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_5201.vcf.gz --id UKB-b:15782 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_5201.txt.gz --cohort_controls 101023 --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": "2019-09-12T23:16:22.006713",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-15782/ukb-b-15782.vcf.gz; Date=Sun May 10 01:37:13 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-15782/UKB-b-15782_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-15782/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:44:25 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-15782/UKB-b-15782_data.vcf.gz ...
Read summary statistics for 9035251 SNPs.
Dropped 8858 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, 1287308 SNPs remain.
After merging with regression SNP LD, 1287308 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0274 (0.005)
Lambda GC: 1.0647
Mean Chi^2: 1.0701
Intercept: 1.0159 (0.0061)
Ratio: 0.2263 (0.0864)
Analysis finished at Thu Oct 17 14:45:59 2019
Total time elapsed: 1.0m:33.99sQC metrics
Metrics
Metrics
{
"af_correlation": 0.948,
"inflation_factor": 1.0475,
"mean_EFFECT": 0.0001,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 3,
"n_p_sig": 116,
"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": 94601,
"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": 1287308,
"ldsc_nsnp_merge_regression_ld": 1287308,
"ldsc_observed_scale_h2_beta": 0.0274,
"ldsc_observed_scale_h2_se": 0.005,
"ldsc_intercept_beta": 1.0159,
"ldsc_intercept_se": 0.0061,
"ldsc_lambda_gc": 1.0647,
"ldsc_mean_chisq": 1.0701,
"ldsc_ratio": 0.2268
}
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 | 9026434 | 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 | 9035251 | 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 | 8.642981e+00 | 5.757968e+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 | 7.878477e+07 | 5.633592e+07 | 828.0000000 | 3.243373e+07 | 6.934933e+07 | 1.145322e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 5.060000e-05 | 1.487380e-02 | -0.1672030 | -5.743400e-03 | 3.850000e-05 | 5.857100e-03 | 1.582750e-01 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.185940e-02 | 8.741400e-03 | 0.0042808 | 5.107600e-03 | 7.843300e-03 | 1.627140e-02 | 1.003720e-01 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.928702e-01 | 2.907916e-01 | 0.0000000 | 2.399999e-01 | 4.899999e-01 | 7.400005e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.928688e-01 | 2.907652e-01 | 0.0000000 | 2.391075e-01 | 4.912546e-01 | 7.449829e-01 | 9.999997e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.196019e-01 | 2.584646e-01 | 0.0034650 | 2.020800e-02 | 1.006860e-01 | 3.462130e-01 | 9.965350e-01 | ▇▂▁▁▁ |
| numeric | AF_reference | 94601 | 0.9895298 | NA | NA | NA | NA | NA | NA | NA | 2.199661e-01 | 2.503628e-01 | 0.0000000 | 1.757190e-02 | 1.182110e-01 | 3.446490e-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 | -0.0046061 | 0.0079033 | 0.5600000 | 0.5600237 | 0.623676 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0102442 | 0.0078514 | 0.1900002 | 0.1919737 | 0.398819 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | -0.0031743 | 0.0124610 | 0.8000000 | 0.7989230 | 0.104021 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0012850 | 0.0077223 | 0.8700001 | 0.8678446 | 0.455648 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | -0.0170845 | 0.0151103 | 0.2599998 | 0.2582013 | 0.074860 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0045391 | 0.0088320 | 0.6100002 | 0.6072902 | 0.240524 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | -0.0006981 | 0.0109367 | 0.9500000 | 0.9491058 | 0.912692 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | 0.0002208 | 0.0073499 | 0.9800000 | 0.9760306 | 0.117603 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | 0.0078826 | 0.0107397 | 0.4600002 | 0.4629686 | 0.067771 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0090927 | 0.0054469 | 0.0949992 | 0.0950518 | 0.514506 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219006 | rs28729663 | G | A | -0.0052993 | 0.0065891 | 0.4199997 | 0.4212498 | 0.137257 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | 0.0014170 | 0.0085793 | 0.8700001 | 0.8688138 | 0.072757 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | 0.0045919 | 0.0114121 | 0.6899999 | 0.6874135 | 0.041907 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | -0.0033573 | 0.0114835 | 0.7700005 | 0.7700105 | 0.049180 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | 0.0023053 | 0.0085822 | 0.7899998 | 0.7882258 | 0.072280 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | -0.0116167 | 0.0100559 | 0.2500000 | 0.2480035 | 0.054393 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | -0.0119622 | 0.0101091 | 0.2399999 | 0.2366863 | 0.053996 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0025644 | 0.0086131 | 0.7700005 | 0.7659087 | 0.072148 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | -0.0186730 | 0.0203152 | 0.3599996 | 0.3580098 | 0.020026 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0040440 | 0.0052396 | 0.4400003 | 0.4402266 | 0.298210 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623676 ES:SE:LP:AF:ID -0.00460611:0.00790333:0.251812:0.623676:rs10399793
1 54676 rs2462492 C T . PASS AF=0.398819 ES:SE:LP:AF:ID 0.0102442:0.00785139:0.721246:0.398819:rs2462492
1 86028 rs114608975 T C . PASS AF=0.104021 ES:SE:LP:AF:ID -0.00317433:0.012461:0.09691:0.104021:rs114608975
1 91536 rs6702460 G T . PASS AF=0.455648 ES:SE:LP:AF:ID 0.00128496:0.00772226:0.0604807:0.455648:rs6702460
1 234313 rs8179466 C T . PASS AF=0.07486 ES:SE:LP:AF:ID -0.0170845:0.0151103:0.585027:0.07486:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240524 ES:SE:LP:AF:ID 0.00453914:0.00883195:0.21467:0.240524:rs6680723
1 546697 rs12025928 A G . PASS AF=0.912692 ES:SE:LP:AF:ID -0.000698086:0.0109367:0.0222764:0.912692:rs12025928
1 693731 rs12238997 A G . PASS AF=0.117603 ES:SE:LP:AF:ID 0.000220834:0.00734993:0.00877392:0.117603:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067771 ES:SE:LP:AF:ID 0.00788259:0.0107397:0.337242:0.067771:rs72631875
1 706368 rs12029736 A G . PASS AF=0.514506 ES:SE:LP:AF:ID 0.00909268:0.0054469:1.02228:0.514506:rs12029736
1 714596 rs149887893 T C . PASS AF=0.033666 ES:SE:LP:AF:ID 0.00253895:0.0135878:0.0705811:0.033666:rs149887893
1 715265 rs12184267 C T . PASS AF=0.037329 ES:SE:LP:AF:ID 0.00318238:0.012358:0.09691:0.037329:rs12184267
1 715367 rs12184277 A G . PASS AF=0.037408 ES:SE:LP:AF:ID 0.00364556:0.0123187:0.113509:0.037408:rs12184277
1 717485 rs12184279 C A . PASS AF=0.037095 ES:SE:LP:AF:ID 0.00312766:0.0124051:0.09691:0.037095:rs12184279
1 717587 rs144155419 G A . PASS AF=0.01672 ES:SE:LP:AF:ID -0.00286229:0.0191354:0.0555173:0.01672:rs144155419
1 720381 rs116801199 G T . PASS AF=0.037664 ES:SE:LP:AF:ID 0.00346634:0.0122662:0.107905:0.037664:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037748 ES:SE:LP:AF:ID 0.00405872:0.0122286:0.130768:0.037748:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101234 ES:SE:LP:AF:ID -0.00740219:0.00900923:0.387216:0.101234:rs116030099
1 723891 rs2977670 G C . PASS AF=0.958064 ES:SE:LP:AF:ID 0.00317679:0.011766:0.102373:0.958064:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031851 ES:SE:LP:AF:ID -0.0638977:0.0214944:2.52288:0.031851:rs12126395
1 725060 rs865924913 A T . PASS AF=0.052574 ES:SE:LP:AF:ID -0.0051772:0.0173879:0.113509:0.052574:rs865924913
1 726794 rs28454925 C G . PASS AF=0.037212 ES:SE:LP:AF:ID 0.00476278:0.0123146:0.154902:0.037212:rs28454925
1 729632 rs116720794 C T . PASS AF=0.037531 ES:SE:LP:AF:ID 0.00578689:0.0122091:0.19382:0.037531:rs116720794
1 729679 rs4951859 C G . PASS AF=0.84107 ES:SE:LP:AF:ID -0.00165137:0.00636699:0.09691:0.84107:rs4951859
1 730087 rs148120343 T C . PASS AF=0.056102 ES:SE:LP:AF:ID -0.00907269:0.0103608:0.420216:0.056102:rs148120343
1 731718 rs58276399 T C . PASS AF=0.123523 ES:SE:LP:AF:ID 0.0023649:0.00697854:0.136677:0.123523:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025819 ES:SE:LP:AF:ID 0.0234937:0.0171434:0.769551:0.025819:rs369030935
1 734349 rs141242758 T C . PASS AF=0.122718 ES:SE:LP:AF:ID 0.00190344:0.00698268:0.102373:0.122718:rs141242758
1 736289 rs79010578 T A . PASS AF=0.133529 ES:SE:LP:AF:ID 0.00282554:0.00688277:0.167491:0.133529:rs79010578
1 736689 rs181876450 T C . PASS AF=0.01125 ES:SE:LP:AF:ID 0.000702944:0.0248894:0.00877392:0.01125:rs181876450
1 740284 rs61770167 C T . PASS AF=0.005992 ES:SE:LP:AF:ID 0.0330167:0.0314078:0.537602:0.005992:rs61770167
1 752478 rs146277091 G A . PASS AF=0.037487 ES:SE:LP:AF:ID 0.00546937:0.0120771:0.187087:0.037487:rs146277091
1 752566 rs3094315 G A . PASS AF=0.836869 ES:SE:LP:AF:ID -3.71767e-05:0.00615958:-0:0.836869:rs3094315
1 752721 rs3131972 A G . PASS AF=0.836433 ES:SE:LP:AF:ID 0.000676481:0.0061528:0.0409586:0.836433:rs3131972
1 753405 rs3115860 C A . PASS AF=0.868211 ES:SE:LP:AF:ID 0.000786263:0.00660102:0.0409586:0.868211:rs3115860
1 753541 rs2073813 G A . PASS AF=0.131476 ES:SE:LP:AF:ID -0.0013231:0.00661509:0.0757207:0.131476:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037918 ES:SE:LP:AF:ID 0.00667501:0.0118896:0.244125:0.037918:rs12184312
1 754105 rs12184325 C T . PASS AF=0.038165 ES:SE:LP:AF:ID 0.00683357:0.0118163:0.251812:0.038165:rs12184325
1 754182 rs3131969 A G . PASS AF=0.867503 ES:SE:LP:AF:ID 0.00151725:0.00658793:0.0861861:0.867503:rs3131969
1 754192 rs3131968 A G . PASS AF=0.867607 ES:SE:LP:AF:ID 0.00137107:0.00659105:0.0757207:0.867607:rs3131968
1 754211 rs12184313 G A . PASS AF=0.038124 ES:SE:LP:AF:ID 0.0066328:0.0118622:0.236572:0.038124:rs12184313
1 754334 rs3131967 T C . PASS AF=0.867501 ES:SE:LP:AF:ID 0.00143972:0.00658752:0.0809219:0.867501:rs3131967
1 754433 rs150578204 G A . PASS AF=0.005085 ES:SE:LP:AF:ID -0.0494201:0.0341134:0.823909:0.005085:rs150578204
1 754458 rs142682604 G T . PASS AF=0.005052 ES:SE:LP:AF:ID -0.0496811:0.034208:0.823909:0.005052:rs142682604
1 754503 rs3115859 G A . PASS AF=0.835976 ES:SE:LP:AF:ID 0.00046849:0.00613944:0.0268721:0.835976:rs3115859
1 754629 rs10454459 A G . PASS AF=0.03814 ES:SE:LP:AF:ID 0.00667532:0.0118782:0.244125:0.03814:rs10454459
1 754964 rs3131966 C T . PASS AF=0.836583 ES:SE:LP:AF:ID 0.000283508:0.00615582:0.0177288:0.836583:rs3131966
1 755240 rs181660517 T G . PASS AF=0.013177 ES:SE:LP:AF:ID -0.0325481:0.0220696:0.853872:0.013177:rs181660517
1 755435 rs184270342 T G . PASS AF=0.005506 ES:SE:LP:AF:ID 0.0082584:0.0333557:0.09691:0.005506:rs184270342
1 755775 rs3131965 A G . PASS AF=0.837857 ES:SE:LP:AF:ID -0.000435021:0.00623953:0.0268721:0.837857:rs3131965