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_41204_Z530.vcf.gz --id UKB-b:9019 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41204_Z530.txt.gz --cohort_cases 4779 --cohort_controls 458231 --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": "2019-09-13T00:40:08.123941",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-9019/ukb-b-9019.vcf.gz; Date=Sun May 10 12:16:29 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-9019/UKB-b-9019_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-9019/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:42:07 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-9019/UKB-b-9019_data.vcf.gz ...
Read summary statistics for 4832700 SNPs.
Dropped 1264 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, 1092262 SNPs remain.
After merging with regression SNP LD, 1092262 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0028 (0.001)
Lambda GC: 1.0312
Mean Chi^2: 1.0279
Intercept: 1.0004 (0.0072)
Ratio: 0.0157 (0.2582)
Analysis finished at Thu Oct 17 14:43:03 2019
Total time elapsed: 55.29sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9023,
"inflation_factor": 1.0475,
"mean_EFFECT": 7.6947e-08,
"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": 40892,
"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": 1092262,
"ldsc_nsnp_merge_regression_ld": 1092262,
"ldsc_observed_scale_h2_beta": 0.0028,
"ldsc_observed_scale_h2_se": 0.001,
"ldsc_intercept_beta": 1.0004,
"ldsc_intercept_se": 0.0072,
"ldsc_lambda_gc": 1.0312,
"ldsc_mean_chisq": 1.0279,
"ldsc_ratio": 0.0143
}
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 | 4831445 | 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 | 4832700 | 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 | 8.667162e+00 | 5.766854e+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 | 7.857521e+07 | 5.665894e+07 | 828.0000000 | 3.185640e+07 | 6.893404e+07 | 1.146481e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.000000e-07 | 2.749000e-04 | -0.0018117 | -1.773000e-04 | -6.000000e-07 | 1.772000e-04 | 1.614000e-03 | ▁▁▇▂▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.662000e-04 | 5.510000e-05 | 0.0002027 | 2.193000e-04 | 2.468000e-04 | 3.019000e-04 | 7.697000e-04 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.947924e-01 | 2.900575e-01 | 0.0000001 | 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 | 4.947962e-01 | 2.900305e-01 | 0.0000001 | 2.415566e-01 | 4.930730e-01 | 7.457617e-01 | 9.999997e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.588369e-01 | 2.348669e-01 | 0.0732380 | 1.561670e-01 | 2.962730e-01 | 5.247560e-01 | 9.267620e-01 | ▇▅▃▂▂ |
| numeric | AF_reference | 40892 | 0.9915385 | NA | NA | NA | NA | NA | NA | NA | 3.515302e-01 | 2.325673e-01 | 0.0000000 | 1.597440e-01 | 2.975240e-01 | 5.137780e-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 | -0.0003655 | 0.0003730 | 0.3300000 | 0.3271715 | 0.623765 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0004398 | 0.0003696 | 0.2300001 | 0.2339924 | 0.400401 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0004464 | 0.0005908 | 0.4500005 | 0.4498809 | 0.103556 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0001129 | 0.0003639 | 0.7600007 | 0.7563660 | 0.456846 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0001216 | 0.0007175 | 0.8700001 | 0.8653995 | 0.074506 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0006646 | 0.0004156 | 0.1100001 | 0.1097987 | 0.240959 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | -0.0007144 | 0.0005185 | 0.1700000 | 0.1683058 | 0.913475 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | 0.0000400 | 0.0003483 | 0.9100000 | 0.9086074 | 0.116329 | 0.1417730 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0001837 | 0.0002580 | 0.4799997 | 0.4764187 | 0.515645 | 0.2751600 | NA |
| 1 | 722670 | rs116030099 | T | C | -0.0003480 | 0.0004257 | 0.4100001 | 0.4136253 | 0.101200 | 0.0413339 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51211392 | rs3888396 | T | C | 0.0005469 | 0.0003395 | 0.1100001 | 0.1071696 | 0.132638 | 0.1641370 | NA |
| 22 | 51212875 | rs2238837 | A | C | -0.0001775 | 0.0002424 | 0.4600002 | 0.4640710 | 0.331457 | 0.3724040 | NA |
| 22 | 51213613 | rs34726907 | C | T | 0.0001733 | 0.0003194 | 0.5900000 | 0.5872683 | 0.127814 | 0.1727240 | NA |
| 22 | 51216564 | rs9616970 | T | C | 0.0001501 | 0.0003180 | 0.6400000 | 0.6368968 | 0.128328 | 0.1563500 | NA |
| 22 | 51217954 | rs9616974 | G | A | -0.0000509 | 0.0004035 | 0.9000000 | 0.8995281 | 0.073312 | 0.0621006 | NA |
| 22 | 51218224 | rs9616975 | C | A | -0.0000588 | 0.0004037 | 0.8800001 | 0.8841065 | 0.073333 | 0.0619010 | NA |
| 22 | 51218377 | rs2519461 | G | C | -0.0000482 | 0.0004032 | 0.9000000 | 0.9048320 | 0.073622 | 0.0826677 | NA |
| 22 | 51219006 | rs28729663 | G | A | 0.0001326 | 0.0003112 | 0.6700003 | 0.6699880 | 0.137950 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | -0.0000117 | 0.0004040 | 0.9800000 | 0.9768894 | 0.073744 | 0.0654952 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0001761 | 0.0002481 | 0.4799997 | 0.4779015 | 0.297974 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623765 ES:SE:LP:AF:ID -0.000365501:0.000373026:0.481486:0.623765:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID -0.00043982:0.000369554:0.638272:0.400401:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103556 ES:SE:LP:AF:ID 0.000446446:0.000590839:0.346787:0.103556:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456846 ES:SE:LP:AF:ID -0.000112893:0.00036387:0.119186:0.456846:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074506 ES:SE:LP:AF:ID 0.000121614:0.000717466:0.0604807:0.074506:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240959 ES:SE:LP:AF:ID -0.000664643:0.000415636:0.958607:0.240959:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913475 ES:SE:LP:AF:ID -0.000714358:0.000518527:0.769551:0.913475:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116329 ES:SE:LP:AF:ID 3.99849e-05:0.000348315:0.0409586:0.116329:rs12238997
1 706368 rs12029736 A G . PASS AF=0.515645 ES:SE:LP:AF:ID 0.000183728:0.000258018:0.318759:0.515645:rs12029736
1 722670 rs116030099 T C . PASS AF=0.1012 ES:SE:LP:AF:ID -0.000348035:0.000425715:0.387216:0.1012:rs116030099
1 729679 rs4951859 C G . PASS AF=0.843204 ES:SE:LP:AF:ID -0.000175775:0.00030186:0.251812:0.843204:rs4951859
1 731718 rs58276399 T C . PASS AF=0.122312 ES:SE:LP:AF:ID 3.68724e-05:0.000330412:0.0409586:0.122312:rs58276399
1 734349 rs141242758 T C . PASS AF=0.121554 ES:SE:LP:AF:ID 4.0744e-05:0.000330551:0.0457575:0.121554:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132335 ES:SE:LP:AF:ID 0.000252597:0.000325791:0.356547:0.132335:rs79010578
1 752566 rs3094315 G A . PASS AF=0.838945 ES:SE:LP:AF:ID -0.000154477:0.000292331:0.221849:0.838945:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838573 ES:SE:LP:AF:ID -0.000150318:0.000292017:0.21467:0.838573:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869776 ES:SE:LP:AF:ID -3.08763e-05:0.000313345:0.0362122:0.869776:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129876 ES:SE:LP:AF:ID 6.67352e-05:0.000313986:0.0809219:0.129876:rs2073813
1 754182 rs3131969 A G . PASS AF=0.869117 ES:SE:LP:AF:ID -2.08926e-05:0.00031273:0.0222764:0.869117:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869215 ES:SE:LP:AF:ID -1.51126e-07:0.000312854:-0:0.869215:rs3131968
1 754334 rs3131967 T C . PASS AF=0.869121 ES:SE:LP:AF:ID -1.23397e-05:0.000312725:0.0132283:0.869121:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838026 ES:SE:LP:AF:ID -0.000120573:0.000291206:0.167491:0.838026:rs3115859
1 754964 rs3131966 C T . PASS AF=0.838657 ES:SE:LP:AF:ID -0.000104227:0.000292025:0.142668:0.838657:rs3131966
1 755775 rs3131965 A G . PASS AF=0.83977 ES:SE:LP:AF:ID -7.61112e-05:0.000295974:0.09691:0.83977:rs3131965
1 755890 rs3115858 A T . PASS AF=0.8694 ES:SE:LP:AF:ID -3.2577e-05:0.000312363:0.0362122:0.8694:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868947 ES:SE:LP:AF:ID -6.10488e-05:0.000311577:0.0757207:0.868947:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867898 ES:SE:LP:AF:ID -7.83305e-05:0.000310979:0.09691:0.867898:rs3115853
1 757734 rs4951929 C T . PASS AF=0.86909 ES:SE:LP:AF:ID -6.10165e-05:0.000311832:0.0757207:0.86909:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869098 ES:SE:LP:AF:ID -6.12303e-05:0.000311856:0.0757207:0.869098:rs4951862
1 758144 rs3131956 A G . PASS AF=0.869106 ES:SE:LP:AF:ID -6.18226e-05:0.000311863:0.0757207:0.869106:rs3131956
1 758626 rs3131954 C T . PASS AF=0.869584 ES:SE:LP:AF:ID -2.73188e-05:0.000312719:0.0315171:0.869584:rs3131954
1 760912 rs1048488 C T . PASS AF=0.838306 ES:SE:LP:AF:ID -0.000120788:0.000290654:0.167491:0.838306:rs1048488
1 761147 rs3115850 T C . PASS AF=0.838427 ES:SE:LP:AF:ID -0.000122814:0.000290859:0.173925:0.838427:rs3115850
1 761732 rs2286139 C T . PASS AF=0.862254 ES:SE:LP:AF:ID -0.000132355:0.000310735:0.173925:0.862254:rs2286139
1 763394 rs3115847 G A . PASS AF=0.706753 ES:SE:LP:AF:ID -0.000285126:0.000302502:0.455932:0.706753:rs3115847
1 766007 rs61768174 A C . PASS AF=0.105145 ES:SE:LP:AF:ID 0.000230336:0.000348476:0.29243:0.105145:rs61768174
1 768253 rs2977608 A C . PASS AF=0.761297 ES:SE:LP:AF:ID -0.000108529:0.000246894:0.180456:0.761297:rs2977608
1 768448 rs12562034 G A . PASS AF=0.10649 ES:SE:LP:AF:ID 0.000112611:0.000340297:0.130768:0.10649:rs12562034
1 769223 rs60320384 C G . PASS AF=0.129581 ES:SE:LP:AF:ID 9.41336e-05:0.000313799:0.119186:0.129581:rs60320384
1 771823 rs2977605 T C . PASS AF=0.868906 ES:SE:LP:AF:ID -8.25293e-05:0.000312122:0.102373:0.868906:rs2977605
1 771967 rs59066358 G A . PASS AF=0.129682 ES:SE:LP:AF:ID 0.000115714:0.000313596:0.148742:0.129682:rs59066358
1 772755 rs2905039 A C . PASS AF=0.868916 ES:SE:LP:AF:ID -7.75021e-05:0.000312128:0.09691:0.868916:rs2905039
1 776546 rs12124819 A G . PASS AF=0.265385 ES:SE:LP:AF:ID -5.00705e-05:0.00027579:0.0655015:0.265385:rs12124819
1 777122 rs2980319 A T . PASS AF=0.870039 ES:SE:LP:AF:ID -0.000110529:0.000312765:0.142668:0.870039:rs2980319
1 777232 rs112618790 C T . PASS AF=0.095141 ES:SE:LP:AF:ID 0.000120308:0.000362489:0.130768:0.095141:rs112618790
1 778745 rs1055606 A G . PASS AF=0.12858 ES:SE:LP:AF:ID 0.000138857:0.000314:0.180456:0.12858:rs1055606
1 779322 rs4040617 A G . PASS AF=0.128877 ES:SE:LP:AF:ID 0.000147803:0.000313467:0.19382:0.128877:rs4040617
1 780785 rs2977612 T A . PASS AF=0.86878 ES:SE:LP:AF:ID -0.000122329:0.00031193:0.161151:0.86878:rs2977612
1 781845 rs61768199 A G . PASS AF=0.101873 ES:SE:LP:AF:ID 0.000271802:0.000353448:0.356547:0.101873:rs61768199
1 782981 rs6594026 C T . PASS AF=0.129518 ES:SE:LP:AF:ID 0.000142856:0.000313366:0.187087:0.129518:rs6594026