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_20001_1059.vcf.gz --id UKB-b:12915 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20001_1059.txt.gz --cohort_cases 3598 --cohort_controls 459335 --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": "2019-09-13T07:58:07.681417",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-12915/ukb-b-12915.vcf.gz; Date=Sun May 10 10:56:55 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-12915/UKB-b-12915_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-12915/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:42:07 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-12915/UKB-b-12915_data.vcf.gz ...
Read summary statistics for 4356465 SNPs.
Dropped 947 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, 1016526 SNPs remain.
After merging with regression SNP LD, 1016526 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0057 (0.0015)
Lambda GC: 1.0375
Mean Chi^2: 1.0474
Intercept: 0.9894 (0.0084)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Thu Oct 17 14:42:57 2019
Total time elapsed: 50.11sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8884,
"inflation_factor": 1.0475,
"mean_EFFECT": -7.643e-08,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 6,
"n_p_sig": 492,
"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": 36041,
"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": 1016526,
"ldsc_nsnp_merge_regression_ld": 1016526,
"ldsc_observed_scale_h2_beta": 0.0057,
"ldsc_observed_scale_h2_se": 0.0015,
"ldsc_intercept_beta": 0.9894,
"ldsc_intercept_se": 0.0084,
"ldsc_lambda_gc": 1.0375,
"ldsc_mean_chisq": 1.0474,
"ldsc_ratio": -0.2236
}
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 | 1.000000 | 3 | 58 | 0 | 4355523 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | REF | 0 | 1.000000 | 1 | 1 | 0 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | ALT | 0 | 1.000000 | 1 | 1 | 0 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | N | 4356465 | 0.000000 | NA | NA | NA | NA | NA | NaN | : | NA | NA | NA | NA | NA | NA | NA | NA |
| numeric | CHROM | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 8.653977e+00 | 5.765348e+00 | 1.0000000 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.200000e+01 | ▇▅▃▂▂ |
| numeric | POS | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 7.860817e+07 | 5.672056e+07 | 828.0000000 | 3.173303e+07 | 6.898282e+07 | 1.147120e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | -1.000000e-07 | 2.282000e-04 | -0.0035541 | -1.488000e-04 | -4.000000e-07 | 1.485000e-04 | 4.047700e-03 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 2.205000e-04 | 3.630000e-05 | 0.0001762 | 1.892000e-04 | 2.082000e-04 | 2.448000e-04 | 6.442000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 4.944743e-01 | 2.907498e-01 | 0.0000000 | 2.399999e-01 | 4.899999e-01 | 7.499995e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 4.944775e-01 | 2.907236e-01 | 0.0000000 | 2.408361e-01 | 4.921695e-01 | 7.471960e-01 | 9.999999e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 3.790273e-01 | 2.232124e-01 | 0.0972770 | 1.851730e-01 | 3.248000e-01 | 5.412380e-01 | 9.027230e-01 | ▇▅▃▂▂ |
| numeric | AF_reference | 36041 | 0.991727 | NA | NA | NA | NA | NA | NA | NA | 3.697050e-01 | 2.241356e-01 | 0.0000000 | 1.849040e-01 | 3.234820e-01 | 5.295530e-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 | -0.0000740 | 0.0003242 | 0.8200001 | 0.8194476 | 0.623763 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0003456 | 0.0003212 | 0.2800000 | 0.2819137 | 0.400401 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | -0.0002600 | 0.0005135 | 0.6100002 | 0.6126541 | 0.103556 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0000903 | 0.0003162 | 0.7800007 | 0.7753170 | 0.456851 | 0.4207270 | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0004086 | 0.0003612 | 0.2599998 | 0.2579194 | 0.240960 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | 0.0002248 | 0.0003027 | 0.4600002 | 0.4577332 | 0.116325 | 0.1417730 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0000171 | 0.0002242 | 0.9400001 | 0.9391841 | 0.515650 | 0.2751600 | NA |
| 1 | 722670 | rs116030099 | T | C | -0.0000025 | 0.0003700 | 0.9900000 | 0.9946404 | 0.101199 | 0.0413339 | NA |
| 1 | 729679 | rs4951859 | C | G | -0.0000608 | 0.0002623 | 0.8200001 | 0.8167350 | 0.843212 | 0.6399760 | NA |
| 1 | 731718 | rs142557973 | T | C | 0.0003253 | 0.0002872 | 0.2599998 | 0.2573391 | 0.122307 | 0.1543530 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51198027 | rs34939255 | A | G | 0.0003294 | 0.0002236 | 0.1400000 | 0.1407694 | 0.254557 | 0.0984425 | NA |
| 22 | 51208537 | rs72619593 | G | A | -0.0005679 | 0.0002989 | 0.0569994 | 0.0574404 | 0.120739 | 0.1142170 | NA |
| 22 | 51210289 | rs112565862 | C | T | 0.0004577 | 0.0002977 | 0.1199999 | 0.1242051 | 0.129955 | 0.1018370 | NA |
| 22 | 51211106 | rs9628250 | T | C | 0.0003438 | 0.0002217 | 0.1199999 | 0.1209812 | 0.271547 | 0.1671330 | NA |
| 22 | 51211392 | rs3888396 | T | C | 0.0004744 | 0.0002950 | 0.1100001 | 0.1078552 | 0.132635 | 0.1641370 | NA |
| 22 | 51212875 | rs2238837 | A | C | -0.0006790 | 0.0002107 | 0.0013000 | 0.0012678 | 0.331455 | 0.3724040 | NA |
| 22 | 51213613 | rs34726907 | C | T | 0.0001428 | 0.0002775 | 0.6100002 | 0.6068415 | 0.127816 | 0.1727240 | NA |
| 22 | 51216564 | rs9616970 | T | C | 0.0001282 | 0.0002764 | 0.6400000 | 0.6426961 | 0.128330 | 0.1563500 | NA |
| 22 | 51219006 | rs28729663 | G | A | 0.0001759 | 0.0002705 | 0.5199996 | 0.5154188 | 0.137953 | 0.2052720 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0006321 | 0.0002157 | 0.0034000 | 0.0033799 | 0.297971 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623763 ES:SE:LP:AF:ID -7.39961e-05:0.000324181:0.0861861:0.623763:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID 0.000345579:0.000321161:0.552842:0.400401:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103556 ES:SE:LP:AF:ID -0.000259966:0.000513474:0.21467:0.103556:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456851 ES:SE:LP:AF:ID -9.026e-05:0.000316228:0.107905:0.456851:rs6702460
1 534192 rs6680723 C T . PASS AF=0.24096 ES:SE:LP:AF:ID -0.000408645:0.00036121:0.585027:0.24096:rs6680723
1 693731 rs12238997 A G . PASS AF=0.116325 ES:SE:LP:AF:ID 0.00022479:0.000302713:0.337242:0.116325:rs12238997
1 706368 rs12029736 A G . PASS AF=0.51565 ES:SE:LP:AF:ID 1.71081e-05:0.000224235:0.0268721:0.51565:rs12029736
1 722670 rs116030099 T C . PASS AF=0.101199 ES:SE:LP:AF:ID -2.48522e-06:0.000369973:0.00436481:0.101199:rs116030099
1 729679 rs4951859 C G . PASS AF=0.843212 ES:SE:LP:AF:ID -6.07961e-05:0.000262339:0.0861861:0.843212:rs4951859
1 731718 rs58276399 T C . PASS AF=0.122307 ES:SE:LP:AF:ID 0.000325259:0.000287153:0.585027:0.122307:rs58276399
1 734349 rs141242758 T C . PASS AF=0.121549 ES:SE:LP:AF:ID 0.000320449:0.000287274:0.585027:0.121549:rs141242758
1 736289 rs79010578 T A . PASS AF=0.13233 ES:SE:LP:AF:ID 0.000206627:0.000283137:0.327902:0.13233:rs79010578
1 752566 rs3094315 G A . PASS AF=0.838951 ES:SE:LP:AF:ID -0.000137517:0.000254057:0.229148:0.838951:rs3094315
1 752721 rs3131972 A G . PASS AF=0.83858 ES:SE:LP:AF:ID -0.000142747:0.000253784:0.244125:0.83858:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869781 ES:SE:LP:AF:ID -0.000314434:0.00027232:0.60206:0.869781:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129871 ES:SE:LP:AF:ID 0.000352741:0.000272877:0.69897:0.129871:rs2073813
1 754182 rs3131969 A G . PASS AF=0.869123 ES:SE:LP:AF:ID -0.000338319:0.000271787:0.677781:0.869123:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869221 ES:SE:LP:AF:ID -0.000334707:0.000271894:0.657577:0.869221:rs3131968
1 754334 rs3131967 T C . PASS AF=0.869126 ES:SE:LP:AF:ID -0.00033796:0.000271781:0.677781:0.869126:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838033 ES:SE:LP:AF:ID -0.00012953:0.000253079:0.21467:0.838033:rs3115859
1 754964 rs3131966 C T . PASS AF=0.838664 ES:SE:LP:AF:ID -0.000134596:0.00025379:0.221849:0.838664:rs3131966
1 755775 rs3131965 A G . PASS AF=0.839777 ES:SE:LP:AF:ID -0.000125273:0.000257223:0.200659:0.839777:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869405 ES:SE:LP:AF:ID -0.000288894:0.000271467:0.537602:0.869405:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868952 ES:SE:LP:AF:ID -0.000268967:0.000270784:0.49485:0.868952:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867905 ES:SE:LP:AF:ID -0.000299641:0.000270265:0.568636:0.867905:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869095 ES:SE:LP:AF:ID -0.000278227:0.000271006:0.522879:0.869095:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869104 ES:SE:LP:AF:ID -0.000278654:0.000271026:0.522879:0.869104:rs4951862
1 758144 rs3131956 A G . PASS AF=0.869112 ES:SE:LP:AF:ID -0.000278994:0.000271033:0.522879:0.869112:rs3131956
1 758626 rs3131954 C T . PASS AF=0.869589 ES:SE:LP:AF:ID -0.000292923:0.000271777:0.552842:0.869589:rs3131954
1 760912 rs1048488 C T . PASS AF=0.838313 ES:SE:LP:AF:ID -6.41105e-05:0.000252599:0.09691:0.838313:rs1048488
1 761147 rs3115850 T C . PASS AF=0.838434 ES:SE:LP:AF:ID -6.752e-05:0.000252777:0.102373:0.838434:rs3115850
1 761732 rs2286139 C T . PASS AF=0.862261 ES:SE:LP:AF:ID -0.000245837:0.000270052:0.443698:0.862261:rs2286139
1 763394 rs3115847 G A . PASS AF=0.706753 ES:SE:LP:AF:ID -0.000189153:0.000262894:0.327902:0.706753:rs3115847
1 766007 rs61768174 A C . PASS AF=0.105142 ES:SE:LP:AF:ID 0.000463342:0.000302852:0.886057:0.105142:rs61768174
1 768253 rs2977608 A C . PASS AF=0.761304 ES:SE:LP:AF:ID -0.000109748:0.000214569:0.21467:0.761304:rs2977608
1 768448 rs12562034 G A . PASS AF=0.106488 ES:SE:LP:AF:ID -9.60758e-05:0.000295741:0.124939:0.106488:rs12562034
1 769223 rs60320384 C G . PASS AF=0.129576 ES:SE:LP:AF:ID 0.000299026:0.000272714:0.568636:0.129576:rs60320384
1 771823 rs2977605 T C . PASS AF=0.868911 ES:SE:LP:AF:ID -0.000277171:0.000271258:0.508638:0.868911:rs2977605
1 771967 rs59066358 G A . PASS AF=0.129677 ES:SE:LP:AF:ID 0.000295477:0.000272538:0.552842:0.129677:rs59066358
1 772755 rs2905039 A C . PASS AF=0.868921 ES:SE:LP:AF:ID -0.000281957:0.000271263:0.522879:0.868921:rs2905039
1 776546 rs12124819 A G . PASS AF=0.26539 ES:SE:LP:AF:ID -6.72957e-05:0.000239676:0.107905:0.26539:rs12124819
1 777122 rs2980319 A T . PASS AF=0.870044 ES:SE:LP:AF:ID -0.000268597:0.000271817:0.49485:0.870044:rs2980319
1 778745 rs1055606 A G . PASS AF=0.128576 ES:SE:LP:AF:ID 0.000282049:0.000272889:0.522879:0.128576:rs1055606
1 779322 rs4040617 A G . PASS AF=0.128873 ES:SE:LP:AF:ID 0.000266603:0.000272426:0.481486:0.128873:rs4040617
1 780785 rs2977612 T A . PASS AF=0.868786 ES:SE:LP:AF:ID -0.000297098:0.000271092:0.568636:0.868786:rs2977612
1 781845 rs61768199 A G . PASS AF=0.10187 ES:SE:LP:AF:ID 0.000461528:0.000307173:0.886057:0.10187:rs61768199
1 782981 rs6594026 C T . PASS AF=0.129513 ES:SE:LP:AF:ID 0.000293586:0.000272339:0.552842:0.129513:rs6594026
1 785050 rs2905062 G A . PASS AF=0.868539 ES:SE:LP:AF:ID -0.00028467:0.000271028:0.537602:0.868539:rs2905062
1 785989 rs2980300 T C . PASS AF=0.86848 ES:SE:LP:AF:ID -0.00027968:0.000271197:0.522879:0.86848:rs2980300
1 787399 rs2905055 G T . PASS AF=0.86078 ES:SE:LP:AF:ID -0.000297343:0.000271011:0.568636:0.86078:rs2905055