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/efdf2918-41f7-4e74-ac72-1943734e8393/call-vcf/inputs/562856133/upload.txt.gz --id ieu-b-18 --json /data/cromwell-executions/qc/efdf2918-41f7-4e74-ac72-1943734e8393/call-vcf/inputs/562856133/ieu-b-18_data.json --ref /data/cromwell-executions/qc/efdf2918-41f7-4e74-ac72-1943734e8393/call-vcf/inputs/1899004205/human_g1k_v37.fasta --dbsnp /data/cromwell-executions/qc/efdf2918-41f7-4e74-ac72-1943734e8393/call-vcf/inputs/-307190728/dbsnp.v153.b37.vcf.gz --out /data/igd/ieu-b-18/ieu-b-18.vcf.gz --rm_chr_prefix --cohort_cases 47429 --cohort_controls 68374; 1.2.1",
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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/efdf2918-41f7-4e74-ac72-1943734e8393/call-ldsc/inputs/562856133/ieu-b-18.vcf.gz \
--ref-ld-chr /data/ref/eur_w_ld_chr/ \
--out /data/igd/ieu-b-18/ldsc.txt \
--w-ld-chr /data/ref/eur_w_ld_chr/
Beginning analysis at Thu Jul 2 11:31:39 2020
Reading summary statistics from /data/cromwell-executions/qc/efdf2918-41f7-4e74-ac72-1943734e8393/call-ldsc/inputs/562856133/ieu-b-18.vcf.gz ...
Read summary statistics for 6304141 SNPs.
Dropped 28044 SNPs with duplicated rs numbers.
Reading reference panel LD Score from /data/ref/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 /data/ref/eur_w_ld_chr/[1-22] ...
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 1208721 SNPs remain.
After merging with regression SNP LD, 1208721 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.1001 (0.0086)
Lambda GC: 1.1459
Mean Chi^2: 1.2449
Intercept: 1.0228 (0.0089)
Ratio: 0.093 (0.0363)
Analysis finished at Thu Jul 2 11:33:21 2020
Total time elapsed: 1.0m:42.64sQC metrics
Metrics
Metrics
{
"af_correlation": "NA",
"inflation_factor": 1.1541,
"mean_EFFECT": -0.0007,
"n": "-Inf",
"n_snps": 6304358,
"n_clumped_hits": 72,
"n_p_sig": 22711,
"n_mono": 0,
"n_ns": 0,
"n_mac": 0,
"is_snpid_unique": false,
"n_miss_EFFECT": 0,
"n_miss_SE": 0,
"n_miss_PVAL": 0,
"n_miss_AF": 6304358,
"n_miss_AF_reference": 133998,
"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": 1208721,
"ldsc_nsnp_merge_regression_ld": 1208721,
"ldsc_observed_scale_h2_beta": 0.1001,
"ldsc_observed_scale_h2_se": 0.0086,
"ldsc_intercept_beta": 1.0228,
"ldsc_intercept_se": 0.0089,
"ldsc_lambda_gc": 1.1459,
"ldsc_mean_chisq": 1.2449,
"ldsc_ratio": 0.0931
}
Flags
| name | value |
|---|---|
| af_correlation | NA |
| 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 | TRUE |
| 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 | 190 | 0.9999699 | 3 | 58 | 0 | 6298247 | 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 | AF | 6304358 | 0.0000000 | NA | NA | NA | NA | NA | NaN | : | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | N | 6304358 | 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 | 8.665710e+00 | 5.763473e+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 | 7.845000e+07 | 5.644676e+07 | 828.0000000 | 3.184148e+07 | 6.887496e+07 | 1.143450e+08 | 2.492223e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -6.620000e-04 | 6.378320e-02 | -4.1783800 | -1.734960e-02 | 4.001000e-04 | 1.816400e-02 | 4.906280e+00 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.318160e-02 | 4.144470e-02 | 0.0107826 | 1.772500e-02 | 2.170220e-02 | 3.229540e-02 | 1.569400e+00 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.755653e-01 | 2.957547e-01 | 0.0000000 | 2.146001e-01 | 4.687001e-01 | 7.319999e-01 | 9.999000e-01 | ▇▇▆▆▆ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.755653e-01 | 2.957547e-01 | 0.0000000 | 2.146008e-01 | 4.686991e-01 | 7.319995e-01 | 9.999000e-01 | ▇▇▆▆▆ |
| numeric | AF_reference | 133998 | 0.9787452 | NA | NA | NA | NA | NA | NA | NA | 3.205810e-01 | 2.522943e-01 | 0.0000000 | 1.130190e-01 | 2.490020e-01 | 4.862220e-01 | 1.000000e+00 | ▇▅▃▂▁ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 51803 | rs62637812 | T | C | -0.0182658 | 0.0722086 | 0.8003000 | 0.8003001 | NA | NA | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0150121 | 0.0586260 | 0.7979001 | 0.7979002 | NA | NA | NA |
| 1 | 62271 | rs28599927 | A | G | -0.0117690 | 0.0514308 | 0.8190000 | 0.8189997 | NA | NA | NA |
| 1 | 66162 | rs201684885 | A | T | -0.0177567 | 0.0754741 | 0.8140000 | 0.8140001 | NA | NA | NA |
| 1 | 74681 | rs13328683 | G | T | 0.0116678 | 0.0506186 | 0.8177000 | 0.8177000 | NA | NA | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0025031 | 0.0864472 | 0.9769000 | 0.9769000 | NA | 0.420727 | NA |
| 1 | 91581 | rs151118460 | G | A | -0.0015011 | 0.0843424 | 0.9858000 | 0.9858000 | NA | 0.423722 | NA |
| 1 | 234481 | rs8179403 | T | A | 0.0019980 | 0.0817444 | 0.9805000 | 0.9805000 | NA | NA | NA |
| 1 | 662622 | rs61769339 | G | A | 0.0046890 | 0.0422833 | 0.9117001 | 0.9116999 | NA | 0.147564 | NA |
| 1 | 729679 | rs4951859 | C | G | -0.0074720 | 0.0438186 | 0.8645999 | 0.8646001 | NA | 0.639976 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51190878 | rs143051537 | G | A | 0.0090588 | 0.0390819 | 0.8167001 | 0.8167002 | NA | 0.4626600 | NA |
| 22 | 51191334 | rs541575022 | C | G | -0.1213320 | 0.0841412 | 0.1493001 | 0.1493011 | NA | 0.1042330 | NA |
| 22 | 51192518 | rs5771005 | C | G | 0.0070749 | 0.0431962 | 0.8698999 | 0.8699000 | NA | 0.2398160 | NA |
| 22 | 51192586 | rs5771006 | G | A | 0.0325640 | 0.0413431 | 0.4308995 | 0.4308999 | NA | 0.0848642 | NA |
| 22 | 51194639 | rs6009964 | T | C | 0.0089597 | 0.0438781 | 0.8382001 | 0.8382002 | NA | 0.2418130 | NA |
| 22 | 51194679 | rs6009965 | T | C | -0.0112632 | 0.0437421 | 0.7968000 | 0.7967997 | NA | 0.4758390 | NA |
| 22 | 51196164 | rs8136603 | A | T | -0.1219520 | 0.0858594 | 0.1554999 | 0.1555004 | NA | 0.1427720 | NA |
| 22 | 51197266 | rs61290853 | A | G | -0.0214279 | 0.0448745 | 0.6330004 | 0.6330009 | NA | 0.4229230 | NA |
| 22 | 51198232 | rs3859893 | C | G | 0.0170538 | 0.0444454 | 0.7012001 | 0.7011991 | NA | 0.2623800 | NA |
| 22 | 51199281 | rs4040021 | T | C | -0.0070246 | 0.0427896 | 0.8695999 | 0.8696001 | NA | 0.4612620 | NA |
bcf preview
1 51803 rs62637812 T C . PASS . ES:SE:LP:ID -0.0182658:0.0722086:0.0967472:rs62637812
1 54676 rs2462492 C T . PASS . ES:SE:LP:ID -0.0150121:0.058626:0.0980515:rs2462492
1 62271 rs28599927 A G . PASS . ES:SE:LP:ID -0.011769:0.0514308:0.0867161:rs28599927
1 66162 rs201684885 A T . PASS . ES:SE:LP:ID -0.0177567:0.0754741:0.0893756:rs201684885
1 74681 rs13328683 G T . PASS . ES:SE:LP:ID 0.0116678:0.0506186:0.087406:rs13328683
1 91536 rs1251109649 G T . PASS . ES:SE:LP:ID -0.00250313:0.0864472:0.0101499:rs1251109649
1 91581 rs1524604 G A . PASS . ES:SE:LP:ID -0.00150113:0.0843424:0.00621119:rs1524604
1 234481 rs8179403 T A . PASS . ES:SE:LP:ID 0.001998:0.0817444:0.0085524:rs8179403
1 662622 rs61769339 G A . PASS . ES:SE:LP:ID 0.00468899:0.0422833:0.040148:rs61769339
1 729679 rs4951859 C G . PASS . ES:SE:LP:ID -0.00747201:0.0438186:0.0631848:rs4951859
1 736523 rs71490526 T C . PASS . ES:SE:LP:ID -0.00608147:0.0496431:0.0445528:rs71490526
1 750055 rs11240771 T C . PASS . ES:SE:LP:ID 0.0988264:0.2019:0.204468:rs11240771
1 750138 rs61770171 G A . PASS . ES:SE:LP:ID 0.0174469:0.0367861:0.197021:rs61770171
1 752566 rs3094315 G A . PASS . ES:SE:LP:ID -0.0192142:0.0364006:0.223589:rs3094315
1 752721 rs3131972 A G . PASS . ES:SE:LP:ID -0.0196065:0.0365637:0.227825:rs3131972
1 752894 rs3131971 T C . PASS . ES:SE:LP:ID -0.0292675:0.0379433:0.356054:rs3131971
1 753474 rs2073814 C G . PASS . ES:SE:LP:ID -0.0242047:0.0364108:0.295678:rs2073814
1 753541 rs1388595942 G A . PASS . ES:SE:LP:ID 0.0179381:0.0366154:0.204676:rs1388595942
1 754182 rs3131969 A G . PASS . ES:SE:LP:ID -0.0180364:0.0365102:0.206699:rs3131969
1 754192 rs3131968 A G . PASS . ES:SE:LP:ID -0.0181346:0.036646:0.207118:rs3131968
1 754334 rs3131967 T C . PASS . ES:SE:LP:ID -0.0255703:0.0370162:0.31007:rs3131967
1 754503 rs3115859 G A . PASS . ES:SE:LP:ID -0.0247902:0.0364135:0.304518:rs3115859
1 754964 rs3131966 C T . PASS . ES:SE:LP:ID -0.0278097:0.0372977:0.34113:rs3131966
1 755775 rs3131965 A G . PASS . ES:SE:LP:ID -0.0199987:0.0373959:0.227092:rs3131965
1 755890 rs1280367067 A T . PASS . ES:SE:LP:ID -0.0224462:0.0361187:0.272215:rs1280367067
1 756380 rs3131963 T A . PASS . ES:SE:LP:ID -0.0160702:0.0442045:0.144966:rs3131963
1 756434 rs61768170 G C . PASS . ES:SE:LP:ID 0.0223484:0.0362002:0.270026:rs61768170
1 756479 rs61768171 C A . PASS . ES:SE:LP:ID 0.0179381:0.0369133:0.202732:rs61768171
1 756604 rs3131962 A G . PASS . ES:SE:LP:ID -0.0200967:0.0365958:0.234406:rs3131962
1 756912 rs6699990 A G . PASS . ES:SE:LP:ID -0.254797:0.306241:0.392116:rs6699990
1 757640 rs3115853 G A . PASS . ES:SE:LP:ID -0.0234235:0.0362958:0.285084:rs3115853
1 757734 rs1557551770 C T . PASS . ES:SE:LP:ID -0.0232281:0.0361572:0.283496:rs1557551770
1 757936 rs1360886751 C A . PASS . ES:SE:LP:ID -0.0233258:0.0362658:0.283913:rs1360886751
1 758144 rs3131956 A G . PASS . ES:SE:LP:ID -0.0116321:0.0347329:0.13212:rs3131956
1 758626 rs3131954 C T . PASS . ES:SE:LP:ID -0.010841:0.0343783:0.123493:rs3131954
1 760912 rs1048488 C T . PASS . ES:SE:LP:ID -0.0164637:0.0356593:0.190912:rs1048488
1 761147 rs3115850 T C . PASS . ES:SE:LP:ID -0.0160702:0.0354697:0.186753:rs3115850
1 761752 rs1057213 C T . PASS . ES:SE:LP:ID -0.0113355:0.0342676:0.130299:rs1057213
1 762273 rs3115849 G A . PASS . ES:SE:LP:ID -0.0116321:0.0345279:0.133004:rs3115849
1 764191 rs7515915 T G . PASS . ES:SE:LP:ID 0.0117309:0.0341218:0.136083:rs7515915
1 766007 rs61768174 A C . PASS . ES:SE:LP:ID 0.0113355:0.0344052:0.129713:rs61768174
1 768253 rs2977608 A C . PASS . ES:SE:LP:ID 0.0455205:0.0343495:0.732594:rs2977608
1 768448 rs12562034 G A . PASS . ES:SE:LP:ID -0.0442083:0.0313504:0.799971:rs12562034
1 769223 rs60320384 C G . PASS . ES:SE:LP:ID 0.0118298:0.0340796:0.13757:rs60320384
1 769963 rs7518545 G A . PASS . ES:SE:LP:ID -0.0457379:0.0326299:0.793174:rs7518545
1 771823 rs2977605 T C . PASS . ES:SE:LP:ID -0.0115332:0.0347818:0.130651:rs2977605
1 771967 rs59066358 G A . PASS . ES:SE:LP:ID 0.0114344:0.0346635:0.129889:rs59066358
1 772755 rs2905039 A C . PASS . ES:SE:LP:ID -0.0115332:0.0347125:0.130944:rs2905039
1 774837 rs1470678699 T A . PASS . ES:SE:LP:ID 0.0148886:0.0381843:0.157017:rs1470678699
1 774874 rs1327209068 A C . PASS . ES:SE:LP:ID -0.0168571:0.0397617:0.172889:rs1327209068