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_3147.vcf.gz --id UKB-b:19234 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_3147.txt.gz --cohort_controls 265753 --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": "2019-09-13T03:44:09.309813",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-19234/ukb-b-19234.vcf.gz; Date=Sun May 10 14:21:34 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-19234/UKB-b-19234_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19234/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:41:09 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19234/UKB-b-19234_data.vcf.gz ...
Read summary statistics for 9674801 SNPs.
Dropped 13097 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, 1288733 SNPs remain.
After merging with regression SNP LD, 1288733 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.2844 (0.0244)
Lambda GC: 1.674
Mean Chi^2: 2.7498
Intercept: 1.2011 (0.0176)
Ratio: 0.1149 (0.0101)
Analysis finished at Thu Oct 17 14:43:01 2019
Total time elapsed: 1.0m:51.69sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9495,
"inflation_factor": 1.4295,
"mean_EFFECT": 0,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 362,
"n_p_sig": 59686,
"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": 159217,
"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": 1288733,
"ldsc_nsnp_merge_regression_ld": 1288733,
"ldsc_observed_scale_h2_beta": 0.2844,
"ldsc_observed_scale_h2_se": 0.0244,
"ldsc_intercept_beta": 1.2011,
"ldsc_intercept_se": 0.0176,
"ldsc_lambda_gc": 1.674,
"ldsc_mean_chisq": 2.7498,
"ldsc_ratio": 0.1149
}
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 | 9661769 | 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 | 9674801 | 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 | 8.625678e+00 | 5.749911e+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 | 7.884118e+07 | 5.629187e+07 | 828.0000000 | 3.256549e+07 | 6.944570e+07 | 1.145707e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.200000e-05 | 1.244450e-02 | -0.2068640 | -4.450000e-03 | 1.030000e-05 | 4.454600e-03 | 3.866430e-01 | ▁▇▁▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 8.266200e-03 | 7.353200e-03 | 0.0024625 | 2.999500e-03 | 4.931000e-03 | 1.112860e-02 | 1.266760e-01 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.374942e-01 | 3.063325e-01 | 0.0000000 | 1.600000e-01 | 4.199997e-01 | 6.999999e-01 | 1.000000e+00 | ▇▅▅▅▅ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.374951e-01 | 3.063107e-01 | 0.0000000 | 1.559183e-01 | 4.157226e-01 | 7.024975e-01 | 9.999998e-01 | ▇▅▅▅▅ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.065163e-01 | 2.569900e-01 | 0.0013180 | 1.442100e-02 | 8.227800e-02 | 3.224240e-01 | 9.986820e-01 | ▇▂▁▁▁ |
| numeric | AF_reference | 159217 | 0.9835431 | NA | NA | NA | NA | NA | NA | NA | 2.089750e-01 | 2.486176e-01 | 0.0000000 | 1.257990e-02 | 1.030350e-01 | 3.248800e-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 | -0.0053496 | 0.0045175 | 0.2399999 | 0.2363364 | 0.623768 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0089475 | 0.0044674 | 0.0449997 | 0.0451932 | 0.400852 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | -0.0053185 | 0.0071682 | 0.4600002 | 0.4581160 | 0.103356 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0002411 | 0.0044027 | 0.9599999 | 0.9563230 | 0.456893 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | -0.0098644 | 0.0086604 | 0.2500000 | 0.2546925 | 0.074614 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0016147 | 0.0050273 | 0.7499995 | 0.7480641 | 0.240666 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | 0.0199062 | 0.0062720 | 0.0015000 | 0.0015044 | 0.913402 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0069206 | 0.0042110 | 0.1000000 | 0.1002872 | 0.116479 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | -0.0085296 | 0.0061742 | 0.1700000 | 0.1671300 | 0.067530 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0022530 | 0.0031259 | 0.4700002 | 0.4710641 | 0.516350 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219704 | rs147475742 | G | A | 0.0017074 | 0.0066042 | 0.8000000 | 0.7959928 | 0.042041 | 0.0473243 | NA |
| 22 | 51219766 | rs182321900 | C | T | -0.0096319 | 0.0305193 | 0.7499995 | 0.7523059 | 0.001975 | NA | NA |
| 22 | 51220146 | rs868950473 | C | T | -0.0102903 | 0.0303148 | 0.7300002 | 0.7342722 | 0.002014 | NA | NA |
| 22 | 51221190 | rs369304721 | G | A | 0.0043219 | 0.0065844 | 0.5099998 | 0.5115756 | 0.049828 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | -0.0019402 | 0.0049292 | 0.6899999 | 0.6938697 | 0.073394 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | -0.0012487 | 0.0058012 | 0.8300000 | 0.8295789 | 0.054628 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | -0.0015483 | 0.0058284 | 0.7899998 | 0.7905108 | 0.054268 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | -0.0016476 | 0.0049469 | 0.7400005 | 0.7390851 | 0.073231 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | -0.0088095 | 0.0116653 | 0.4500005 | 0.4501339 | 0.020022 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0028709 | 0.0030322 | 0.3400001 | 0.3437335 | 0.297736 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623768 ES:SE:LP:AF:ID -0.00534956:0.00451747:0.619789:0.623768:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400852 ES:SE:LP:AF:ID 0.00894754:0.0044674:1.34679:0.400852:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103356 ES:SE:LP:AF:ID -0.00531848:0.00716822:0.337242:0.103356:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456893 ES:SE:LP:AF:ID -0.00024113:0.00440272:0.0177288:0.456893:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074614 ES:SE:LP:AF:ID -0.00986444:0.00866042:0.60206:0.074614:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240666 ES:SE:LP:AF:ID -0.00161474:0.00502732:0.124939:0.240666:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913402 ES:SE:LP:AF:ID 0.0199062:0.00627198:2.82391:0.913402:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116479 ES:SE:LP:AF:ID -0.00692059:0.00421098:1:0.116479:rs12238997
1 705882 rs72631875 G A . PASS AF=0.06753 ES:SE:LP:AF:ID -0.00852959:0.00617422:0.769551:0.06753:rs72631875
1 706368 rs12029736 A G . PASS AF=0.51635 ES:SE:LP:AF:ID -0.00225301:0.00312593:0.327902:0.51635:rs12029736
1 714596 rs149887893 T C . PASS AF=0.032869 ES:SE:LP:AF:ID -0.00149645:0.0079026:0.0705811:0.032869:rs149887893
1 715265 rs12184267 C T . PASS AF=0.036472 ES:SE:LP:AF:ID -0.00214742:0.00717734:0.119186:0.036472:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036591 ES:SE:LP:AF:ID -0.000545382:0.00714989:0.0268721:0.036591:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036304 ES:SE:LP:AF:ID -0.000468462:0.00720055:0.0222764:0.036304:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016401 ES:SE:LP:AF:ID 0.00251081:0.0110435:0.0861861:0.016401:rs144155419
1 720381 rs116801199 G T . PASS AF=0.036814 ES:SE:LP:AF:ID -0.000146736:0.00712306:0.00877392:0.036814:rs116801199
1 721290 rs12565286 G C . PASS AF=0.036918 ES:SE:LP:AF:ID -0.000666843:0.0070974:0.0315171:0.036918:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101106 ES:SE:LP:AF:ID 0.0029197:0.00516229:0.244125:0.101106:rs116030099
1 723891 rs2977670 G C . PASS AF=0.959291 ES:SE:LP:AF:ID -0.000736973:0.00684329:0.0409586:0.959291:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031365 ES:SE:LP:AF:ID -0.00199144:0.0124018:0.0604807:0.031365:rs12126395
1 725060 rs865924913 A T . PASS AF=0.053453 ES:SE:LP:AF:ID 0.00639704:0.00980033:0.29243:0.053453:rs865924913
1 726794 rs28454925 C G . PASS AF=0.036459 ES:SE:LP:AF:ID -0.00160887:0.00714125:0.0861861:0.036459:rs28454925
1 729632 rs116720794 C T . PASS AF=0.036767 ES:SE:LP:AF:ID -0.00209528:0.00707615:0.113509:0.036767:rs116720794
1 729679 rs4951859 C G . PASS AF=0.843248 ES:SE:LP:AF:ID 0.00548487:0.00365369:0.886057:0.843248:rs4951859
1 730087 rs148120343 T C . PASS AF=0.056046 ES:SE:LP:AF:ID -0.0106849:0.00590451:1.1549:0.056046:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122476 ES:SE:LP:AF:ID -0.00787315:0.00399454:1.3098:0.122476:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025604 ES:SE:LP:AF:ID -0.00122632:0.00986758:0.0457575:0.025604:rs369030935
1 734349 rs141242758 T C . PASS AF=0.12173 ES:SE:LP:AF:ID -0.00812756:0.00399574:1.37675:0.12173:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132455 ES:SE:LP:AF:ID -0.00711239:0.00394113:1.14874:0.132455:rs79010578
1 736689 rs181876450 T C . PASS AF=0.010991 ES:SE:LP:AF:ID -0.0147789:0.0144731:0.508638:0.010991:rs181876450
1 740284 rs61770167 C T . PASS AF=0.00576 ES:SE:LP:AF:ID -0.0140166:0.018415:0.346787:0.00576:rs61770167
1 742813 rs112573343 C T . PASS AF=0.002278 ES:SE:LP:AF:ID 0.0487528:0.030938:0.920819:0.002278:rs112573343
1 752478 rs146277091 G A . PASS AF=0.036687 ES:SE:LP:AF:ID -0.000536888:0.00700343:0.0268721:0.036687:rs146277091
1 752566 rs3094315 G A . PASS AF=0.838959 ES:SE:LP:AF:ID 0.00401028:0.0035383:0.585027:0.838959:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838586 ES:SE:LP:AF:ID 0.00399029:0.00353454:0.585027:0.838586:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869675 ES:SE:LP:AF:ID 0.0051491:0.00379165:0.769551:0.869675:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129962 ES:SE:LP:AF:ID -0.00534197:0.00379897:0.79588:0.129962:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037186 ES:SE:LP:AF:ID 2.02215e-06:0.00688482:-0:0.037186:rs12184312
1 754105 rs12184325 C T . PASS AF=0.037424 ES:SE:LP:AF:ID 1.90913e-05:0.00684165:-0:0.037424:rs12184325
1 754182 rs3131969 A G . PASS AF=0.869017 ES:SE:LP:AF:ID 0.00496565:0.00378423:0.721246:0.869017:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869099 ES:SE:LP:AF:ID 0.00491962:0.0037855:0.721246:0.869099:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037399 ES:SE:LP:AF:ID -0.000217549:0.00687075:0.0132283:0.037399:rs12184313
1 754334 rs3131967 T C . PASS AF=0.869022 ES:SE:LP:AF:ID 0.00494956:0.00378427:0.721246:0.869022:rs3131967
1 754433 rs150578204 G A . PASS AF=0.00515 ES:SE:LP:AF:ID 0.0190445:0.0193614:0.481486:0.00515:rs150578204
1 754458 rs142682604 G T . PASS AF=0.005114 ES:SE:LP:AF:ID 0.0200902:0.0194148:0.522879:0.005114:rs142682604
1 754503 rs3115859 G A . PASS AF=0.838018 ES:SE:LP:AF:ID 0.00360961:0.00352425:0.508638:0.838018:rs3115859
1 754629 rs10454459 A G . PASS AF=0.037416 ES:SE:LP:AF:ID -0.000477262:0.00688032:0.0268721:0.037416:rs10454459
1 754964 rs3131966 C T . PASS AF=0.838653 ES:SE:LP:AF:ID 0.00379966:0.00353421:0.552842:0.838653:rs3131966
1 755240 rs181660517 T G . PASS AF=0.014036 ES:SE:LP:AF:ID 0.0196265:0.0122:0.958607:0.014036:rs181660517
1 755435 rs184270342 T G . PASS AF=0.005534 ES:SE:LP:AF:ID -0.0266457:0.0190988:0.79588:0.005534:rs184270342