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_20003_1140867888.vcf.gz --id UKB-b:10258 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20003_1140867888.txt.gz --cohort_cases 1554 --cohort_controls 461379 --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": "2019-09-13T14:21:59.265736",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-10258/ukb-b-10258.vcf.gz; Date=Sun May 10 13:26:10 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-10258/UKB-b-10258_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-10258/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-10258/UKB-b-10258_data.vcf.gz ...
Read summary statistics for 2588512 SNPs.
Dropped 305 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, 653483 SNPs remain.
After merging with regression SNP LD, 653483 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0031 (0.0012)
Lambda GC: 1.0286
Mean Chi^2: 1.0277
Intercept: 0.9945 (0.0098)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Thu Oct 17 14:40:53 2019
Total time elapsed: 34.08sQC metrics
Metrics
Metrics
{
"af_correlation": 0.7785,
"inflation_factor": 1.0475,
"mean_EFFECT": -1.1159e-06,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 0,
"n_p_sig": 0,
"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": 20538,
"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": 653483,
"ldsc_nsnp_merge_regression_ld": 653483,
"ldsc_observed_scale_h2_beta": 0.0031,
"ldsc_observed_scale_h2_se": 0.0012,
"ldsc_intercept_beta": 0.9945,
"ldsc_intercept_se": 0.0098,
"ldsc_lambda_gc": 1.0286,
"ldsc_mean_chisq": 1.0277,
"ldsc_ratio": -0.1986
}
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 | 4 | 58 | 0 | 2588210 | 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 | 2588512 | 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 | 8.657808e+00 | 5.766757e+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 | 7.855756e+07 | 5.662102e+07 | 5687.0000000 | 3.170565e+07 | 6.897098e+07 | 1.147418e+08 | 2.491917e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -1.100000e-06 | 1.306000e-04 | -0.0006439 | -8.890000e-05 | -1.000000e-06 | 8.670000e-05 | 6.428000e-04 | ▁▂▇▂▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.284000e-04 | 7.400000e-06 | 0.0001160 | 1.220000e-04 | 1.262000e-04 | 1.335000e-04 | 2.515000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.946778e-01 | 2.900844e-01 | 0.0000019 | 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 | 4.946760e-01 | 2.900584e-01 | 0.0000019 | 2.413345e-01 | 4.926743e-01 | 7.459176e-01 | 9.999998e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.501258e-01 | 1.559790e-01 | 0.2252260 | 3.129160e-01 | 4.264180e-01 | 5.747605e-01 | 7.747740e-01 | ▇▆▅▃▃ |
| numeric | AF_reference | 20538 | 0.9920657 | NA | NA | NA | NA | NA | NA | NA | 4.321603e-01 | 1.791550e-01 | 0.0001997 | 2.903350e-01 | 4.151360e-01 | 5.632990e-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 | 0.0000571 | 0.0002135 | 0.7899998 | 0.7890907 | 0.623763 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0000765 | 0.0002115 | 0.7199992 | 0.7174076 | 0.400401 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0001835 | 0.0002082 | 0.3800004 | 0.3781875 | 0.456851 | 0.4207270 | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0002804 | 0.0002379 | 0.2399999 | 0.2384306 | 0.240960 | NA | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0000489 | 0.0001477 | 0.7400005 | 0.7405071 | 0.515650 | 0.2751600 | NA |
| 1 | 763394 | rs369924889 | G | A | -0.0001080 | 0.0001731 | 0.5300002 | 0.5327049 | 0.706753 | 0.6176120 | NA |
| 1 | 768253 | rs2977608 | A | C | -0.0001012 | 0.0001413 | 0.4700002 | 0.4738157 | 0.761304 | 0.4894170 | NA |
| 1 | 776546 | rs12124819 | A | G | -0.0001538 | 0.0001578 | 0.3300000 | 0.3299097 | 0.265390 | 0.0756789 | NA |
| 1 | 808631 | rs11240779 | G | A | -0.0000565 | 0.0001435 | 0.6899999 | 0.6934836 | 0.772626 | 0.4534740 | NA |
| 1 | 808928 | rs11240780 | C | T | -0.0000542 | 0.0001437 | 0.7099994 | 0.7059882 | 0.772854 | 0.4522760 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51180501 | rs5770999 | T | C | 0.0001221 | 0.0001390 | 0.3800004 | 0.3794877 | 0.713658 | 0.6369810 | NA |
| 22 | 51181919 | rs9616825 | G | C | 0.0000102 | 0.0001383 | 0.9400001 | 0.9411520 | 0.695471 | 0.6194090 | NA |
| 22 | 51182485 | rs6009961 | A | G | 0.0001105 | 0.0001394 | 0.4299995 | 0.4280818 | 0.715505 | 0.6383790 | NA |
| 22 | 51186143 | rs2879914 | T | C | 0.0000597 | 0.0001293 | 0.6400000 | 0.6442702 | 0.381826 | 0.2733630 | NA |
| 22 | 51186228 | rs3865766 | C | T | 0.0000000 | 0.0001260 | 1.0000000 | 0.9997516 | 0.451063 | 0.4532750 | NA |
| 22 | 51197266 | rs61290853 | A | G | -0.0000502 | 0.0001301 | 0.6999999 | 0.6994658 | 0.386333 | 0.4229230 | NA |
| 22 | 51198027 | rs34939255 | A | G | 0.0001682 | 0.0001473 | 0.2500000 | 0.2533167 | 0.254557 | 0.0984425 | NA |
| 22 | 51211106 | rs9628250 | T | C | 0.0000912 | 0.0001460 | 0.5300002 | 0.5324100 | 0.271547 | 0.1671330 | NA |
| 22 | 51212875 | rs2238837 | A | C | 0.0000244 | 0.0001387 | 0.8600001 | 0.8606270 | 0.331455 | 0.3724040 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0000313 | 0.0001420 | 0.8300000 | 0.8253628 | 0.297971 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623763 ES:SE:LP:AF:ID 5.71049e-05:0.000213483:0.102373:0.623763:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID -7.65451e-05:0.000211494:0.142668:0.400401:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456851 ES:SE:LP:AF:ID 0.000183514:0.000208245:0.420216:0.456851:rs6702460
1 534192 rs6680723 C T . PASS AF=0.24096 ES:SE:LP:AF:ID -0.000280427:0.000237868:0.619789:0.24096:rs6680723
1 706368 rs12029736 A G . PASS AF=0.51565 ES:SE:LP:AF:ID -4.89043e-05:0.000147666:0.130768:0.51565:rs12029736
1 763394 rs3115847 G A . PASS AF=0.706753 ES:SE:LP:AF:ID -0.000108009:0.000173124:0.275724:0.706753:rs3115847
1 768253 rs2977608 A C . PASS AF=0.761304 ES:SE:LP:AF:ID -0.000101211:0.0001413:0.327902:0.761304:rs2977608
1 776546 rs12124819 A G . PASS AF=0.26539 ES:SE:LP:AF:ID -0.000153777:0.000157834:0.481486:0.26539:rs12124819
1 808631 rs11240779 G A . PASS AF=0.772626 ES:SE:LP:AF:ID -5.65406e-05:0.000143456:0.161151:0.772626:rs11240779
1 808928 rs11240780 C T . PASS AF=0.772854 ES:SE:LP:AF:ID -5.421e-05:0.000143698:0.148742:0.772854:rs11240780
1 814495 rs74461805 C A . PASS AF=0.340397 ES:SE:LP:AF:ID -0.000358515:0.000202478:1.11351:0.340397:rs74461805
1 830181 rs28444699 A G . PASS AF=0.697259 ES:SE:LP:AF:ID -8.10161e-06:0.000135464:0.0222764:0.697259:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705403 ES:SE:LP:AF:ID 2.40735e-05:0.000133013:0.0655015:0.705403:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705448 ES:SE:LP:AF:ID 1.75429e-05:0.000133009:0.0457575:0.705448:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705634 ES:SE:LP:AF:ID 1.50382e-05:0.000133015:0.0409586:0.705634:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705662 ES:SE:LP:AF:ID 1.45969e-05:0.000133029:0.0409586:0.705662:rs4500250
1 832398 rs4553118 T C . PASS AF=0.730134 ES:SE:LP:AF:ID 2.86462e-05:0.000136654:0.0809219:0.730134:rs4553118
1 832918 rs28765502 T C . PASS AF=0.294371 ES:SE:LP:AF:ID -1.53433e-05:0.000133023:0.0409586:0.294371:rs28765502
1 833223 rs13303211 C T . PASS AF=0.236696 ES:SE:LP:AF:ID -2.70723e-05:0.000141624:0.0705811:0.236696:rs13303211
1 833302 rs28752186 C T . PASS AF=0.236684 ES:SE:LP:AF:ID -3.5393e-05:0.000141625:0.09691:0.236684:rs28752186
1 833641 rs28594623 T C . PASS AF=0.239743 ES:SE:LP:AF:ID -1.68581e-05:0.000141171:0.0457575:0.239743:rs28594623
1 833824 rs28484835 T C . PASS AF=0.236686 ES:SE:LP:AF:ID -3.54902e-05:0.000141624:0.09691:0.236686:rs28484835
1 834832 rs4411087 G C . PASS AF=0.237171 ES:SE:LP:AF:ID -3.47458e-05:0.000141516:0.091515:0.237171:rs4411087
1 835499 rs4422948 A G . PASS AF=0.241155 ES:SE:LP:AF:ID -9.92636e-06:0.000140528:0.0268721:0.241155:rs4422948
1 836896 rs28705752 T C . PASS AF=0.269503 ES:SE:LP:AF:ID 1.07605e-05:0.000135598:0.0268721:0.269503:rs28705752
1 838555 rs4970383 C A . PASS AF=0.246197 ES:SE:LP:AF:ID -1.20874e-05:0.000139562:0.0315171:0.246197:rs4970383
1 839103 rs28562941 A G . PASS AF=0.27001 ES:SE:LP:AF:ID 1.30226e-05:0.000135694:0.0362122:0.27001:rs28562941
1 840753 rs4970382 T C . PASS AF=0.400106 ES:SE:LP:AF:ID -2.05748e-05:0.000122694:0.0604807:0.400106:rs4970382
1 841085 rs1574243 C G . PASS AF=0.237094 ES:SE:LP:AF:ID -1.94899e-05:0.000142514:0.05061:0.237094:rs1574243
1 842362 rs28540380 C T . PASS AF=0.235321 ES:SE:LP:AF:ID 5.46466e-05:0.000144646:0.148742:0.235321:rs28540380
1 843405 rs11516185 A G . PASS AF=0.362599 ES:SE:LP:AF:ID -9.61661e-05:0.000152311:0.275724:0.362599:rs11516185
1 850218 rs6664536 T A . PASS AF=0.590333 ES:SE:LP:AF:ID -0.000137734:0.000122334:0.585027:0.590333:rs6664536
1 850371 rs6679046 G T . PASS AF=0.603726 ES:SE:LP:AF:ID -0.00013286:0.000123021:0.552842:0.603726:rs6679046
1 850780 rs6657440 C T . PASS AF=0.603944 ES:SE:LP:AF:ID -0.000139338:0.000123003:0.585027:0.603944:rs6657440
1 852037 rs4970463 G A . PASS AF=0.589688 ES:SE:LP:AF:ID -0.000136241:0.000122533:0.568636:0.589688:rs4970463
1 852063 rs28436996 G A . PASS AF=0.589667 ES:SE:LP:AF:ID -0.000134773:0.000122478:0.568636:0.589667:rs28436996
1 852875 rs13303369 C T . PASS AF=0.607674 ES:SE:LP:AF:ID -0.000115722:0.000123279:0.455932:0.607674:rs13303369
1 853954 rs1806509 C A . PASS AF=0.607833 ES:SE:LP:AF:ID -0.000110193:0.000123295:0.431798:0.607833:rs1806509
1 854777 rs13303019 A G . PASS AF=0.610318 ES:SE:LP:AF:ID -0.000115824:0.000123416:0.455932:0.610318:rs13303019
1 854978 rs13303057 A C . PASS AF=0.603286 ES:SE:LP:AF:ID -0.00013275:0.000123051:0.552842:0.603286:rs13303057
1 855075 rs6673914 C G . PASS AF=0.610338 ES:SE:LP:AF:ID -0.000120788:0.000123419:0.481486:0.610338:rs6673914
1 856099 rs28534711 T G . PASS AF=0.389935 ES:SE:LP:AF:ID 0.000116041:0.000123442:0.455932:0.389935:rs28534711
1 856108 rs28742275 A G . PASS AF=0.389918 ES:SE:LP:AF:ID 0.000111396:0.000123448:0.431798:0.389918:rs28742275
1 856476 rs4040605 A G . PASS AF=0.350351 ES:SE:LP:AF:ID 9.53447e-05:0.000126817:0.346787:0.350351:rs4040605
1 858801 rs7418179 A G . PASS AF=0.765845 ES:SE:LP:AF:ID -5.81539e-06:0.000142358:0.0132283:0.765845:rs7418179
1 860416 rs61464428 G A . PASS AF=0.766637 ES:SE:LP:AF:ID 1.47531e-05:0.000142428:0.0362122:0.766637:rs61464428
1 860688 rs60837925 G A . PASS AF=0.766103 ES:SE:LP:AF:ID 2.78383e-05:0.00014233:0.0757207:0.766103:rs60837925
1 861630 rs2879816 G A . PASS AF=0.766255 ES:SE:LP:AF:ID 2.22923e-05:0.000142365:0.0555173:0.766255:rs2879816
1 862866 rs3892970 C T . PASS AF=0.763121 ES:SE:LP:AF:ID 3.31887e-05:0.000142235:0.0861861:0.763121:rs3892970
1 864938 rs2340587 G A . PASS AF=0.760005 ES:SE:LP:AF:ID 4.56275e-05:0.00014118:0.124939:0.760005:rs2340587