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/d9d317f8-4d99-4e8e-87ff-a7c078fa2c1e/call-vcf/inputs/268670776/upload.txt.gz --id ieu-b-116 --json /data/cromwell-executions/qc/d9d317f8-4d99-4e8e-87ff-a7c078fa2c1e/call-vcf/inputs/268670776/ieu-b-116_data.json --ref /data/cromwell-executions/qc/d9d317f8-4d99-4e8e-87ff-a7c078fa2c1e/call-vcf/inputs/1899004205/human_g1k_v37.fasta --dbsnp /data/cromwell-executions/qc/d9d317f8-4d99-4e8e-87ff-a7c078fa2c1e/call-vcf/inputs/-307190728/dbsnp.v153.b37.vcf.gz --out /data/igd/ieu-b-116/ieu-b-116.vcf.gz --alias alias.txt; 1.3.0",
"file_date": "2021-04-01T15:28:09.476818",
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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/d9d317f8-4d99-4e8e-87ff-a7c078fa2c1e/call-ldsc/inputs/268670776/ieu-b-116.vcf.gz \
--ref-ld-chr /data/ref/eur_w_ld_chr/ \
--out /data/igd/ieu-b-116/ldsc.txt \
--w-ld-chr /data/ref/eur_w_ld_chr/
Beginning analysis at Thu Apr 1 15:28:48 2021
Reading summary statistics from /data/cromwell-executions/qc/d9d317f8-4d99-4e8e-87ff-a7c078fa2c1e/call-ldsc/inputs/268670776/ieu-b-116.vcf.gz ...
Read summary statistics for 64421 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.
After merging with reference panel LD, 41416 SNPs remain.
After merging with regression SNP LD, 41416 SNPs remain.
WARNING: number of SNPs less than 200k; this is almost always bad.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.207 (0.0292)
Lambda GC: 1.2855
Mean Chi^2: 1.4905
Intercept: 1.0836 (0.0295)
Ratio: 0.1704 (0.0601)
Analysis finished at Thu Apr 1 15:28:55 2021
Total time elapsed: 7.46sQC metrics
Metrics
Metrics
{
"af_correlation": "NA",
"inflation_factor": 1.2901,
"mean_EFFECT": -0,
"n": 108557,
"n_snps": 64421,
"n_clumped_hits": 14,
"n_p_sig": 73,
"n_mono": 0,
"n_ns": 0,
"n_mac": 0,
"is_snpid_unique": true,
"n_miss_EFFECT": 0,
"n_miss_SE": 0,
"n_miss_PVAL": 0,
"n_miss_AF": 64421,
"n_miss_AF_reference": 515,
"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": 41416,
"ldsc_nsnp_merge_regression_ld": 41416,
"ldsc_observed_scale_h2_beta": 0.207,
"ldsc_observed_scale_h2_se": 0.0292,
"ldsc_intercept_beta": 1.0836,
"ldsc_intercept_se": 0.0295,
"ldsc_lambda_gc": 1.2855,
"ldsc_mean_chisq": 1.4905,
"ldsc_ratio": 0.1704
}
Flags
| name | value |
|---|---|
| af_correlation | NA |
| inflation_factor | TRUE |
| n | FALSE |
| is_snpid_non_unique | FALSE |
| mean_EFFECT_nonfinite | FALSE |
| mean_EFFECT_05 | FALSE |
| mean_EFFECT_01 | FALSE |
| mean_chisq | TRUE |
| 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 | 0 | 1.0000000 | 5 | 33 | 0 | 64421 | 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 | 64421 | 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 | 8.820012e+00 | 5.794708e+00 | 1.00000e+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 | 7.722352e+07 | 5.590941e+07 | 1.30340e+04 | 3.159054e+07 | 6.644051e+07 | 1.137371e+08 | 2.491546e+08 | ▇▆▃▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -2.860000e-05 | 5.002400e-03 | -1.30000e-01 | -2.300000e-03 | -2.200000e-05 | 2.300000e-03 | 1.500000e-01 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.576300e-03 | 2.741800e-03 | 2.00000e-03 | 2.200000e-03 | 2.700000e-03 | 3.800000e-03 | 5.500000e-02 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.570792e-01 | 3.016161e-01 | 0.00000e+00 | 1.829641e-01 | 4.436107e-01 | 7.184888e-01 | 9.999780e-01 | ▇▆▆▅▅ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.570669e-01 | 3.016654e-01 | 0.00000e+00 | 1.824224e-01 | 4.444467e-01 | 7.188471e-01 | 1.000000e+00 | ▇▆▆▆▅ |
| numeric | AF_reference | 515 | 0.9920057 | NA | NA | NA | NA | NA | NA | NA | 3.393791e-01 | 2.596465e-01 | 1.99700e-04 | 1.236020e-01 | 2.690695e-01 | 5.185700e-01 | 9.996010e-01 | ▇▅▃▂▂ |
| numeric | N | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.085570e+05 | 0.000000e+00 | 1.08557e+05 | 1.085570e+05 | 1.085570e+05 | 1.085570e+05 | 1.085570e+05 | ▁▁▇▁▁ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 785050 | rs2905062 | G | A | -0.00150 | 0.0043 | 0.7320690 | 0.7272115 | NA | 0.626997 | 108557 |
| 1 | 1140435 | rs1815606 | G | T | 0.00085 | 0.0025 | 0.7303987 | 0.7338565 | NA | 0.712061 | 108557 |
| 1 | 1706136 | rs6603811 | T | C | -0.00450 | 0.0049 | 0.3600427 | 0.3584266 | NA | 0.746605 | 108557 |
| 1 | 1706160 | rs7531583 | A | G | -0.00860 | 0.0025 | 0.0004520 | 0.0005817 | NA | 0.583267 | 108557 |
| 1 | 1708801 | rs12044597 | A | G | -0.00510 | 0.0021 | 0.0133869 | 0.0151584 | NA | 0.360224 | 108557 |
| 1 | 1723031 | rs9660180 | G | A | -0.00570 | 0.0022 | 0.0092210 | 0.0095723 | NA | 0.358027 | 108557 |
| 1 | 1812688 | rs6603803 | A | G | -0.00520 | 0.0020 | 0.0109590 | 0.0093224 | NA | 0.360623 | 108557 |
| 1 | 1886519 | rs2748975 | C | A | -0.01300 | 0.0042 | 0.0030250 | 0.0019665 | NA | 0.607428 | 108557 |
| 1 | 1892325 | rs2803291 | T | C | -0.00380 | 0.0030 | 0.2064310 | 0.2052745 | NA | 0.835863 | 108557 |
| 1 | 2035379 | rs10910029 | A | G | -0.00098 | 0.0022 | 0.6505849 | 0.6559913 | NA | 0.531550 | 108557 |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 50878927 | rs12171249 | G | A | 0.00110 | 0.0024 | 0.6354963 | 0.6467130 | NA | 0.2062700 | 108557 |
| 22 | 50960682 | rs140524 | C | T | -0.00300 | 0.0029 | 0.3046162 | 0.3009105 | NA | 0.2302320 | 108557 |
| 22 | 50971266 | rs140522 | T | C | 0.00300 | 0.0023 | 0.1823509 | 0.1921150 | NA | 0.6190100 | 108557 |
| 22 | 51015674 | rs3213446 | C | T | 0.00140 | 0.0045 | 0.7609007 | 0.7557162 | NA | 0.1859030 | 108557 |
| 22 | 51018911 | rs2269382 | C | T | -0.00037 | 0.0047 | 0.9376991 | 0.9372526 | NA | 0.1222040 | 108557 |
| 22 | 51057923 | rs131718 | C | T | 0.00150 | 0.0021 | 0.4641539 | 0.4750505 | NA | 0.5517170 | 108557 |
| 22 | 51062832 | rs8142033 | G | A | 0.00300 | 0.0041 | 0.4535164 | 0.4643472 | NA | 0.1773160 | 108557 |
| 22 | 51163138 | rs715586 | C | T | 0.00550 | 0.0032 | 0.0884158 | 0.0856599 | NA | 0.0902556 | 108557 |
| 22 | 51165664 | rs8137951 | G | A | 0.00300 | 0.0023 | 0.1876031 | 0.1921150 | NA | 0.4063500 | 108557 |
| 22 | 51178090 | rs2285395 | G | A | 0.00560 | 0.0046 | 0.2267003 | 0.2234554 | NA | 0.0666933 | 108557 |
bcf preview
1 785050 rs2905062 G A . PASS . ES:SE:LP:SS:ID -0.0015:0.0043:0.135448:108557:rs2905062
1 1140435 rs1815606 G T . PASS . ES:SE:LP:SS:ID 0.00085:0.0025:0.13644:108557:rs1815606
1 1706136 rs6603811 T C . PASS . ES:SE:LP:SS:ID -0.0045:0.0049:0.443646:108557:rs6603811
1 1706160 rs7531583 A G . PASS . ES:SE:LP:SS:ID -0.0086:0.0025:3.34486:108557:rs7531583
1 1708801 rs12044597 A G . PASS . ES:SE:LP:SS:ID -0.0051:0.0021:1.87332:108557:rs12044597
1 1723031 rs9660180 G A . PASS . ES:SE:LP:SS:ID -0.0057:0.0022:2.03522:108557:rs9660180
1 1812688 rs6603803 A G . PASS . ES:SE:LP:SS:ID -0.0052:0.002:1.96023:108557:rs6603803
1 1886519 rs1558021383 C A . PASS . ES:SE:LP:SS:ID -0.013:0.0042:2.51927:108557:rs1558021383
1 1892325 rs2803291 T C . PASS . ES:SE:LP:SS:ID -0.0038:0.003:0.685225:108557:rs2803291
1 2035379 rs10910029 A G . PASS . ES:SE:LP:SS:ID -0.00098:0.0022:0.186696:108557:rs10910029
1 2069172 rs425277 C T . PASS . ES:SE:LP:SS:ID 0.0016:0.0022:0.312303:108557:rs425277
1 2119833 rs2460002 A G . PASS . ES:SE:LP:SS:ID 0.006:0.0023:2.10896:108557:rs2460002
1 2142518 rs380190 A C . PASS . ES:SE:LP:SS:ID 0.0014:0.0025:0.244121:108557:rs380190
1 2180524 rs260513 G A . PASS . ES:SE:LP:SS:ID 0.0012:0.0021:0.251529:108557:rs260513
1 2182342 rs10797416 T C . PASS . ES:SE:LP:SS:ID 0.0012:0.0021:0.244211:108557:rs10797416
1 2184855 rs12385717 G C . PASS . ES:SE:LP:SS:ID 0.0051:0.0045:0.595515:108557:rs12385717
1 2204755 rs7553178 G A . PASS . ES:SE:LP:SS:ID 0.0035:0.0023:0.915024:108557:rs7553178
1 2224645 rs796896153 A G . PASS . ES:SE:LP:SS:ID 0.0016:0.003:0.221728:108557:rs796896153
1 2231595 rs1809822 A C . PASS . ES:SE:LP:SS:ID 0.0054:0.0055:0.485378:108557:rs1809822
1 2251357 rs12080256 C A . PASS . ES:SE:LP:SS:ID -0.00069:0.0028:0.0932395:108557:rs12080256
1 2280661 rs2055204 G A . PASS . ES:SE:LP:SS:ID -0.00092:0.002:0.18697:108557:rs2055204
1 2291680 rs2840542 T C . PASS . ES:SE:LP:SS:ID -0.00076:0.0026:0.113934:108557:rs2840542
1 2390331 rs10910077 G A . PASS . ES:SE:LP:SS:ID 0.0015:0.0025:0.257863:108557:rs10910077
1 2409383 rs12742193 G A . PASS . ES:SE:LP:SS:ID 0.0017:0.0037:0.185448:108557:rs12742193
1 2420913 rs6668720 A G . PASS . ES:SE:LP:SS:ID -0.00076:0.0023:0.128576:108557:rs6668720
1 2490898 rs1553129604 C A . PASS . ES:SE:LP:SS:ID -0.0027:0.0021:0.680798:108557:rs1553129604
1 2490942 rs2281852 C A . PASS . ES:SE:LP:SS:ID -0.0017:0.0021:0.402705:108557:rs2281852
1 2541727 rs11589185 C T . PASS . ES:SE:LP:SS:ID 0.0023:0.0053:0.179172:108557:rs11589185
1 2723345 rs4648360 C T . PASS . ES:SE:LP:SS:ID 0.00031:0.002:0.0566371:108557:rs4648360
1 2725952 rs1192117797 C T . PASS . ES:SE:LP:SS:ID -0.0035:0.0029:0.646353:108557:rs1192117797
1 2727804 rs10909880 C T . PASS . ES:SE:LP:SS:ID 0.00091:0.0022:0.167939:108557:rs10909880
1 2829551 rs1869970 A G . PASS . ES:SE:LP:SS:ID 0.0035:0.0025:0.80112:108557:rs1869970
1 2839757 rs1557731784 A C . PASS . ES:SE:LP:SS:ID -0.00038:0.0023:0.0614578:108557:rs1557731784
1 2842270 rs2045331 G A . PASS . ES:SE:LP:SS:ID -0.00019:0.0023:0.0304182:108557:rs2045331
1 2890345 rs4648445 G A . PASS . ES:SE:LP:SS:ID -0.00077:0.0024:0.124172:108557:rs4648445
1 2904056 rs10797373 G A . PASS . ES:SE:LP:SS:ID 1.5e-05:0.0025:0.00207043:108557:rs10797373
1 2976816 rs1138513 T C . PASS . ES:SE:LP:SS:ID -0.0017:0.004:0.175287:108557:rs1138513
1 2996196 rs7525173 C G . PASS . ES:SE:LP:SS:ID -0.0044:0.0022:1.31873:108557:rs7525173
1 3037613 rs941540736 C T . PASS . ES:SE:LP:SS:ID -0.00094:0.0022:0.173713:108557:rs941540736
1 3074306 rs2651902 C T . PASS . ES:SE:LP:SS:ID -0.0023:0.0028:0.381129:108557:rs2651902
1 3089849 rs17390062 T C . PASS . ES:SE:LP:SS:ID -0.0026:0.0036:0.326223:108557:rs17390062
1 3103826 rs4233024 C T . PASS . ES:SE:LP:SS:ID 0.0034:0.0034:0.506025:108557:rs4233024
1 3105276 rs2817138 T G . PASS . ES:SE:LP:SS:ID 0.003:0.0032:0.451769:108557:rs2817138
1 3110735 rs2651912 T C . PASS . ES:SE:LP:SS:ID 0.002:0.0032:0.27974:108557:rs2651912
1 3117678 rs12095716 G C . PASS . ES:SE:LP:SS:ID -0.003:0.0031:0.475656:108557:rs12095716
1 3138136 rs2817148 A G . PASS . ES:SE:LP:SS:ID 0.0044:0.0072:0.26607:108557:rs2817148
1 3144068 rs10158583 G A . PASS . ES:SE:LP:SS:ID -0.0022:0.0047:0.194915:108557:rs10158583
1 3144745 rs1553288 C T . PASS . ES:SE:LP:SS:ID -0.0018:0.0048:0.147508:108557:rs1553288
1 3197747 rs10492940 C A . PASS . ES:SE:LP:SS:ID 0.002:0.0031:0.282351:108557:rs10492940
1 3209631 rs4648376 G A . PASS . ES:SE:LP:SS:ID -0.0025:0.0021:0.647848:108557:rs4648376