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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}
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/c1cd802f-c9a3-4069-a667-a1644161b95c/call-ldsc/inputs/-261043389/ieu-b-4977.vcf.gz \
--ref-ld-chr /data/ref/eur_w_ld_chr/ \
--out /data/igd/ieu-b-4977/ldsc.txt \
--w-ld-chr /data/ref/eur_w_ld_chr/
Beginning analysis at Sat Apr 23 02:08:40 2022
Reading summary statistics from /data/cromwell-executions/qc/c1cd802f-c9a3-4069-a667-a1644161b95c/call-ldsc/inputs/-261043389/ieu-b-4977.vcf.gz ...
Read summary statistics for 12242326 SNPs.
Dropped 231208 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: -0.0188 (0.0119)
Lambda GC: 0.9851
Mean Chi^2: 0.9814
Intercept: 0.993 (0.0062)
Ratio: NA (mean chi^2 < 1)
Analysis finished at Sat Apr 23 02:10:42 2022
Total time elapsed: 2.0m:2.42sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9501,
"inflation_factor": 0.995,
"mean_EFFECT": -0.0024,
"n": "-Inf",
"n_snps": 12243326,
"n_clumped_hits": 0,
"n_p_sig": 0,
"n_mono": 0,
"n_ns": 1279864,
"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": 404067,
"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": "NA",
"ldsc_observed_scale_h2_se": "NA",
"ldsc_intercept_beta": 0.993,
"ldsc_intercept_se": 0.0062,
"ldsc_lambda_gc": 0.9851,
"ldsc_mean_chisq": 0.9814,
"ldsc_ratio": 0.3763
}
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 | 12193961 | 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 | 34525 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | N | 12243326 | 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 | 8.635971e+00 | 5.758643e+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 | 7.869401e+07 | 5.629936e+07 | 302.0000000 | 3.250310e+07 | 6.911930e+07 | 1.143454e+08 | 2.492405e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -2.442900e-03 | 3.365622e-01 | -97.7937000 | -9.018450e-02 | -3.214000e-03 | 8.105100e-02 | 1.643960e+02 | ▁▇▁▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.200761e-01 | 4.300992e-01 | 0.0603435 | 7.314980e-02 | 1.183020e-01 | 2.731620e-01 | 1.017700e+02 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 5.017463e-01 | 2.871968e-01 | 0.0000004 | 2.536682e-01 | 5.010684e-01 | 7.506250e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 5.017463e-01 | 2.871968e-01 | 0.0000004 | 2.536680e-01 | 5.010683e-01 | 7.506256e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.128012e-01 | 2.601902e-01 | 0.0000061 | 1.458480e-02 | 8.853150e-02 | 3.382510e-01 | 9.999930e-01 | ▇▂▁▁▁ |
| numeric | AF_reference | 404067 | 0.9669970 | NA | NA | NA | NA | NA | NA | NA | 2.160796e-01 | 2.512168e-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 | 0.1243240 | 0.0931497 | 0.1819852 | 0.1819848 | 0.3989430 | 0.4253190 | NA |
| 1 | 10352 | rs555500075 | T | TA | 0.1220560 | 0.0960082 | 0.2036179 | 0.2036191 | 0.3930400 | 0.4375000 | NA |
| 1 | 11008 | rs575272151 | C | G | -0.2765480 | 0.1596100 | 0.0831572 | 0.0831582 | 0.0863651 | 0.0880591 | NA |
| 1 | 11012 | rs544419019 | C | G | -0.2765480 | 0.1596100 | 0.0831572 | 0.0831582 | 0.0863651 | 0.0880591 | NA |
| 1 | 13110 | rs540538026 | G | A | 0.0479652 | 0.2127520 | 0.8216283 | 0.8216283 | 0.0586161 | 0.0267572 | NA |
| 1 | 13116 | rs62635286 | T | G | -0.0183789 | 0.1258150 | 0.8838588 | 0.8838592 | 0.1890610 | 0.0970447 | NA |
| 1 | 13118 | rs200579949 | A | G | -0.0183789 | 0.1258150 | 0.8838588 | 0.8838592 | 0.1890610 | 0.0970447 | NA |
| 1 | 13273 | rs531730856 | G | C | -0.0912087 | 0.1476570 | 0.5367697 | 0.5367688 | 0.1341500 | 0.0950479 | NA |
| 1 | 14464 | rs546169444 | A | T | -0.1962390 | 0.1335780 | 0.1418045 | 0.1418066 | 0.1568120 | 0.0958466 | NA |
| 1 | 14599 | rs531646671 | T | A | 0.0257014 | 0.1219050 | 0.8330192 | 0.8330188 | 0.1914280 | 0.1475640 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51233182 | rs4040317 | G | A | 0.0791523 | 0.0760854 | 0.2981968 | 0.2981965 | 0.3197730 | 0.2633790 | NA |
| 22 | 51233300 | rs9616839 | C | T | 0.0965733 | 0.0760306 | 0.2040168 | 0.2040170 | 0.3336920 | 0.3146960 | NA |
| 22 | 51233312 | rs62240043 | A | G | 0.1222670 | 0.0800771 | 0.1267929 | 0.1267943 | 0.2724880 | 0.2134580 | NA |
| 22 | 51233347 | rs62240044 | T | C | 0.1222670 | 0.0800771 | 0.1267929 | 0.1267943 | 0.2724880 | 0.2134580 | NA |
| 22 | 51234799 | rs191117135 | G | A | -0.5271240 | 0.2855000 | 0.0648455 | 0.0648459 | 0.0158080 | 0.0059904 | NA |
| 22 | 51235959 | rs200189535 | T | C | -0.0217564 | 0.0931878 | 0.8153981 | 0.8153978 | 0.1956440 | 0.1996810 | NA |
| 22 | 51235979 | rs62240045 | G | A | 0.1365160 | 0.1040010 | 0.1893006 | 0.1893039 | 0.2634720 | 0.2400160 | NA |
| 22 | 51236013 | rs200507571 | A | AT | -0.0362332 | 0.0804479 | 0.6524271 | 0.6524268 | 0.2512440 | 0.1487620 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.1001060 | 0.0736950 | 0.1743420 | 0.1743423 | 0.2979830 | 0.2050720 | NA |
| 22 | 51237712 | rs370652263 | G | A | -0.0619645 | 0.1471460 | 0.6736759 | 0.6736755 | 0.0554577 | 0.0690895 | NA |
bcf preview
1 10177 rs1264289758 A AC . PASS AF=0.398943 ES:SE:LP:AF:ID 0.124324:0.0931497:0.739964:0.398943:rs1264289758
1 10352 rs1557426776 T TA . PASS AF=0.39304 ES:SE:LP:AF:ID 0.122056:0.0960082:0.691184:0.39304:rs1557426776
1 11008 rs575272151 C G . PASS AF=0.0863651 ES:SE:LP:AF:ID -0.276548:0.15961:1.0801:0.0863651:rs575272151
1 11012 rs544419019 C G . PASS AF=0.0863651 ES:SE:LP:AF:ID -0.276548:0.15961:1.0801:0.0863651:rs544419019
1 13110 rs540538026 G A . PASS AF=0.0586161 ES:SE:LP:AF:ID 0.0479652:0.212752:0.0853246:0.0586161:rs540538026
1 13116 rs62635286 T G . PASS AF=0.189061 ES:SE:LP:AF:ID -0.0183789:0.125815:0.0536171:0.189061:rs62635286
1 13118 rs62028691 A G . PASS AF=0.189061 ES:SE:LP:AF:ID -0.0183789:0.125815:0.0536171:0.189061:rs62028691
1 13273 rs531730856 G C . PASS AF=0.13415 ES:SE:LP:AF:ID -0.0912087:0.147657:0.270212:0.13415:rs531730856
1 14464 rs546169444 A T . PASS AF=0.156812 ES:SE:LP:AF:ID -0.196239:0.133578:0.84831:0.156812:rs546169444
1 14599 rs707680 T A . PASS AF=0.191428 ES:SE:LP:AF:ID 0.0257014:0.121905:0.079345:0.191428:rs707680
1 14604 rs1418508701 A G . PASS AF=0.191428 ES:SE:LP:AF:ID 0.0257014:0.121905:0.079345:0.191428:rs1418508701
1 14930 rs6682385 A G . PASS AF=0.474471 ES:SE:LP:AF:ID 0.0377333:0.0945225:0.161311:0.474471:rs6682385
1 14933 rs199856693 G A . PASS AF=0.0448431 ES:SE:LP:AF:ID 0.179871:0.235106:0.352389:0.0448431:rs199856693
1 15211 rs3982632 T G . PASS AF=0.740981 ES:SE:LP:AF:ID 0.00297618:0.109437:0.0095262:0.740981:rs3982632
1 15820 rs1316988498 G T . PASS AF=0.275358 ES:SE:LP:AF:ID -0.0834041:0.112476:0.338779:0.275358:rs1316988498
1 15903 rs557514207 G GC . PASS AF=0.418903 ES:SE:LP:AF:ID -0.119812:0.0929524:0.704627:0.418903:rs557514207
1 28590 rs1344649620 T TTGG . PASS AF=0.956694 ES:SE:LP:AF:ID 0.282097:0.264025:0.54467:0.956694:rs1344649620
1 30923 rs1165072081 G T . PASS AF=0.910086 ES:SE:LP:AF:ID -0.122663:0.174727:0.316358:0.910086:rs1165072081
1 47159 rs540662756 T C . PASS AF=0.0628754 ES:SE:LP:AF:ID -0.0894749:0.200133:0.183878:0.0628754:rs540662756
1 49298 rs10399793 T C . PASS AF=0.624138 ES:SE:LP:AF:ID -0.0289243:0.111057:0.0998953:0.624138:rs10399793
1 49554 rs539322794 A G . PASS AF=0.0930177 ES:SE:LP:AF:ID 0.315003:0.170707:1.18711:0.0930177:rs539322794
1 51479 rs116400033 T A . PASS AF=0.213359 ES:SE:LP:AF:ID -0.0394845:0.118799:0.130995:0.213359:rs116400033
1 54490 rs141149254 G A . PASS AF=0.154366 ES:SE:LP:AF:ID 0.00903608:0.133388:0.0241132:0.154366:rs141149254
1 54676 rs2462492 C T . PASS AF=0.400197 ES:SE:LP:AF:ID 0.246258:0.110256:1.5932:0.400197:rs2462492
1 54712 rs573184866 T TTTTC . PASS AF=0.584992 ES:SE:LP:AF:ID -0.0559314:0.0868647:0.284292:0.584992:rs573184866
1 54716 rs1166278911 C T . PASS AF=0.416967 ES:SE:LP:AF:ID -0.0643687:0.100305:0.283121:0.416967:rs1166278911
1 55545 rs28396308 C T . PASS AF=0.253628 ES:SE:LP:AF:ID 0.0435614:0.113067:0.15488:0.253628:rs28396308
1 58814 rs114420996 G A . PASS AF=0.089982 ES:SE:LP:AF:ID 0.214514:0.172827:0.668513:0.089982:rs114420996
1 59040 rs62637815 T C . PASS AF=0.0887216 ES:SE:LP:AF:ID 0.25766:0.172993:0.865265:0.0887216:rs62637815
1 60351 rs62637817 A G . PASS AF=0.0809189 ES:SE:LP:AF:ID 0.259761:0.179539:0.829896:0.0809189:rs62637817
1 62777 rs3844233 A T . PASS AF=0.437735 ES:SE:LP:AF:ID -0.0840694:0.0954964:0.421733:0.437735:rs3844233
1 63268 rs28664618 T C . PASS AF=0.381703 ES:SE:LP:AF:ID 0.0380243:0.102653:0.148085:0.381703:rs28664618
1 63671 rs80011619 G A . PASS AF=0.158572 ES:SE:LP:AF:ID 0.0466291:0.131008:0.141525:0.158572:rs80011619
1 63735 rs61158452 CCTA C . PASS AF=0.316293 ES:SE:LP:AF:ID -0.0662908:0.10191:0.287873:0.316293:rs61158452
1 64931 rs62639104 G A . PASS AF=0.07951 ES:SE:LP:AF:ID 0.268247:0.18188:0.853097:0.07951:rs62639104
1 68082 rs367789441 T C . PASS AF=0.0706186 ES:SE:LP:AF:ID 0.059299:0.184816:0.125913:0.0706186:rs367789441
1 69428 rs140739101 T G . PASS AF=0.0325994 ES:SE:LP:AF:ID -0.525796:0.280268:1.21718:0.0325994:rs140739101
1 69761 rs200505207 A T . PASS AF=0.0736849 ES:SE:LP:AF:ID 0.150817:0.181735:0.390823:0.0736849:rs200505207
1 69897 rs200676709 T C . PASS AF=0.751157 ES:SE:LP:AF:ID -0.0895032:0.11536:0.358695:0.751157:rs200676709
1 74790 rs13328700 C G . PASS AF=0.0342591 ES:SE:LP:AF:ID -0.366842:0.26309:0.787254:0.0342591:rs13328700
1 74792 rs1335672253 G A . PASS AF=0.0342591 ES:SE:LP:AF:ID -0.366842:0.26309:0.787254:0.0342591:rs1335672253
1 76838 rs563953605 T G . PASS AF=0.0771777 ES:SE:LP:AF:ID 0.28881:0.184857:0.927355:0.0771777:rs563953605
1 76854 rs367666799 A G . PASS AF=0.0776044 ES:SE:LP:AF:ID -0.187193:0.179333:0.527881:0.0776044:rs367666799
1 77866 rs563593912 C T . PASS AF=0.0769797 ES:SE:LP:AF:ID 0.287436:0.184891:0.920691:0.0769797:rs563593912
1 77874 rs62641297 G A . PASS AF=0.0769797 ES:SE:LP:AF:ID 0.287436:0.184891:0.920691:0.0769797:rs62641297
1 81260 rs571136476 C T . PASS AF=0.0415022 ES:SE:LP:AF:ID 0.83993:0.255896:2.98732:0.0415022:rs571136476
1 81587 rs536406113 C T . PASS AF=0.0608357 ES:SE:LP:AF:ID 0.0222894:0.197427:0.040906:0.0608357:rs536406113
1 82163 rs139113303 G A . PASS AF=0.0755592 ES:SE:LP:AF:ID -0.192802:0.181616:0.539974:0.0755592:rs139113303
1 82609 rs149189449 C G . PASS AF=0.075589 ES:SE:LP:AF:ID -0.191717:0.181583:0.536024:0.075589:rs149189449
1 83514 rs201754587 C T . PASS AF=0.352949 ES:SE:LP:AF:ID -0.0594399:0.103245:0.248099:0.352949:rs201754587