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_5085.vcf.gz --id UKB-b:7500 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_5085.txt.gz --cohort_controls 99071 --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": "2019-09-12T23:07:45.774359",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-7500/ukb-b-7500.vcf.gz; Date=Sun May 10 06:55:06 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-7500/UKB-b-7500_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-7500/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:40:18 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-7500/UKB-b-7500_data.vcf.gz ...
Read summary statistics for 9019439 SNPs.
Dropped 8786 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, 1287265 SNPs remain.
After merging with regression SNP LD, 1287265 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.2986 (0.0149)
Lambda GC: 1.4152
Mean Chi^2: 1.6378
Intercept: 1.0762 (0.0105)
Ratio: 0.1194 (0.0165)
Analysis finished at Thu Oct 17 14:42:42 2019
Total time elapsed: 2.0m:24.64sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9479,
"inflation_factor": 1.3107,
"mean_EFFECT": 0.0001,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 140,
"n_p_sig": 12186,
"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": 93942,
"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": 1287265,
"ldsc_nsnp_merge_regression_ld": 1287265,
"ldsc_observed_scale_h2_beta": 0.2986,
"ldsc_observed_scale_h2_se": 0.0149,
"ldsc_intercept_beta": 1.0762,
"ldsc_intercept_se": 0.0105,
"ldsc_lambda_gc": 1.4152,
"ldsc_mean_chisq": 1.6378,
"ldsc_ratio": 0.1195
}
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 | 9010693 | 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 | 9019439 | 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 | 8.642992e+00 | 5.758157e+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 | 7.878496e+07 | 5.633944e+07 | 828.0000000 | 3.242930e+07 | 6.934579e+07 | 1.145385e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 6.150000e-05 | 1.569930e-02 | -0.2564280 | -6.545500e-03 | 3.740000e-05 | 6.628900e-03 | 2.712450e-01 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.179940e-02 | 8.667500e-03 | 0.0042673 | 5.099500e-03 | 7.817700e-03 | 1.618920e-02 | 9.926470e-02 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.576698e-01 | 3.006522e-01 | 0.0000000 | 1.900002e-01 | 4.400003e-01 | 7.199992e-01 | 1.000000e+00 | ▇▆▆▅▆ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.576734e-01 | 3.006273e-01 | 0.0000000 | 1.868245e-01 | 4.434869e-01 | 7.182599e-01 | 9.999999e-01 | ▇▆▆▆▆ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.199583e-01 | 2.585105e-01 | 0.0035330 | 2.038700e-02 | 1.011670e-01 | 3.468000e-01 | 9.964670e-01 | ▇▂▁▁▁ |
| numeric | AF_reference | 93942 | 0.9895845 | NA | NA | NA | NA | NA | NA | NA | 2.202909e-01 | 2.504123e-01 | 0.0000000 | 1.777160e-02 | 1.186100e-01 | 3.452480e-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 | -0.0127441 | 0.0078859 | 0.1100001 | 0.1060832 | 0.623764 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0084052 | 0.0078342 | 0.2800000 | 0.2833284 | 0.398686 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0022019 | 0.0124330 | 0.8600001 | 0.8594314 | 0.103973 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0014425 | 0.0077005 | 0.8499999 | 0.8514061 | 0.455613 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | -0.0000463 | 0.0150981 | 1.0000000 | 0.9975558 | 0.074778 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0008141 | 0.0088077 | 0.9299999 | 0.9263578 | 0.240464 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | -0.0077826 | 0.0109165 | 0.4799997 | 0.4758953 | 0.912871 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0149074 | 0.0073237 | 0.0420001 | 0.0418007 | 0.117768 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | 0.0081693 | 0.0107183 | 0.4500005 | 0.4459522 | 0.067645 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0103715 | 0.0054306 | 0.0560003 | 0.0561566 | 0.514180 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219006 | rs28729663 | G | A | -0.0091438 | 0.0066001 | 0.1700000 | 0.1659294 | 0.137223 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | -0.0112907 | 0.0086024 | 0.1900002 | 0.1893485 | 0.072606 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | -0.0090305 | 0.0114347 | 0.4299995 | 0.4296745 | 0.041858 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | -0.0167228 | 0.0115081 | 0.1499999 | 0.1461868 | 0.049110 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | -0.0108451 | 0.0086050 | 0.2099999 | 0.2075494 | 0.072139 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | -0.0104690 | 0.0100574 | 0.2999998 | 0.2979103 | 0.054533 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | -0.0111772 | 0.0101099 | 0.2700001 | 0.2689128 | 0.054145 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | -0.0111900 | 0.0086361 | 0.2000000 | 0.1950727 | 0.072001 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | -0.0306186 | 0.0203586 | 0.1299999 | 0.1325907 | 0.020054 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0079044 | 0.0052502 | 0.1299999 | 0.1321859 | 0.298305 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623764 ES:SE:LP:AF:ID -0.0127441:0.00788595:0.958607:0.623764:rs10399793
1 54676 rs2462492 C T . PASS AF=0.398686 ES:SE:LP:AF:ID 0.00840515:0.00783425:0.552842:0.398686:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103973 ES:SE:LP:AF:ID 0.00220186:0.012433:0.0655015:0.103973:rs114608975
1 91536 rs6702460 G T . PASS AF=0.455613 ES:SE:LP:AF:ID -0.00144249:0.00770048:0.0705811:0.455613:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074778 ES:SE:LP:AF:ID -4.62504e-05:0.0150981:-0:0.074778:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240464 ES:SE:LP:AF:ID 0.000814083:0.00880773:0.0315171:0.240464:rs6680723
1 546697 rs12025928 A G . PASS AF=0.912871 ES:SE:LP:AF:ID -0.00778259:0.0109165:0.318759:0.912871:rs12025928
1 693731 rs12238997 A G . PASS AF=0.117768 ES:SE:LP:AF:ID -0.0149074:0.00732371:1.37675:0.117768:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067645 ES:SE:LP:AF:ID 0.00816928:0.0107183:0.346787:0.067645:rs72631875
1 706368 rs12029736 A G . PASS AF=0.51418 ES:SE:LP:AF:ID 0.0103715:0.00543062:1.25181:0.51418:rs12029736
1 714596 rs149887893 T C . PASS AF=0.033663 ES:SE:LP:AF:ID -0.00845409:0.0135497:0.275724:0.033663:rs149887893
1 715265 rs12184267 C T . PASS AF=0.037324 ES:SE:LP:AF:ID -0.00898327:0.0123238:0.327902:0.037324:rs12184267
1 715367 rs12184277 A G . PASS AF=0.037404 ES:SE:LP:AF:ID -0.00872669:0.0122845:0.318759:0.037404:rs12184277
1 717485 rs12184279 C A . PASS AF=0.037098 ES:SE:LP:AF:ID -0.00956682:0.012369:0.356547:0.037098:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016724 ES:SE:LP:AF:ID -0.0157857:0.019078:0.387216:0.016724:rs144155419
1 720381 rs116801199 G T . PASS AF=0.037665 ES:SE:LP:AF:ID -0.00981074:0.0122311:0.376751:0.037665:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037752 ES:SE:LP:AF:ID -0.00896398:0.0121936:0.337242:0.037752:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101285 ES:SE:LP:AF:ID -0.0104926:0.00897823:0.619789:0.101285:rs116030099
1 723891 rs2977670 G C . PASS AF=0.958033 ES:SE:LP:AF:ID 0.00717394:0.0117286:0.267606:0.958033:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031831 ES:SE:LP:AF:ID 0.0328719:0.0214544:0.886057:0.031831:rs12126395
1 725060 rs865924913 A T . PASS AF=0.052516 ES:SE:LP:AF:ID 0.00213879:0.0173554:0.0457575:0.052516:rs865924913
1 726794 rs28454925 C G . PASS AF=0.037209 ES:SE:LP:AF:ID -0.00943051:0.0122799:0.356547:0.037209:rs28454925
1 729632 rs116720794 C T . PASS AF=0.037525 ES:SE:LP:AF:ID -0.00973637:0.0121758:0.376751:0.037525:rs116720794
1 729679 rs4951859 C G . PASS AF=0.840936 ES:SE:LP:AF:ID 0.0100612:0.00634233:0.958607:0.840936:rs4951859
1 730087 rs148120343 T C . PASS AF=0.056184 ES:SE:LP:AF:ID -0.0126952:0.0103182:0.657577:0.056184:rs148120343
1 731718 rs58276399 T C . PASS AF=0.123696 ES:SE:LP:AF:ID -0.0112292:0.00695168:0.958607:0.123696:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025917 ES:SE:LP:AF:ID 0.0297343:0.0170592:1.09151:0.025917:rs369030935
1 734349 rs141242758 T C . PASS AF=0.12289 ES:SE:LP:AF:ID -0.0108561:0.00695522:0.920819:0.12289:rs141242758
1 736289 rs79010578 T A . PASS AF=0.133662 ES:SE:LP:AF:ID -0.0102453:0.00685548:0.853872:0.133662:rs79010578
1 736689 rs181876450 T C . PASS AF=0.011245 ES:SE:LP:AF:ID 0.0182296:0.0248149:0.337242:0.011245:rs181876450
1 740284 rs61770167 C T . PASS AF=0.006012 ES:SE:LP:AF:ID 0.00678903:0.0312517:0.0809219:0.006012:rs61770167
1 752478 rs146277091 G A . PASS AF=0.037495 ES:SE:LP:AF:ID -0.00714999:0.0120405:0.259637:0.037495:rs146277091
1 752566 rs3094315 G A . PASS AF=0.836696 ES:SE:LP:AF:ID 0.00901453:0.00613735:0.853872:0.836696:rs3094315
1 752721 rs3131972 A G . PASS AF=0.836264 ES:SE:LP:AF:ID 0.00912442:0.00613038:0.853872:0.836264:rs3131972
1 753405 rs3115860 C A . PASS AF=0.867973 ES:SE:LP:AF:ID 0.00970404:0.00657577:0.853872:0.867973:rs3115860
1 753541 rs2073813 G A . PASS AF=0.131689 ES:SE:LP:AF:ID -0.00983732:0.0065899:0.853872:0.131689:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037937 ES:SE:LP:AF:ID -0.00724251:0.011851:0.267606:0.037937:rs12184312
1 754105 rs12184325 C T . PASS AF=0.038185 ES:SE:LP:AF:ID -0.00683847:0.0117776:0.251812:0.038185:rs12184325
1 754182 rs3131969 A G . PASS AF=0.867277 ES:SE:LP:AF:ID 0.00922788:0.00656249:0.79588:0.867277:rs3131969
1 754192 rs3131968 A G . PASS AF=0.867378 ES:SE:LP:AF:ID 0.00914024:0.00656556:0.79588:0.867378:rs3131968
1 754211 rs12184313 G A . PASS AF=0.038137 ES:SE:LP:AF:ID -0.0062165:0.0118246:0.221849:0.038137:rs12184313
1 754334 rs3131967 T C . PASS AF=0.867276 ES:SE:LP:AF:ID 0.00926955:0.00656208:0.79588:0.867276:rs3131967
1 754433 rs150578204 G A . PASS AF=0.005088 ES:SE:LP:AF:ID -0.00406759:0.0339915:0.0457575:0.005088:rs150578204
1 754458 rs142682604 G T . PASS AF=0.005055 ES:SE:LP:AF:ID -0.00567585:0.0340862:0.0604807:0.005055:rs142682604
1 754503 rs3115859 G A . PASS AF=0.835809 ES:SE:LP:AF:ID 0.00882146:0.00611729:0.823909:0.835809:rs3115859
1 754629 rs10454459 A G . PASS AF=0.038155 ES:SE:LP:AF:ID -0.00661049:0.0118401:0.236572:0.038155:rs10454459
1 754964 rs3131966 C T . PASS AF=0.836413 ES:SE:LP:AF:ID 0.00906473:0.00613367:0.853872:0.836413:rs3131966
1 755240 rs181660517 T G . PASS AF=0.013199 ES:SE:LP:AF:ID -0.0154871:0.0219907:0.318759:0.013199:rs181660517
1 755435 rs184270342 T G . PASS AF=0.005513 ES:SE:LP:AF:ID 0.0338414:0.0332343:0.508638:0.005513:rs184270342
1 755775 rs3131965 A G . PASS AF=0.837704 ES:SE:LP:AF:ID 0.00959595:0.00621763:0.920819:0.837704:rs3131965