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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"Gwas2VCF_command": "--data /data/cromwell-executions/qc/792065d3-b2ea-4ce7-98a2-59d682087fdf/call-vcf/inputs/-261022278/upload.txt.gz --id ieu-b-5067 --json /data/cromwell-executions/qc/792065d3-b2ea-4ce7-98a2-59d682087fdf/call-vcf/inputs/-261022278/ieu-b-5067_data.json --ref /data/cromwell-executions/qc/792065d3-b2ea-4ce7-98a2-59d682087fdf/call-vcf/inputs/1899004205/human_g1k_v37.fasta --dbsnp /data/cromwell-executions/qc/792065d3-b2ea-4ce7-98a2-59d682087fdf/call-vcf/inputs/-307190728/dbsnp.v153.b37.vcf.gz --out /data/igd/ieu-b-5067/ieu-b-5067.vcf.gz --alias alias.txt --cohort_cases 954 --cohort_controls 487331; 1.3.0",
"file_date": "2022-04-23T14:05:40.880189",
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}
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 /data/cromwell-executions/qc/792065d3-b2ea-4ce7-98a2-59d682087fdf/call-ldsc/inputs/-261022278/ieu-b-5067.vcf.gz \
--ref-ld-chr /data/ref/eur_w_ld_chr/ \
--out /data/igd/ieu-b-5067/ldsc.txt \
--w-ld-chr /data/ref/eur_w_ld_chr/
Beginning analysis at Sat Apr 23 14:22:04 2022
Reading summary statistics from /data/cromwell-executions/qc/792065d3-b2ea-4ce7-98a2-59d682087fdf/call-ldsc/inputs/-261022278/ieu-b-5067.vcf.gz ...
Read summary statistics for 12320830 SNPs.
Dropped 206220 SNPs with duplicated rs numbers.
Reading reference panel LD Score from /data/ref/eur_w_ld_chr/[1-22] ... (ldscore_fromlist)
Read reference panel LD Scores for 1290028 SNPs.
Removing partitioned LD Scores with zero variance.
Reading regression weight LD Score from /data/ref/eur_w_ld_chr/[1-22] ... (ldscore_fromlist)
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 1220578 SNPs remain.
After merging with regression SNP LD, 1220578 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0012 (0.0009)
Lambda GC: 1.0082
Mean Chi^2: 1.0103
Intercept: 0.9993 (0.0062)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Sat Apr 23 14:23:57 2022
Total time elapsed: 1.0m:52.91sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9503,
"inflation_factor": 1,
"mean_EFFECT": 4.4092e-07,
"n": "-Inf",
"n_snps": 12321875,
"n_clumped_hits": 5,
"n_p_sig": 211,
"n_mono": 0,
"n_ns": 1293897,
"n_mac": 0,
"is_snpid_unique": false,
"n_miss_EFFECT": 0,
"n_miss_SE": 0,
"n_miss_PVAL": 0,
"n_miss_AF": 0,
"n_miss_AF_reference": 1423040,
"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": 1220578,
"ldsc_nsnp_merge_regression_ld": 1220578,
"ldsc_observed_scale_h2_beta": 0.0012,
"ldsc_observed_scale_h2_se": 0.0009,
"ldsc_intercept_beta": 0.9993,
"ldsc_intercept_se": 0.0062,
"ldsc_lambda_gc": 1.0082,
"ldsc_mean_chisq": 1.0103,
"ldsc_ratio": -0.068
}
Flags
| name | value |
|---|---|
| af_correlation | FALSE |
| inflation_factor | FALSE |
| n | TRUE |
| is_snpid_non_unique | TRUE |
| 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 | 675 | 0.9999452 | 3 | 58 | 0 | 12276232 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | REF | 0 | 1.0000000 | 1 | 88 | 0 | 17566 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | ALT | 0 | 1.0000000 | 1 | 662 | 0 | 71704 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | N | 12321875 | 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 | 8.757492e+00 | 5.885387e+00 | 1.0000000 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.300000e+01 | ▇▅▅▂▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.871202e+07 | 5.622121e+07 | 302.0000000 | 3.252510e+07 | 6.928045e+07 | 1.144492e+08 | 2.492405e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.000000e-07 | 4.228000e-04 | -0.0065663 | -1.302000e-04 | -3.400000e-06 | 1.214000e-04 | 8.510400e-03 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.074000e-04 | 2.953000e-04 | 0.0000763 | 1.076000e-04 | 1.728000e-04 | 3.988000e-04 | 4.693600e-03 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 5.002115e-01 | 2.881284e-01 | 0.0000000 | 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 | 5.002273e-01 | 2.880976e-01 | 0.0000000 | 2.512473e-01 | 4.998708e-01 | 7.495362e-01 | 9.999999e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.594005e-01 | 3.020419e-01 | 0.0009980 | 1.660000e-02 | 1.145680e-01 | 4.379330e-01 | 9.990010e-01 | ▇▂▁▁▁ |
| numeric | AF_reference | 1423040 | 0.8845111 | NA | NA | NA | NA | NA | NA | NA | 2.132160e-01 | 2.513550e-01 | 0.0000000 | 1.218050e-02 | 1.056310e-01 | 3.376600e-01 | 1.000000e+00 | ▇▂▁▁▁ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 10177 | rs1264289758 | AC | A | -0.0000361 | 0.0001381 | 0.7800007 | 0.7936200 | 0.601092 | NA | NA |
| 1 | 10352 | rs1557426776 | TA | T | -0.0000779 | 0.0001422 | 0.5700002 | 0.5835933 | 0.606764 | NA | NA |
| 1 | 11008 | rs575272151 | C | G | 0.0001805 | 0.0002364 | 0.4500005 | 0.4450304 | 0.086225 | 0.0880591 | NA |
| 1 | 11012 | rs544419019 | C | G | 0.0001805 | 0.0002364 | 0.4500005 | 0.4450304 | 0.086225 | 0.0880591 | NA |
| 1 | 13110 | rs540538026 | G | A | -0.0001479 | 0.0003144 | 0.6300007 | 0.6380144 | 0.058685 | 0.0267572 | NA |
| 1 | 13116 | rs62635286 | T | G | 0.0001901 | 0.0001861 | 0.3100002 | 0.3070958 | 0.189177 | 0.0970447 | NA |
| 1 | 13118 | rs200579949 | A | G | 0.0001901 | 0.0001861 | 0.3100002 | 0.3070958 | 0.189177 | 0.0970447 | NA |
| 1 | 13273 | rs531730856 | G | C | 0.0002088 | 0.0002177 | 0.3400001 | 0.3375016 | 0.134135 | 0.0950479 | NA |
| 1 | 14464 | rs546169444 | A | T | -0.0000501 | 0.0001984 | 0.8100000 | 0.8006472 | 0.156897 | 0.0958466 | NA |
| 1 | 14599 | rs531646671 | T | A | 0.0000763 | 0.0001805 | 0.6700003 | 0.6724162 | 0.191556 | 0.1475640 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 23 | 154901936 | rs697727 | A | G | 0.0000126 | 8.87e-05 | 0.8800001 | 0.8873662 | 0.751936 | 0.643179 | NA |
| 23 | 154901964 | rs697726 | G | A | -0.0001005 | 9.36e-05 | 0.2800000 | 0.2831318 | 0.347899 | 0.117616 | NA |
| 23 | 154902105 | rs696316 | G | T | 0.0000128 | 8.87e-05 | 0.8800001 | 0.8853971 | 0.751891 | 0.636026 | NA |
| 23 | 154902889 | rs697725 | A | T | 0.0000066 | 8.87e-05 | 0.9400001 | 0.9402976 | 0.751565 | 0.584106 | NA |
| 23 | 154903118 | rs479770 | G | A | 0.0000123 | 8.87e-05 | 0.8900000 | 0.8902009 | 0.751980 | 0.643444 | NA |
| 23 | 154903224 | rs480725 | A | T | 0.0000123 | 8.87e-05 | 0.8900000 | 0.8901049 | 0.751983 | 0.643444 | NA |
| 23 | 154903937 | rs674707 | G | A | 0.0000122 | 8.88e-05 | 0.8900000 | 0.8906957 | 0.751993 | 0.643444 | NA |
| 23 | 154909055 | rs473529 | C | G | 0.0000189 | 8.36e-05 | 0.8200001 | 0.8206071 | 0.304737 | 0.463046 | NA |
| 23 | 154918266 | rs642043 | C | T | 0.0000297 | 8.38e-05 | 0.7199992 | 0.7232336 | 0.302582 | 0.478675 | NA |
| 23 | 154927581 | rs644138 | G | A | 0.0000304 | 8.38e-05 | 0.7199992 | 0.7171048 | 0.302456 | 0.463576 | NA |
bcf preview
1 10177 rs1264289758 AC A . PASS AF=0.601092 ES:SE:LP:AF:ID -3.6135e-05:0.000138124:0.107905:0.601092:rs1264289758
1 10352 rs1557426776 TA T . PASS AF=0.606764 ES:SE:LP:AF:ID -7.79252e-05:0.000142162:0.244125:0.606764:rs1557426776
1 11008 rs575272151 C G . PASS AF=0.086225 ES:SE:LP:AF:ID 0.000180538:0.000236391:0.346787:0.086225:rs575272151
1 11012 rs544419019 C G . PASS AF=0.086225 ES:SE:LP:AF:ID 0.000180538:0.000236391:0.346787:0.086225:rs544419019
1 13110 rs540538026 G A . PASS AF=0.058685 ES:SE:LP:AF:ID -0.00014794:0.000314447:0.200659:0.058685:rs540538026
1 13116 rs62635286 T G . PASS AF=0.189177 ES:SE:LP:AF:ID 0.000190051:0.000186081:0.508638:0.189177:rs62635286
1 13118 rs62028691 A G . PASS AF=0.189177 ES:SE:LP:AF:ID 0.000190051:0.000186081:0.508638:0.189177:rs62028691
1 13273 rs531730856 G C . PASS AF=0.134135 ES:SE:LP:AF:ID 0.000208846:0.000217749:0.468521:0.134135:rs531730856
1 14464 rs546169444 A T . PASS AF=0.156897 ES:SE:LP:AF:ID -5.00883e-05:0.000198362:0.091515:0.156897:rs546169444
1 14599 rs707680 T A . PASS AF=0.191556 ES:SE:LP:AF:ID 7.63072e-05:0.000180466:0.173925:0.191556:rs707680
1 14604 rs1418508701 A G . PASS AF=0.191556 ES:SE:LP:AF:ID 7.63072e-05:0.000180466:0.173925:0.191556:rs1418508701
1 14930 rs6682385 A G . PASS AF=0.473818 ES:SE:LP:AF:ID 3.65473e-05:0.00014003:0.102373:0.473818:rs6682385
1 14933 rs199856693 G A . PASS AF=0.045079 ES:SE:LP:AF:ID 0.000187699:0.000344219:0.229148:0.045079:rs199856693
1 15211 rs3982632 T G . PASS AF=0.741354 ES:SE:LP:AF:ID 5.75894e-05:0.000161922:0.130768:0.741354:rs3982632
1 15820 rs1316988498 G T . PASS AF=0.275123 ES:SE:LP:AF:ID -0.000283168:0.000166086:1.05061:0.275123:rs1316988498
1 15903 rs557514207 GC G . PASS AF=0.581159 ES:SE:LP:AF:ID 0.000125366:0.000136946:0.443698:0.581159:rs557514207
1 28590 rs1344649620 T TTGG . PASS AF=0.956571 ES:SE:LP:AF:ID -0.000228295:0.000389946:0.251812:0.956571:rs1344649620
1 30923 rs1165072081 G T . PASS AF=0.910221 ES:SE:LP:AF:ID 0.000108586:0.000258524:0.161151:0.910221:rs1165072081
1 47159 rs540662756 T C . PASS AF=0.062712 ES:SE:LP:AF:ID -0.000313337:0.000295161:0.522879:0.062712:rs540662756
1 49298 rs10399793 T C . PASS AF=0.623767 ES:SE:LP:AF:ID 2.53493e-05:0.000164244:0.0555173:0.623767:rs10399793
1 49554 rs539322794 A G . PASS AF=0.092954 ES:SE:LP:AF:ID 3.48594e-05:0.000252818:0.05061:0.092954:rs539322794
1 51479 rs116400033 T A . PASS AF=0.21326 ES:SE:LP:AF:ID 0.000122702:0.000175754:0.309804:0.21326:rs116400033
1 54490 rs141149254 G A . PASS AF=0.154391 ES:SE:LP:AF:ID 0.000141327:0.000197455:0.327902:0.154391:rs141149254
1 54676 rs2462492 C T . PASS AF=0.400395 ES:SE:LP:AF:ID -0.000176717:0.000162731:0.568636:0.400395:rs2462492
1 54712 rs573184866 TTTTC T . PASS AF=0.414731 ES:SE:LP:AF:ID -0.000196449:0.000128705:0.920819:0.414731:rs573184866
1 54716 rs1166278911 C T . PASS AF=0.41737 ES:SE:LP:AF:ID -0.000118042:0.000148614:0.366532:0.41737:rs1166278911
1 55545 rs28396308 C T . PASS AF=0.253776 ES:SE:LP:AF:ID 9.31796e-05:0.000167186:0.244125:0.253776:rs28396308
1 58814 rs114420996 G A . PASS AF=0.089998 ES:SE:LP:AF:ID 7.06424e-05:0.000254835:0.102373:0.089998:rs114420996
1 59040 rs62637815 T C . PASS AF=0.088788 ES:SE:LP:AF:ID -2.85862e-05:0.000255873:0.0409586:0.088788:rs62637815
1 60351 rs62637817 A G . PASS AF=0.080983 ES:SE:LP:AF:ID -1.93746e-05:0.000265367:0.0315171:0.080983:rs62637817
1 62777 rs3844233 A T . PASS AF=0.438272 ES:SE:LP:AF:ID 0.000297073:0.000141262:1.4437:0.438272:rs3844233
1 63268 rs28664618 T C . PASS AF=0.382184 ES:SE:LP:AF:ID -3.81159e-05:0.000151846:0.091515:0.382184:rs28664618
1 63671 rs80011619 G A . PASS AF=0.158758 ES:SE:LP:AF:ID -0.000139738:0.000193389:0.327902:0.158758:rs80011619
1 63735 rs61158452 C CCTA . PASS AF=0.684027 ES:SE:LP:AF:ID 0.000118379:0.000150592:0.366532:0.684027:rs61158452
1 64931 rs62639104 G A . PASS AF=0.079579 ES:SE:LP:AF:ID -5.78159e-05:0.00026864:0.0861861:0.079579:rs62639104
1 68082 rs367789441 T C . PASS AF=0.070781 ES:SE:LP:AF:ID -0.00011906:0.000271993:0.180456:0.070781:rs367789441
1 69428 rs140739101 T G . PASS AF=0.032919 ES:SE:LP:AF:ID -9.2192e-05:0.000411557:0.0861861:0.032919:rs140739101
1 69761 rs200505207 A T . PASS AF=0.073875 ES:SE:LP:AF:ID -4.50863e-06:0.000267729:0.00877392:0.073875:rs200505207
1 69897 rs200676709 T C . PASS AF=0.751221 ES:SE:LP:AF:ID 4.04069e-05:0.000170609:0.0861861:0.751221:rs200676709
1 74790 rs13328700 C G . PASS AF=0.034093 ES:SE:LP:AF:ID 0.000484739:0.00039105:0.657577:0.034093:rs13328700
1 74792 rs1335672253 G A . PASS AF=0.034093 ES:SE:LP:AF:ID 0.000484739:0.00039105:0.657577:0.034093:rs1335672253
1 76838 rs563953605 T G . PASS AF=0.077135 ES:SE:LP:AF:ID -7.06748e-05:0.00027376:0.102373:0.077135:rs563953605
1 76854 rs367666799 A G . PASS AF=0.077503 ES:SE:LP:AF:ID -0.000343308:0.000264035:0.69897:0.077503:rs367666799
1 77866 rs563593912 C T . PASS AF=0.07694 ES:SE:LP:AF:ID -7.10739e-05:0.000273827:0.102373:0.07694:rs563593912
1 77874 rs62641297 G A . PASS AF=0.07694 ES:SE:LP:AF:ID -7.10739e-05:0.000273827:0.102373:0.07694:rs62641297
1 81260 rs571136476 C T . PASS AF=0.041463 ES:SE:LP:AF:ID -0.000184602:0.000381781:0.207608:0.041463:rs571136476
1 81587 rs536406113 C T . PASS AF=0.060944 ES:SE:LP:AF:ID -0.000148861:0.000289892:0.221849:0.060944:rs536406113
1 82163 rs139113303 G A . PASS AF=0.075448 ES:SE:LP:AF:ID -0.000316781:0.000267443:0.619789:0.075448:rs139113303
1 82609 rs149189449 C G . PASS AF=0.075475 ES:SE:LP:AF:ID -0.000322451:0.000267384:0.619789:0.075475:rs149189449
1 83514 rs201754587 C T . PASS AF=0.352797 ES:SE:LP:AF:ID -4.97383e-05:0.000152917:0.130768:0.352797:rs201754587