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_1080.vcf.gz --id UKB-b:11247 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41245_1080.txt.gz --cohort_cases 5378 --cohort_controls 455759 --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": "2019-09-12T19:13:03.805135",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-11247/ukb-b-11247.vcf.gz; Date=Sun May 10 13:02:33 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-11247/UKB-b-11247_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-11247/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:40:19 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-11247/UKB-b-11247_data.vcf.gz ...
Read summary statistics for 5019473 SNPs.
Dropped 1448 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, 1118131 SNPs remain.
After merging with regression SNP LD, 1118131 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0055 (0.0011)
Lambda GC: 1.0545
Mean Chi^2: 1.0631
Intercept: 1.0114 (0.0077)
Ratio: 0.1815 (0.1222)
Analysis finished at Thu Oct 17 14:41:16 2019
Total time elapsed: 56.69sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9069,
"inflation_factor": 1.0475,
"mean_EFFECT": 3.8399e-07,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 3,
"n_p_sig": 153,
"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": 42898,
"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": 1118131,
"ldsc_nsnp_merge_regression_ld": 1118131,
"ldsc_observed_scale_h2_beta": 0.0055,
"ldsc_observed_scale_h2_se": 0.0011,
"ldsc_intercept_beta": 1.0114,
"ldsc_intercept_se": 0.0077,
"ldsc_lambda_gc": 1.0545,
"ldsc_mean_chisq": 1.0631,
"ldsc_ratio": 0.1807
}
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 | 5018036 | 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 | 5019473 | 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 | 8.669032e+00 | 5.765828e+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 | 7.856802e+07 | 5.663521e+07 | 828.0000000 | 3.188530e+07 | 6.896582e+07 | 1.145980e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.000000e-07 | 3.049000e-04 | -0.0021752 | -1.932000e-04 | 1.000000e-07 | 1.926000e-04 | 3.766800e-03 | ▁▇▂▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.888000e-04 | 6.500000e-05 | 0.0002153 | 2.337000e-04 | 2.655000e-04 | 3.303000e-04 | 8.177000e-04 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.913366e-01 | 2.910561e-01 | 0.0000000 | 2.399999e-01 | 4.899999e-01 | 7.400005e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.913360e-01 | 2.910291e-01 | 0.0000000 | 2.376490e-01 | 4.887069e-01 | 7.431095e-01 | 9.999995e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.509482e-01 | 2.387144e-01 | 0.0650800 | 1.453660e-01 | 2.851450e-01 | 5.174210e-01 | 9.349200e-01 | ▇▅▃▂▂ |
| numeric | AF_reference | 42898 | 0.9914537 | NA | NA | NA | NA | NA | NA | NA | 3.443878e-01 | 2.353838e-01 | 0.0000000 | 1.501600e-01 | 2.875400e-01 | 5.067890e-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 | 0.0004953 | 0.0003962 | 0.2099999 | 0.2112956 | 0.623777 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0003751 | 0.0003926 | 0.3400001 | 0.3394226 | 0.400402 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | -0.0000095 | 0.0006277 | 0.9900000 | 0.9879142 | 0.103551 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0000714 | 0.0003865 | 0.8499999 | 0.8534169 | 0.456858 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0005389 | 0.0007620 | 0.4799997 | 0.4794302 | 0.074516 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0002002 | 0.0004415 | 0.6499995 | 0.6501824 | 0.240944 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | -0.0002341 | 0.0005508 | 0.6700003 | 0.6707661 | 0.913468 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | 0.0000500 | 0.0003701 | 0.8900000 | 0.8924802 | 0.116327 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | -0.0003607 | 0.0005422 | 0.5099998 | 0.5058571 | 0.067307 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0000999 | 0.0002741 | 0.7199992 | 0.7155779 | 0.515674 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51213613 | rs34726907 | C | T | -0.0001063 | 0.0003393 | 0.7499995 | 0.7540912 | 0.127818 | 0.1727240 | NA |
| 22 | 51216564 | rs9616970 | T | C | -0.0000656 | 0.0003378 | 0.8499999 | 0.8461207 | 0.128329 | 0.1563500 | NA |
| 22 | 51217954 | rs9616974 | G | A | 0.0003442 | 0.0004286 | 0.4199997 | 0.4219492 | 0.073306 | 0.0621006 | NA |
| 22 | 51218224 | rs9616975 | C | A | 0.0003669 | 0.0004288 | 0.3900004 | 0.3921984 | 0.073326 | 0.0619010 | NA |
| 22 | 51218377 | rs2519461 | G | C | 0.0003705 | 0.0004283 | 0.3900004 | 0.3869556 | 0.073614 | 0.0826677 | NA |
| 22 | 51219006 | rs28729663 | G | A | 0.0000206 | 0.0003306 | 0.9500000 | 0.9503249 | 0.137952 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | 0.0003257 | 0.0004292 | 0.4500005 | 0.4479409 | 0.073739 | 0.0654952 | NA |
| 22 | 51221731 | rs115055839 | T | C | 0.0003953 | 0.0004294 | 0.3599996 | 0.3572718 | 0.073228 | 0.0625000 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0003898 | 0.0004310 | 0.3700002 | 0.3657971 | 0.073066 | 0.0730831 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0004659 | 0.0002636 | 0.0769999 | 0.0771758 | 0.297976 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623777 ES:SE:LP:AF:ID 0.000495293:0.000396231:0.677781:0.623777:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400402 ES:SE:LP:AF:ID -0.000375084:0.000392632:0.468521:0.400402:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103551 ES:SE:LP:AF:ID -9.50874e-06:0.000627725:0.00436481:0.103551:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456858 ES:SE:LP:AF:ID 7.14166e-05:0.000386536:0.0705811:0.456858:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074516 ES:SE:LP:AF:ID 0.000538933:0.000762045:0.318759:0.074516:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240944 ES:SE:LP:AF:ID 0.000200245:0.000441546:0.187087:0.240944:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913468 ES:SE:LP:AF:ID -0.000234148:0.000550811:0.173925:0.913468:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116327 ES:SE:LP:AF:ID 5.00185e-05:0.000370051:0.05061:0.116327:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067307 ES:SE:LP:AF:ID -0.000360717:0.000542185:0.29243:0.067307:rs72631875
1 706368 rs12029736 A G . PASS AF=0.515674 ES:SE:LP:AF:ID 9.98836e-05:0.000274123:0.142668:0.515674:rs12029736
1 722670 rs116030099 T C . PASS AF=0.101206 ES:SE:LP:AF:ID 0.000358504:0.000452246:0.366532:0.101206:rs116030099
1 729679 rs4951859 C G . PASS AF=0.843193 ES:SE:LP:AF:ID 0.000110603:0.000320681:0.136677:0.843193:rs4951859
1 731718 rs58276399 T C . PASS AF=0.122317 ES:SE:LP:AF:ID -8.63766e-05:0.000351022:0.091515:0.122317:rs58276399
1 734349 rs141242758 T C . PASS AF=0.12156 ES:SE:LP:AF:ID -5.93871e-05:0.000351169:0.0604807:0.12156:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132336 ES:SE:LP:AF:ID -0.000365169:0.000346118:0.537602:0.132336:rs79010578
1 752566 rs3094315 G A . PASS AF=0.838936 ES:SE:LP:AF:ID 0.000133645:0.000310557:0.173925:0.838936:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838563 ES:SE:LP:AF:ID 0.000141307:0.000310222:0.187087:0.838563:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869771 ES:SE:LP:AF:ID 3.7453e-05:0.000332888:0.0409586:0.869771:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129882 ES:SE:LP:AF:ID -6.31667e-05:0.000333562:0.0705811:0.129882:rs2073813
1 754182 rs3131969 A G . PASS AF=0.869112 ES:SE:LP:AF:ID 3.14744e-05:0.000332233:0.0362122:0.869112:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869209 ES:SE:LP:AF:ID 2.12852e-05:0.000332364:0.0222764:0.869209:rs3131968
1 754334 rs3131967 T C . PASS AF=0.869115 ES:SE:LP:AF:ID 3.09717e-05:0.000332227:0.0315171:0.869115:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838016 ES:SE:LP:AF:ID 0.000148877:0.000309361:0.200659:0.838016:rs3115859
1 754964 rs3131966 C T . PASS AF=0.838646 ES:SE:LP:AF:ID 0.000127661:0.00031023:0.167491:0.838646:rs3131966
1 755775 rs3131965 A G . PASS AF=0.839762 ES:SE:LP:AF:ID 0.000109458:0.000314428:0.136677:0.839762:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869396 ES:SE:LP:AF:ID 4.17677e-05:0.000331845:0.0457575:0.869396:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868946 ES:SE:LP:AF:ID 6.75187e-05:0.000331013:0.0757207:0.868946:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867895 ES:SE:LP:AF:ID 7.28697e-05:0.000330374:0.0809219:0.867895:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869088 ES:SE:LP:AF:ID 5.4665e-05:0.000331282:0.0604807:0.869088:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869096 ES:SE:LP:AF:ID 5.43141e-05:0.000331308:0.0604807:0.869096:rs4951862
1 758144 rs3131956 A G . PASS AF=0.869104 ES:SE:LP:AF:ID 5.37387e-05:0.000331315:0.0604807:0.869104:rs3131956
1 758626 rs3131954 C T . PASS AF=0.86958 ES:SE:LP:AF:ID 4.90982e-05:0.000332223:0.0555173:0.86958:rs3131954
1 760912 rs1048488 C T . PASS AF=0.838296 ES:SE:LP:AF:ID 0.000107218:0.000308772:0.136677:0.838296:rs1048488
1 761147 rs3115850 T C . PASS AF=0.838417 ES:SE:LP:AF:ID 0.000114457:0.00030899:0.148742:0.838417:rs3115850
1 761732 rs2286139 C T . PASS AF=0.862251 ES:SE:LP:AF:ID 1.37124e-05:0.000330112:0.0132283:0.862251:rs2286139
1 763394 rs3115847 G A . PASS AF=0.706754 ES:SE:LP:AF:ID -0.000192314:0.00032137:0.259637:0.706754:rs3115847
1 766007 rs61768174 A C . PASS AF=0.10513 ES:SE:LP:AF:ID -0.000186389:0.000370238:0.21467:0.10513:rs61768174
1 768253 rs2977608 A C . PASS AF=0.761335 ES:SE:LP:AF:ID 0.000402469:0.000262315:0.920819:0.761335:rs2977608
1 768448 rs12562034 G A . PASS AF=0.106455 ES:SE:LP:AF:ID -0.000709802:0.000361572:1.30103:0.106455:rs12562034
1 769223 rs60320384 C G . PASS AF=0.129579 ES:SE:LP:AF:ID -5.62464e-05:0.000333371:0.0604807:0.129579:rs60320384
1 771823 rs2977605 T C . PASS AF=0.868907 ES:SE:LP:AF:ID 1.44048e-05:0.000331593:0.0132283:0.868907:rs2977605
1 771967 rs59066358 G A . PASS AF=0.12968 ES:SE:LP:AF:ID -5.06898e-05:0.000333156:0.0555173:0.12968:rs59066358
1 772755 rs2905039 A C . PASS AF=0.868918 ES:SE:LP:AF:ID 1.232e-05:0.000331601:0.0132283:0.868918:rs2905039
1 776546 rs12124819 A G . PASS AF=0.265367 ES:SE:LP:AF:ID 0.000817277:0.000292986:2.27572:0.265367:rs12124819
1 777122 rs2980319 A T . PASS AF=0.870036 ES:SE:LP:AF:ID 1.29601e-05:0.000332274:0.0132283:0.870036:rs2980319
1 777232 rs112618790 C T . PASS AF=0.095116 ES:SE:LP:AF:ID -0.000662134:0.000385142:1.0655:0.095116:rs112618790
1 778745 rs1055606 A G . PASS AF=0.128581 ES:SE:LP:AF:ID -4.24143e-05:0.000333583:0.0457575:0.128581:rs1055606
1 779322 rs4040617 A G . PASS AF=0.12888 ES:SE:LP:AF:ID -4.51566e-05:0.000333015:0.05061:0.12888:rs4040617
1 780785 rs2977612 T A . PASS AF=0.868777 ES:SE:LP:AF:ID -2.03161e-05:0.000331386:0.0222764:0.868777:rs2977612
1 781845 rs61768199 A G . PASS AF=0.101863 ES:SE:LP:AF:ID -0.000167251:0.000375521:0.180456:0.101863:rs61768199