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_3710.vcf.gz --id UKB-b:3278 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_3710.txt.gz --cohort_controls 43125 --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": "2019-09-13T03:45:43.808779",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-3278/ukb-b-3278.vcf.gz; Date=Sun May 10 10:24:06 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-3278/UKB-b-3278_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-3278/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:43:23 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-3278/UKB-b-3278_data.vcf.gz ...
Read summary statistics for 8032987 SNPs.
Dropped 6194 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, 1282926 SNPs remain.
After merging with regression SNP LD, 1282926 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0939 (0.0139)
Lambda GC: 1.0594
Mean Chi^2: 1.0816
Intercept: 1.0028 (0.0065)
Ratio: 0.0338 (0.0796)
Analysis finished at Thu Oct 17 14:44:58 2019
Total time elapsed: 1.0m:35.27sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9434,
"inflation_factor": 1.0475,
"mean_EFFECT": -0,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 8,
"n_p_sig": 667,
"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": 75056,
"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": 1282926,
"ldsc_nsnp_merge_regression_ld": 1282926,
"ldsc_observed_scale_h2_beta": 0.0939,
"ldsc_observed_scale_h2_se": 0.0139,
"ldsc_intercept_beta": 1.0028,
"ldsc_intercept_se": 0.0065,
"ldsc_lambda_gc": 1.0594,
"ldsc_mean_chisq": 1.0816,
"ldsc_ratio": 0.0343
}
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 | 8026821 | 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 | 8032987 | 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 | 8.660446e+00 | 5.763290e+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 | 7.870913e+07 | 5.640817e+07 | 828.0000000 | 3.227366e+07 | 6.918540e+07 | 1.145363e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -2.180000e-05 | 1.537780e-02 | -0.1617290 | -7.111600e-03 | 6.800000e-06 | 7.094300e-03 | 1.602370e-01 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.297230e-02 | 7.860000e-03 | 0.0058317 | 6.792900e-03 | 9.485100e-03 | 1.709180e-02 | 7.157320e-02 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.924406e-01 | 2.914545e-01 | 0.0000000 | 2.399999e-01 | 4.899999e-01 | 7.499995e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.924395e-01 | 2.914289e-01 | 0.0000000 | 2.379035e-01 | 4.901080e-01 | 7.452801e-01 | 9.999998e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.446806e-01 | 2.606770e-01 | 0.0081160 | 3.483400e-02 | 1.352940e-01 | 3.876955e-01 | 9.918840e-01 | ▇▂▂▁▁ |
| numeric | AF_reference | 75056 | 0.9906565 | NA | NA | NA | NA | NA | NA | NA | 2.440144e-01 | 2.525340e-01 | 0.0000000 | 3.654150e-02 | 1.507590e-01 | 3.827880e-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 | 0.0113718 | 0.0107778 | 0.2900000 | 0.2913735 | 0.622802 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0006047 | 0.0107039 | 0.9500000 | 0.9549464 | 0.400722 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | -0.0054220 | 0.0170565 | 0.7499995 | 0.7505738 | 0.103694 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0118065 | 0.0105334 | 0.2599998 | 0.2623461 | 0.457631 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0172199 | 0.0207649 | 0.4100001 | 0.4069464 | 0.074412 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0016492 | 0.0119864 | 0.8900000 | 0.8905664 | 0.241381 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | -0.0471624 | 0.0149880 | 0.0017000 | 0.0016514 | 0.913289 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | 0.0000119 | 0.0099922 | 1.0000000 | 0.9990522 | 0.117665 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | 0.0172386 | 0.0148258 | 0.2399999 | 0.2449337 | 0.066529 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0015289 | 0.0074360 | 0.8400000 | 0.8370949 | 0.514343 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219006 | rs28729663 | G | A | 0.0034664 | 0.0090109 | 0.6999999 | 0.7004646 | 0.138547 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | -0.0198944 | 0.0116472 | 0.0879995 | 0.0876207 | 0.074103 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | -0.0360346 | 0.0154830 | 0.0200000 | 0.0199458 | 0.042876 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | -0.0311360 | 0.0156128 | 0.0460002 | 0.0461235 | 0.049894 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | -0.0193549 | 0.0116542 | 0.0969996 | 0.0967604 | 0.073612 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | 0.0266486 | 0.0138156 | 0.0539995 | 0.0537461 | 0.054322 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | 0.0263981 | 0.0138718 | 0.0569994 | 0.0570399 | 0.054022 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | -0.0191686 | 0.0116960 | 0.1000000 | 0.1012336 | 0.073491 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | 0.0120192 | 0.0274154 | 0.6600001 | 0.6610887 | 0.020316 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0153385 | 0.0072172 | 0.0340001 | 0.0335648 | 0.294687 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.622802 ES:SE:LP:AF:ID 0.0113718:0.0107778:0.537602:0.622802:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400722 ES:SE:LP:AF:ID 0.000604731:0.0107039:0.0222764:0.400722:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103694 ES:SE:LP:AF:ID -0.00542197:0.0170565:0.124939:0.103694:rs114608975
1 91536 rs6702460 G T . PASS AF=0.457631 ES:SE:LP:AF:ID 0.0118065:0.0105334:0.585027:0.457631:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074412 ES:SE:LP:AF:ID 0.0172199:0.0207649:0.387216:0.074412:rs8179466
1 534192 rs6680723 C T . PASS AF=0.241381 ES:SE:LP:AF:ID -0.00164918:0.0119864:0.05061:0.241381:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913289 ES:SE:LP:AF:ID -0.0471624:0.014988:2.76955:0.913289:rs12025928
1 693731 rs12238997 A G . PASS AF=0.117665 ES:SE:LP:AF:ID 1.18702e-05:0.00999225:-0:0.117665:rs12238997
1 705882 rs72631875 G A . PASS AF=0.066529 ES:SE:LP:AF:ID 0.0172386:0.0148258:0.619789:0.066529:rs72631875
1 706368 rs12029736 A G . PASS AF=0.514343 ES:SE:LP:AF:ID 0.00152892:0.00743599:0.0757207:0.514343:rs12029736
1 714596 rs149887893 T C . PASS AF=0.033318 ES:SE:LP:AF:ID 0.0032945:0.0187206:0.0655015:0.033318:rs149887893
1 715265 rs12184267 C T . PASS AF=0.036934 ES:SE:LP:AF:ID 0.00168634:0.0170198:0.0362122:0.036934:rs12184267
1 715367 rs12184277 A G . PASS AF=0.03704 ES:SE:LP:AF:ID 0.00312043:0.0169678:0.0705811:0.03704:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036742 ES:SE:LP:AF:ID 0.00193851:0.0170847:0.0409586:0.036742:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016881 ES:SE:LP:AF:ID -0.0017597:0.0260809:0.0222764:0.016881:rs144155419
1 720381 rs116801199 G T . PASS AF=0.037302 ES:SE:LP:AF:ID 0.000699478:0.0168949:0.0132283:0.037302:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037434 ES:SE:LP:AF:ID 0.00308418:0.0168247:0.0705811:0.037434:rs12565286
1 722670 rs116030099 T C . PASS AF=0.100228 ES:SE:LP:AF:ID -0.007075:0.0123929:0.244125:0.100228:rs116030099
1 723891 rs2977670 G C . PASS AF=0.958318 ES:SE:LP:AF:ID -0.00300946:0.0161514:0.0705811:0.958318:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031743 ES:SE:LP:AF:ID 0.0302479:0.0292316:0.522879:0.031743:rs12126395
1 725060 rs865924913 A T . PASS AF=0.053524 ES:SE:LP:AF:ID 0.00917813:0.0233091:0.161151:0.053524:rs865924913
1 726794 rs28454925 C G . PASS AF=0.036857 ES:SE:LP:AF:ID -0.000460882:0.0169533:0.00877392:0.036857:rs28454925
1 729632 rs116720794 C T . PASS AF=0.037105 ES:SE:LP:AF:ID 0.000402788:0.0168127:0.00877392:0.037105:rs116720794
1 729679 rs4951859 C G . PASS AF=0.841467 ES:SE:LP:AF:ID 0.00018419:0.00867759:0.00877392:0.841467:rs4951859
1 730087 rs148120343 T C . PASS AF=0.056323 ES:SE:LP:AF:ID -0.00135282:0.0141002:0.0362122:0.056323:rs148120343
1 731718 rs58276399 T C . PASS AF=0.123407 ES:SE:LP:AF:ID 4.64446e-05:0.00949602:-0:0.123407:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025496 ES:SE:LP:AF:ID 0.0166614:0.0235556:0.318759:0.025496:rs369030935
1 734349 rs141242758 T C . PASS AF=0.122594 ES:SE:LP:AF:ID 0.000147607:0.00949991:0.00436481:0.122594:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132859 ES:SE:LP:AF:ID -0.000689315:0.00937475:0.0268721:0.132859:rs79010578
1 736689 rs181876450 T C . PASS AF=0.011177 ES:SE:LP:AF:ID 0.0726598:0.0345199:1.45593:0.011177:rs181876450
1 752478 rs146277091 G A . PASS AF=0.037061 ES:SE:LP:AF:ID 0.00136654:0.0166386:0.0315171:0.037061:rs146277091
1 752566 rs3094315 G A . PASS AF=0.837329 ES:SE:LP:AF:ID -0.0034044:0.00839649:0.161151:0.837329:rs3094315
1 752721 rs3131972 A G . PASS AF=0.836955 ES:SE:LP:AF:ID -0.00292857:0.00838402:0.136677:0.836955:rs3131972
1 753405 rs3115860 C A . PASS AF=0.868336 ES:SE:LP:AF:ID -0.00580061:0.00898893:0.283997:0.868336:rs3115860
1 753541 rs2073813 G A . PASS AF=0.131304 ES:SE:LP:AF:ID 0.00501671:0.00900794:0.236572:0.131304:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037566 ES:SE:LP:AF:ID -0.000367519:0.0163468:0.00877392:0.037566:rs12184312
1 754105 rs12184325 C T . PASS AF=0.03781 ES:SE:LP:AF:ID -0.000156035:0.0162431:0.00436481:0.03781:rs12184325
1 754182 rs3131969 A G . PASS AF=0.867645 ES:SE:LP:AF:ID -0.00534061:0.00896864:0.259637:0.867645:rs3131969
1 754192 rs3131968 A G . PASS AF=0.867742 ES:SE:LP:AF:ID -0.00556379:0.0089732:0.267606:0.867742:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037791 ES:SE:LP:AF:ID 0.000470173:0.016313:0.00877392:0.037791:rs12184313
1 754334 rs3131967 T C . PASS AF=0.867659 ES:SE:LP:AF:ID -0.00540258:0.00896848:0.259637:0.867659:rs3131967
1 754503 rs3115859 G A . PASS AF=0.836346 ES:SE:LP:AF:ID -0.00382605:0.00836221:0.187087:0.836346:rs3115859
1 754629 rs10454459 A G . PASS AF=0.037804 ES:SE:LP:AF:ID 0.00157744:0.016343:0.0362122:0.037804:rs10454459
1 754964 rs3131966 C T . PASS AF=0.836959 ES:SE:LP:AF:ID -0.00357024:0.00838533:0.173925:0.836959:rs3131966
1 755240 rs181660517 T G . PASS AF=0.014048 ES:SE:LP:AF:ID -0.022754:0.0291438:0.366532:0.014048:rs181660517
1 755775 rs3131965 A G . PASS AF=0.837918 ES:SE:LP:AF:ID -0.00480571:0.00849496:0.244125:0.837918:rs3131965
1 755890 rs3115858 A T . PASS AF=0.86799 ES:SE:LP:AF:ID -0.00545819:0.00896111:0.267606:0.86799:rs3115858
1 756604 rs3131962 A G . PASS AF=0.867507 ES:SE:LP:AF:ID -0.00569166:0.00893731:0.283997:0.867507:rs3131962
1 757640 rs3115853 G A . PASS AF=0.86646 ES:SE:LP:AF:ID -0.00549101:0.00892258:0.267606:0.86646:rs3115853
1 757734 rs4951929 C T . PASS AF=0.867691 ES:SE:LP:AF:ID -0.00558231:0.00894712:0.275724:0.867691:rs4951929