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/1fe2a70c-c481-49ce-a572-74286011733d/call-vcf/inputs/-261043393/upload.txt.gz --id ieu-b-4973 --json /data/cromwell-executions/qc/1fe2a70c-c481-49ce-a572-74286011733d/call-vcf/inputs/-261043393/ieu-b-4973_data.json --ref /data/cromwell-executions/qc/1fe2a70c-c481-49ce-a572-74286011733d/call-vcf/inputs/1899004205/human_g1k_v37.fasta --dbsnp /data/cromwell-executions/qc/1fe2a70c-c481-49ce-a572-74286011733d/call-vcf/inputs/-307190728/dbsnp.v153.b37.vcf.gz --out /data/igd/ieu-b-4973/ieu-b-4973.vcf.gz --alias alias.txt --cohort_cases 14135 --cohort_controls 472349; 1.3.0",
"file_date": "2022-04-23T01:26:03.428125",
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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/1fe2a70c-c481-49ce-a572-74286011733d/call-ldsc/inputs/-261043393/ieu-b-4973.vcf.gz \
--ref-ld-chr /data/ref/eur_w_ld_chr/ \
--out /data/igd/ieu-b-4973/ldsc.txt \
--w-ld-chr /data/ref/eur_w_ld_chr/
Beginning analysis at Sat Apr 23 01:41:56 2022
Reading summary statistics from /data/cromwell-executions/qc/1fe2a70c-c481-49ce-a572-74286011733d/call-ldsc/inputs/-261043393/ieu-b-4973.vcf.gz ...
Read summary statistics for 12242540 SNPs.
Dropped 231422 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: 0.0039 (0.001)
Lambda GC: 1.0515
Mean Chi^2: 1.0538
Intercept: 1.0175 (0.0062)
Ratio: 0.3257 (0.1156)
Analysis finished at Sat Apr 23 01:44:07 2022
Total time elapsed: 2.0m:11.32sQC metrics
Metrics
Metrics
{
"af_correlation": 0.95,
"inflation_factor": 1.0445,
"mean_EFFECT": 0.0002,
"n": "-Inf",
"n_snps": 12243540,
"n_clumped_hits": 2,
"n_p_sig": 147,
"n_mono": 0,
"n_ns": 1279904,
"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": 404183,
"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": 0.0039,
"ldsc_observed_scale_h2_se": 0.001,
"ldsc_intercept_beta": 1.0175,
"ldsc_intercept_se": 0.0062,
"ldsc_lambda_gc": 1.0515,
"ldsc_mean_chisq": 1.0538,
"ldsc_ratio": 0.3253
}
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 | 566 | 0.9999538 | 3 | 58 | 0 | 12193991 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | REF | 0 | 1.0000000 | 1 | 101 | 0 | 56052 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | ALT | 0 | 1.0000000 | 1 | 662 | 0 | 34526 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | N | 12243540 | 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 | 8.635975e+00 | 5.758638e+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 | 7.869397e+07 | 5.629934e+07 | 302.0000000 | 3.250311e+07 | 6.911925e+07 | 1.143454e+08 | 2.492405e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.939000e-04 | 8.368900e-02 | -38.9543000 | -1.739470e-02 | 2.100000e-06 | 1.750650e-02 | 5.545200e+01 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.353050e-02 | 7.667270e-02 | 0.0120141 | 1.457090e-02 | 2.355970e-02 | 5.439840e-02 | 1.917730e+01 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.931089e-01 | 2.905030e-01 | 0.0000000 | 2.403239e-01 | 4.906186e-01 | 7.446188e-01 | 9.999999e-01 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.931089e-01 | 2.905030e-01 | 0.0000000 | 2.403240e-01 | 4.906191e-01 | 7.446190e-01 | 9.999999e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.127948e-01 | 2.602010e-01 | 0.0000054 | 1.457910e-02 | 8.849500e-02 | 3.382500e-01 | 9.999950e-01 | ▇▂▁▁▁ |
| numeric | AF_reference | 404183 | 0.9669881 | NA | NA | NA | NA | NA | NA | NA | 2.160784e-01 | 2.512172e-01 | 0.0000000 | 1.357830e-02 | 1.106230e-01 | 3.426520e-01 | 1.000000e+00 | ▇▂▁▁▁ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 10177 | rs367896724 | A | AC | 0.0089052 | 0.0185758 | 0.6316536 | 0.6316544 | 0.3989060 | 0.4253190 | NA |
| 1 | 10352 | rs555500075 | T | TA | -0.0174995 | 0.0191290 | 0.3602890 | 0.3602887 | 0.3932360 | 0.4375000 | NA |
| 1 | 11008 | rs575272151 | C | G | 0.0377545 | 0.0317909 | 0.2349957 | 0.2349957 | 0.0862242 | 0.0880591 | NA |
| 1 | 11012 | rs544419019 | C | G | 0.0377545 | 0.0317909 | 0.2349957 | 0.2349957 | 0.0862242 | 0.0880591 | NA |
| 1 | 13110 | rs540538026 | G | A | -0.0261537 | 0.0422704 | 0.5360990 | 0.5360984 | 0.0586843 | 0.0267572 | NA |
| 1 | 13116 | rs62635286 | T | G | 0.0233078 | 0.0250285 | 0.3517240 | 0.3517241 | 0.1891810 | 0.0970447 | NA |
| 1 | 13118 | rs200579949 | A | G | 0.0233078 | 0.0250285 | 0.3517240 | 0.3517241 | 0.1891810 | 0.0970447 | NA |
| 1 | 13273 | rs531730856 | G | C | -0.0207973 | 0.0293019 | 0.4778527 | 0.4778533 | 0.1341340 | 0.0950479 | NA |
| 1 | 14464 | rs546169444 | A | T | -0.0240068 | 0.0266655 | 0.3679620 | 0.3679636 | 0.1568980 | 0.0958466 | NA |
| 1 | 14599 | rs531646671 | T | A | 0.0170412 | 0.0242813 | 0.4827897 | 0.4827889 | 0.1915570 | 0.1475640 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51233182 | rs4040317 | G | A | 0.0022808 | 0.0151915 | 0.8806548 | 0.8806548 | 0.3200860 | 0.2633790 | NA |
| 22 | 51233300 | rs9616839 | C | T | 0.0111979 | 0.0151644 | 0.4602492 | 0.4602511 | 0.3343140 | 0.3146960 | NA |
| 22 | 51233312 | rs62240043 | A | G | 0.0013828 | 0.0159708 | 0.9310005 | 0.9310005 | 0.2729950 | 0.2134580 | NA |
| 22 | 51233347 | rs62240044 | T | C | 0.0013828 | 0.0159708 | 0.9310005 | 0.9310005 | 0.2729950 | 0.2134580 | NA |
| 22 | 51234799 | rs191117135 | G | A | 0.0435317 | 0.0564427 | 0.4405559 | 0.4405560 | 0.0156550 | 0.0059904 | NA |
| 22 | 51235959 | rs200189535 | T | C | -0.0071811 | 0.0185798 | 0.6991253 | 0.6991253 | 0.1953500 | 0.1996810 | NA |
| 22 | 51235979 | rs62240045 | G | A | 0.0143413 | 0.0206661 | 0.4877104 | 0.4877117 | 0.2637980 | 0.2400160 | NA |
| 22 | 51236013 | rs200507571 | A | AT | 0.0102970 | 0.0159955 | 0.5197398 | 0.5197417 | 0.2513400 | 0.1487620 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0027954 | 0.0146998 | 0.8491770 | 0.8491776 | 0.2979820 | 0.2050720 | NA |
| 22 | 51237712 | rs370652263 | G | A | 0.0186695 | 0.0293358 | 0.5245115 | 0.5245114 | 0.0553522 | 0.0690895 | NA |
bcf preview
1 10177 rs1264289758 A AC . PASS AF=0.398906 ES:SE:LP:AF:ID 0.00890523:0.0185758:0.199521:0.398906:rs1264289758
1 10352 rs1557426776 T TA . PASS AF=0.393236 ES:SE:LP:AF:ID -0.0174995:0.019129:0.443349:0.393236:rs1557426776
1 11008 rs575272151 C G . PASS AF=0.0862242 ES:SE:LP:AF:ID 0.0377545:0.0317909:0.62894:0.0862242:rs575272151
1 11012 rs544419019 C G . PASS AF=0.0862242 ES:SE:LP:AF:ID 0.0377545:0.0317909:0.62894:0.0862242:rs544419019
1 13110 rs540538026 G A . PASS AF=0.0586843 ES:SE:LP:AF:ID -0.0261537:0.0422704:0.270755:0.0586843:rs540538026
1 13116 rs62635286 T G . PASS AF=0.189181 ES:SE:LP:AF:ID 0.0233078:0.0250285:0.453798:0.189181:rs62635286
1 13118 rs62028691 A G . PASS AF=0.189181 ES:SE:LP:AF:ID 0.0233078:0.0250285:0.453798:0.189181:rs62028691
1 13273 rs531730856 G C . PASS AF=0.134134 ES:SE:LP:AF:ID -0.0207973:0.0293019:0.320706:0.134134:rs531730856
1 14464 rs546169444 A T . PASS AF=0.156898 ES:SE:LP:AF:ID -0.0240068:0.0266655:0.434197:0.156898:rs546169444
1 14599 rs707680 T A . PASS AF=0.191557 ES:SE:LP:AF:ID 0.0170412:0.0242813:0.316242:0.191557:rs707680
1 14604 rs1418508701 A G . PASS AF=0.191557 ES:SE:LP:AF:ID 0.0170412:0.0242813:0.316242:0.191557:rs1418508701
1 14930 rs6682385 A G . PASS AF=0.473819 ES:SE:LP:AF:ID 0.0095995:0.0188354:0.21446:0.473819:rs6682385
1 14933 rs199856693 G A . PASS AF=0.0450802 ES:SE:LP:AF:ID -0.0193591:0.0461784:0.170663:0.0450802:rs199856693
1 15211 rs3982632 T G . PASS AF=0.741355 ES:SE:LP:AF:ID -0.0295854:0.0217661:0.759279:0.741355:rs3982632
1 15820 rs1316988498 G T . PASS AF=0.275124 ES:SE:LP:AF:ID 0.0104046:0.0223449:0.192821:0.275124:rs1316988498
1 15903 rs557514207 G GC . PASS AF=0.41884 ES:SE:LP:AF:ID 0.00449311:0.0184091:0.0930313:0.41884:rs557514207
1 28590 rs1344649620 T TTGG . PASS AF=0.956571 ES:SE:LP:AF:ID -0.0053855:0.0525649:0.03697:0.956571:rs1344649620
1 30923 rs1165072081 G T . PASS AF=0.910221 ES:SE:LP:AF:ID -0.0487189:0.0347052:0.794849:0.910221:rs1165072081
1 47159 rs540662756 T C . PASS AF=0.0627121 ES:SE:LP:AF:ID 0.0539475:0.0395875:0.762043:0.0627121:rs540662756
1 49298 rs10399793 T C . PASS AF=0.623768 ES:SE:LP:AF:ID -0.0301214:0.0220927:0.762573:0.623768:rs10399793
1 49554 rs539322794 A G . PASS AF=0.0929554 ES:SE:LP:AF:ID -0.000161794:0.0340275:0.00165074:0.0929554:rs539322794
1 51479 rs116400033 T A . PASS AF=0.21326 ES:SE:LP:AF:ID 0.0195761:0.0236111:0.390358:0.21326:rs116400033
1 54490 rs141149254 G A . PASS AF=0.154391 ES:SE:LP:AF:ID 0.0276535:0.0265114:0.527374:0.154391:rs141149254
1 54676 rs2462492 C T . PASS AF=0.400396 ES:SE:LP:AF:ID 0.0413425:0.0219089:1.22799:0.400396:rs2462492
1 54712 rs573184866 T TTTTC . PASS AF=0.585268 ES:SE:LP:AF:ID -0.00405992:0.0173123:0.089062:0.585268:rs573184866
1 54716 rs1166278911 C T . PASS AF=0.417371 ES:SE:LP:AF:ID 0.012139:0.0199841:0.264749:0.417371:rs1166278911
1 55545 rs28396308 C T . PASS AF=0.253777 ES:SE:LP:AF:ID 0.028166:0.0224936:0.676738:0.253777:rs28396308
1 58814 rs114420996 G A . PASS AF=0.0899979 ES:SE:LP:AF:ID -0.039495:0.0343545:0.601545:0.0899979:rs114420996
1 59040 rs62637815 T C . PASS AF=0.0887883 ES:SE:LP:AF:ID -0.0426338:0.034473:0.665169:0.0887883:rs62637815
1 60351 rs62637817 A G . PASS AF=0.0809837 ES:SE:LP:AF:ID -0.0454436:0.0357691:0.690546:0.0809837:rs62637817
1 62777 rs3844233 A T . PASS AF=0.438273 ES:SE:LP:AF:ID -0.0111412:0.0190086:0.253521:0.438273:rs3844233
1 63268 rs28664618 T C . PASS AF=0.382186 ES:SE:LP:AF:ID 0.0136417:0.0204304:0.297298:0.382186:rs28664618
1 63671 rs80011619 G A . PASS AF=0.158756 ES:SE:LP:AF:ID 0.00246451:0.0259901:0.0341148:0.158756:rs80011619
1 63735 rs61158452 CCTA C . PASS AF=0.315972 ES:SE:LP:AF:ID 0.00532279:0.0202336:0.101001:0.315972:rs61158452
1 64931 rs62639104 G A . PASS AF=0.0795793 ES:SE:LP:AF:ID -0.0491166:0.0362167:0.756864:0.0795793:rs62639104
1 68082 rs367789441 T C . PASS AF=0.0707801 ES:SE:LP:AF:ID -0.0190997:0.0365743:0.22075:0.0707801:rs367789441
1 69428 rs140739101 T G . PASS AF=0.0329199 ES:SE:LP:AF:ID 0.0704493:0.0555573:0.688711:0.0329199:rs140739101
1 69761 rs200505207 A T . PASS AF=0.0738738 ES:SE:LP:AF:ID -0.0190763:0.0360221:0.224456:0.0738738:rs200505207
1 69897 rs200676709 T C . PASS AF=0.751218 ES:SE:LP:AF:ID 0.000672377:0.0229721:0.0102611:0.751218:rs200676709
1 74790 rs13328700 C G . PASS AF=0.0340921 ES:SE:LP:AF:ID 0.0161068:0.0526121:0.119475:0.0340921:rs13328700
1 74792 rs1335672253 G A . PASS AF=0.0340921 ES:SE:LP:AF:ID 0.0161068:0.0526121:0.119475:0.0340921:rs1335672253
1 76838 rs563953605 T G . PASS AF=0.0771356 ES:SE:LP:AF:ID -0.0274701:0.0369004:0.340453:0.0771356:rs563953605
1 76854 rs367666799 A G . PASS AF=0.0775037 ES:SE:LP:AF:ID 0.0326451:0.0354132:0.4478:0.0775037:rs367666799
1 77866 rs563593912 C T . PASS AF=0.0769408 ES:SE:LP:AF:ID -0.0291971:0.0369085:0.36764:0.0769408:rs563593912
1 77874 rs62641297 G A . PASS AF=0.0769408 ES:SE:LP:AF:ID -0.0291971:0.0369085:0.36764:0.0769408:rs62641297
1 81260 rs571136476 C T . PASS AF=0.0414632 ES:SE:LP:AF:ID 0.0866956:0.0513233:1.0401:0.0414632:rs571136476
1 81587 rs536406113 C T . PASS AF=0.0609432 ES:SE:LP:AF:ID -0.0302122:0.0389697:0.35835:0.0609432:rs536406113
1 82163 rs139113303 G A . PASS AF=0.0754482 ES:SE:LP:AF:ID 0.0286499:0.035874:0.372114:0.0754482:rs139113303
1 82609 rs149189449 C G . PASS AF=0.0754758 ES:SE:LP:AF:ID 0.0279998:0.0358676:0.361498:0.0754758:rs149189449
1 83514 rs201754587 C T . PASS AF=0.352798 ES:SE:LP:AF:ID -0.0210683:0.0205742:0.514522:0.352798:rs201754587