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_4104.vcf.gz --id UKB-b:845 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_4104.txt.gz --cohort_controls 146226 --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": "2019-09-13T02:30:30.844883",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-845/ukb-b-845.vcf.gz; Date=Sun May 10 14:51:53 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-845/UKB-b-845_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-845/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:41:40 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-845/UKB-b-845_data.vcf.gz ...
Read summary statistics for 9301974 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: 0.2866 (0.0258)
Lambda GC: 1.4208
Mean Chi^2: 1.9526
Intercept: 1.0962 (0.0137)
Ratio: 0.101 (0.0144)
Analysis finished at Thu Oct 17 14:43:22 2019
Total time elapsed: 1.0m:41.41sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9487,
"inflation_factor": 1.3107,
"mean_EFFECT": 0.0001,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 210,
"n_p_sig": 26341,
"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": 111647,
"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": 0.2866,
"ldsc_observed_scale_h2_se": 0.0258,
"ldsc_intercept_beta": 1.0962,
"ldsc_intercept_se": 0.0137,
"ldsc_lambda_gc": 1.4208,
"ldsc_mean_chisq": 1.9526,
"ldsc_ratio": 0.101
}
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 | 9291738 | 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 | 9301974 | 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 | 5.754200e+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 | 7.880990e+07 | 5.630848e+07 | 828.0000000 | 3.250016e+07 | 6.939227e+07 | 1.145403e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 5.450000e-05 | 1.375620e-02 | -0.2508050 | -5.337200e-03 | 6.300000e-06 | 5.365300e-03 | 3.979770e-01 | ▁▇▂▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 9.917500e-03 | 7.817200e-03 | 0.0033253 | 4.007600e-03 | 6.327700e-03 | 1.359660e-02 | 8.359010e-02 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.554452e-01 | 3.018308e-01 | 0.0000000 | 1.800002e-01 | 4.400003e-01 | 7.199992e-01 | 1.000000e+00 | ▇▆▆▅▆ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.554462e-01 | 3.018064e-01 | 0.0000000 | 1.832993e-01 | 4.400417e-01 | 7.171574e-01 | 9.999997e-01 | ▇▆▆▅▅ |
| 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 | 3.358530e-01 | 9.976060e-01 | ▇▂▁▁▁ |
| numeric | AF_reference | 111647 | 0.9879975 | 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 | 0.0013373 | 0.0061350 | 0.8300000 | 0.8274494 | 0.623783 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0047286 | 0.0060914 | 0.4400003 | 0.4375896 | 0.399281 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0001669 | 0.0097038 | 0.9900000 | 0.9862735 | 0.103822 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0059776 | 0.0060013 | 0.3200000 | 0.3192222 | 0.456049 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0041304 | 0.0118588 | 0.7300002 | 0.7276193 | 0.074426 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0017018 | 0.0068542 | 0.8000000 | 0.8039091 | 0.241234 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | 0.0284612 | 0.0085103 | 0.0008200 | 0.0008248 | 0.913029 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0004824 | 0.0057231 | 0.9299999 | 0.9328283 | 0.116832 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | -0.0196381 | 0.0083536 | 0.0189998 | 0.0187309 | 0.067746 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0025749 | 0.0042351 | 0.5400003 | 0.5431954 | 0.515132 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219006 | rs28729663 | G | A | 0.0067674 | 0.0051514 | 0.1900002 | 0.1889460 | 0.137093 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | 0.0023896 | 0.0066977 | 0.7199992 | 0.7212593 | 0.072776 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | -0.0057826 | 0.0089416 | 0.5199996 | 0.5178267 | 0.041651 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | -0.0030358 | 0.0089605 | 0.7300002 | 0.7347672 | 0.049132 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | 0.0026374 | 0.0067028 | 0.6899999 | 0.6939696 | 0.072255 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | 0.0062896 | 0.0078519 | 0.4199997 | 0.4231128 | 0.054400 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | 0.0067847 | 0.0078927 | 0.3900004 | 0.3900007 | 0.054006 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0029444 | 0.0067270 | 0.6600001 | 0.6616077 | 0.072125 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | 0.0089495 | 0.0157635 | 0.5700002 | 0.5702145 | 0.020154 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0060940 | 0.0040909 | 0.1400000 | 0.1363191 | 0.297636 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623783 ES:SE:LP:AF:ID 0.00133727:0.006135:0.0809219:0.623783:rs10399793
1 54676 rs2462492 C T . PASS AF=0.399281 ES:SE:LP:AF:ID -0.00472861:0.00609145:0.356547:0.399281:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103822 ES:SE:LP:AF:ID 0.000166949:0.00970382:0.00436481:0.103822:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456049 ES:SE:LP:AF:ID 0.0059776:0.00600126:0.49485:0.456049:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074426 ES:SE:LP:AF:ID 0.00413035:0.0118588:0.136677:0.074426:rs8179466
1 534192 rs6680723 C T . PASS AF=0.241234 ES:SE:LP:AF:ID -0.00170185:0.00685425:0.09691:0.241234:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913029 ES:SE:LP:AF:ID 0.0284612:0.0085103:3.08619:0.913029:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116832 ES:SE:LP:AF:ID -0.000482385:0.00572313:0.0315171:0.116832:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067746 ES:SE:LP:AF:ID -0.0196381:0.00835364:1.72125:0.067746:rs72631875
1 706368 rs12029736 A G . PASS AF=0.515132 ES:SE:LP:AF:ID -0.00257489:0.0042351:0.267606:0.515132:rs12029736
1 714596 rs149887893 T C . PASS AF=0.033488 ES:SE:LP:AF:ID 0.00465214:0.0105935:0.180456:0.033488:rs149887893
1 715265 rs12184267 C T . PASS AF=0.037195 ES:SE:LP:AF:ID 0.0039474:0.00961758:0.167491:0.037195:rs12184267
1 715367 rs12184277 A G . PASS AF=0.037322 ES:SE:LP:AF:ID 0.00349175:0.00957978:0.142668:0.037322:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036979 ES:SE:LP:AF:ID 0.0019763:0.00965265:0.0757207:0.036979:rs12184279
1 717587 rs144155419 G A . PASS AF=0.01633 ES:SE:LP:AF:ID -0.00419657:0.015065:0.107905:0.01633:rs144155419
1 720381 rs116801199 G T . PASS AF=0.037556 ES:SE:LP:AF:ID 0.00322093:0.00954229:0.130768:0.037556:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037647 ES:SE:LP:AF:ID 0.00295492:0.00951259:0.119186:0.037647:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101556 ES:SE:LP:AF:ID 0.0113029:0.00697751:0.958607:0.101556:rs116030099
1 723891 rs2977670 G C . PASS AF=0.958405 ES:SE:LP:AF:ID -0.0060247:0.00917787:0.29243:0.958405:rs2977670
1 724849 rs12126395 C A . PASS AF=0.03177 ES:SE:LP:AF:ID -0.0143706:0.0167217:0.408935:0.03177:rs12126395
1 725060 rs865924913 A T . PASS AF=0.052678 ES:SE:LP:AF:ID -0.0108612:0.0135354:0.376751:0.052678:rs865924913
1 726794 rs28454925 C G . PASS AF=0.03713 ES:SE:LP:AF:ID 0.0043931:0.00957752:0.187087:0.03713:rs28454925
1 729632 rs116720794 C T . PASS AF=0.037443 ES:SE:LP:AF:ID 0.00237394:0.0094941:0.09691:0.037443:rs116720794
1 729679 rs4951859 C G . PASS AF=0.84215 ES:SE:LP:AF:ID -1.41858e-05:0.00495356:-0:0.84215:rs4951859
1 730087 rs148120343 T C . PASS AF=0.056062 ES:SE:LP:AF:ID -0.00736916:0.00804259:0.443698:0.056062:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122752 ES:SE:LP:AF:ID -0.000545313:0.00543193:0.0362122:0.122752:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025686 ES:SE:LP:AF:ID -0.00712583:0.0133693:0.229148:0.025686:rs369030935
1 734349 rs141242758 T C . PASS AF=0.121975 ES:SE:LP:AF:ID -0.00109727:0.0054346:0.0757207:0.121975:rs141242758
1 736289 rs79010578 T A . PASS AF=0.133228 ES:SE:LP:AF:ID -0.00109533:0.00534529:0.0757207:0.133228:rs79010578
1 736689 rs181876450 T C . PASS AF=0.011238 ES:SE:LP:AF:ID 0.00442306:0.0193699:0.0861861:0.011238:rs181876450
1 740284 rs61770167 C T . PASS AF=0.005839 ES:SE:LP:AF:ID 0.0315442:0.0247554:0.69897:0.005839:rs61770167
1 752478 rs146277091 G A . PASS AF=0.037388 ES:SE:LP:AF:ID 0.00231074:0.00939693:0.091515:0.037388:rs146277091
1 752566 rs3094315 G A . PASS AF=0.837789 ES:SE:LP:AF:ID -0.00159831:0.00479418:0.130768:0.837789:rs3094315
1 752721 rs3131972 A G . PASS AF=0.837406 ES:SE:LP:AF:ID -0.00163964:0.00478862:0.136677:0.837406:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869058 ES:SE:LP:AF:ID -0.00279532:0.0051406:0.229148:0.869058:rs3115860
1 753541 rs2073813 G A . PASS AF=0.130635 ES:SE:LP:AF:ID 0.00279398:0.00515082:0.229148:0.130635:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037882 ES:SE:LP:AF:ID 0.00459093:0.0092428:0.207608:0.037882:rs12184312
1 754105 rs12184325 C T . PASS AF=0.03814 ES:SE:LP:AF:ID 0.00509071:0.00918346:0.236572:0.03814:rs12184325
1 754182 rs3131969 A G . PASS AF=0.868393 ES:SE:LP:AF:ID -0.00297494:0.00513034:0.251812:0.868393:rs3131969
1 754192 rs3131968 A G . PASS AF=0.868495 ES:SE:LP:AF:ID -0.00311482:0.00513233:0.267606:0.868495:rs3131968
1 754211 rs12184313 G A . PASS AF=0.038064 ES:SE:LP:AF:ID 0.00476086:0.00922427:0.21467:0.038064:rs12184313
1 754334 rs3131967 T C . PASS AF=0.868386 ES:SE:LP:AF:ID -0.00295307:0.00513:0.251812:0.868386:rs3131967
1 754433 rs150578204 G A . PASS AF=0.005181 ES:SE:LP:AF:ID 0.025613:0.0262687:0.481486:0.005181:rs150578204
1 754458 rs142682604 G T . PASS AF=0.005152 ES:SE:LP:AF:ID 0.0253848:0.0263248:0.481486:0.005152:rs142682604
1 754503 rs3115859 G A . PASS AF=0.8369 ES:SE:LP:AF:ID -0.00204238:0.00477643:0.173925:0.8369:rs3115859
1 754629 rs10454459 A G . PASS AF=0.038074 ES:SE:LP:AF:ID 0.00492115:0.00923682:0.229148:0.038074:rs10454459
1 754964 rs3131966 C T . PASS AF=0.837533 ES:SE:LP:AF:ID -0.00181568:0.00478978:0.154902:0.837533:rs3131966
1 755240 rs181660517 T G . PASS AF=0.013254 ES:SE:LP:AF:ID 0.00840105:0.0171247:0.207608:0.013254:rs181660517
1 755435 rs184270342 T G . PASS AF=0.005491 ES:SE:LP:AF:ID -0.024114:0.0259476:0.455932:0.005491:rs184270342
1 755775 rs3131965 A G . PASS AF=0.838766 ES:SE:LP:AF:ID -0.00183643:0.00485577:0.148742:0.838766:rs3131965