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_1160.vcf.gz --id UKB-b:20300 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41246_1160.txt.gz --cohort_cases 27510 --cohort_controls 418183 --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": "2019-09-13T13:16:30.312077",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-20300/ukb-b-20300.vcf.gz; Date=Sun May 10 04:49:03 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-20300/UKB-b-20300_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-20300/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:42:12 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-20300/UKB-b-20300_data.vcf.gz ...
Read summary statistics for 7386643 SNPs.
Dropped 4963 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, 1273763 SNPs remain.
After merging with regression SNP LD, 1273763 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0183 (0.0016)
Lambda GC: 1.1716
Mean Chi^2: 1.1995
Intercept: 1.0404 (0.0081)
Ratio: 0.2025 (0.0404)
Analysis finished at Thu Oct 17 14:43:40 2019
Total time elapsed: 1.0m:28.13sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9389,
"inflation_factor": 1.1474,
"mean_EFFECT": 3.2148e-06,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 10,
"n_p_sig": 556,
"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": 68358,
"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": 1273763,
"ldsc_nsnp_merge_regression_ld": 1273763,
"ldsc_observed_scale_h2_beta": 0.0183,
"ldsc_observed_scale_h2_se": 0.0016,
"ldsc_intercept_beta": 1.0404,
"ldsc_intercept_se": 0.0081,
"ldsc_lambda_gc": 1.1716,
"ldsc_mean_chisq": 1.1995,
"ldsc_ratio": 0.2025
}
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 | 7381702 | 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 | 7386643 | 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 | 8.663779e+00 | 5.763627e+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 | 7.863974e+07 | 5.644509e+07 | 828.0000000 | 3.217884e+07 | 6.905666e+07 | 1.145198e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.200000e-06 | 1.124800e-03 | -0.0096820 | -5.772000e-04 | 3.500000e-06 | 5.859000e-04 | 2.276810e-02 | ▁▇▁▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 9.499000e-04 | 4.929000e-04 | 0.0004893 | 5.577000e-04 | 7.389000e-04 | 1.215500e-03 | 5.315700e-03 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.772492e-01 | 2.944812e-01 | 0.0000000 | 2.200002e-01 | 4.700002e-01 | 7.300002e-01 | 1.000000e+00 | ▇▇▇▆▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.772517e-01 | 2.944572e-01 | 0.0000000 | 2.166917e-01 | 4.683615e-01 | 7.320286e-01 | 9.999995e-01 | ▇▇▆▆▆ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.635440e-01 | 2.606734e-01 | 0.0127230 | 4.907600e-02 | 1.620870e-01 | 4.152990e-01 | 9.872760e-01 | ▇▂▂▁▁ |
| numeric | AF_reference | 68358 | 0.9907457 | NA | NA | NA | NA | NA | NA | NA | 2.622650e-01 | 2.525414e-01 | 0.0000000 | 5.491210e-02 | 1.755190e-01 | 4.091450e-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 | 0.0011422 | 0.0009002 | 0.2000000 | 0.2044911 | 0.623782 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0002194 | 0.0008918 | 0.8100000 | 0.8057125 | 0.400412 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | -0.0000143 | 0.0014257 | 0.9900000 | 0.9919980 | 0.103539 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0009237 | 0.0008779 | 0.2900000 | 0.2927370 | 0.456877 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0009805 | 0.0017314 | 0.5700002 | 0.5711589 | 0.074468 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0022358 | 0.0010029 | 0.0259998 | 0.0257916 | 0.240938 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | -0.0001802 | 0.0012512 | 0.8900000 | 0.8855011 | 0.913476 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0003010 | 0.0008407 | 0.7199992 | 0.7203061 | 0.116360 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | -0.0008844 | 0.0012316 | 0.4700002 | 0.4727320 | 0.067338 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0000086 | 0.0006227 | 0.9900000 | 0.9889474 | 0.515733 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219006 | rs28729663 | G | A | 0.0010375 | 0.0007512 | 0.1700000 | 0.1672574 | 0.137985 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | 0.0012790 | 0.0009748 | 0.1900002 | 0.1894870 | 0.073787 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | 0.0016050 | 0.0013063 | 0.2200002 | 0.2191768 | 0.041974 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | 0.0025479 | 0.0013039 | 0.0510000 | 0.0506896 | 0.049768 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | 0.0012951 | 0.0009754 | 0.1800002 | 0.1842380 | 0.073279 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | 0.0004660 | 0.0011490 | 0.6899999 | 0.6850507 | 0.054474 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | 0.0003951 | 0.0011546 | 0.7300002 | 0.7321984 | 0.054103 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0013173 | 0.0009789 | 0.1800002 | 0.1784174 | 0.073116 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | -0.0010490 | 0.0023076 | 0.6499995 | 0.6494028 | 0.020045 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0012780 | 0.0005989 | 0.0329997 | 0.0328451 | 0.298169 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623782 ES:SE:LP:AF:ID 0.00114222:0.000900194:0.69897:0.623782:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400412 ES:SE:LP:AF:ID -0.000219359:0.000891845:0.091515:0.400412:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103539 ES:SE:LP:AF:ID -1.42989e-05:0.00142573:0.00436481:0.103539:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456877 ES:SE:LP:AF:ID 0.000923662:0.000877892:0.537602:0.456877:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074468 ES:SE:LP:AF:ID 0.000980548:0.00173136:0.244125:0.074468:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240938 ES:SE:LP:AF:ID 0.0022358:0.0010029:1.58503:0.240938:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913476 ES:SE:LP:AF:ID -0.000180179:0.00125125:0.05061:0.913476:rs12025928
1 693731 rs12238997 A G . PASS AF=0.11636 ES:SE:LP:AF:ID -0.00030103:0.000840749:0.142668:0.11636:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067338 ES:SE:LP:AF:ID -0.000884368:0.00123164:0.327902:0.067338:rs72631875
1 706368 rs12029736 A G . PASS AF=0.515733 ES:SE:LP:AF:ID -8.62666e-06:0.000622736:0.00436481:0.515733:rs12029736
1 714596 rs149887893 T C . PASS AF=0.033056 ES:SE:LP:AF:ID 0.000907256:0.00156884:0.251812:0.033056:rs149887893
1 715265 rs12184267 C T . PASS AF=0.036676 ES:SE:LP:AF:ID 0.000899442:0.00142512:0.275724:0.036676:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036792 ES:SE:LP:AF:ID 0.00090186:0.00141973:0.275724:0.036792:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036488 ES:SE:LP:AF:ID 0.000911016:0.00143002:0.283997:0.036488:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016432 ES:SE:LP:AF:ID 0.00252864:0.00220226:0.60206:0.016432:rs144155419
1 720381 rs116801199 G T . PASS AF=0.037026 ES:SE:LP:AF:ID 0.00100333:0.00141424:0.318759:0.037026:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037127 ES:SE:LP:AF:ID 0.00105002:0.00140923:0.337242:0.037127:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101226 ES:SE:LP:AF:ID -0.000807775:0.00102743:0.366532:0.101226:rs116030099
1 723891 rs2977670 G C . PASS AF=0.959059 ES:SE:LP:AF:ID -0.00072157:0.00135955:0.221849:0.959059:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031438 ES:SE:LP:AF:ID -0.000873164:0.00246919:0.142668:0.031438:rs12126395
1 725060 rs865924913 A T . PASS AF=0.0533 ES:SE:LP:AF:ID -0.00473967:0.00196181:1.79588:0.0533:rs865924913
1 726794 rs28454925 C G . PASS AF=0.036635 ES:SE:LP:AF:ID 0.000939871:0.00141859:0.29243:0.036635:rs28454925
1 729632 rs116720794 C T . PASS AF=0.036953 ES:SE:LP:AF:ID 0.000949918:0.00140561:0.30103:0.036953:rs116720794
1 729679 rs4951859 C G . PASS AF=0.843132 ES:SE:LP:AF:ID 3.32139e-05:0.000728542:0.0177288:0.843132:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055909 ES:SE:LP:AF:ID -0.00056084:0.00117973:0.200659:0.055909:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122347 ES:SE:LP:AF:ID -0.000391848:0.00079746:0.207608:0.122347:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025701 ES:SE:LP:AF:ID 0.00219904:0.00196231:0.585027:0.025701:rs369030935
1 734349 rs141242758 T C . PASS AF=0.121589 ES:SE:LP:AF:ID -0.0002789:0.000797789:0.136677:0.121589:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132415 ES:SE:LP:AF:ID -0.000224089:0.000786135:0.107905:0.132415:rs79010578
1 752478 rs146277091 G A . PASS AF=0.036865 ES:SE:LP:AF:ID 0.000735447:0.00139147:0.221849:0.036865:rs146277091
1 752566 rs3094315 G A . PASS AF=0.83883 ES:SE:LP:AF:ID -4.72605e-05:0.000705406:0.0222764:0.83883:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838451 ES:SE:LP:AF:ID -1.30209e-05:0.000704624:0.00436481:0.838451:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869706 ES:SE:LP:AF:ID 0.000358324:0.000756153:0.19382:0.869706:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129957 ES:SE:LP:AF:ID -0.000424606:0.000757663:0.236572:0.129957:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037385 ES:SE:LP:AF:ID 0.000687274:0.00136768:0.207608:0.037385:rs12184312
1 754105 rs12184325 C T . PASS AF=0.037629 ES:SE:LP:AF:ID 0.000622164:0.00135905:0.187087:0.037629:rs12184325
1 754182 rs3131969 A G . PASS AF=0.869045 ES:SE:LP:AF:ID 0.000386552:0.000754649:0.21467:0.869045:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869145 ES:SE:LP:AF:ID 0.000381168:0.000754957:0.21467:0.869145:rs3131968
1 754211 rs12184313 G A . PASS AF=0.03759 ES:SE:LP:AF:ID 0.000631482:0.00136487:0.19382:0.03759:rs12184313
1 754334 rs3131967 T C . PASS AF=0.869049 ES:SE:LP:AF:ID 0.000381768:0.000754636:0.21467:0.869049:rs3131967
1 754503 rs3115859 G A . PASS AF=0.837899 ES:SE:LP:AF:ID 2.33524e-05:0.000702656:0.0132283:0.837899:rs3115859
1 754629 rs10454459 A G . PASS AF=0.037604 ES:SE:LP:AF:ID 0.000672753:0.00136676:0.207608:0.037604:rs10454459
1 754964 rs3131966 C T . PASS AF=0.83853 ES:SE:LP:AF:ID -2.98164e-05:0.000704628:0.0132283:0.83853:rs3131966
1 755240 rs181660517 T G . PASS AF=0.01377 ES:SE:LP:AF:ID -0.00239794:0.00246066:0.481486:0.01377:rs181660517
1 755775 rs3131965 A G . PASS AF=0.839655 ES:SE:LP:AF:ID 5.28246e-05:0.000714214:0.0268721:0.839655:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869334 ES:SE:LP:AF:ID 0.000341819:0.000753797:0.187087:0.869334:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868885 ES:SE:LP:AF:ID 0.000289732:0.000751933:0.154902:0.868885:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867829 ES:SE:LP:AF:ID 0.000242512:0.000750441:0.124939:0.867829:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869024 ES:SE:LP:AF:ID 0.000278563:0.000752534:0.148742:0.869024:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869033 ES:SE:LP:AF:ID 0.00027602:0.000752591:0.148742:0.869033:rs4951862