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_2644.vcf.gz --id UKB-b:8133 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_2644.txt.gz --cohort_cases 57021 --cohort_controls 66873 --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": "2019-09-12T19:33:13.721508",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-8133/ukb-b-8133.vcf.gz; Date=Sun May 10 01:16:15 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-8133/UKB-b-8133_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-8133/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:41:22 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-8133/UKB-b-8133_data.vcf.gz ...
Read summary statistics for 8431016 SNPs.
Dropped 6983 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, 1285027 SNPs remain.
After merging with regression SNP LD, 1285027 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0718 (0.0059)
Lambda GC: 1.1532
Mean Chi^2: 1.1905
Intercept: 1.02 (0.0072)
Ratio: 0.1048 (0.0379)
Analysis finished at Thu Oct 17 14:42:52 2019
Total time elapsed: 1.0m:29.42sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9453,
"inflation_factor": 1.1474,
"mean_EFFECT": 0,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 3,
"n_p_sig": 344,
"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": 79885,
"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": 1285027,
"ldsc_nsnp_merge_regression_ld": 1285027,
"ldsc_observed_scale_h2_beta": 0.0718,
"ldsc_observed_scale_h2_se": 0.0059,
"ldsc_intercept_beta": 1.02,
"ldsc_intercept_se": 0.0072,
"ldsc_lambda_gc": 1.1532,
"ldsc_mean_chisq": 1.1905,
"ldsc_ratio": 0.105
}
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 | 8424065 | 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 | 8431016 | 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 | 8.653897e+00 | 5.761826e+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 | 7.874243e+07 | 5.637643e+07 | 828.0000000 | 3.234895e+07 | 6.924670e+07 | 1.145466e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.940000e-05 | 5.729800e-03 | -0.0580853 | -2.519000e-03 | 2.130000e-05 | 2.578000e-03 | 5.941860e-02 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.633600e-03 | 3.049800e-03 | 0.0019162 | 2.249700e-03 | 3.256200e-03 | 6.215700e-03 | 2.923430e-02 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.798988e-01 | 2.942692e-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 | 4.798973e-01 | 2.942441e-01 | 0.0000000 | 2.199294e-01 | 4.731556e-01 | 7.345528e-01 | 9.999999e-01 | ▇▇▇▆▆ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.339864e-01 | 2.599683e-01 | 0.0061390 | 2.805600e-02 | 1.206220e-01 | 3.705230e-01 | 9.938610e-01 | ▇▂▂▁▁ |
| numeric | AF_reference | 79885 | 0.9905249 | NA | NA | NA | NA | NA | NA | NA | 2.335772e-01 | 2.518892e-01 | 0.0000000 | 2.695690e-02 | 1.367810e-01 | 3.670130e-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 | -0.0010403 | 0.0035220 | 0.7700005 | 0.7677011 | 0.623878 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0031332 | 0.0034990 | 0.3700002 | 0.3705441 | 0.399138 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0047846 | 0.0055634 | 0.3900004 | 0.3897791 | 0.103792 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0030954 | 0.0034398 | 0.3700002 | 0.3681852 | 0.456146 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0105901 | 0.0068243 | 0.1199999 | 0.1207064 | 0.074084 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0002035 | 0.0039351 | 0.9599999 | 0.9587523 | 0.240520 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | -0.0007004 | 0.0048813 | 0.8900000 | 0.8859048 | 0.912789 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | 0.0037887 | 0.0033031 | 0.2500000 | 0.2513784 | 0.116010 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | 0.0006878 | 0.0047858 | 0.8900000 | 0.8857176 | 0.068321 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0006602 | 0.0024371 | 0.7899998 | 0.7864702 | 0.516568 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219006 | rs28729663 | G | A | -0.0022574 | 0.0029441 | 0.4400003 | 0.4432386 | 0.138555 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | -0.0012764 | 0.0038054 | 0.7400005 | 0.7373124 | 0.074824 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | -0.0030097 | 0.0050771 | 0.5500004 | 0.5533109 | 0.042812 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | -0.0050611 | 0.0050665 | 0.3200000 | 0.3178294 | 0.050666 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | -0.0014100 | 0.0038066 | 0.7099994 | 0.7110805 | 0.074334 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | -0.0031782 | 0.0045245 | 0.4799997 | 0.4824124 | 0.054135 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | -0.0031836 | 0.0045453 | 0.4799997 | 0.4836688 | 0.053799 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | -0.0014379 | 0.0038207 | 0.7099994 | 0.7066585 | 0.074131 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | -0.0009369 | 0.0089851 | 0.9199999 | 0.9169556 | 0.020172 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0033020 | 0.0023545 | 0.1600000 | 0.1607873 | 0.297382 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623878 ES:SE:LP:AF:ID -0.00104034:0.003522:0.113509:0.623878:rs10399793
1 54676 rs2462492 C T . PASS AF=0.399138 ES:SE:LP:AF:ID 0.00313316:0.00349896:0.431798:0.399138:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103792 ES:SE:LP:AF:ID 0.00478465:0.00556343:0.408935:0.103792:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456146 ES:SE:LP:AF:ID 0.0030954:0.0034398:0.431798:0.456146:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074084 ES:SE:LP:AF:ID 0.0105901:0.00682433:0.920819:0.074084:rs8179466
1 534192 rs6680723 C T . PASS AF=0.24052 ES:SE:LP:AF:ID -0.000203522:0.00393512:0.0177288:0.24052:rs6680723
1 546697 rs12025928 A G . PASS AF=0.912789 ES:SE:LP:AF:ID -0.000700402:0.00488126:0.05061:0.912789:rs12025928
1 693731 rs12238997 A G . PASS AF=0.11601 ES:SE:LP:AF:ID 0.00378867:0.00330309:0.60206:0.11601:rs12238997
1 705882 rs72631875 G A . PASS AF=0.068321 ES:SE:LP:AF:ID 0.000687845:0.00478584:0.05061:0.068321:rs72631875
1 706368 rs12029736 A G . PASS AF=0.516568 ES:SE:LP:AF:ID 0.000660195:0.00243707:0.102373:0.516568:rs12029736
1 714596 rs149887893 T C . PASS AF=0.033235 ES:SE:LP:AF:ID -0.00576292:0.00613486:0.455932:0.033235:rs149887893
1 715265 rs12184267 C T . PASS AF=0.036864 ES:SE:LP:AF:ID -0.00406801:0.00557413:0.327902:0.036864:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036974 ES:SE:LP:AF:ID -0.00345738:0.00555299:0.275724:0.036974:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036653 ES:SE:LP:AF:ID -0.00354919:0.00559577:0.275724:0.036653:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016408 ES:SE:LP:AF:ID -0.00663465:0.00868577:0.356547:0.016408:rs144155419
1 720381 rs116801199 G T . PASS AF=0.037216 ES:SE:LP:AF:ID -0.00345124:0.0055302:0.275724:0.037216:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037301 ES:SE:LP:AF:ID -0.00419931:0.00551324:0.346787:0.037301:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101602 ES:SE:LP:AF:ID -0.0025536:0.00401862:0.275724:0.101602:rs116030099
1 723891 rs2977670 G C . PASS AF=0.958854 ES:SE:LP:AF:ID 0.00293677:0.00531661:0.236572:0.958854:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031355 ES:SE:LP:AF:ID 0.00328132:0.00971287:0.130768:0.031355:rs12126395
1 725060 rs865924913 A T . PASS AF=0.053411 ES:SE:LP:AF:ID 0.00506961:0.00768102:0.29243:0.053411:rs865924913
1 726794 rs28454925 C G . PASS AF=0.036802 ES:SE:LP:AF:ID -0.00347523:0.00554998:0.275724:0.036802:rs28454925
1 729632 rs116720794 C T . PASS AF=0.037162 ES:SE:LP:AF:ID -0.00342563:0.00549437:0.275724:0.037162:rs116720794
1 729679 rs4951859 C G . PASS AF=0.843352 ES:SE:LP:AF:ID -0.00111533:0.00286244:0.154902:0.843352:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055629 ES:SE:LP:AF:ID 0.00356867:0.00462693:0.356547:0.055629:rs148120343
1 731718 rs58276399 T C . PASS AF=0.121785 ES:SE:LP:AF:ID 0.0032675:0.00313847:0.522879:0.121785:rs58276399
1 732989 rs369030935 C T . PASS AF=0.0254 ES:SE:LP:AF:ID 0.00635983:0.0077352:0.387216:0.0254:rs369030935
1 734349 rs141242758 T C . PASS AF=0.121021 ES:SE:LP:AF:ID 0.00311831:0.00314004:0.49485:0.121021:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132138 ES:SE:LP:AF:ID 0.00132739:0.00308759:0.173925:0.132138:rs79010578
1 736689 rs181876450 T C . PASS AF=0.011032 ES:SE:LP:AF:ID -0.00793355:0.0112892:0.318759:0.011032:rs181876450
1 752478 rs146277091 G A . PASS AF=0.036981 ES:SE:LP:AF:ID -0.00409252:0.00544812:0.346787:0.036981:rs146277091
1 752566 rs3094315 G A . PASS AF=0.838847 ES:SE:LP:AF:ID -0.00148142:0.00277027:0.229148:0.838847:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838468 ES:SE:LP:AF:ID -0.00175349:0.00276752:0.275724:0.838468:rs3131972
1 753405 rs3115860 C A . PASS AF=0.870093 ES:SE:LP:AF:ID -0.00350889:0.00297363:0.619789:0.870093:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129563 ES:SE:LP:AF:ID 0.0040184:0.0029801:0.744727:0.129563:rs2073813
1 754063 rs12184312 G T . PASS AF=0.0375 ES:SE:LP:AF:ID -0.00354322:0.00535629:0.29243:0.0375:rs12184312
1 754105 rs12184325 C T . PASS AF=0.037751 ES:SE:LP:AF:ID -0.00365641:0.00532194:0.309804:0.037751:rs12184325
1 754182 rs3131969 A G . PASS AF=0.869429 ES:SE:LP:AF:ID -0.00382366:0.0029679:0.69897:0.869429:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869543 ES:SE:LP:AF:ID -0.0038331:0.00296971:0.69897:0.869543:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037706 ES:SE:LP:AF:ID -0.00365056:0.0053455:0.309804:0.037706:rs12184313
1 754334 rs3131967 T C . PASS AF=0.86942 ES:SE:LP:AF:ID -0.00381012:0.00296781:0.69897:0.86942:rs3131967
1 754503 rs3115859 G A . PASS AF=0.837961 ES:SE:LP:AF:ID -0.0016667:0.00276004:0.259637:0.837961:rs3115859
1 754629 rs10454459 A G . PASS AF=0.037709 ES:SE:LP:AF:ID -0.0037872:0.00535333:0.318759:0.037709:rs10454459
1 754964 rs3131966 C T . PASS AF=0.838588 ES:SE:LP:AF:ID -0.00176982:0.00276782:0.283997:0.838588:rs3131966
1 755240 rs181660517 T G . PASS AF=0.013973 ES:SE:LP:AF:ID 0.0093677:0.00954765:0.481486:0.013973:rs181660517
1 755775 rs3131965 A G . PASS AF=0.839759 ES:SE:LP:AF:ID -0.00172093:0.00280586:0.267606:0.839759:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869692 ES:SE:LP:AF:ID -0.00350143:0.00296407:0.619789:0.869692:rs3115858
1 756604 rs3131962 A G . PASS AF=0.869257 ES:SE:LP:AF:ID -0.00346354:0.00295676:0.619789:0.869257:rs3131962
1 757640 rs3115853 G A . PASS AF=0.868134 ES:SE:LP:AF:ID -0.0035992:0.00295056:0.657577:0.868134:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869357 ES:SE:LP:AF:ID -0.00343787:0.0029588:0.60206:0.869357:rs4951929