677 | NA |
| 22 | 51219006 | rs28729663 | G | A | -0.0004942 | 0.0003297 | 0.1299999 | 0.1338959 | 0.138020 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | -0.0003469 | 0.0004278 | 0.4199997 | 0.4175153 | 0.073789 | 0.0654952 | NA |
| 22 | 51221731 | rs115055839 | T | C | -0.0003036 | 0.0004281 | 0.4799997 | 0.4781881 | 0.073284 | 0.0625000 | NA |
| 22 | 51229805 | rs9616985 | T | C | -0.0002704 | 0.0004297 | 0.5300002 | 0.5291157 | 0.073121 | 0.0730831 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0000719 | 0.0002629 | 0.7800007 | 0.7844307 | 0.298146 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623717 ES:SE:LP:AF:ID -0.000252618:0.000395183:0.283997:0.623717:rs10399793
1 54676 rs2462492 C T . PASS AF=0.40043 ES:SE:LP:AF:ID 0.000694702:0.000391534:1.11919:0.40043:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103543 ES:SE:LP:AF:ID 0.0005825:0.000625979:0.455932:0.103543:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456852 ES:SE:LP:AF:ID 0.00052045:0.000385458:0.744727:0.456852:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074437 ES:SE:LP:AF:ID 0.000255242:0.000760665:0.130768:0.074437:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240923 ES:SE:LP:AF:ID 0.000162035:0.000440393:0.148742:0.240923:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913485 ES:SE:LP:AF:ID 0.000632422:0.000549398:0.60206:0.913485:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116351 ES:SE:LP:AF:ID 0.000106687:0.000369113:0.113509:0.116351:rs12238997
1 706368 rs12029736 A G . PASS AF=0.515718 ES:SE:LP:AF:ID -7.05842e-05:0.000273413:0.09691:0.515718:rs12029736
1 722670 rs116030099 T C . PASS AF=0.101244 ES:SE:LP:AF:ID -0.000140154:0.000451047:0.119186:0.101244:rs116030099
1 729679 rs4951859 C G . PASS AF=0.843138 ES:SE:LP:AF:ID -3.36572e-05:0.000319859:0.0362122:0.843138:rs4951859
1 731718 rs58276399 T C . PASS AF=0.122339 ES:SE:LP:AF:ID 4.7246e-05:0.000350095:0.05061:0.122339:rs58276399
1 734349 rs141242758 T C . PASS AF=0.121582 ES:SE:LP:AF:ID 6.60567e-05:0.00035024:0.0705811:0.121582:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132405 ES:SE:LP:AF:ID -0.000102522:0.00034516:0.113509:0.132405:rs79010578
1 752566 rs3094315 G A . PASS AF=0.838827 ES:SE:LP:AF:ID 4.71554e-05:0.000309684:0.0555173:0.838827:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838449 ES:SE:LP:AF:ID 5.6435e-05:0.000309338:0.0655015:0.838449:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869709 ES:SE:LP:AF:ID 6.65699e-05:0.00033195:0.0757207:0.869709:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129955 ES:SE:LP:AF:ID -0.000140935:0.00033261:0.173925:0.129955:rs2073813
1 754182 rs3131969 A G . PASS AF=0.869048 ES:SE:LP:AF:ID 8.58714e-05:0.00033129:0.09691:0.869048:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869149 ES:SE:LP:AF:ID 8.68549e-05:0.000331425:0.102373:0.869149:rs3131968
1 754334 rs3131967 T C . PASS AF=0.869052 ES:SE:LP:AF:ID 8.50452e-05:0.000331284:0.09691:0.869052:rs3131967
1 754503 rs3115859 G A . PASS AF=0.837898 ES:SE:LP:AF:ID 4.96257e-05:0.000308478:0.0604807:0.837898:rs3115859
1 754964 rs3131966 C T . PASS AF=0.83853 ES:SE:LP:AF:ID 3.90901e-05:0.000309345:0.0457575:0.83853:rs3131966
1 755775 rs3131965 A G . PASS AF=0.839647 ES:SE:LP:AF:ID 7.1547e-06:0.000313546:0.00877392:0.839647:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869338 ES:SE:LP:AF:ID 5.3158e-05:0.00033092:0.0604807:0.869338:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868891 ES:SE:LP:AF:ID 4.54109e-05:0.000330103:0.05061:0.868891:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867834 ES:SE:LP:AF:ID 8.9566e-05:0.00032945:0.102373:0.867834:rs3115853
1 757734 rs4951929 C T . PASS AF=0.86903 ES:SE:LP:AF:ID 5.18198e-05:0.000330367:0.0555173:0.86903:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869038 ES:SE:LP:AF:ID 5.1746e-05:0.000330392:0.0555173:0.869038:rs4951862
1 758144 rs3131956 A G . PASS AF=0.869046 ES:SE:LP:AF:ID 5.56249e-05:0.0003304:0.0604807:0.869046:rs3131956
1 758626 rs3131954 C T . PASS AF=0.869521 ES:SE:LP:AF:ID 5.75668e-05:0.000331295:0.0655015:0.869521:rs3131954
1 760912 rs1048488 C T . PASS AF=0.838184 ES:SE:LP:AF:ID 4.45344e-05:0.000307908:0.0555173:0.838184:rs1048488
1 761147 rs3115850 T C . PASS AF=0.838304 ES:SE:LP:AF:ID 4.16399e-05:0.000308126:0.05061:0.838304:rs3115850
1 761732 rs2286139 C T . PASS AF=0.862192 ES:SE:LP:AF:ID 6.82042e-05:0.000329219:0.0757207:0.862192:rs2286139
1 763394 rs3115847 G A . PASS AF=0.706661 ES:SE:LP:AF:ID -7.59569e-05:0.000320508:0.091515:0.706661:rs3115847
1 766007 rs61768174 A C . PASS AF=0.105158 ES:SE:LP:AF:ID -0.000162883:0.000369251:0.180456:0.105158:rs61768174
1 768253 rs2977608 A C . PASS AF=0.76134 ES:SE:LP:AF:ID 9.83253e-05:0.000261718:0.148742:0.76134:rs2977608
1 768448 rs12562034 G A . PASS AF=0.106399 ES:SE:LP:AF:ID -5.92632e-05:0.000360797:0.0604807:0.106399:rs12562034
1 769223 rs60320384 C G . PASS AF=0.129642 ES:SE:LP:AF:ID -0.000111185:0.000332453:0.130768:0.129642:rs60320384
1 771823 rs2977605 T C . PASS AF=0.868853 ES:SE:LP:AF:ID 4.03968e-05:0.000330681:0.0457575:0.868853:rs2977605
1 771967 rs59066358 G A . PASS AF=0.129742 ES:SE:LP:AF:ID -0.0001097:0.000332237:0.130768:0.129742:rs59066358
1 772755 rs2905039 A C . PASS AF=0.868863 ES:SE:LP:AF:ID 3.78184e-05:0.000330687:0.0409586:0.868863:rs2905039
1 776546 rs12124819 A G . PASS AF=0.265282 ES:SE:LP:AF:ID -0.000128199:0.000292288:0.180456:0.265282:rs12124819
1 777122 rs2980319 A T . PASS AF=0.870009 ES:SE:LP:AF:ID 7.60683e-06:0.000331384:0.00877392:0.870009:rs2980319
1 777232 rs112618790 C T . PASS AF=0.095082 ES:SE:LP:AF:ID 4.06306e-06:0.000384252:0.00436481:0.095082:rs112618790
1 778745 rs1055606 A G . PASS AF=0.128612 ES:SE:LP:AF:ID -8.23618e-05:0.000332692:0.09691:0.128612:rs1055606
1 779322 rs4040617 A G . PASS AF=0.128907 ES:SE:LP:AF:ID -6.06878e-05:0.000332133:0.0655015:0.128907:rs4040617
1 780785 rs2977612 T A . PASS AF=0.868748 ES:SE:LP:AF:ID 3.97894e-05:0.000330502:0.0457575:0.868748:rs2977612
1 781845 rs61768199 A G . PASS AF=0.101861 ES:SE:LP:AF:ID -0.0001079:0.000374555:0.113509:0.101861:rs61768199
1 782981 rs6594026 C T . PASS AF=0.129556 ES:SE:LP:AF:ID -8.64068e-05:0.000332023:0.102373:0.129556:rs6594026