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/54cd6381-c7c1-435f-a015-99090b4fb45e/call-vcf/inputs/562856313/upload.txt.gz --id ieu-b-72 --json /data/cromwell-executions/qc/54cd6381-c7c1-435f-a015-99090b4fb45e/call-vcf/inputs/562856313/ieu-b-72_data.json --ref /data/cromwell-executions/qc/54cd6381-c7c1-435f-a015-99090b4fb45e/call-vcf/inputs/1899004205/human_g1k_v37.fasta --dbsnp /data/cromwell-executions/qc/54cd6381-c7c1-435f-a015-99090b4fb45e/call-vcf/inputs/-307190728/dbsnp.v153.b37.vcf.gz --out /data/igd/ieu-b-72/ieu-b-72.vcf.gz --alias alias.txt; 1.3.0",
"file_date": "2020-11-24T17:15:09.327486",
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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/54cd6381-c7c1-435f-a015-99090b4fb45e/call-ldsc/inputs/562856313/ieu-b-72.vcf.gz \
--ref-ld-chr /data/ref/eur_w_ld_chr/ \
--out /data/igd/ieu-b-72/ldsc.txt \
--w-ld-chr /data/ref/eur_w_ld_chr/
Beginning analysis at Tue Nov 24 17:34:21 2020
Reading summary statistics from /data/cromwell-executions/qc/54cd6381-c7c1-435f-a015-99090b4fb45e/call-ldsc/inputs/562856313/ieu-b-72.vcf.gz ...
Read summary statistics for 0 SNPs.
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.
Traceback (most recent call last):
File "./ldsc/ldsc.py", line 647, in <module>
sumstats.estimate_h2(args, log)
File "/ldsc/ldscore/sumstats.py", line 330, in estimate_h2
args, log, args.h2)
File "/ldsc/ldscore/sumstats.py", line 252, in _read_ld_sumstats
sumstats = _merge_and_log(ref_ld, sumstats, 'reference panel LD', log)
File "/ldsc/ldscore/sumstats.py", line 238, in _merge_and_log
raise ValueError(msg.format(N=len(sumstats), F=noun))
ValueError: After merging with reference panel LD, 0 SNPs remain.
Analysis finished at Tue Nov 24 17:36:02 2020
Total time elapsed: 1.0m:40.42sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9604,
"inflation_factor": 1.1247,
"mean_EFFECT": 0.0001,
"n": "-Inf",
"n_snps": 19420026,
"n_clumped_hits": 28,
"n_p_sig": 1649,
"n_mono": 0,
"n_ns": 1434996,
"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": 2207239,
"n_est": "NA",
"ratio_se_n": "NA",
"mean_diff": "NaN",
"ratio_diff": "NaN",
"sd_y_est1": "NA",
"sd_y_est2": "NA",
"r2_sum1": 0,
"r2_sum2": 0,
"r2_sum3": 0,
"r2_sum4": 0,
"ldsc_nsnp_merge_refpanel_ld": 0,
"ldsc_nsnp_merge_regression_ld": "NA",
"ldsc_observed_scale_h2_beta": "NA",
"ldsc_observed_scale_h2_se": "NA",
"ldsc_intercept_beta": "NA",
"ldsc_intercept_se": "NA",
"ldsc_lambda_gc": "NA",
"ldsc_mean_chisq": "NA",
"ldsc_ratio": "NA"
}
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 | NA |
| n_p_sig | TRUE |
| miss_EFFECT | FALSE |
| miss_SE | FALSE |
| miss_PVAL | FALSE |
| ldsc_ratio | NA |
| ldsc_intercept_beta | NA |
| 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 | 2643 | 0.9998639 | 3 | 58 | 0 | 19391228 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | REF | 0 | 1.0000000 | 1 | 101 | 0 | 67060 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | ALT | 0 | 1.0000000 | 1 | 412 | 0 | 36950 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | N | 19420026 | 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 | 8.541396e+00 | 5.726077e+00 | 1.0000000 | 4.000000e+00 | 7.000000e+00 | 1.200000e+01 | 2.200000e+01 | ▇▅▃▂▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.949689e+07 | 5.600429e+07 | 302.0000000 | 3.363084e+07 | 7.038981e+07 | 1.149796e+08 | 2.492309e+08 | ▇▇▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.051000e-04 | 5.712940e-02 | -0.7950540 | -7.669800e-03 | 4.440000e-05 | 7.792400e-03 | 9.001950e-01 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.562890e-02 | 4.428030e-02 | 0.0023177 | 3.599300e-03 | 1.347360e-02 | 5.488070e-02 | 2.185270e-01 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.806997e-01 | 2.943628e-01 | 0.0000000 | 2.213421e-01 | 4.744145e-01 | 7.356089e-01 | 1.000000e+00 | ▇▇▇▆▆ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.806992e-01 | 2.943631e-01 | 0.0000000 | 2.213412e-01 | 4.744136e-01 | 7.356083e-01 | 1.000000e+00 | ▇▇▇▆▆ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.345051e-01 | 2.340645e-01 | 0.0000368 | 5.611000e-04 | 9.171300e-03 | 1.600760e-01 | 9.999600e-01 | ▇▁▁▁▁ |
| numeric | AF_reference | 2207239 | 0.8863421 | NA | NA | NA | NA | NA | NA | NA | 1.519943e-01 | 2.333090e-01 | 0.0000000 | 2.196500e-03 | 2.855430e-02 | 2.090650e-01 | 1.000000e+00 | ▇▁▁▁▁ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 13259 | rs562993331 | G | A | -0.0187179 | 0.0792272 | 0.8132339 | 0.8132340 | 0.0002704 | 0.0003994 | NA |
| 1 | 17641 | rs578081284 | G | A | 0.0357238 | 0.0451137 | 0.4284420 | 0.4284415 | 0.0008258 | 0.0001997 | NA |
| 1 | 52144 | rs190291950 | T | A | -0.0020751 | 0.0600382 | 0.9724281 | 0.9724281 | 0.0004966 | 0.0009984 | NA |
| 1 | 57222 | rs576081345 | T | C | -0.0723460 | 0.0503887 | 0.1510720 | 0.1510711 | 0.0006738 | 0.0001997 | NA |
| 1 | 58396 | rs570371753 | T | C | -0.0433553 | 0.0754908 | 0.5657558 | 0.5657564 | 0.0002521 | 0.0005990 | NA |
| 1 | 62157 | rs10399597 | G | A | -0.0123519 | 0.1349680 | 0.9270810 | 0.9270816 | 0.0000878 | 0.0021965 | NA |
| 1 | 62595 | rs577275305 | C | T | -0.0419547 | 0.0754583 | 0.5782132 | 0.5782119 | 0.0002776 | 0.0013978 | NA |
| 1 | 69569 | rs2531267 | T | C | 0.0446168 | 0.0905509 | 0.6222072 | 0.6222061 | 0.0001914 | 0.0001997 | NA |
| 1 | 79192 | rs557418932 | T | G | 0.0740495 | 0.1412570 | 0.6001254 | 0.6001267 | 0.0000776 | 0.0001997 | NA |
| 1 | 91588 | rs554639997 | G | A | -0.0462944 | 0.0967476 | 0.6322895 | 0.6322894 | 0.0001563 | 0.0003994 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51229717 | rs200078515 | A | T | 0.0006917 | 0.0029194 | 0.8127139 | 0.8127141 | 0.2957390 | 0.2294330 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0126851 | 0.0046521 | 0.0063966 | 0.0063962 | 0.0725830 | 0.0730831 | NA |
| 22 | 51229813 | rs191731586 | T | C | 0.0511277 | 0.0765046 | 0.5039448 | 0.5039448 | 0.0002783 | 0.0057907 | NA |
| 22 | 51230139 | rs367819077 | T | G | -0.0218509 | 0.0601465 | 0.7163842 | 0.7163848 | 0.0004513 | 0.0003994 | NA |
| 22 | 51231220 | rs368226325 | A | G | 0.0009374 | 0.0057144 | 0.8696961 | 0.8696956 | 0.0535365 | 0.0601038 | NA |
| 22 | 51232581 | rs200771213 | T | C | 0.0007451 | 0.0029290 | 0.7991951 | 0.7991947 | 0.2972490 | 0.2270370 | NA |
| 22 | 51236013 | rs200507571 | A | AT | -0.0037888 | 0.0030831 | 0.2191170 | 0.2191167 | 0.2517780 | 0.1487620 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0017990 | 0.0028316 | 0.5252245 | 0.5252238 | 0.2989010 | 0.2050720 | NA |
| 22 | 51237215 | rs536109858 | C | T | -0.0100418 | 0.0585550 | 0.8638360 | 0.8638358 | 0.0004584 | 0.0001997 | NA |
| 22 | 51237712 | rs370652263 | G | A | 0.0013399 | 0.0056675 | 0.8131109 | 0.8131113 | 0.0550333 | 0.0690895 | NA |
bcf preview
1 13259 rs562993331 G A . PASS AF=0.00027045 ES:SE:LP:AF:ID -0.0187179:0.0792272:0.0897845:0.00027045:rs562993331
1 17641 rs578081284 G A . PASS AF=0.000825751 ES:SE:LP:AF:ID 0.0357238:0.0451137:0.368108:0.000825751:rs578081284
1 52144 rs190291950 T A . PASS AF=0.000496639 ES:SE:LP:AF:ID -0.00207511:0.0600382:0.0121425:0.000496639:rs190291950
1 57222 rs576081345 T C . PASS AF=0.000673828 ES:SE:LP:AF:ID -0.072346:0.0503887:0.820816:0.000673828:rs576081345
1 58396 rs570371753 T C . PASS AF=0.000252076 ES:SE:LP:AF:ID -0.0433553:0.0754908:0.247371:0.000252076:rs570371753
1 62157 rs10399597 G A . PASS AF=8.78379e-05 ES:SE:LP:AF:ID -0.0123519:0.134968:0.0328823:8.78379e-05:rs10399597
1 62595 rs577275305 C T . PASS AF=0.000277588 ES:SE:LP:AF:ID -0.0419547:0.0754583:0.237912:0.000277588:rs577275305
1 69569 rs2531267 T C . PASS AF=0.000191386 ES:SE:LP:AF:ID 0.0446168:0.0905509:0.206065:0.000191386:rs2531267
1 79192 rs557418932 T G . PASS AF=7.76378e-05 ES:SE:LP:AF:ID 0.0740495:0.141257:0.221758:7.76378e-05:rs557418932
1 91588 rs554639997 G A . PASS AF=0.000156291 ES:SE:LP:AF:ID -0.0462944:0.0967476:0.199084:0.000156291:rs554639997
1 255847 rs575228382 G A . PASS AF=0.000168441 ES:SE:LP:AF:ID -0.0336195:0.10356:0.127579:0.000168441:rs575228382
1 267132 rs550489103 T C . PASS AF=5.35072e-05 ES:SE:LP:AF:ID 0.379805:0.172001:1.56489:5.35072e-05:rs550489103
1 533573 rs575442534 G A . PASS AF=0.000640454 ES:SE:LP:AF:ID 0.00247171:0.0492809:0.0177292:0.000640454:rs575442534
1 565148 rs547117545 T C . PASS AF=0.000127417 ES:SE:LP:AF:ID -0.0191354:0.115071:0.0615168:0.000127417:rs547117545
1 565169 rs539230054 T C . PASS AF=0.000141863 ES:SE:LP:AF:ID -0.0425835:0.103331:0.167324:0.000141863:rs539230054
1 565724 rs547930359 C T . PASS AF=0.000520968 ES:SE:LP:AF:ID 0.0501372:0.0582008:0.410062:0.000520968:rs547930359
1 566012 rs529231313 C A . PASS AF=0.00013545 ES:SE:LP:AF:ID 0.0404101:0.114899:0.139624:0.00013545:rs529231313
1 570578 rs532537403 T G . PASS AF=0.000515446 ES:SE:LP:AF:ID 0.0279468:0.0586728:0.198014:0.000515446:rs532537403
1 601158 rs779794883 GAGTA G . PASS AF=0.000883176 ES:SE:LP:AF:ID -0.0509566:0.0441556:0.604689:0.000883176:rs779794883
1 603010 rs368808541 C A . PASS AF=0.000276205 ES:SE:LP:AF:ID 0.0312754:0.0804816:0.156412:0.000276205:rs368808541
1 668332 rs1316928168 T C . PASS AF=0.000107339 ES:SE:LP:AF:ID 0.125455:0.128162:0.484603:0.000107339:rs1316928168
1 692794 rs530212009 CA C . PASS AF=0.110713 ES:SE:LP:AF:ID -0.00157412:0.00422275:0.149158:0.110713:rs530212009
1 693731 rs12238997 A G . PASS AF=0.115904 ES:SE:LP:AF:ID -0.00023317:0.00399002:0.0207249:0.115904:rs12238997
1 707522 rs371890604 G C . PASS AF=0.097385 ES:SE:LP:AF:ID 0.00136485:0.00448648:0.118635:0.097385:rs371890604
1 713979 rs117217250 C G . PASS AF=0.000182259 ES:SE:LP:AF:ID -0.0204729:0.0913645:0.0847606:0.000182259:rs117217250
1 714596 rs149887893 T C . PASS AF=0.032024 ES:SE:LP:AF:ID -0.00478013:0.00754133:0.27887:0.032024:rs149887893
1 714856 rs540438253 G A . PASS AF=0.000257803 ES:SE:LP:AF:ID -0.0211151:0.0792335:0.10245:0.000257803:rs540438253
1 715265 rs12184267 C T . PASS AF=0.0355535 ES:SE:LP:AF:ID -0.00286802:0.00684766:0.170478:0.0355535:rs12184267
1 715367 rs12184277 A G . PASS AF=0.0356417 ES:SE:LP:AF:ID -0.00338483:0.00682495:0.207657:0.0356417:rs12184277
1 717485 rs12184279 C A . PASS AF=0.0353784 ES:SE:LP:AF:ID -0.00242868:0.00687064:0.140427:0.0353784:rs12184279
1 717587 rs144155419 G A . PASS AF=0.0156305 ES:SE:LP:AF:ID -0.0114767:0.0107316:0.545347:0.0156305:rs144155419
1 720381 rs116801199 G T . PASS AF=0.035881 ES:SE:LP:AF:ID -0.00350708:0.00679665:0.217632:0.035881:rs116801199
1 721290 rs12565286 G C . PASS AF=0.0359803 ES:SE:LP:AF:ID -0.00237314:0.0067729:0.139035:0.0359803:rs12565286
1 723329 rs189787166 A T . PASS AF=0.00178778 ES:SE:LP:AF:ID 0.00287682:0.0310387:0.0333168:0.00178778:rs189787166
1 723891 rs2977670 G C . PASS AF=0.960848 ES:SE:LP:AF:ID 0.00378756:0.0065775:0.248162:0.960848:rs2977670
1 724187 rs1214800179 G C . PASS AF=0.000164093 ES:SE:LP:AF:ID -0.0346782:0.10362:0.132017:0.000164093:rs1214800179
1 726794 rs28454925 C G . PASS AF=0.0355108 ES:SE:LP:AF:ID -0.00273943:0.00681633:0.162561:0.0355108:rs28454925
1 727841 rs116587930 G A . PASS AF=0.0526637 ES:SE:LP:AF:ID 0.0073252:0.00600593:0.652487:0.0526637:rs116587930
1 729632 rs116720794 C T . PASS AF=0.0358098 ES:SE:LP:AF:ID -0.00260465:0.00675508:0.155023:0.0358098:rs116720794
1 729679 rs4951859 C G . PASS AF=0.845269 ES:SE:LP:AF:ID 0.000872359:0.00347148:0.0960493:0.845269:rs4951859
1 730087 rs148120343 T C . PASS AF=0.0564779 ES:SE:LP:AF:ID 0.00544103:0.00555807:0.484644:0.0564779:rs148120343
1 731718 rs58276399 T C . PASS AF=0.121797 ES:SE:LP:AF:ID -0.000112859:0.00378505:0.0104554:0.121797:rs58276399
1 731959 rs555463208 G A . PASS AF=0.000140729 ES:SE:LP:AF:ID -0.0359035:0.103654:0.137238:0.000140729:rs555463208
1 732032 rs61770163 A C . PASS AF=0.121248 ES:SE:LP:AF:ID -0.00120908:0.00403764:0.116569:0.121248:rs61770163
1 734349 rs141242758 T C . PASS AF=0.121016 ES:SE:LP:AF:ID 3.89261e-05:0.00378729:0.00357633:0.121016:rs141242758
1 734914 rs542807748 T A . PASS AF=8.6969e-05 ES:SE:LP:AF:ID -0.179835:0.134193:0.744226:8.6969e-05:rs542807748
1 736289 rs1254887344 T A . PASS AF=0.131999 ES:SE:LP:AF:ID 0.000855489:0.00372798:0.0869824:0.131999:rs1254887344
1 736736 rs10454457 A G . PASS AF=0.0373336 ES:SE:LP:AF:ID -0.00194283:0.00707913:0.105827:0.0373336:rs10454457
1 740284 rs61770167 C T . PASS AF=0.0058259 ES:SE:LP:AF:ID -0.00160383:0.0172542:0.0334167:0.0058259:rs61770167
1 742220 rs60070474 T C . PASS AF=5.4614e-05 ES:SE:LP:AF:ID 0.0184782:0.178116:0.0374536:5.4614e-05:rs60070474