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_5098.vcf.gz --id UKB-b:13416 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_5098.txt.gz --cohort_controls 84303 --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": "2019-09-12T23:09:04.726676",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-13416/ukb-b-13416.vcf.gz; Date=Sun May 10 07:04:54 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-13416/UKB-b-13416_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-13416/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:42:30 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-13416/UKB-b-13416_data.vcf.gz ...
Read summary statistics for 8887262 SNPs.
Dropped 8243 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, 1286829 SNPs remain.
After merging with regression SNP LD, 1286829 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.3882 (0.0212)
Lambda GC: 1.4118
Mean Chi^2: 1.7224
Intercept: 1.0927 (0.012)
Ratio: 0.1283 (0.0166)
Analysis finished at Thu Oct 17 14:44:06 2019
Total time elapsed: 1.0m:35.39sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9474,
"inflation_factor": 1.3107,
"mean_EFFECT": 0,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 193,
"n_p_sig": 15678,
"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": 89248,
"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": 1286829,
"ldsc_nsnp_merge_regression_ld": 1286829,
"ldsc_observed_scale_h2_beta": 0.3882,
"ldsc_observed_scale_h2_se": 0.0212,
"ldsc_intercept_beta": 1.0927,
"ldsc_intercept_se": 0.012,
"ldsc_lambda_gc": 1.4118,
"ldsc_mean_chisq": 1.7224,
"ldsc_ratio": 0.1283
}
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 | 8879058 | 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 | 8887262 | 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 | 8.646363e+00 | 5.759728e+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 | 7.877882e+07 | 5.634575e+07 | 828.0000000 | 3.241416e+07 | 6.933239e+07 | 1.145492e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.500000e-05 | 1.580580e-02 | -0.2319810 | -6.674300e-03 | 7.100000e-06 | 6.727800e-03 | 3.789460e-01 | ▁▇▁▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.177050e-02 | 8.416100e-03 | 0.0043891 | 5.230900e-03 | 7.911500e-03 | 1.609300e-02 | 1.006970e-01 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.566177e-01 | 3.015648e-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 | 4.566174e-01 | 3.015406e-01 | 0.0000000 | 1.847633e-01 | 4.420732e-01 | 7.180683e-01 | 1.000000e+00 | ▇▆▆▆▅ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.229324e-01 | 2.588377e-01 | 0.0041520 | 2.190300e-02 | 1.054090e-01 | 3.518930e-01 | 9.958480e-01 | ▇▂▁▁▁ |
| numeric | AF_reference | 89248 | 0.9899578 | NA | NA | NA | NA | NA | NA | NA | 2.230286e-01 | 2.507842e-01 | 0.0000000 | 1.936900e-02 | 1.224040e-01 | 3.500400e-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 | 0.0194534 | 0.0081178 | 0.0170000 | 0.0165573 | 0.623328 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0180042 | 0.0080627 | 0.0259998 | 0.0255476 | 0.399068 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | -0.0009721 | 0.0127706 | 0.9400001 | 0.9393225 | 0.104087 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0027925 | 0.0079150 | 0.7199992 | 0.7242342 | 0.455590 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0172834 | 0.0154854 | 0.2599998 | 0.2643753 | 0.074857 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0018831 | 0.0090572 | 0.8400000 | 0.8352945 | 0.240530 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | -0.0094309 | 0.0112150 | 0.4000000 | 0.4003946 | 0.912604 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | 0.0087290 | 0.0075232 | 0.2500000 | 0.2459342 | 0.117886 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | -0.0072565 | 0.0110527 | 0.5099998 | 0.5114802 | 0.067380 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0038052 | 0.0055800 | 0.5000000 | 0.4952775 | 0.514497 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219006 | rs28729663 | G | A | -0.0034568 | 0.0067945 | 0.6100002 | 0.6109236 | 0.137315 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | 0.0070307 | 0.0088690 | 0.4299995 | 0.4279402 | 0.072445 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | 0.0167494 | 0.0117861 | 0.1600000 | 0.1552834 | 0.041844 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | 0.0140505 | 0.0118592 | 0.2399999 | 0.2361059 | 0.049027 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | 0.0067302 | 0.0088709 | 0.4500005 | 0.4480441 | 0.071981 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | -0.0180459 | 0.0103421 | 0.0810009 | 0.0810028 | 0.054759 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | -0.0183958 | 0.0103954 | 0.0769999 | 0.0767922 | 0.054381 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0061978 | 0.0089052 | 0.4899999 | 0.4864458 | 0.071827 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | -0.0453204 | 0.0209359 | 0.0299999 | 0.0304090 | 0.020127 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0049290 | 0.0054028 | 0.3599996 | 0.3616046 | 0.298658 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623328 ES:SE:LP:AF:ID 0.0194534:0.00811778:1.76955:0.623328:rs10399793
1 54676 rs2462492 C T . PASS AF=0.399068 ES:SE:LP:AF:ID -0.0180042:0.00806271:1.58503:0.399068:rs2462492
1 86028 rs114608975 T C . PASS AF=0.104087 ES:SE:LP:AF:ID -0.000972116:0.0127706:0.0268721:0.104087:rs114608975
1 91536 rs6702460 G T . PASS AF=0.45559 ES:SE:LP:AF:ID 0.00279247:0.00791504:0.142668:0.45559:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074857 ES:SE:LP:AF:ID 0.0172834:0.0154854:0.585027:0.074857:rs8179466
1 534192 rs6680723 C T . PASS AF=0.24053 ES:SE:LP:AF:ID -0.00188314:0.00905721:0.0757207:0.24053:rs6680723
1 546697 rs12025928 A G . PASS AF=0.912604 ES:SE:LP:AF:ID -0.00943088:0.011215:0.39794:0.912604:rs12025928
1 693731 rs12238997 A G . PASS AF=0.117886 ES:SE:LP:AF:ID 0.00872903:0.0075232:0.60206:0.117886:rs12238997
1 705882 rs72631875 G A . PASS AF=0.06738 ES:SE:LP:AF:ID -0.00725648:0.0110527:0.29243:0.06738:rs72631875
1 706368 rs12029736 A G . PASS AF=0.514497 ES:SE:LP:AF:ID -0.00380524:0.00558003:0.30103:0.514497:rs12029736
1 714596 rs149887893 T C . PASS AF=0.033641 ES:SE:LP:AF:ID 0.0113143:0.0139484:0.376751:0.033641:rs149887893
1 715265 rs12184267 C T . PASS AF=0.037343 ES:SE:LP:AF:ID 0.0139797:0.012675:0.568636:0.037343:rs12184267
1 715367 rs12184277 A G . PASS AF=0.037429 ES:SE:LP:AF:ID 0.0133366:0.0126347:0.537602:0.037429:rs12184277
1 717485 rs12184279 C A . PASS AF=0.037126 ES:SE:LP:AF:ID 0.0159719:0.0127216:0.677781:0.037126:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016858 ES:SE:LP:AF:ID 0.00186471:0.019539:0.0362122:0.016858:rs144155419
1 720381 rs116801199 G T . PASS AF=0.037677 ES:SE:LP:AF:ID 0.015258:0.0125815:0.638272:0.037677:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037773 ES:SE:LP:AF:ID 0.0146851:0.0125403:0.619789:0.037773:rs12565286
1 722670 rs116030099 T C . PASS AF=0.10073 ES:SE:LP:AF:ID -0.00151642:0.00927393:0.0604807:0.10073:rs116030099
1 723891 rs2977670 G C . PASS AF=0.957986 ES:SE:LP:AF:ID -0.00953687:0.0120529:0.366532:0.957986:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031745 ES:SE:LP:AF:ID -0.0445272:0.0221181:1.35655:0.031745:rs12126395
1 725060 rs865924913 A T . PASS AF=0.052556 ES:SE:LP:AF:ID -0.0015686:0.0178158:0.0315171:0.052556:rs865924913
1 726794 rs28454925 C G . PASS AF=0.037261 ES:SE:LP:AF:ID 0.0160864:0.0126215:0.69897:0.037261:rs28454925
1 729632 rs116720794 C T . PASS AF=0.03756 ES:SE:LP:AF:ID 0.0169513:0.0125187:0.744727:0.03756:rs116720794
1 729679 rs4951859 C G . PASS AF=0.840764 ES:SE:LP:AF:ID -0.00819033:0.00651812:0.677781:0.840764:rs4951859
1 730087 rs148120343 T C . PASS AF=0.056161 ES:SE:LP:AF:ID -0.00167218:0.010611:0.0604807:0.056161:rs148120343
1 731718 rs58276399 T C . PASS AF=0.123753 ES:SE:LP:AF:ID 0.00767987:0.00714542:0.552842:0.123753:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025786 ES:SE:LP:AF:ID -0.00886608:0.0175985:0.21467:0.025786:rs369030935
1 734349 rs141242758 T C . PASS AF=0.122943 ES:SE:LP:AF:ID 0.00703969:0.0071495:0.49485:0.122943:rs141242758
1 736289 rs79010578 T A . PASS AF=0.133622 ES:SE:LP:AF:ID 0.0110776:0.00705047:0.920819:0.133622:rs79010578
1 736689 rs181876450 T C . PASS AF=0.01134 ES:SE:LP:AF:ID -0.0285663:0.0254175:0.585027:0.01134:rs181876450
1 740284 rs61770167 C T . PASS AF=0.006117 ES:SE:LP:AF:ID 0.0377696:0.0318479:0.619789:0.006117:rs61770167
1 752478 rs146277091 G A . PASS AF=0.037535 ES:SE:LP:AF:ID 0.0146885:0.0123815:0.619789:0.037535:rs146277091
1 752566 rs3094315 G A . PASS AF=0.8367 ES:SE:LP:AF:ID -0.00650866:0.00630836:0.522879:0.8367:rs3094315
1 752721 rs3131972 A G . PASS AF=0.836251 ES:SE:LP:AF:ID -0.00626558:0.00630057:0.49485:0.836251:rs3131972
1 753405 rs3115860 C A . PASS AF=0.868092 ES:SE:LP:AF:ID -0.00363045:0.00676174:0.229148:0.868092:rs3115860
1 753541 rs2073813 G A . PASS AF=0.131595 ES:SE:LP:AF:ID 0.00425939:0.00677482:0.275724:0.131595:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037984 ES:SE:LP:AF:ID 0.0151224:0.0121854:0.677781:0.037984:rs12184312
1 754105 rs12184325 C T . PASS AF=0.038243 ES:SE:LP:AF:ID 0.0148801:0.0121085:0.657577:0.038243:rs12184325
1 754182 rs3131969 A G . PASS AF=0.867391 ES:SE:LP:AF:ID -0.00319699:0.00674777:0.19382:0.867391:rs3131969
1 754192 rs3131968 A G . PASS AF=0.867486 ES:SE:LP:AF:ID -0.00332681:0.00675115:0.207608:0.867486:rs3131968
1 754211 rs12184313 G A . PASS AF=0.038174 ES:SE:LP:AF:ID 0.0156809:0.0121588:0.69897:0.038174:rs12184313
1 754334 rs3131967 T C . PASS AF=0.86738 ES:SE:LP:AF:ID -0.00314978:0.00674725:0.19382:0.86738:rs3131967
1 754433 rs150578204 G A . PASS AF=0.005063 ES:SE:LP:AF:ID 0.000207342:0.0351272:-0:0.005063:rs150578204
1 754458 rs142682604 G T . PASS AF=0.00503 ES:SE:LP:AF:ID -0.00123544:0.0352268:0.0132283:0.00503:rs142682604
1 754503 rs3115859 G A . PASS AF=0.835803 ES:SE:LP:AF:ID -0.00617542:0.00628729:0.481486:0.835803:rs3115859
1 754629 rs10454459 A G . PASS AF=0.038191 ES:SE:LP:AF:ID 0.0159315:0.0121751:0.721246:0.038191:rs10454459
1 754964 rs3131966 C T . PASS AF=0.836415 ES:SE:LP:AF:ID -0.00617085:0.0063046:0.481486:0.836415:rs3131966
1 755240 rs181660517 T G . PASS AF=0.013196 ES:SE:LP:AF:ID 0.0126794:0.0226365:0.236572:0.013196:rs181660517
1 755435 rs184270342 T G . PASS AF=0.005531 ES:SE:LP:AF:ID 0.0019206:0.0341064:0.0177288:0.005531:rs184270342
1 755775 rs3131965 A G . PASS AF=0.837734 ES:SE:LP:AF:ID -0.00648598:0.00639184:0.508638:0.837734:rs3131965