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_20094_3.vcf.gz --id UKB-b:4310 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20094_3.txt.gz --cohort_cases 1997 --cohort_controls 62952 --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": "2019-09-13T11:46:19.547729",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-4310/ukb-b-4310.vcf.gz; Date=Sat May 9 18:51:32 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-4310/UKB-b-4310_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4310/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:44:09 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4310/UKB-b-4310_data.vcf.gz ...
Read summary statistics for 3186283 SNPs.
Dropped 443 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, 788232 SNPs remain.
After merging with regression SNP LD, 788232 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: -0.0035 (0.0079)
Lambda GC: 0.9942
Mean Chi^2: 0.9896
Intercept: 0.9947 (0.0085)
Ratio: NA (mean chi^2 < 1)
Analysis finished at Thu Oct 17 14:44:51 2019
Total time elapsed: 42.76sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8312,
"inflation_factor": 1,
"mean_EFFECT": 9.6932e-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": 25458,
"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": 788232,
"ldsc_nsnp_merge_regression_ld": 788232,
"ldsc_observed_scale_h2_beta": "NA",
"ldsc_observed_scale_h2_se": "NA",
"ldsc_intercept_beta": 0.9947,
"ldsc_intercept_se": 0.0085,
"ldsc_lambda_gc": 0.9942,
"ldsc_mean_chisq": 0.9896,
"ldsc_ratio": 0.5096
}
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 | TRUE |
| 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 | 3185843 | 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 | 3186283 | 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 | 8.663172e+00 | 5.772936e+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 | 7.853880e+07 | 5.670232e+07 | 828.0000000 | 3.160216e+07 | 6.892478e+07 | 1.147754e+08 | 2.492013e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 9.700000e-06 | 1.053500e-03 | -0.0053140 | -6.947000e-04 | 9.300000e-06 | 7.141000e-04 | 5.677000e-03 | ▁▂▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.057200e-03 | 9.090000e-05 | 0.0009251 | 9.782000e-04 | 1.029300e-03 | 1.121500e-03 | 2.287900e-03 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 5.022027e-01 | 2.882003e-01 | 0.0000008 | 2.500000e-01 | 5.000000e-01 | 7.499995e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 5.022043e-01 | 2.881722e-01 | 0.0000008 | 2.523990e-01 | 5.027239e-01 | 7.519656e-01 | 9.999999e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.277488e-01 | 1.830682e-01 | 0.1752630 | 2.672110e-01 | 3.940360e-01 | 5.697185e-01 | 8.247370e-01 | ▇▆▅▃▃ |
| numeric | AF_reference | 25458 | 0.9920101 | NA | NA | NA | NA | NA | NA | NA | 4.126669e-01 | 1.966369e-01 | 0.0000000 | 2.537940e-01 | 3.863820e-01 | 5.569090e-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 | 0.0004987 | 0.0017048 | 0.7700005 | 0.7699078 | 0.623812 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0015524 | 0.0016999 | 0.3599996 | 0.3611156 | 0.399144 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0035770 | 0.0016720 | 0.0320000 | 0.0324014 | 0.455916 | 0.4207270 | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0011771 | 0.0019043 | 0.5400003 | 0.5364836 | 0.242057 | NA | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0012410 | 0.0011786 | 0.2900000 | 0.2923629 | 0.513304 | 0.2751600 | NA |
| 1 | 763394 | rs369924889 | G | A | 0.0008242 | 0.0013808 | 0.5500004 | 0.5505923 | 0.705804 | 0.6176120 | NA |
| 1 | 768253 | rs2977608 | A | C | 0.0003017 | 0.0011192 | 0.7899998 | 0.7874590 | 0.758252 | 0.4894170 | NA |
| 1 | 776546 | rs12124819 | A | G | 0.0008620 | 0.0012646 | 0.5000000 | 0.4954877 | 0.263729 | 0.0756789 | NA |
| 1 | 798400 | rs10900604 | A | G | -0.0014374 | 0.0011932 | 0.2300001 | 0.2283249 | 0.209824 | 0.4105430 | NA |
| 1 | 798959 | rs11240777 | G | A | -0.0014202 | 0.0011938 | 0.2300001 | 0.2341940 | 0.209642 | 0.4099440 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51180501 | rs5770999 | T | C | 0.0000470 | 0.0011044 | 0.9699999 | 0.9660478 | 0.712571 | 0.6369810 | NA |
| 22 | 51181919 | rs9616825 | G | C | 0.0001024 | 0.0010999 | 0.9299999 | 0.9258088 | 0.695031 | 0.6194090 | NA |
| 22 | 51182485 | rs6009961 | A | G | -0.0000071 | 0.0011081 | 0.9900000 | 0.9948556 | 0.714237 | 0.6383790 | NA |
| 22 | 51186143 | rs2879914 | T | C | 0.0005179 | 0.0010320 | 0.6200004 | 0.6157418 | 0.380077 | 0.2733630 | NA |
| 22 | 51186228 | rs3865766 | C | T | 0.0011246 | 0.0010052 | 0.2599998 | 0.2632145 | 0.449547 | 0.4532750 | NA |
| 22 | 51197266 | rs61290853 | A | G | 0.0011924 | 0.0010358 | 0.2500000 | 0.2496694 | 0.386693 | 0.4229230 | NA |
| 22 | 51198027 | rs34939255 | A | G | -0.0022976 | 0.0011748 | 0.0500000 | 0.0504931 | 0.254586 | 0.0984425 | NA |
| 22 | 51211106 | rs9628250 | T | C | -0.0019008 | 0.0011654 | 0.1000000 | 0.1028654 | 0.271468 | 0.1671330 | NA |
| 22 | 51212875 | rs2238837 | A | C | 0.0002276 | 0.0011078 | 0.8400000 | 0.8372031 | 0.331351 | 0.3724040 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0007082 | 0.0011317 | 0.5300002 | 0.5314584 | 0.298393 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623812 ES:SE:LP:AF:ID 0.000498661:0.00170485:0.113509:0.623812:rs10399793
1 54676 rs2462492 C T . PASS AF=0.399144 ES:SE:LP:AF:ID 0.00155241:0.00169989:0.443698:0.399144:rs2462492
1 91536 rs6702460 G T . PASS AF=0.455916 ES:SE:LP:AF:ID 0.00357703:0.00167196:1.49485:0.455916:rs6702460
1 534192 rs6680723 C T . PASS AF=0.242057 ES:SE:LP:AF:ID 0.00117711:0.00190428:0.267606:0.242057:rs6680723
1 706368 rs12029736 A G . PASS AF=0.513304 ES:SE:LP:AF:ID -0.00124102:0.00117861:0.537602:0.513304:rs12029736
1 763394 rs3115847 G A . PASS AF=0.705804 ES:SE:LP:AF:ID 0.000824162:0.0013808:0.259637:0.705804:rs3115847
1 768253 rs2977608 A C . PASS AF=0.758252 ES:SE:LP:AF:ID 0.000301736:0.00111915:0.102373:0.758252:rs2977608
1 776546 rs12124819 A G . PASS AF=0.263729 ES:SE:LP:AF:ID 0.000861953:0.00126459:0.30103:0.263729:rs12124819
1 798400 rs10900604 A G . PASS AF=0.209824 ES:SE:LP:AF:ID -0.00143737:0.00119315:0.638272:0.209824:rs10900604
1 798959 rs11240777 G A . PASS AF=0.209642 ES:SE:LP:AF:ID -0.00142015:0.00119378:0.638272:0.209642:rs11240777
1 808631 rs11240779 G A . PASS AF=0.76828 ES:SE:LP:AF:ID 0.00138455:0.00113459:0.657577:0.76828:rs11240779
1 808928 rs11240780 C T . PASS AF=0.768417 ES:SE:LP:AF:ID 0.00136712:0.00113604:0.638272:0.768417:rs11240780
1 814495 rs74461805 C A . PASS AF=0.340108 ES:SE:LP:AF:ID 0.000798224:0.00161398:0.207608:0.340108:rs74461805
1 830181 rs28444699 A G . PASS AF=0.696612 ES:SE:LP:AF:ID -0.00100927:0.00107979:0.455932:0.696612:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705031 ES:SE:LP:AF:ID -0.00141272:0.00106023:0.744727:0.705031:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705083 ES:SE:LP:AF:ID -0.00139701:0.00106023:0.721246:0.705083:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705261 ES:SE:LP:AF:ID -0.00141451:0.00106018:0.744727:0.705261:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705286 ES:SE:LP:AF:ID -0.0014027:0.00106033:0.721246:0.705286:rs4500250
1 832398 rs4553118 T C . PASS AF=0.730154 ES:SE:LP:AF:ID -0.00200103:0.00108958:1.18046:0.730154:rs4553118
1 832918 rs28765502 T C . PASS AF=0.294729 ES:SE:LP:AF:ID 0.00139568:0.0010603:0.721246:0.294729:rs28765502
1 833223 rs13303211 C T . PASS AF=0.237157 ES:SE:LP:AF:ID 0.0011866:0.00112881:0.537602:0.237157:rs13303211
1 833302 rs28752186 C T . PASS AF=0.237168 ES:SE:LP:AF:ID 0.00118521:0.00112883:0.537602:0.237168:rs28752186
1 833641 rs28594623 T C . PASS AF=0.240204 ES:SE:LP:AF:ID 0.000983869:0.00112548:0.420216:0.240204:rs28594623
1 833824 rs28484835 T C . PASS AF=0.23716 ES:SE:LP:AF:ID 0.00118675:0.00112888:0.537602:0.23716:rs28484835
1 833927 rs28593608 T C . PASS AF=0.212467 ES:SE:LP:AF:ID 0.00181821:0.00117346:0.920819:0.212467:rs28593608
1 834198 rs28385272 T C . PASS AF=0.21236 ES:SE:LP:AF:ID 0.00184011:0.00117376:0.920819:0.21236:rs28385272
1 834832 rs4411087 G C . PASS AF=0.237633 ES:SE:LP:AF:ID 0.00118319:0.00112803:0.537602:0.237633:rs4411087
1 834928 rs4422949 A G . PASS AF=0.213048 ES:SE:LP:AF:ID 0.00175977:0.00117171:0.886057:0.213048:rs4422949
1 834999 rs28570054 G A . PASS AF=0.213007 ES:SE:LP:AF:ID 0.00176972:0.00117193:0.886057:0.213007:rs28570054
1 835499 rs4422948 A G . PASS AF=0.241744 ES:SE:LP:AF:ID 0.00134924:0.0011196:0.638272:0.241744:rs4422948
1 836529 rs28731045 C G . PASS AF=0.213504 ES:SE:LP:AF:ID 0.001765:0.00117053:0.886057:0.213504:rs28731045
1 836896 rs28705752 T C . PASS AF=0.269687 ES:SE:LP:AF:ID 0.000567214:0.00108183:0.221849:0.269687:rs28705752
1 836924 rs72890788 G A . PASS AF=0.213501 ES:SE:LP:AF:ID 0.00177084:0.00117068:0.886057:0.213501:rs72890788
1 838387 rs4970384 T C . PASS AF=0.214599 ES:SE:LP:AF:ID 0.00171682:0.00116842:0.853872:0.214599:rs4970384
1 838555 rs4970383 C A . PASS AF=0.245985 ES:SE:LP:AF:ID 0.00116006:0.00111375:0.522879:0.245985:rs4970383
1 839103 rs28562941 A G . PASS AF=0.270067 ES:SE:LP:AF:ID 0.000604554:0.00108271:0.236572:0.270067:rs28562941
1 840753 rs4970382 T C . PASS AF=0.400406 ES:SE:LP:AF:ID 0.000913069:0.000978759:0.455932:0.400406:rs4970382
1 841085 rs1574243 C G . PASS AF=0.236925 ES:SE:LP:AF:ID 0.00131607:0.00113785:0.60206:0.236925:rs1574243
1 842013 rs7419119 T G . PASS AF=0.215763 ES:SE:LP:AF:ID 0.00207556:0.00116928:1.11919:0.215763:rs7419119
1 842362 rs28540380 C T . PASS AF=0.234687 ES:SE:LP:AF:ID 0.00163122:0.00115567:0.79588:0.234687:rs28540380
1 843405 rs11516185 A G . PASS AF=0.362367 ES:SE:LP:AF:ID -0.00207774:0.00121774:1.05552:0.362367:rs11516185
1 844300 rs61769713 C G . PASS AF=0.819531 ES:SE:LP:AF:ID -0.000158121:0.00124569:0.0457575:0.819531:rs61769713
1 845283 rs7366404 G T . PASS AF=0.815154 ES:SE:LP:AF:ID 6.65456e-05:0.00123491:0.0177288:0.815154:rs7366404
1 845635 rs117086422 C T . PASS AF=0.206525 ES:SE:LP:AF:ID 0.00197395:0.00118569:1.01773:0.206525:rs117086422
1 845938 rs57760052 G A . PASS AF=0.212239 ES:SE:LP:AF:ID 0.0015919:0.00117437:0.744727:0.212239:rs57760052
1 846078 rs28612348 C T . PASS AF=0.197605 ES:SE:LP:AF:ID 0.00183503:0.00120355:0.886057:0.197605:rs28612348
1 846338 rs4970334 A G . PASS AF=0.814547 ES:SE:LP:AF:ID 9.28761e-05:0.00123411:0.0268721:0.814547:rs4970334
1 846398 rs58781670 G A . PASS AF=0.205375 ES:SE:LP:AF:ID 0.00137438:0.00119141:0.60206:0.205375:rs58781670
1 846489 rs4970333 T C . PASS AF=0.81468 ES:SE:LP:AF:ID 4.7562e-05:0.00123505:0.0132283:0.81468:rs4970333
1 846808 rs4475691 C T . PASS AF=0.199243 ES:SE:LP:AF:ID 0.00167215:0.00120002:0.79588:0.199243:rs4475691