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_4101.vcf.gz --id UKB-b:5447 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_4101.txt.gz --cohort_controls 146218 --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": "2019-09-13T09:26:57.441054",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-5447/ukb-b-5447.vcf.gz; Date=Sun May 10 14:40:37 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-5447/UKB-b-5447_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5447/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:45:20 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5447/UKB-b-5447_data.vcf.gz ...
Read summary statistics for 9301976 SNPs.
Dropped 10288 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, 1287947 SNPs remain.
After merging with regression SNP LD, 1287947 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.2568 (0.0231)
Lambda GC: 1.3867
Mean Chi^2: 1.8697
Intercept: 1.0891 (0.0131)
Ratio: 0.1024 (0.0151)
Analysis finished at Thu Oct 17 14:46:55 2019
Total time elapsed: 1.0m:34.22sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9487,
"inflation_factor": 1.2544,
"mean_EFFECT": 0.0001,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 188,
"n_p_sig": 25269,
"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": 111648,
"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": 1287947,
"ldsc_nsnp_merge_regression_ld": 1287947,
"ldsc_observed_scale_h2_beta": 0.2568,
"ldsc_observed_scale_h2_se": 0.0231,
"ldsc_intercept_beta": 1.0891,
"ldsc_intercept_se": 0.0131,
"ldsc_lambda_gc": 1.3867,
"ldsc_mean_chisq": 1.8697,
"ldsc_ratio": 0.1024
}
Flags
| name | value |
|---|---|
| af_correlation | FALSE |
| inflation_factor | TRUE |
| n | TRUE |
| is_snpid_non_unique | FALSE |
| mean_EFFECT_nonfinite | FALSE |
| mean_EFFECT_05 | FALSE |
| mean_EFFECT_01 | FALSE |
| mean_chisq | TRUE |
| 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 | 9291740 | 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 | 9301976 | 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 | 8.635421e+00 | 5.754204e+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 | 7.880994e+07 | 5.630847e+07 | 828.0000000 | 3.250018e+07 | 6.939232e+07 | 1.145403e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 5.030000e-05 | 1.344040e-02 | -0.2824930 | -5.181400e-03 | 6.200000e-06 | 5.221100e-03 | 3.188370e-01 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 9.761400e-03 | 7.694200e-03 | 0.0032743 | 3.944600e-03 | 6.228000e-03 | 1.338260e-02 | 8.229050e-02 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.586993e-01 | 3.011736e-01 | 0.0000000 | 1.900002e-01 | 4.500005e-01 | 7.199992e-01 | 1.000000e+00 | ▇▆▆▅▆ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.587010e-01 | 3.011481e-01 | 0.0000000 | 1.873054e-01 | 4.452776e-01 | 7.200313e-01 | 1.000000e+00 | ▇▆▆▆▆ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.138806e-01 | 2.578296e-01 | 0.0023940 | 1.754400e-02 | 9.266250e-02 | 3.358550e-01 | 9.976060e-01 | ▇▂▁▁▁ |
| numeric | AF_reference | 111648 | 0.9879974 | NA | NA | NA | NA | NA | NA | NA | 2.148773e-01 | 2.496214e-01 | 0.0000000 | 1.477640e-02 | 1.110220e-01 | 3.356630e-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 | 0.0041700 | 0.0060396 | 0.4899999 | 0.4899152 | 0.623786 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0057213 | 0.0059970 | 0.3400001 | 0.3400693 | 0.399273 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | -0.0016873 | 0.0095526 | 0.8600001 | 0.8597950 | 0.103824 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0061092 | 0.0059080 | 0.2999998 | 0.3011183 | 0.456045 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0097130 | 0.0116748 | 0.4100001 | 0.4054304 | 0.074426 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0028930 | 0.0067476 | 0.6700003 | 0.6681122 | 0.241233 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | 0.0254905 | 0.0083780 | 0.0023000 | 0.0023457 | 0.913029 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | 0.0000700 | 0.0056340 | 0.9900000 | 0.9900799 | 0.116835 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | -0.0193400 | 0.0082236 | 0.0189998 | 0.0186843 | 0.067746 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0023572 | 0.0041692 | 0.5700002 | 0.5718080 | 0.515130 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219006 | rs28729663 | G | A | 0.0075109 | 0.0050679 | 0.1400000 | 0.1383262 | 0.137097 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | 0.0051394 | 0.0065893 | 0.4400003 | 0.4354152 | 0.072776 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | -0.0026942 | 0.0087969 | 0.7600007 | 0.7594062 | 0.041651 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | 0.0021899 | 0.0088154 | 0.8000000 | 0.8038116 | 0.049132 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | 0.0051735 | 0.0065942 | 0.4299995 | 0.4327167 | 0.072255 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | 0.0044660 | 0.0077246 | 0.5600000 | 0.5631645 | 0.054402 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | 0.0048035 | 0.0077648 | 0.5400003 | 0.5361623 | 0.054009 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0055504 | 0.0066180 | 0.4000000 | 0.4016521 | 0.072125 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | 0.0039297 | 0.0155080 | 0.8000000 | 0.7999590 | 0.020155 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0091982 | 0.0040247 | 0.0219999 | 0.0222869 | 0.297633 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623786 ES:SE:LP:AF:ID 0.00416999:0.00603958:0.309804:0.623786:rs10399793
1 54676 rs2462492 C T . PASS AF=0.399273 ES:SE:LP:AF:ID -0.00572127:0.00599696:0.468521:0.399273:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103824 ES:SE:LP:AF:ID -0.00168733:0.00955263:0.0655015:0.103824:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456045 ES:SE:LP:AF:ID 0.00610915:0.00590805:0.522879:0.456045:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074426 ES:SE:LP:AF:ID 0.00971299:0.0116748:0.387216:0.074426:rs8179466
1 534192 rs6680723 C T . PASS AF=0.241233 ES:SE:LP:AF:ID -0.00289296:0.00674758:0.173925:0.241233:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913029 ES:SE:LP:AF:ID 0.0254905:0.00837797:2.63827:0.913029:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116835 ES:SE:LP:AF:ID 7.00493e-05:0.005634:0.00436481:0.116835:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067746 ES:SE:LP:AF:ID -0.01934:0.00822359:1.72125:0.067746:rs72631875
1 706368 rs12029736 A G . PASS AF=0.51513 ES:SE:LP:AF:ID -0.00235721:0.00416917:0.244125:0.51513:rs12029736
1 714596 rs149887893 T C . PASS AF=0.03349 ES:SE:LP:AF:ID 0.00246826:0.0104284:0.091515:0.03349:rs149887893
1 715265 rs12184267 C T . PASS AF=0.037197 ES:SE:LP:AF:ID 0.00170738:0.00946765:0.0655015:0.037197:rs12184267
1 715367 rs12184277 A G . PASS AF=0.037324 ES:SE:LP:AF:ID 0.00147502:0.00943044:0.0555173:0.037324:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036981 ES:SE:LP:AF:ID -0.00037329:0.00950217:0.0132283:0.036981:rs12184279
1 717587 rs144155419 G A . PASS AF=0.01633 ES:SE:LP:AF:ID -0.0128085:0.0148301:0.408935:0.01633:rs144155419
1 720381 rs116801199 G T . PASS AF=0.037558 ES:SE:LP:AF:ID 0.00154198:0.00939354:0.0604807:0.037558:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037649 ES:SE:LP:AF:ID 0.00101015:0.0093643:0.0409586:0.037649:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101559 ES:SE:LP:AF:ID 0.00884836:0.00686879:0.69897:0.101559:rs116030099
1 723891 rs2977670 G C . PASS AF=0.958403 ES:SE:LP:AF:ID -0.00505275:0.0090348:0.236572:0.958403:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031768 ES:SE:LP:AF:ID -0.0167514:0.0164635:0.508638:0.031768:rs12126395
1 725060 rs865924913 A T . PASS AF=0.052677 ES:SE:LP:AF:ID -0.0069213:0.0133246:0.221849:0.052677:rs865924913
1 726794 rs28454925 C G . PASS AF=0.037132 ES:SE:LP:AF:ID 0.00222367:0.00942822:0.091515:0.037132:rs28454925
1 729632 rs116720794 C T . PASS AF=0.037445 ES:SE:LP:AF:ID 0.000955996:0.00934609:0.0362122:0.037445:rs116720794
1 729679 rs4951859 C G . PASS AF=0.842145 ES:SE:LP:AF:ID -0.000758466:0.00487641:0.0555173:0.842145:rs4951859
1 730087 rs148120343 T C . PASS AF=0.056066 ES:SE:LP:AF:ID -0.00528673:0.00791722:0.30103:0.056066:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122756 ES:SE:LP:AF:ID 0.000348296:0.00534733:0.0222764:0.122756:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025688 ES:SE:LP:AF:ID -0.00226298:0.0131609:0.0655015:0.025688:rs369030935
1 734349 rs141242758 T C . PASS AF=0.121978 ES:SE:LP:AF:ID -0.000204245:0.00534996:0.0132283:0.121978:rs141242758
1 736289 rs79010578 T A . PASS AF=0.133232 ES:SE:LP:AF:ID 0.000173572:0.00526203:0.0132283:0.133232:rs79010578
1 736689 rs181876450 T C . PASS AF=0.011239 ES:SE:LP:AF:ID -0.000550769:0.0190679:0.00877392:0.011239:rs181876450
1 740284 rs61770167 C T . PASS AF=0.005839 ES:SE:LP:AF:ID 0.035572:0.0243694:0.853872:0.005839:rs61770167
1 752478 rs146277091 G A . PASS AF=0.03739 ES:SE:LP:AF:ID 0.00117:0.00925044:0.0457575:0.03739:rs146277091
1 752566 rs3094315 G A . PASS AF=0.837783 ES:SE:LP:AF:ID -0.00154565:0.00471951:0.130768:0.837783:rs3094315
1 752721 rs3131972 A G . PASS AF=0.8374 ES:SE:LP:AF:ID -0.00158771:0.00471404:0.130768:0.8374:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869054 ES:SE:LP:AF:ID -0.00299846:0.00506053:0.259637:0.869054:rs3115860
1 753541 rs2073813 G A . PASS AF=0.130639 ES:SE:LP:AF:ID 0.00277257:0.00507059:0.236572:0.130639:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037884 ES:SE:LP:AF:ID 0.0031242:0.00909871:0.136677:0.037884:rs12184312
1 754105 rs12184325 C T . PASS AF=0.038142 ES:SE:LP:AF:ID 0.00348164:0.00904029:0.154902:0.038142:rs12184325
1 754182 rs3131969 A G . PASS AF=0.868389 ES:SE:LP:AF:ID -0.00317924:0.00505043:0.275724:0.868389:rs3131969
1 754192 rs3131968 A G . PASS AF=0.868491 ES:SE:LP:AF:ID -0.00338962:0.00505239:0.30103:0.868491:rs3131968
1 754211 rs12184313 G A . PASS AF=0.038066 ES:SE:LP:AF:ID 0.00333158:0.00908047:0.148742:0.038066:rs12184313
1 754334 rs3131967 T C . PASS AF=0.868382 ES:SE:LP:AF:ID -0.0031778:0.0050501:0.275724:0.868382:rs3131967
1 754433 rs150578204 G A . PASS AF=0.005181 ES:SE:LP:AF:ID 0.011789:0.0258592:0.187087:0.005181:rs150578204
1 754458 rs142682604 G T . PASS AF=0.005153 ES:SE:LP:AF:ID 0.0116796:0.0259144:0.187087:0.005153:rs142682604
1 754503 rs3115859 G A . PASS AF=0.836895 ES:SE:LP:AF:ID -0.00193622:0.00470203:0.167491:0.836895:rs3115859
1 754629 rs10454459 A G . PASS AF=0.038076 ES:SE:LP:AF:ID 0.00334499:0.00909282:0.148742:0.038076:rs10454459
1 754964 rs3131966 C T . PASS AF=0.837527 ES:SE:LP:AF:ID -0.00169:0.00471518:0.142668:0.837527:rs3131966
1 755240 rs181660517 T G . PASS AF=0.013255 ES:SE:LP:AF:ID 0.0087777:0.0168577:0.221849:0.013255:rs181660517
1 755435 rs184270342 T G . PASS AF=0.005488 ES:SE:LP:AF:ID -0.0136985:0.0255527:0.229148:0.005488:rs184270342
1 755775 rs3131965 A G . PASS AF=0.83876 ES:SE:LP:AF:ID -0.00187519:0.00478014:0.161151:0.83876:rs3131965