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_20107_9.vcf.gz --id UKB-b:20211 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20107_9.txt.gz --cohort_cases 38850 --cohort_controls 361837 --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": "2019-09-13T11:47:23.636058",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-20211/ukb-b-20211.vcf.gz; Date=Sat May 9 23:26:10 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-20211/UKB-b-20211_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-20211/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-20211/UKB-b-20211_data.vcf.gz ...
Read summary statistics for 7880914 SNPs.
Dropped 5900 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, 1281152 SNPs remain.
After merging with regression SNP LD, 1281152 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0198 (0.002)
Lambda GC: 1.1948
Mean Chi^2: 1.2339
Intercept: 1.0767 (0.0076)
Ratio: 0.3279 (0.0324)
Analysis finished at Thu Oct 17 14:43:35 2019
Total time elapsed: 1.0m:28.18sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9422,
"inflation_factor": 1.1474,
"mean_EFFECT": 0,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 26,
"n_p_sig": 1035,
"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": 73491,
"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": 1281152,
"ldsc_nsnp_merge_regression_ld": 1281152,
"ldsc_observed_scale_h2_beta": 0.0198,
"ldsc_observed_scale_h2_se": 0.002,
"ldsc_intercept_beta": 1.0767,
"ldsc_intercept_se": 0.0076,
"ldsc_lambda_gc": 1.1948,
"ldsc_mean_chisq": 1.2339,
"ldsc_ratio": 0.3279
}
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 | TRUE |
| 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 | 7875041 | 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 | 7880914 | 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 | 8.661094e+00 | 5.763697e+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 | 7.870569e+07 | 5.642567e+07 | 828.0000000 | 3.224675e+07 | 6.917336e+07 | 1.145607e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.360000e-05 | 1.680100e-03 | -0.0185597 | -8.042000e-04 | 2.400000e-06 | 8.127000e-04 | 2.240080e-02 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.366200e-03 | 8.006000e-04 | 0.0006365 | 7.352000e-04 | 1.013600e-03 | 1.789000e-03 | 7.857800e-03 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.742790e-01 | 2.956388e-01 | 0.0000000 | 2.099999e-01 | 4.700002e-01 | 7.300002e-01 | 1.000000e+00 | ▇▇▇▆▆ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.742811e-01 | 2.956150e-01 | 0.0000000 | 2.120319e-01 | 4.650958e-01 | 7.304220e-01 | 1.000000e+00 | ▇▇▆▆▆ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.488467e-01 | 2.608021e-01 | 0.0090100 | 3.768800e-02 | 1.411960e-01 | 3.938850e-01 | 9.909900e-01 | ▇▂▂▁▁ |
| numeric | AF_reference | 73491 | 0.9906748 | NA | NA | NA | NA | NA | NA | NA | 2.480206e-01 | 2.526351e-01 | 0.0000000 | 4.033550e-02 | 1.563500e-01 | 3.889780e-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 | 0.0008335 | 0.0011719 | 0.4799997 | 0.4769282 | 0.623696 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0002775 | 0.0011606 | 0.8100000 | 0.8110019 | 0.400449 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0000633 | 0.0018564 | 0.9699999 | 0.9728168 | 0.103515 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0003941 | 0.0011438 | 0.7300002 | 0.7304242 | 0.457020 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | -0.0001580 | 0.0022571 | 0.9400001 | 0.9441936 | 0.074447 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0009590 | 0.0013058 | 0.4600002 | 0.4627091 | 0.241061 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | -0.0018884 | 0.0016287 | 0.2500000 | 0.2462678 | 0.913473 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | 0.0000867 | 0.0010934 | 0.9400001 | 0.9367873 | 0.116328 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | 0.0005778 | 0.0016022 | 0.7199992 | 0.7183803 | 0.067402 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0008893 | 0.0008103 | 0.2700001 | 0.2724381 | 0.515668 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219006 | rs28729663 | G | A | -0.0010579 | 0.0009787 | 0.2800000 | 0.2796903 | 0.137834 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | -0.0001837 | 0.0012702 | 0.8800001 | 0.8849749 | 0.073730 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | -0.0002434 | 0.0017033 | 0.8900000 | 0.8863917 | 0.041900 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | -0.0013108 | 0.0016988 | 0.4400003 | 0.4403292 | 0.049691 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | -0.0002980 | 0.0012709 | 0.8100000 | 0.8146058 | 0.073216 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | -0.0011782 | 0.0014964 | 0.4299995 | 0.4310920 | 0.054388 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | -0.0010756 | 0.0015036 | 0.4700002 | 0.4744000 | 0.054022 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | -0.0003644 | 0.0012754 | 0.7800007 | 0.7751173 | 0.073051 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | -0.0003881 | 0.0030077 | 0.9000000 | 0.8973261 | 0.019981 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0013841 | 0.0007796 | 0.0759994 | 0.0758388 | 0.297982 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623696 ES:SE:LP:AF:ID 0.000833516:0.0011719:0.318759:0.623696:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400449 ES:SE:LP:AF:ID 0.000277541:0.00116061:0.091515:0.400449:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103515 ES:SE:LP:AF:ID 6.32563e-05:0.00185635:0.0132283:0.103515:rs114608975
1 91536 rs6702460 G T . PASS AF=0.45702 ES:SE:LP:AF:ID 0.000394092:0.00114375:0.136677:0.45702:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074447 ES:SE:LP:AF:ID -0.000157995:0.00225707:0.0268721:0.074447:rs8179466
1 534192 rs6680723 C T . PASS AF=0.241061 ES:SE:LP:AF:ID -0.000959:0.00130584:0.337242:0.241061:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913473 ES:SE:LP:AF:ID -0.00188837:0.00162866:0.60206:0.913473:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116328 ES:SE:LP:AF:ID 8.6715e-05:0.00109339:0.0268721:0.116328:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067402 ES:SE:LP:AF:ID 0.000577785:0.00160218:0.142668:0.067402:rs72631875
1 706368 rs12029736 A G . PASS AF=0.515668 ES:SE:LP:AF:ID 0.00088931:0.000810331:0.568636:0.515668:rs12029736
1 714596 rs149887893 T C . PASS AF=0.033063 ES:SE:LP:AF:ID -0.00194932:0.00204157:0.468521:0.033063:rs149887893
1 715265 rs12184267 C T . PASS AF=0.036695 ES:SE:LP:AF:ID -0.00140495:0.00185409:0.346787:0.036695:rs12184267
1 715367 rs12184277 A G . PASS AF=0.03681 ES:SE:LP:AF:ID -0.00154389:0.00184719:0.39794:0.03681:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036507 ES:SE:LP:AF:ID -0.00148026:0.00186051:0.366532:0.036507:rs12184279
1 717587 rs144155419 G A . PASS AF=0.01645 ES:SE:LP:AF:ID 0.00092343:0.00286196:0.124939:0.01645:rs144155419
1 720381 rs116801199 G T . PASS AF=0.03705 ES:SE:LP:AF:ID -0.00139839:0.00183988:0.346787:0.03705:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037144 ES:SE:LP:AF:ID -0.00151623:0.00183366:0.387216:0.037144:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101363 ES:SE:LP:AF:ID -0.00104193:0.00133558:0.356547:0.101363:rs116030099
1 723891 rs2977670 G C . PASS AF=0.959106 ES:SE:LP:AF:ID 0.000518109:0.00177041:0.113509:0.959106:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031527 ES:SE:LP:AF:ID 0.00285652:0.00320254:0.431798:0.031527:rs12126395
1 725060 rs865924913 A T . PASS AF=0.053255 ES:SE:LP:AF:ID -0.00152848:0.00255612:0.259637:0.053255:rs865924913
1 726794 rs28454925 C G . PASS AF=0.036652 ES:SE:LP:AF:ID -0.00112891:0.00184555:0.267606:0.036652:rs28454925
1 729632 rs116720794 C T . PASS AF=0.036976 ES:SE:LP:AF:ID -0.00150327:0.00182862:0.387216:0.036976:rs116720794
1 729679 rs4951859 C G . PASS AF=0.843197 ES:SE:LP:AF:ID -2.91565e-05:0.000947643:0.00877392:0.843197:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055911 ES:SE:LP:AF:ID 0.000249935:0.00153454:0.0604807:0.055911:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122359 ES:SE:LP:AF:ID 0.000152914:0.00103688:0.0555173:0.122359:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025695 ES:SE:LP:AF:ID -0.00226064:0.00255326:0.420216:0.025695:rs369030935
1 734349 rs141242758 T C . PASS AF=0.121594 ES:SE:LP:AF:ID 0.000147666:0.00103733:0.05061:0.121594:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132419 ES:SE:LP:AF:ID -0.000608039:0.00102257:0.259637:0.132419:rs79010578
1 736689 rs181876450 T C . PASS AF=0.011046 ES:SE:LP:AF:ID 0.00268827:0.00373943:0.327902:0.011046:rs181876450
1 752478 rs146277091 G A . PASS AF=0.036894 ES:SE:LP:AF:ID -0.00166134:0.00180998:0.443698:0.036894:rs146277091
1 752566 rs3094315 G A . PASS AF=0.838841 ES:SE:LP:AF:ID -0.000379068:0.000917614:0.167491:0.838841:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838495 ES:SE:LP:AF:ID -0.000481647:0.000916671:0.221849:0.838495:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869732 ES:SE:LP:AF:ID -0.000719765:0.000983678:0.337242:0.869732:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129896 ES:SE:LP:AF:ID 0.000623029:0.000985709:0.275724:0.129896:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037415 ES:SE:LP:AF:ID -0.00176141:0.00177904:0.49485:0.037415:rs12184312
1 754105 rs12184325 C T . PASS AF=0.037651 ES:SE:LP:AF:ID -0.00174801:0.0017681:0.49485:0.037651:rs12184325
1 754182 rs3131969 A G . PASS AF=0.869101 ES:SE:LP:AF:ID -0.000816442:0.00098181:0.387216:0.869101:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869205 ES:SE:LP:AF:ID -0.000794775:0.000982197:0.376751:0.869205:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037614 ES:SE:LP:AF:ID -0.00177568:0.00177554:0.49485:0.037614:rs12184313
1 754334 rs3131967 T C . PASS AF=0.869102 ES:SE:LP:AF:ID -0.000833265:0.000981791:0.39794:0.869102:rs3131967
1 754503 rs3115859 G A . PASS AF=0.837942 ES:SE:LP:AF:ID -0.000361721:0.000914101:0.161151:0.837942:rs3115859
1 754629 rs10454459 A G . PASS AF=0.037627 ES:SE:LP:AF:ID -0.00184734:0.00177809:0.522879:0.037627:rs10454459
1 754964 rs3131966 C T . PASS AF=0.838576 ES:SE:LP:AF:ID -0.000353323:0.000916668:0.154902:0.838576:rs3131966
1 755240 rs181660517 T G . PASS AF=0.013804 ES:SE:LP:AF:ID -0.00280854:0.0031998:0.420216:0.013804:rs181660517
1 755775 rs3131965 A G . PASS AF=0.839653 ES:SE:LP:AF:ID -0.00018874:0.000928924:0.0757207:0.839653:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869356 ES:SE:LP:AF:ID -0.000684732:0.000980587:0.318759:0.869356:rs3115858
1 756604 rs3131962 A G . PASS AF=0.8689 ES:SE:LP:AF:ID -0.000624972:0.000978096:0.283997:0.8689:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867876 ES:SE:LP:AF:ID -0.000983096:0.000976261:0.508638:0.867876:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869039 ES:SE:LP:AF:ID -0.000654505:0.000978888:0.30103:0.869039:rs4951929