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_20004_1435.vcf.gz --id UKB-b:6266 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20004_1435.txt.gz --cohort_cases 4957 --cohort_controls 457976 --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": "2019-09-13T01:15:15.176406",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-6266/ukb-b-6266.vcf.gz; Date=Fri May 8 00:48:30 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-6266/UKB-b-6266_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-6266/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:40:19 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-6266/UKB-b-6266_data.vcf.gz ...
Read summary statistics for 4891124 SNPs.
Dropped 1327 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, 1100592 SNPs remain.
After merging with regression SNP LD, 1100592 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0038 (0.0011)
Lambda GC: 1.0513
Mean Chi^2: 1.055
Intercept: 1.0178 (0.0069)
Ratio: 0.3231 (0.1248)
Analysis finished at Thu Oct 17 14:41:17 2019
Total time elapsed: 58.18sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9038,
"inflation_factor": 1.0475,
"mean_EFFECT": 2.5746e-06,
"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": 41525,
"n_est": "NA",
"ratio_se_n": "NA",
"mean_diff": "NaN",
"ratio_diff": "NaN",
"sd_y_est1": "NA",
"sd_y_est2": "NA",
"r2_sum1": 0,
"r2_sum2": 0,
"r2_sum3": 0,
"r2_sum4": 0,
"ldsc_nsnp_merge_refpanel_ld": 1100592,
"ldsc_nsnp_merge_regression_ld": 1100592,
"ldsc_observed_scale_h2_beta": 0.0038,
"ldsc_observed_scale_h2_se": 0.0011,
"ldsc_intercept_beta": 1.0178,
"ldsc_intercept_se": 0.0069,
"ldsc_lambda_gc": 1.0513,
"ldsc_mean_chisq": 1.055,
"ldsc_ratio": 0.3236
}
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 | 4889806 | 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 | 4891124 | 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 | 8.665452e+00 | 5.766098e+00 | 1.0000000 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.200000e+01 | <U+2587><U+2585><U+2585><U+2582><U+2582> |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.858327e+07 | 5.666107e+07 | 828.0000000 | 3.186604e+07 | 6.895154e+07 | 1.146584e+08 | 2.492385e+08 | <U+2587><U+2586><U+2585><U+2582><U+2581> |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.600000e-06 | 2.857000e-04 | -0.0019053 | -1.811000e-04 | 1.900000e-06 | 1.853000e-04 | 3.119200e-03 | <U+2581><U+2587><U+2585><U+2581><U+2581> |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.728000e-04 | 5.800000e-05 | 0.0002064 | 2.235000e-04 | 2.523000e-04 | 3.103000e-04 | 7.839000e-04 | <U+2587><U+2582><U+2581><U+2581><U+2581> |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.917813e-01 | 2.907304e-01 | 0.0000000 | 2.399999e-01 | 4.899999e-01 | 7.400005e-01 | 1.000000e+00 | <U+2587><U+2587><U+2587><U+2587><U+2587> |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.917819e-01 | 2.907041e-01 | 0.0000000 | 2.384940e-01 | 4.894002e-01 | 7.431832e-01 | 9.999999e-01 | <U+2587><U+2587><U+2587><U+2587><U+2587> |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.563490e-01 | 2.361032e-01 | 0.0706080 | 1.526810e-01 | 2.927860e-01 | 5.225020e-01 | 9.293920e-01 | <U+2587><U+2585><U+2583><U+2582><U+2582> |
| numeric | AF_reference | 41525 | 0.9915101 | NA | NA | NA | NA | NA | NA | NA | 3.492800e-01 | 2.334647e-01 | 0.0000000 | 1.567490e-01 | 2.943290e-01 | 5.115810e-01 | 1.000000e+00 | <U+2587><U+2587><U+2585><U+2583><U+2581> |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 49298 | rs200943160 | T | C | 0.0001276 | 0.0003799 | 0.7400005 | 0.7369808 | 0.623763 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0002598 | 0.0003764 | 0.4899999 | 0.4899921 | 0.400401 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0006088 | 0.0006017 | 0.3100002 | 0.3116872 | 0.103556 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0001950 | 0.0003706 | 0.5999997 | 0.5986942 | 0.456851 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | -0.0009891 | 0.0007307 | 0.1800002 | 0.1758323 | 0.074508 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0003088 | 0.0004233 | 0.4700002 | 0.4656093 | 0.240960 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | -0.0000150 | 0.0005281 | 0.9800000 | 0.9773284 | 0.913473 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | 0.0002270 | 0.0003547 | 0.5199996 | 0.5222898 | 0.116325 | 0.1417730 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0003089 | 0.0002628 | 0.2399999 | 0.2397260 | 0.515650 | 0.2751600 | NA |
| 1 | 722670 | rs116030099 | T | C | 0.0001997 | 0.0004336 | 0.6499995 | 0.6450339 | 0.101199 | 0.0413339 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51213613 | rs34726907 | C | T | 0.0000566 | 0.0003252 | 0.8600001 | 0.8618575 | 0.127816 | 0.1727240 | NA |
| 22 | 51216564 | rs9616970 | T | C | 0.0000859 | 0.0003239 | 0.7899998 | 0.7909216 | 0.128330 | 0.1563500 | NA |
| 22 | 51217954 | rs9616974 | G | A | 0.0001318 | 0.0004109 | 0.7499995 | 0.7484172 | 0.073314 | 0.0621006 | NA |
| 22 | 51218224 | rs9616975 | C | A | 0.0001310 | 0.0004111 | 0.7499995 | 0.7500591 | 0.073336 | 0.0619010 | NA |
| 22 | 51218377 | rs2519461 | G | C | 0.0001762 | 0.0004106 | 0.6700003 | 0.6679148 | 0.073625 | 0.0826677 | NA |
| 22 | 51219006 | rs28729663 | G | A | 0.0001404 | 0.0003170 | 0.6600001 | 0.6577181 | 0.137953 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | 0.0001244 | 0.0004114 | 0.7600007 | 0.7624673 | 0.073747 | 0.0654952 | NA |
| 22 | 51221731 | rs115055839 | T | C | 0.0001386 | 0.0004117 | 0.7400005 | 0.7363865 | 0.073238 | 0.0625000 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0001582 | 0.0004132 | 0.6999999 | 0.7017895 | 0.073073 | 0.0730831 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0000961 | 0.0002527 | 0.6999999 | 0.7036968 | 0.297971 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623763 ES:SE:LP:AF:ID 0.00012759:0.000379897:0.130768:0.623763:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID 0.000259808:0.000376358:0.309804:0.400401:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103556 ES:SE:LP:AF:ID 0.000608756:0.000601723:0.508638:0.103556:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456851 ES:SE:LP:AF:ID -0.000195027:0.000370577:0.221849:0.456851:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074508 ES:SE:LP:AF:ID -0.000989113:0.000730674:0.744727:0.074508:rs8179466
1 534192 rs6680723 C T . PASS AF=0.24096 ES:SE:LP:AF:ID -0.00030885:0.00042329:0.327902:0.24096:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913473 ES:SE:LP:AF:ID -1.50072e-05:0.000528079:0.00877392:0.913473:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116325 ES:SE:LP:AF:ID 0.000226969:0.000354739:0.283997:0.116325:rs12238997
1 706368 rs12029736 A G . PASS AF=0.51565 ES:SE:LP:AF:ID -0.000308936:0.000262774:0.619789:0.51565:rs12029736
1 722670 rs116030099 T C . PASS AF=0.101199 ES:SE:LP:AF:ID 0.000199729:0.00043356:0.187087:0.101199:rs116030099
1 729679 rs4951859 C G . PASS AF=0.843212 ES:SE:LP:AF:ID -8.83148e-05:0.000307427:0.113509:0.843212:rs4951859
1 731718 rs58276399 T C . PASS AF=0.122307 ES:SE:LP:AF:ID 0.000137202:0.000336505:0.167491:0.122307:rs58276399
1 734349 rs141242758 T C . PASS AF=0.121549 ES:SE:LP:AF:ID 0.000134491:0.000336647:0.161151:0.121549:rs141242758
1 736289 rs79010578 T A . PASS AF=0.13233 ES:SE:LP:AF:ID 4.22334e-06:0.000331798:0.00436481:0.13233:rs79010578
1 752566 rs3094315 G A . PASS AF=0.838951 ES:SE:LP:AF:ID -2.92683e-06:0.000297721:0.00436481:0.838951:rs3094315
1 752721 rs3131972 A G . PASS AF=0.83858 ES:SE:LP:AF:ID 2.12617e-07:0.000297401:-0:0.83858:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869781 ES:SE:LP:AF:ID -5.80225e-07:0.000319123:-0:0.869781:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129871 ES:SE:LP:AF:ID 7.12922e-07:0.000319776:-0:0.129871:rs2073813
1 754182 rs3131969 A G . PASS AF=0.869123 ES:SE:LP:AF:ID 1.91485e-05:0.000318498:0.0222764:0.869123:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869221 ES:SE:LP:AF:ID 2.89496e-05:0.000318624:0.0315171:0.869221:rs3131968
1 754334 rs3131967 T C . PASS AF=0.869126 ES:SE:LP:AF:ID 1.93598e-05:0.000318491:0.0222764:0.869126:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838033 ES:SE:LP:AF:ID 3.2182e-05:0.000296575:0.0409586:0.838033:rs3115859
1 754964 rs3131966 C T . PASS AF=0.838664 ES:SE:LP:AF:ID 4.06259e-05:0.000297409:0.05061:0.838664:rs3131966
1 755775 rs3131965 A G . PASS AF=0.839777 ES:SE:LP:AF:ID 2.93087e-05:0.000301431:0.0362122:0.839777:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869405 ES:SE:LP:AF:ID 1.06183e-05:0.000318123:0.0132283:0.869405:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868952 ES:SE:LP:AF:ID -2.05243e-05:0.000317323:0.0222764:0.868952:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867905 ES:SE:LP:AF:ID 3.91055e-05:0.000316715:0.0457575:0.867905:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869095 ES:SE:LP:AF:ID 8.21525e-06:0.000317582:0.00877392:0.869095:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869104 ES:SE:LP:AF:ID 7.41384e-06:0.000317607:0.00877392:0.869104:rs4951862
1 758144 rs3131956 A G . PASS AF=0.869112 ES:SE:LP:AF:ID 7.58908e-06:0.000317614:0.00877392:0.869112:rs3131956
1 758626 rs3131954 C T . PASS AF=0.869589 ES:SE:LP:AF:ID 1.75532e-05:0.000318486:0.0177288:0.869589:rs3131954
1 760912 rs1048488 C T . PASS AF=0.838313 ES:SE:LP:AF:ID 3.98781e-05:0.000296012:0.05061:0.838313:rs1048488
1 761147 rs3115850 T C . PASS AF=0.838434 ES:SE:LP:AF:ID 3.81908e-05:0.000296222:0.0457575:0.838434:rs3115850
1 761732 rs2286139 C T . PASS AF=0.862261 ES:SE:LP:AF:ID -4.8227e-05:0.000316465:0.0555173:0.862261:rs2286139
1 763394 rs3115847 G A . PASS AF=0.706753 ES:SE:LP:AF:ID 0.000402448:0.000308077:0.721246:0.706753:rs3115847
1 766007 rs61768174 A C . PASS AF=0.105142 ES:SE:LP:AF:ID 0.000103826:0.000354902:0.113509:0.105142:rs61768174
1 768253 rs2977608 A C . PASS AF=0.761304 ES:SE:LP:AF:ID -0.000372858:0.000251446:0.853872:0.761304:rs2977608
1 768448 rs12562034 G A . PASS AF=0.106488 ES:SE:LP:AF:ID 0.000630006:0.000346569:1.16115:0.106488:rs12562034
1 769223 rs60320384 C G . PASS AF=0.129576 ES:SE:LP:AF:ID 2.82033e-05:0.000319585:0.0315171:0.129576:rs60320384
1 771823 rs2977605 T C . PASS AF=0.868911 ES:SE:LP:AF:ID -2.21538e-05:0.000317878:0.0268721:0.868911:rs2977605
1 771967 rs59066358 G A . PASS AF=0.129677 ES:SE:LP:AF:ID 1.82773e-05:0.000319379:0.0222764:0.129677:rs59066358
1 772755 rs2905039 A C . PASS AF=0.868921 ES:SE:LP:AF:ID -4.29073e-05:0.000317884:0.05061:0.868921:rs2905039
1 776546 rs12124819 A G . PASS AF=0.26539 ES:SE:LP:AF:ID -0.000448925:0.000280869:0.958607:0.26539:rs12124819
1 777122 rs2980319 A T . PASS AF=0.870044 ES:SE:LP:AF:ID -4.28111e-05:0.000318533:0.05061:0.870044:rs2980319
1 777232 rs112618790 C T . PASS AF=0.095139 ES:SE:LP:AF:ID 0.000532985:0.000369167:0.823909:0.095139:rs112618790
1 778745 rs1055606 A G . PASS AF=0.128576 ES:SE:LP:AF:ID 7.47955e-05:0.00031979:0.0861861:0.128576:rs1055606
1 779322 rs4040617 A G . PASS AF=0.128873 ES:SE:LP:AF:ID 0.000104338:0.000319248:0.130768:0.128873:rs4040617
1 780785 rs2977612 T A . PASS AF=0.868786 ES:SE:LP:AF:ID -4.4769e-05:0.000317683:0.05061:0.868786:rs2977612
1 781845 rs61768199 A G . PASS AF=0.10187 ES:SE:LP:AF:ID 7.46828e-05:0.000359966:0.0757207:0.10187:rs61768199
1 782981 rs6594026 C T . PASS AF=0.129513 ES:SE:LP:AF:ID 6.92238e-05:0.000319145:0.0809219:0.129513:rs6594026