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/04dec220-ab7c-4ff6-a049-276132db855e/call-vcf/inputs/-261044410/upload.txt.gz --id ieu-b-4859 --json /data/cromwell-executions/qc/04dec220-ab7c-4ff6-a049-276132db855e/call-vcf/inputs/-261044410/ieu-b-4859_data.json --ref /data/cromwell-executions/qc/04dec220-ab7c-4ff6-a049-276132db855e/call-vcf/inputs/1899004205/human_g1k_v37.fasta --dbsnp /data/cromwell-executions/qc/04dec220-ab7c-4ff6-a049-276132db855e/call-vcf/inputs/-307190728/dbsnp.v153.b37.vcf.gz --out /data/igd/ieu-b-4859/ieu-b-4859.vcf.gz --alias alias.txt; 1.3.0",
"file_date": "2022-01-05T19:20:24.773826",
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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/04dec220-ab7c-4ff6-a049-276132db855e/call-ldsc/inputs/-261044410/ieu-b-4859.vcf.gz \
--ref-ld-chr /data/ref/eur_w_ld_chr/ \
--out /data/igd/ieu-b-4859/ldsc.txt \
--w-ld-chr /data/ref/eur_w_ld_chr/
Beginning analysis at Wed Jan 5 19:33:08 2022
Reading summary statistics from /data/cromwell-executions/qc/04dec220-ab7c-4ff6-a049-276132db855e/call-ldsc/inputs/-261044410/ieu-b-4859.vcf.gz ...
Read summary statistics for 7986977 SNPs.
Dropped 39184 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, 1202971 SNPs remain.
After merging with regression SNP LD, 1202971 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0007 (0.0184)
Lambda GC: 1.0122
Mean Chi^2: 1.0092
Intercept: 1.0088 (0.0062)
Ratio: 0.9617 (0.6731)
Analysis finished at Wed Jan 5 19:34:51 2022
Total time elapsed: 1.0m:43.55sQC metrics
Metrics
Metrics
{
"af_correlation": "NA",
"inflation_factor": 1.0112,
"mean_EFFECT": -0.0001,
"n": 24264,
"n_snps": 7987053,
"n_clumped_hits": 0,
"n_p_sig": 0,
"n_mono": 0,
"n_ns": 285767,
"n_mac": 0,
"is_snpid_unique": false,
"n_miss_EFFECT": 0,
"n_miss_SE": 0,
"n_miss_PVAL": 0,
"n_miss_AF": 7987053,
"n_miss_AF_reference": 166434,
"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": 1202971,
"ldsc_nsnp_merge_regression_ld": 1202971,
"ldsc_observed_scale_h2_beta": 0.0007,
"ldsc_observed_scale_h2_se": 0.0184,
"ldsc_intercept_beta": 1.0088,
"ldsc_intercept_se": 0.0062,
"ldsc_lambda_gc": 1.0122,
"ldsc_mean_chisq": 1.0092,
"ldsc_ratio": 0.9565
}
Flags
| name | value |
|---|---|
| af_correlation | NA |
| inflation_factor | FALSE |
| n | FALSE |
| 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 | TRUE |
| 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 | 26 | 0.9999967 | 3 | 58 | 0 | 7986884 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | REF | 0 | 1.0000000 | 1 | 43 | 0 | 16651 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | ALT | 0 | 1.0000000 | 1 | 44 | 0 | 6034 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | AF | 7987053 | 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 | 8.646238e+00 | 5.754043e+00 | 1.0000e+00 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.200000e+01 | ▇▅▅▂▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.884892e+07 | 5.635741e+07 | 3.0200e+02 | 3.246384e+07 | 6.941758e+07 | 1.146302e+08 | 2.492223e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -7.430000e-05 | 9.125000e-03 | -9.0100e-02 | -4.500000e-03 | 0.000000e+00 | 4.400000e-03 | 9.520000e-02 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.894700e-03 | 4.445500e-03 | 3.5000e-03 | 4.500000e-03 | 5.900000e-03 | 1.010000e-02 | 2.550000e-02 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.982891e-01 | 2.888598e-01 | 1.0000e-07 | 2.478997e-01 | 4.975996e-01 | 7.480007e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.982997e-01 | 2.888911e-01 | 1.0000e-07 | 2.478630e-01 | 4.970464e-01 | 7.486740e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | AF_reference | 166434 | 0.9791620 | NA | NA | NA | NA | NA | NA | NA | 2.642209e-01 | 2.522116e-01 | 0.0000e+00 | 5.670930e-02 | 1.783150e-01 | 4.125400e-01 | 1.000000e+00 | ▇▃▂▁▁ |
| numeric | N | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.285987e+04 | 1.983384e+03 | 1.3594e+04 | 2.295600e+04 | 2.386700e+04 | 2.426400e+04 | 2.426400e+04 | ▁▁▂▁▇ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 797440 | rs58013264 | T | C | -0.0184 | 0.0066 | 0.0056930 | 0.0053054 | NA | 0.189497 | 20919 |
| 1 | 798400 | rs10900604 | A | G | -0.0106 | 0.0054 | 0.0507598 | 0.0496505 | NA | 0.410543 | 20919 |
| 1 | 798959 | rs11240777 | G | A | -0.0102 | 0.0052 | 0.0485099 | 0.0498162 | NA | 0.409944 | 21618 |
| 1 | 801467 | rs61768212 | G | C | -0.0186 | 0.0066 | 0.0049440 | 0.0048296 | NA | 0.193091 | 20919 |
| 1 | 804759 | rs7526310 | C | T | -0.0190 | 0.0066 | 0.0042250 | 0.0039921 | NA | 0.193890 | 20919 |
| 1 | 808223 | rs4951933 | G | C | 0.0125 | 0.0056 | 0.0250801 | 0.0256055 | NA | 0.452077 | 19081 |
| 1 | 808631 | rs11240779 | G | A | 0.0119 | 0.0052 | 0.0226898 | 0.0221107 | NA | 0.453474 | 20919 |
| 1 | 808928 | rs11240780 | C | T | 0.0118 | 0.0052 | 0.0241502 | 0.0232543 | NA | 0.452276 | 20919 |
| 1 | 845274 | rs112856858 | G | T | 0.0006 | 0.0058 | 0.9173999 | 0.9176072 | NA | 0.374002 | 19081 |
| 1 | 845635 | rs117086422 | C | T | 0.0017 | 0.0054 | 0.7498994 | 0.7529023 | NA | 0.158546 | 21618 |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51218377 | rs2519461 | G | C | -0.0136 | 0.0092 | 0.1391000 | 0.1393380 | NA | 0.0826677 | 15631 |
| 22 | 51219006 | rs28729663 | G | A | -0.0169 | 0.0073 | 0.0208200 | 0.0206092 | NA | 0.2052720 | 14932 |
| 22 | 51219387 | rs9616832 | T | C | -0.0140 | 0.0092 | 0.1278000 | 0.1280745 | NA | 0.0654952 | 15631 |
| 22 | 51219704 | rs147475742 | G | A | -0.0283 | 0.0126 | 0.0243002 | 0.0247020 | NA | 0.0473243 | 14932 |
| 22 | 51221190 | rs369304721 | G | A | -0.0270 | 0.0112 | 0.0163799 | 0.0159213 | NA | NA | 14932 |
| 22 | 51221731 | rs115055839 | T | C | -0.0144 | 0.0092 | 0.1198001 | 0.1175319 | NA | 0.0625000 | 15631 |
| 22 | 51222100 | rs114553188 | G | T | -0.0071 | 0.0106 | 0.5002004 | 0.5029781 | NA | 0.0880591 | 15631 |
| 22 | 51223637 | rs375798137 | G | A | -0.0077 | 0.0106 | 0.4681004 | 0.4675843 | NA | 0.0788738 | 15631 |
| 22 | 51229805 | rs9616985 | T | C | -0.0139 | 0.0092 | 0.1320000 | 0.1308217 | NA | 0.0730831 | 15631 |
| 22 | 51237063 | rs3896457 | T | C | -0.0019 | 0.0055 | 0.7350993 | 0.7297527 | NA | 0.2050720 | 14932 |
bcf preview
1 797440 rs58013264 T C . PASS . ES:SE:LP:SS:ID -0.0184:0.0066:2.24466:20919:rs58013264
1 798400 rs10900604 A G . PASS . ES:SE:LP:SS:ID -0.0106:0.0054:1.29448:20919:rs10900604
1 798959 rs11240777 G A . PASS . ES:SE:LP:SS:ID -0.0102:0.0052:1.31417:21618:rs11240777
1 801467 rs61768212 G C . PASS . ES:SE:LP:SS:ID -0.0186:0.0066:2.30592:20919:rs61768212
1 804759 rs7526310 C T . PASS . ES:SE:LP:SS:ID -0.019:0.0066:2.37417:20919:rs7526310
1 808223 rs1557576983 G C . PASS . ES:SE:LP:SS:ID 0.0125:0.0056:1.60067:19081:rs1557576983
1 808631 rs11240779 G A . PASS . ES:SE:LP:SS:ID 0.0119:0.0052:1.64417:20919:rs11240779
1 808928 rs1247187939 C T . PASS . ES:SE:LP:SS:ID 0.0118:0.0052:1.61708:20919:rs1247187939
1 845274 rs1324283754 G T . PASS . ES:SE:LP:SS:ID 0.0006:0.0058:0.0374413:19081:rs1324283754
1 845635 rs117086422 C T . PASS . ES:SE:LP:SS:ID 0.0017:0.0054:0.124997:21618:rs117086422
1 845938 rs57760052 G A . PASS . ES:SE:LP:SS:ID 0.0013:0.0053:0.090765:21618:rs57760052
1 846078 rs778265812 C T . PASS . ES:SE:LP:SS:ID 0.0019:0.0055:0.133654:21618:rs778265812
1 846398 rs58781670 G A . PASS . ES:SE:LP:SS:ID 0.0006:0.0054:0.0376307:21618:rs58781670
1 846465 rs60454217 C T . PASS . ES:SE:LP:SS:ID -0.0059:0.017:0.136499:20919:rs60454217
1 846543 rs79396034 G T . PASS . ES:SE:LP:SS:ID 0.0018:0.0178:0.037536:20919:rs79396034
1 846808 rs4475691 C T . PASS . ES:SE:LP:SS:ID 0.0017:0.0055:0.11702:21618:rs4475691
1 846864 rs1269142199 G C . PASS . ES:SE:LP:SS:ID 0.0021:0.0055:0.15484:21618:rs1269142199
1 847228 rs3905286 C T . PASS . ES:SE:LP:SS:ID 0.0018:0.0054:0.129421:21618:rs3905286
1 847491 rs1158719307 G A . PASS . ES:SE:LP:SS:ID 0.0034:0.0053:0.277202:21618:rs1158719307
1 848090 rs4246505 G A . PASS . ES:SE:LP:SS:ID 0.0039:0.0053:0.3347:21618:rs4246505
1 848445 rs1156895099 G A . PASS . ES:SE:LP:SS:ID 0.005:0.0054:0.452471:21618:rs1156895099
1 848456 rs11507767 A G . PASS . ES:SE:LP:SS:ID 0.005:0.0054:0.458795:21618:rs11507767
1 848738 rs3829741 C T . PASS . ES:SE:LP:SS:ID 0.0039:0.0053:0.332174:21618:rs3829741
1 850062 rs28723578 A T . PASS . ES:SE:LP:SS:ID 0.0029:0.0053:0.233215:21618:rs28723578
1 850123 rs28622257 C T . PASS . ES:SE:LP:SS:ID 0.0036:0.0053:0.305746:21618:rs28622257
1 851190 rs28609852 G A . PASS . ES:SE:LP:SS:ID 0.0029:0.0053:0.227605:21618:rs28609852
1 851204 rs28552953 G C . PASS . ES:SE:LP:SS:ID 0.0024:0.0053:0.185087:21618:rs28552953
1 852664 rs28605311 C T . PASS . ES:SE:LP:SS:ID 0.0043:0.0054:0.368962:21618:rs28605311
1 852758 rs4970462 G C . PASS . ES:SE:LP:SS:ID 0.0042:0.0054:0.359419:21618:rs4970462
1 853239 rs4970380 A G . PASS . ES:SE:LP:SS:ID 0.0041:0.0054:0.35125:21618:rs4970380
1 853805 rs3748591 C T . PASS . ES:SE:LP:SS:ID -0.0001:0.0153:0.00139197:20919:rs3748591
1 854250 rs7537756 A G . PASS . ES:SE:LP:SS:ID 0.0032:0.0054:0.262489:21618:rs7537756
1 854429 rs72902552 G A . PASS . ES:SE:LP:SS:ID 0:0.0153:0.000391041:20919:rs72902552
1 856436 rs34105146 TG T . PASS . ES:SE:LP:SS:ID 0.0039:0.0059:0.293538:19081:rs34105146
1 856476 rs4040605 A G . PASS . ES:SE:LP:SS:ID 0.0059:0.0046:0.684449:20919:rs4040605
1 857177 rs386627408 T C . PASS . ES:SE:LP:SS:ID -0.0054:0.015:0.142245:20919:rs386627408
1 858040 rs4970460 C A . PASS . ES:SE:LP:SS:ID 0.0041:0.0055:0.343136:20919:rs4970460
1 858051 rs4970459 C T . PASS . ES:SE:LP:SS:ID 0.0041:0.0055:0.344381:20919:rs4970459
1 858801 rs7418179 A G . PASS . ES:SE:LP:SS:ID -0.0035:0.0053:0.28929:20919:rs7418179
1 859404 rs71509444 C G . PASS . ES:SE:LP:SS:ID 0.001:0.0165:0.0223678:19081:rs71509444
1 859685 rs111572704 G A . PASS . ES:SE:LP:SS:ID 0.0013:0.0057:0.0858685:19081:rs111572704
1 859690 rs71509445 C G . PASS . ES:SE:LP:SS:ID -0.006:0.0175:0.136142:19081:rs71509445
1 859701 rs71509446 C G . PASS . ES:SE:LP:SS:ID -0.006:0.0175:0.136142:19081:rs71509446
1 859913 rs1187056171 A G . PASS . ES:SE:LP:SS:ID 0.0016:0.0164:0.0360234:20919:rs1187056171
1 860416 rs61464428 G A . PASS . ES:SE:LP:SS:ID -0.0039:0.0053:0.333295:20919:rs61464428
1 860461 rs57465118 G A . PASS . ES:SE:LP:SS:ID 0.0011:0.0146:0.026595:20919:rs57465118
1 860521 rs57924093 C A . PASS . ES:SE:LP:SS:ID 0.0011:0.0146:0.026595:20919:rs57924093
1 860688 rs60837925 G A . PASS . ES:SE:LP:SS:ID -0.0037:0.0053:0.316593:20919:rs60837925
1 860854 rs57816555 T C . PASS . ES:SE:LP:SS:ID -0.0045:0.0192:0.0886826:19081:rs57816555
1 861008 rs28521172 G C . PASS . ES:SE:LP:SS:ID 0.0011:0.0146:0.026595:20919:rs28521172