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_20092_2.vcf.gz --id UKB-b:14381 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20092_2.txt.gz --cohort_cases 1427 --cohort_controls 63522 --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": "2019-09-13T11:45:57.008801",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-14381/ukb-b-14381.vcf.gz; Date=Sun May 10 05:07:45 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-14381/UKB-b-14381_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-14381/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:43:27 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-14381/UKB-b-14381_data.vcf.gz ...
Read summary statistics for 2370203 SNPs.
Dropped 265 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, 601976 SNPs remain.
After merging with regression SNP LD, 601976 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0071 (0.0082)
Lambda GC: 0.996
Mean Chi^2: 1.0032
Intercept: 0.9924 (0.0098)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Thu Oct 17 14:43:57 2019
Total time elapsed: 29.25sQC metrics
Metrics
Metrics
{
"af_correlation": 0.7525,
"inflation_factor": 1,
"mean_EFFECT": -4.1301e-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": 18759,
"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": 601976,
"ldsc_nsnp_merge_regression_ld": 601976,
"ldsc_observed_scale_h2_beta": 0.0071,
"ldsc_observed_scale_h2_se": 0.0082,
"ldsc_intercept_beta": 0.9924,
"ldsc_intercept_se": 0.0098,
"ldsc_lambda_gc": 0.996,
"ldsc_mean_chisq": 1.0032,
"ldsc_ratio": -2.375
}
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 | 2369940 | 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 | 2370203 | 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 | 8.650453e+00 | 5.767368e+00 | 1.0000e+00 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.200000e+01 | ▇▅▃▂▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.860354e+07 | 5.664450e+07 | 5.6870e+03 | 3.172173e+07 | 6.903317e+07 | 1.147858e+08 | 2.491722e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -4.100000e-06 | 8.649000e-04 | -4.6949e-03 | -5.829000e-04 | -3.900000e-06 | 5.725000e-04 | 4.660600e-03 | ▁▂▇▂▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 8.595000e-04 | 4.250000e-05 | 7.8520e-04 | 8.238000e-04 | 8.475000e-04 | 8.884000e-04 | 1.701900e-03 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.992396e-01 | 2.896702e-01 | 1.1000e-06 | 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 | 4.992394e-01 | 2.896425e-01 | 1.1000e-06 | 2.472313e-01 | 5.006516e-01 | 7.501487e-01 | 9.999989e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.577819e-01 | 1.450577e-01 | 2.4527e-01 | 3.301795e-01 | 4.375030e-01 | 5.749200e-01 | 7.547300e-01 | ▇▆▅▅▃ |
| numeric | AF_reference | 18759 | 0.9920855 | NA | NA | NA | NA | NA | NA | NA | 4.389358e-01 | 1.723008e-01 | 1.9970e-04 | 3.039140e-01 | 4.251200e-01 | 5.646960e-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 | 0.0002073 | 0.0014470 | 0.8900000 | 0.8860862 | 0.623812 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0002806 | 0.0014428 | 0.8499999 | 0.8457923 | 0.399144 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0018108 | 0.0014191 | 0.2000000 | 0.2019623 | 0.455916 | 0.4207270 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0004176 | 0.0010004 | 0.6800001 | 0.6763504 | 0.513304 | 0.2751600 | NA |
| 1 | 763394 | rs369924889 | G | A | -0.0000156 | 0.0011720 | 0.9900000 | 0.9893600 | 0.705804 | 0.6176120 | NA |
| 1 | 776546 | rs12124819 | A | G | -0.0012446 | 0.0010734 | 0.2500000 | 0.2462528 | 0.263729 | 0.0756789 | NA |
| 1 | 814495 | rs74461805 | C | A | -0.0013301 | 0.0013699 | 0.3300000 | 0.3315711 | 0.340108 | NA | NA |
| 1 | 830181 | rs28444699 | A | G | -0.0004270 | 0.0009165 | 0.6400000 | 0.6413151 | 0.696612 | 0.6912940 | NA |
| 1 | 831489 | rs4970385 | C | T | -0.0001716 | 0.0008999 | 0.8499999 | 0.8487994 | 0.705031 | 0.6491610 | NA |
| 1 | 831909 | rs9697642 | C | T | -0.0001464 | 0.0008999 | 0.8700001 | 0.8707636 | 0.705083 | 0.6485620 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51180501 | rs5770999 | T | C | 0.0022892 | 0.0009374 | 0.0150000 | 0.0146050 | 0.712571 | 0.6369810 | NA |
| 22 | 51181919 | rs9616825 | G | C | 0.0015830 | 0.0009336 | 0.0899995 | 0.0899524 | 0.695031 | 0.6194090 | NA |
| 22 | 51182485 | rs6009961 | A | G | 0.0022221 | 0.0009405 | 0.0179999 | 0.0181431 | 0.714237 | 0.6383790 | NA |
| 22 | 51186143 | rs2879914 | T | C | 0.0001575 | 0.0008759 | 0.8600001 | 0.8572835 | 0.380077 | 0.2733630 | NA |
| 22 | 51186228 | rs3865766 | C | T | 0.0007187 | 0.0008532 | 0.4000000 | 0.3995794 | 0.449547 | 0.4532750 | NA |
| 22 | 51197266 | rs61290853 | A | G | 0.0010842 | 0.0008792 | 0.2200002 | 0.2175302 | 0.386693 | 0.4229230 | NA |
| 22 | 51198027 | rs34939255 | A | G | 0.0017115 | 0.0009971 | 0.0860003 | 0.0860838 | 0.254586 | 0.0984425 | NA |
| 22 | 51211106 | rs9628250 | T | C | 0.0020482 | 0.0009891 | 0.0379997 | 0.0383883 | 0.271468 | 0.1671330 | NA |
| 22 | 51212875 | rs2238837 | A | C | -0.0004846 | 0.0009402 | 0.6100002 | 0.6062881 | 0.331351 | 0.3724040 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0003484 | 0.0009606 | 0.7199992 | 0.7168399 | 0.298393 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623812 ES:SE:LP:AF:ID 0.000207299:0.00144703:0.05061:0.623812:rs10399793
1 54676 rs2462492 C T . PASS AF=0.399144 ES:SE:LP:AF:ID 0.000280614:0.00144282:0.0705811:0.399144:rs2462492
1 91536 rs6702460 G T . PASS AF=0.455916 ES:SE:LP:AF:ID -0.00181077:0.00141912:0.69897:0.455916:rs6702460
1 706368 rs12029736 A G . PASS AF=0.513304 ES:SE:LP:AF:ID -0.000417603:0.00100037:0.167491:0.513304:rs12029736
1 763394 rs3115847 G A . PASS AF=0.705804 ES:SE:LP:AF:ID -1.56293e-05:0.00117199:0.00436481:0.705804:rs3115847
1 776546 rs12124819 A G . PASS AF=0.263729 ES:SE:LP:AF:ID -0.00124456:0.00107336:0.60206:0.263729:rs12124819
1 814495 rs74461805 C A . PASS AF=0.340108 ES:SE:LP:AF:ID -0.00133011:0.0013699:0.481486:0.340108:rs74461805
1 830181 rs28444699 A G . PASS AF=0.696612 ES:SE:LP:AF:ID -0.000426962:0.000916501:0.19382:0.696612:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705031 ES:SE:LP:AF:ID -0.000171566:0.000899898:0.0705811:0.705031:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705083 ES:SE:LP:AF:ID -0.000146403:0.000899897:0.0604807:0.705083:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705261 ES:SE:LP:AF:ID -0.000160557:0.000899855:0.0655015:0.705261:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705286 ES:SE:LP:AF:ID -0.000162091:0.000899981:0.0655015:0.705286:rs4500250
1 832398 rs4553118 T C . PASS AF=0.730154 ES:SE:LP:AF:ID 0.00013857:0.000924808:0.0555173:0.730154:rs4553118
1 832918 rs28765502 T C . PASS AF=0.294729 ES:SE:LP:AF:ID 0.000161714:0.000899953:0.0655015:0.294729:rs28765502
1 836896 rs28705752 T C . PASS AF=0.269687 ES:SE:LP:AF:ID -0.000928007:0.000918228:0.508638:0.269687:rs28705752
1 838555 rs4970383 C A . PASS AF=0.245985 ES:SE:LP:AF:ID -0.00114661:0.00094532:0.638272:0.245985:rs4970383
1 839103 rs28562941 A G . PASS AF=0.270067 ES:SE:LP:AF:ID -0.00106156:0.000918981:0.60206:0.270067:rs28562941
1 840753 rs4970382 T C . PASS AF=0.400406 ES:SE:LP:AF:ID 0.00133683:0.000830746:0.958607:0.400406:rs4970382
1 843405 rs11516185 A G . PASS AF=0.362367 ES:SE:LP:AF:ID 0.000729264:0.00103359:0.318759:0.362367:rs11516185
1 850218 rs6664536 T A . PASS AF=0.589315 ES:SE:LP:AF:ID 0.000413369:0.000826242:0.207608:0.589315:rs6664536
1 850371 rs6679046 G T . PASS AF=0.603035 ES:SE:LP:AF:ID 0.000359192:0.000830194:0.173925:0.603035:rs6679046
1 850780 rs6657440 C T . PASS AF=0.603381 ES:SE:LP:AF:ID 0.000476524:0.000830209:0.244125:0.603381:rs6657440
1 852037 rs4970463 G A . PASS AF=0.588673 ES:SE:LP:AF:ID 0.000394612:0.000827421:0.200659:0.588673:rs4970463
1 852063 rs28436996 G A . PASS AF=0.588649 ES:SE:LP:AF:ID 0.000442747:0.000827048:0.229148:0.588649:rs28436996
1 852875 rs13303369 C T . PASS AF=0.606715 ES:SE:LP:AF:ID 0.00026162:0.000831575:0.124939:0.606715:rs13303369
1 853954 rs1806509 C A . PASS AF=0.60688 ES:SE:LP:AF:ID 0.000252216:0.000831661:0.119186:0.60688:rs1806509
1 854777 rs13303019 A G . PASS AF=0.609504 ES:SE:LP:AF:ID 0.000373217:0.000832493:0.187087:0.609504:rs13303019
1 854978 rs13303057 A C . PASS AF=0.602505 ES:SE:LP:AF:ID 0.000364044:0.000830339:0.180456:0.602505:rs13303057
1 855075 rs6673914 C G . PASS AF=0.609473 ES:SE:LP:AF:ID 0.000391868:0.000832383:0.19382:0.609473:rs6673914
1 856099 rs28534711 T G . PASS AF=0.390835 ES:SE:LP:AF:ID -0.000381445:0.000832721:0.187087:0.390835:rs28534711
1 856108 rs28742275 A G . PASS AF=0.390777 ES:SE:LP:AF:ID -0.000377326:0.000832807:0.187087:0.390777:rs28742275
1 856476 rs4040605 A G . PASS AF=0.352504 ES:SE:LP:AF:ID 7.54704e-05:0.000854852:0.0315171:0.352504:rs4040605
1 866893 rs2880024 T C . PASS AF=0.608083 ES:SE:LP:AF:ID -9.84357e-05:0.000840086:0.0409586:0.608083:rs2880024
1 868418 rs28546443 C T . PASS AF=0.301063 ES:SE:LP:AF:ID 0.00054205:0.000920095:0.251812:0.301063:rs28546443
1 870645 rs28576697 T C . PASS AF=0.293389 ES:SE:LP:AF:ID -0.000537252:0.000910038:0.259637:0.293389:rs28576697
1 871334 rs4072383 G T . PASS AF=0.717959 ES:SE:LP:AF:ID 0.000507859:0.000905314:0.244125:0.717959:rs4072383
1 872352 rs1806780 G C . PASS AF=0.269716 ES:SE:LP:AF:ID -0.000632617:0.000916554:0.309804:0.269716:rs1806780
1 873558 rs1110052 G T . PASS AF=0.71249 ES:SE:LP:AF:ID 0.000484249:0.000898537:0.229148:0.71249:rs1110052
1 875770 rs4970379 A G . PASS AF=0.598464 ES:SE:LP:AF:ID -0.000283886:0.000846369:0.130768:0.598464:rs4970379
1 881627 rs2272757 G A . PASS AF=0.65048 ES:SE:LP:AF:ID -2.44441e-05:0.000854987:0.00877392:0.65048:rs2272757
1 890104 rs28631199 G A . PASS AF=0.248374 ES:SE:LP:AF:ID -0.000611321:0.000945023:0.283997:0.248374:rs28631199
1 891059 rs13303065 C T . PASS AF=0.650626 ES:SE:LP:AF:ID 2.47128e-05:0.000854903:0.00877392:0.650626:rs13303065
1 891945 rs13303106 A G . PASS AF=0.650689 ES:SE:LP:AF:ID -3.9794e-05:0.000855881:0.0177288:0.650689:rs13303106
1 900505 rs28705211 G C . PASS AF=0.273272 ES:SE:LP:AF:ID -0.000578975:0.000922665:0.275724:0.273272:rs28705211
1 903245 rs28690976 A G . PASS AF=0.565133 ES:SE:LP:AF:ID 0.000122506:0.00084988:0.05061:0.565133:rs28690976
1 909073 rs3892467 C T . PASS AF=0.386217 ES:SE:LP:AF:ID -0.000118062:0.000848104:0.05061:0.386217:rs3892467
1 909238 rs3829740 G C . PASS AF=0.572164 ES:SE:LP:AF:ID 3.40269e-05:0.000820495:0.0132283:0.572164:rs3829740
1 910394 rs28477686 C T . PASS AF=0.322663 ES:SE:LP:AF:ID 0.00110539:0.00089122:0.677781:0.322663:rs28477686
1 912049 rs7367995 T C . PASS AF=0.585242 ES:SE:LP:AF:ID -0.00129938:0.00082845:0.920819:0.585242:rs7367995
1 913889 rs2340596 G A . PASS AF=0.600025 ES:SE:LP:AF:ID -0.00131867:0.000830097:0.958607:0.600025:rs2340596