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_1845.vcf.gz --id UKB-b:13686 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_1845.txt.gz --cohort_controls 180094 --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": "2019-09-13T07:54:49.518199",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-13686/ukb-b-13686.vcf.gz; Date=Sat May 9 17:48:31 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-13686/UKB-b-13686_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-13686/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:42:51 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-13686/UKB-b-13686_data.vcf.gz ...
Read summary statistics for 9443465 SNPs.
Dropped 11166 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, 1288253 SNPs remain.
After merging with regression SNP LD, 1288253 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0325 (0.0036)
Lambda GC: 1.1828
Mean Chi^2: 1.2019
Intercept: 1.0852 (0.0069)
Ratio: 0.4219 (0.0342)
Analysis finished at Thu Oct 17 14:44:37 2019
Total time elapsed: 1.0m:46.24sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9491,
"inflation_factor": 1.1474,
"mean_EFFECT": -0.0003,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 2,
"n_p_sig": 138,
"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": 125156,
"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": 1288253,
"ldsc_nsnp_merge_regression_ld": 1288253,
"ldsc_observed_scale_h2_beta": 0.0325,
"ldsc_observed_scale_h2_se": 0.0036,
"ldsc_intercept_beta": 1.0852,
"ldsc_intercept_se": 0.0069,
"ldsc_lambda_gc": 1.1828,
"ldsc_mean_chisq": 1.2019,
"ldsc_ratio": 0.422
}
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 | 9432358 | 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 | 9443465 | 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 | 8.629834e+00 | 5.752564e+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 | 7.883803e+07 | 5.631724e+07 | 828.0000000 | 3.252800e+07 | 6.941890e+07 | 1.145766e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -2.916000e-04 | 1.350500e-02 | -0.1946400 | -4.954800e-03 | -1.027000e-04 | 4.644200e-03 | 1.771560e-01 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 9.909400e-03 | 8.137000e-03 | 0.0031896 | 3.857300e-03 | 6.181200e-03 | 1.352840e-02 | 1.085330e-01 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.771953e-01 | 2.949299e-01 | 0.0000000 | 2.200002e-01 | 4.700002e-01 | 7.300002e-01 | 1.000000e+00 | ▇▇▇▆▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.771963e-01 | 2.949030e-01 | 0.0000000 | 2.155894e-01 | 4.692897e-01 | 7.329367e-01 | 9.999997e-01 | ▇▇▆▆▆ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.110058e-01 | 2.574878e-01 | 0.0019440 | 1.626900e-02 | 8.851400e-02 | 3.305090e-01 | 9.980560e-01 | ▇▂▁▁▁ |
| numeric | AF_reference | 125156 | 0.9867468 | NA | NA | NA | NA | NA | NA | NA | 2.124764e-01 | 2.492131e-01 | 0.0000000 | 1.377800e-02 | 1.078270e-01 | 3.314700e-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 | 0.0027050 | 0.0058804 | 0.6499995 | 0.6455202 | 0.623823 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0014468 | 0.0058316 | 0.8000000 | 0.8040613 | 0.399969 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | -0.0040048 | 0.0093484 | 0.6700003 | 0.6683672 | 0.103070 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0072641 | 0.0057366 | 0.2099999 | 0.2054098 | 0.456345 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0111739 | 0.0112320 | 0.3200000 | 0.3198203 | 0.074986 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0015922 | 0.0065683 | 0.8100000 | 0.8084715 | 0.240729 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | -0.0044961 | 0.0081687 | 0.5800000 | 0.5820433 | 0.913413 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0014676 | 0.0054814 | 0.7899998 | 0.7888935 | 0.116341 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | 0.0105868 | 0.0080428 | 0.1900002 | 0.1880680 | 0.067169 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0032291 | 0.0040613 | 0.4299995 | 0.4265601 | 0.515782 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219704 | rs147475742 | G | A | -0.0119817 | 0.0085574 | 0.1600000 | 0.1614640 | 0.041811 | 0.0473243 | NA |
| 22 | 51219766 | rs182321900 | C | T | -0.0235528 | 0.0395213 | 0.5500004 | 0.5512073 | 0.001953 | NA | NA |
| 22 | 51220146 | rs868950473 | C | T | -0.0289902 | 0.0391869 | 0.4600002 | 0.4594255 | 0.002002 | NA | NA |
| 22 | 51221190 | rs369304721 | G | A | -0.0046432 | 0.0085449 | 0.5900000 | 0.5868595 | 0.049443 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | -0.0067846 | 0.0063957 | 0.2900000 | 0.2887784 | 0.072808 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | -0.0013522 | 0.0074833 | 0.8600001 | 0.8566044 | 0.054812 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | -0.0013067 | 0.0075180 | 0.8600001 | 0.8620115 | 0.054459 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | -0.0074353 | 0.0064207 | 0.2500000 | 0.2468556 | 0.072630 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | -0.0030140 | 0.0149649 | 0.8400000 | 0.8403848 | 0.020242 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0043818 | 0.0039140 | 0.2599998 | 0.2629264 | 0.297063 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623823 ES:SE:LP:AF:ID 0.00270497:0.00588044:0.187087:0.623823:rs10399793
1 54676 rs2462492 C T . PASS AF=0.399969 ES:SE:LP:AF:ID -0.00144678:0.00583157:0.09691:0.399969:rs2462492
1 86028 rs114608975 T C . PASS AF=0.10307 ES:SE:LP:AF:ID -0.00400477:0.00934842:0.173925:0.10307:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456345 ES:SE:LP:AF:ID -0.00726414:0.00573656:0.677781:0.456345:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074986 ES:SE:LP:AF:ID 0.0111739:0.011232:0.49485:0.074986:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240729 ES:SE:LP:AF:ID 0.00159215:0.00656832:0.091515:0.240729:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913413 ES:SE:LP:AF:ID -0.00449609:0.00816873:0.236572:0.913413:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116341 ES:SE:LP:AF:ID -0.00146763:0.00548139:0.102373:0.116341:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067169 ES:SE:LP:AF:ID 0.0105868:0.00804275:0.721246:0.067169:rs72631875
1 706368 rs12029736 A G . PASS AF=0.515782 ES:SE:LP:AF:ID 0.00322913:0.00406133:0.366532:0.515782:rs12029736
1 714596 rs149887893 T C . PASS AF=0.033375 ES:SE:LP:AF:ID -0.00109779:0.0101912:0.0409586:0.033375:rs149887893
1 715265 rs12184267 C T . PASS AF=0.037069 ES:SE:LP:AF:ID -0.00249225:0.00925335:0.102373:0.037069:rs12184267
1 715367 rs12184277 A G . PASS AF=0.037183 ES:SE:LP:AF:ID -0.00308708:0.0092191:0.130768:0.037183:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036848 ES:SE:LP:AF:ID -0.00214565:0.00929024:0.0861861:0.036848:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016438 ES:SE:LP:AF:ID 0.0117222:0.0143741:0.387216:0.016438:rs144155419
1 720381 rs116801199 G T . PASS AF=0.037422 ES:SE:LP:AF:ID -0.00280699:0.00918466:0.119186:0.037422:rs116801199
1 721290 rs12565286 G C . PASS AF=0.0375 ES:SE:LP:AF:ID -0.00328305:0.00915546:0.142668:0.0375:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101182 ES:SE:LP:AF:ID 0.0068862:0.00672072:0.508638:0.101182:rs116030099
1 723891 rs2977670 G C . PASS AF=0.958463 ES:SE:LP:AF:ID 0.00238934:0.00880954:0.102373:0.958463:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031694 ES:SE:LP:AF:ID -0.00605968:0.0159333:0.154902:0.031694:rs12126395
1 725060 rs865924913 A T . PASS AF=0.053223 ES:SE:LP:AF:ID -0.0208311:0.012846:1:0.053223:rs865924913
1 726794 rs28454925 C G . PASS AF=0.037029 ES:SE:LP:AF:ID -0.00246882:0.00921236:0.102373:0.037029:rs28454925
1 729632 rs116720794 C T . PASS AF=0.037338 ES:SE:LP:AF:ID -0.00175498:0.00912848:0.0705811:0.037338:rs116720794
1 729679 rs4951859 C G . PASS AF=0.842801 ES:SE:LP:AF:ID 0.000230181:0.00474489:0.0177288:0.842801:rs4951859
1 730087 rs148120343 T C . PASS AF=0.056027 ES:SE:LP:AF:ID 0.00259501:0.00768788:0.130768:0.056027:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122314 ES:SE:LP:AF:ID 0.00128276:0.0052002:0.091515:0.122314:rs58276399
1 732989 rs369030935 C T . PASS AF=0.02553 ES:SE:LP:AF:ID 0.0177944:0.0128574:0.769551:0.02553:rs369030935
1 734349 rs141242758 T C . PASS AF=0.121537 ES:SE:LP:AF:ID 0.00158682:0.00520269:0.119186:0.121537:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132514 ES:SE:LP:AF:ID -0.00190615:0.00512793:0.148742:0.132514:rs79010578
1 736689 rs181876450 T C . PASS AF=0.010835 ES:SE:LP:AF:ID 0.0252441:0.0189796:0.744727:0.010835:rs181876450
1 740284 rs61770167 C T . PASS AF=0.00566 ES:SE:LP:AF:ID 0.0125632:0.0242132:0.221849:0.00566:rs61770167
1 742813 rs112573343 C T . PASS AF=0.002392 ES:SE:LP:AF:ID -0.0218575:0.0392072:0.236572:0.002392:rs112573343
1 752478 rs146277091 G A . PASS AF=0.037291 ES:SE:LP:AF:ID -0.00288629:0.00903194:0.124939:0.037291:rs146277091
1 752566 rs3094315 G A . PASS AF=0.838446 ES:SE:LP:AF:ID 0.000575281:0.00459176:0.0457575:0.838446:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838069 ES:SE:LP:AF:ID 0.000298864:0.00458672:0.0222764:0.838069:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869608 ES:SE:LP:AF:ID -0.000101646:0.00492408:0.00877392:0.869608:rs3115860
1 753541 rs2073813 G A . PASS AF=0.130011 ES:SE:LP:AF:ID 0.000756549:0.00493531:0.0555173:0.130011:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037782 ES:SE:LP:AF:ID -0.00334307:0.00887921:0.148742:0.037782:rs12184312
1 754105 rs12184325 C T . PASS AF=0.038028 ES:SE:LP:AF:ID -0.00271015:0.00882287:0.119186:0.038028:rs12184325
1 754182 rs3131969 A G . PASS AF=0.868925 ES:SE:LP:AF:ID -0.00048104:0.00491398:0.0362122:0.868925:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869022 ES:SE:LP:AF:ID -0.000486246:0.00491585:0.0362122:0.869022:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037998 ES:SE:LP:AF:ID -0.00420422:0.00886041:0.19382:0.037998:rs12184313
1 754334 rs3131967 T C . PASS AF=0.868927 ES:SE:LP:AF:ID -0.000421816:0.00491396:0.0315171:0.868927:rs3131967
1 754433 rs150578204 G A . PASS AF=0.005181 ES:SE:LP:AF:ID -0.0376715:0.0251788:0.886057:0.005181:rs150578204
1 754458 rs142682604 G T . PASS AF=0.005149 ES:SE:LP:AF:ID -0.0367203:0.0252336:0.823909:0.005149:rs142682604
1 754503 rs3115859 G A . PASS AF=0.837524 ES:SE:LP:AF:ID 0.000458019:0.00457419:0.0362122:0.837524:rs3115859
1 754629 rs10454459 A G . PASS AF=0.038018 ES:SE:LP:AF:ID -0.00389492:0.00887265:0.180456:0.038018:rs10454459
1 754964 rs3131966 C T . PASS AF=0.838173 ES:SE:LP:AF:ID 0.000644535:0.00458744:0.05061:0.838173:rs3131966
1 755240 rs181660517 T G . PASS AF=0.013707 ES:SE:LP:AF:ID 0.0129711:0.0160752:0.376751:0.013707:rs181660517
1 755435 rs184270342 T G . PASS AF=0.005496 ES:SE:LP:AF:ID 0.0659963:0.0248641:2.10237:0.005496:rs184270342