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 /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-780/ieu-a-780.vcf.gz \
--ref-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-780/ldsc.txt \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/
Beginning analysis at Wed Feb 5 11:36:46 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-780/ieu-a-780.vcf.gz ...
Read summary statistics for 119687 SNPs.
Reading reference panel LD Score from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/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 /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/[1-22] ...
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 53315 SNPs remain.
After merging with regression SNP LD, 53315 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: 1.2999 (0.4634)
Lambda GC: 1.5767
Mean Chi^2: 3.1264
Intercept: 1.2689 (0.0539)
Ratio: 0.1265 (0.0253)
Analysis finished at Wed Feb 5 11:36:55 2020
Total time elapsed: 8.89sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9366,
"inflation_factor": 1.405,
"mean_EFFECT": 0.0002,
"n": 92859.8,
"n_snps": 119687,
"n_clumped_hits": 42,
"n_p_sig": 1613,
"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": 30,
"n_miss_AF_reference": 760,
"n_est": 90739.0116,
"ratio_se_n": 0.9885,
"mean_diff": -0,
"ratio_diff": 16.3961,
"sd_y_est1": 1.0609,
"sd_y_est2": 1.0487,
"r2_sum1": 0.0623,
"r2_sum2": 0.0553,
"r2_sum3": 0.0566,
"r2_sum4": 0.0589,
"ldsc_nsnp_merge_refpanel_ld": 53315,
"ldsc_nsnp_merge_regression_ld": 53315,
"ldsc_observed_scale_h2_beta": 1.2999,
"ldsc_observed_scale_h2_se": 0.4634,
"ldsc_intercept_beta": 1.2689,
"ldsc_intercept_se": 0.0539,
"ldsc_lambda_gc": 1.5767,
"ldsc_mean_chisq": 3.1264,
"ldsc_ratio": 0.1265
}
Flags
| name | value |
|---|---|
| af_correlation | FALSE |
| 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 | 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 | numeric.mean | numeric.sd | numeric.p0 | numeric.p25 | numeric.p50 | numeric.p75 | numeric.p100 | numeric.hist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| character | ID | 0 | 1.0000000 | 5 | 23 | 0 | 119687 | 0 | 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 |
| character | ALT | 0 | 1.0000000 | 1 | 1 | 0 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA | NA |
| numeric | CHROM | 0 | 1.0000000 | NA | NA | NA | NA | NA | 8.657950e+00 | 5.546209e+00 | 1.0000e+00 | 4.000000e+00 | 8.000000e+00 | 1.200000e+01 | 2.200000e+01 | ▇▆▆▃▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | 7.372507e+07 | 5.531398e+07 | 4.0483e+04 | 2.794585e+07 | 6.160932e+07 | 1.108098e+08 | 2.491546e+08 | ▇▅▃▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | 2.352000e-04 | 2.095100e-02 | -5.3080e-01 | -6.400000e-03 | 2.000000e-04 | 6.800000e-03 | 3.134000e-01 | ▁▁▁▇▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | 1.053450e-02 | 1.153350e-02 | 4.8000e-03 | 5.300000e-03 | 6.700000e-03 | 1.080000e-02 | 2.333000e-01 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | 4.419682e-01 | 3.086670e-01 | 0.0000e+00 | 1.574001e-01 | 4.240001e-01 | 7.121000e-01 | 9.999000e-01 | ▇▅▅▅▅ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | 4.233108e-01 | 3.113564e-01 | 0.0000e+00 | 1.311879e-01 | 3.936919e-01 | 6.930371e-01 | 1.000000e+00 | ▇▅▅▅▅ |
| numeric | AF | 30 | 0.9997493 | NA | NA | NA | NA | NA | 2.824370e-01 | 2.629376e-01 | 1.3190e-03 | 6.332000e-02 | 1.939000e-01 | 4.446000e-01 | 9.986810e-01 | ▇▃▂▂▁ |
| numeric | AF_reference | 760 | 0.9936501 | NA | NA | NA | NA | NA | 2.859455e-01 | 2.549693e-01 | 1.9970e-04 | 7.887380e-02 | 2.038740e-01 | 4.388980e-01 | 1.000000e+00 | ▇▃▂▂▁ |
| numeric | N | 0 | 1.0000000 | NA | NA | NA | NA | NA | 8.646054e+04 | 1.282965e+04 | 2.0004e+04 | 8.610630e+04 | 9.260690e+04 | 9.275910e+04 | 9.285980e+04 | ▁▁▁▁▇ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 721290 | rs12565286 | G | C | 0.0472 | 0.0250 | 0.0739997 | 0.0590260 | 0.04354 | 0.0371406 | 33029.6 |
| 1 | 1140435 | rs1815606 | G | T | 0.0080 | 0.0053 | 0.1627002 | 0.1311879 | 0.31270 | 0.7120610 | 84436.0 |
| 1 | 1706136 | rs6603811 | T | C | 0.0120 | 0.0101 | 0.2099999 | 0.2347866 | 0.93668 | 0.7466050 | 92098.8 |
| 1 | 1706160 | rs7531583 | A | G | -0.0169 | 0.0059 | 0.0064840 | 0.0041779 | 0.75730 | 0.5832670 | 86770.1 |
| 1 | 1708801 | rs12044597 | A | G | -0.0203 | 0.0049 | 0.0000899 | 0.0000343 | 0.51060 | 0.3602240 | 86751.0 |
| 1 | 1723031 | rs9660180 | G | A | -0.0194 | 0.0050 | 0.0002388 | 0.0001045 | 0.51320 | 0.3580270 | 83925.1 |
| 1 | 1812688 | rs6603803 | A | G | -0.0184 | 0.0049 | 0.0003863 | 0.0001733 | 0.50790 | 0.3606230 | 86371.0 |
| 1 | 1823922 | rs6688000 | A | G | 0.0170 | 0.0107 | 0.1315001 | 0.1121089 | 0.94195 | 0.7651760 | 92103.3 |
| 1 | 1874326 | rs2803328 | C | G | -0.0216 | 0.0052 | 0.0000730 | 0.0000327 | 0.50000 | 0.3835860 | 78906.9 |
| 1 | 2035379 | rs10910029 | A | G | 0.0020 | 0.0050 | 0.6782003 | 0.6891565 | 0.62270 | 0.5315500 | 92088.0 |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 50661869 | rs5771242 | T | C | 0.0141 | 0.0056 | 0.0168000 | 0.0118071 | 0.76780 | 0.8512380 | 92100.0 |
| 22 | 50672097 | rs17248301 | T | C | -0.0004 | 0.0180 | 0.7781996 | 0.9822707 | 0.01583 | 0.0517173 | 80022.2 |
| 22 | 50858813 | rs7410608 | G | C | 0.0000 | 0.0050 | 0.9967000 | 1.0000000 | 0.34960 | 0.4916130 | 91949.1 |
| 22 | 50873497 | rs1134848 | C | T | 0.0247 | 0.0099 | 0.0134899 | 0.0125975 | 0.13980 | 0.0529153 | 52898.6 |
| 22 | 50873694 | rs2273259 | T | C | 0.0009 | 0.0053 | 0.8877000 | 0.8651585 | 0.33910 | 0.4802320 | 80371.0 |
| 22 | 50878927 | rs12171249 | G | A | 0.0062 | 0.0058 | 0.2880998 | 0.2850852 | 0.28760 | 0.2062700 | 82984.0 |
| 22 | 51062832 | rs8142033 | G | A | 0.0020 | 0.0096 | 0.9425999 | 0.8349687 | 0.08575 | 0.1773160 | 92091.2 |
| 22 | 51163138 | rs715586 | C | T | 0.0041 | 0.0071 | 0.7161995 | 0.5636255 | 0.12140 | 0.0902556 | 89822.0 |
| 22 | 51165664 | rs8137951 | G | A | 0.0062 | 0.0052 | 0.2704002 | 0.2331406 | 0.28100 | 0.4063500 | 92112.0 |
| 22 | 51178090 | rs2285395 | G | A | -0.0081 | 0.0109 | 0.5535999 | 0.4574095 | 0.05145 | 0.0666933 | 85455.4 |
bcf preview
1 721290 rs12565286 G C . PASS AF=0.04354 ES:SE:LP:AF:SS:ID 0.0472:0.025:1.13077:0.04354:33029.6:rs12565286
1 1140435 rs1815606 G T . PASS AF=0.3127 ES:SE:LP:AF:SS:ID 0.008:0.0053:0.788612:0.3127:84436:rs1815606
1 1706136 rs6603811 T C . PASS AF=0.93668 ES:SE:LP:AF:SS:ID 0.012:0.0101:0.677781:0.93668:92098.8:rs6603811
1 1706160 rs7531583 A G . PASS AF=0.7573 ES:SE:LP:AF:SS:ID -0.0169:0.0059:2.18816:0.7573:86770.1:rs7531583
1 1708801 rs12044597 A G . PASS AF=0.5106 ES:SE:LP:AF:SS:ID -0.0203:0.0049:4.04648:0.5106:86751:rs12044597
1 1723031 rs9660180 G A . PASS AF=0.5132 ES:SE:LP:AF:SS:ID -0.0194:0.005:3.62197:0.5132:83925.1:rs9660180
1 1812688 rs6603803 A G . PASS AF=0.5079 ES:SE:LP:AF:SS:ID -0.0184:0.0049:3.41308:0.5079:86371:rs6603803
1 1823922 rs6688000 A G . PASS AF=0.94195 ES:SE:LP:AF:SS:ID 0.017:0.0107:0.881074:0.94195:92103.3:rs6688000
1 1874326 rs2803328 C G . PASS AF=0.5 ES:SE:LP:AF:SS:ID -0.0216:0.0052:4.13668:0.5:78906.9:rs2803328
1 2035379 rs10910029 A G . PASS AF=0.6227 ES:SE:LP:AF:SS:ID 0.002:0.005:0.168642:0.6227:92088:rs10910029
1 2069172 rs425277 C T . PASS AF=0.2836 ES:SE:LP:AF:SS:ID 0.0038:0.0054:0.269864:0.2836:92169:rs425277
1 2119833 rs2460002 A G . PASS AF=0.6755 ES:SE:LP:AF:SS:ID -0.0045:0.0051:0.391367:0.6755:92145.9:rs2460002
1 2142518 rs380190 A C . PASS AF=0.2863 ES:SE:LP:AF:SS:ID 0.0051:0.0057:0.333576:0.2863:78909.9:rs380190
1 2182342 rs10797416 T C . PASS AF=0.3654 ES:SE:LP:AF:SS:ID 0.004:0.005:0.30557:0.3654:92162:rs10797416
1 2184855 rs12385717 G C . PASS AF=0.06464 ES:SE:LP:AF:SS:ID 0.0129:0.0102:0.581367:0.06464:92030.1:rs12385717
1 2204755 rs7553178 G A . PASS AF=0.6596 ES:SE:LP:AF:SS:ID -0.0071:0.0052:0.700711:0.6596:92105.1:rs7553178
1 2251357 rs12080256 C A . PASS AF=0.2177 ES:SE:LP:AF:SS:ID -0.0207:0.0069:2.32486:0.2177:72384.9:rs12080256
1 2280661 rs2055204 G A . PASS AF=0.5158 ES:SE:LP:AF:SS:ID 0.011:0.0049:1.46559:0.5158:92054:rs2055204
1 2390331 rs10910077 G A . PASS AF=0.2916 ES:SE:LP:AF:SS:ID 0.0001:0.0053:0.00489154:0.2916:89083:rs10910077
1 2409383 rs12742193 G A . PASS AF=0.1253 ES:SE:LP:AF:SS:ID -0.0063:0.0103:0.25516:0.1253:46460.7:rs12742193
1 2420913 rs6668720 A G . PASS AF=0.6359 ES:SE:LP:AF:SS:ID 0.0028:0.005:0.214741:0.6359:91968:rs6668720
1 2541727 rs11589185 C T . PASS AF=0.07916 ES:SE:LP:AF:SS:ID 0.0196:0.0089:1.44382:0.07916:79918.3:rs11589185
1 2723345 rs4648360 C T . PASS AF=0.4288 ES:SE:LP:AF:SS:ID 0.0091:0.0049:1.11031:0.4288:92118.9:rs4648360
1 2725952 rs7538063 C T . PASS AF=0.1715 ES:SE:LP:AF:SS:ID -0.0164:0.0066:1.77832:0.1715:87102:rs7538063
1 2727804 rs10909880 C T . PASS AF=0.4274 ES:SE:LP:AF:SS:ID 0.0094:0.006:0.837436:0.4274:62155.9:rs10909880
1 2829551 rs1869970 A G . PASS AF=0.2322 ES:SE:LP:AF:SS:ID -0.0043:0.0057:0.315514:0.2322:92081.9:rs1869970
1 2839757 rs4648432 A C . PASS AF=0.2691 ES:SE:LP:AF:SS:ID 0.0075:0.0054:0.67469:0.2691:92023:rs4648432
1 2842270 rs2045331 G A . PASS AF=0.2691 ES:SE:LP:AF:SS:ID 0.0075:0.0054:0.681937:0.2691:92078.9:rs2045331
1 2890345 rs4648445 G A . PASS AF=0.7111 ES:SE:LP:AF:SS:ID 0.0077:0.0051:0.84649:0.7111:90893:rs4648445
1 2904056 rs10797373 G A . PASS AF=0.7058 ES:SE:LP:AF:SS:ID 0.0144:0.0064:1.46623:0.7058:63106:rs10797373
1 2996196 rs7525173 C G . PASS AF=0.4974 ES:SE:LP:AF:SS:ID -0.0067:0.0052:0.612966:0.4974:77372.9:rs7525173
1 3089849 rs17390062 T C . PASS AF=0.1135 ES:SE:LP:AF:SS:ID 0.0065:0.0083:0.22614:0.1135:92332.3:rs17390062
1 3103826 rs4233024 C T . PASS AF=0.8522 ES:SE:LP:AF:SS:ID 0.0032:0.0085:0.215454:0.8522:63236.1:rs4233024
1 3105276 rs2817138 T G . PASS AF=0.8588 ES:SE:LP:AF:SS:ID 0.002:0.0072:0.148925:0.8588:92228.9:rs2817138
1 3117678 rs12095716 G C . PASS AF=0.2256 ES:SE:LP:AF:SS:ID -0.0041:0.006:0.282662:0.2256:92087:rs12095716
1 3138136 rs2817148 A G . PASS AF=0.02375 ES:SE:LP:AF:SS:ID -0.0291:0.016:1.32331:0.02375:77046.1:rs2817148
1 3144068 rs10158583 G A . PASS AF=0.08971 ES:SE:LP:AF:SS:ID 0.0046:0.0095:0.291749:0.08971:79866.5:rs10158583
1 3209631 rs4648376 G A . PASS AF=0.562 ES:SE:LP:AF:SS:ID -0.0074:0.0049:0.791559:0.562:92298:rs4648376
1 3214170 rs7553849 T C . PASS AF=0.7625 ES:SE:LP:AF:SS:ID 0.0022:0.0058:0.128427:0.7625:92307:rs7553849
1 3229185 rs12743017 G A . PASS AF=0.07652 ES:SE:LP:AF:SS:ID 0.0111:0.0089:0.590913:0.07652:92303.3:rs12743017
1 3264190 rs2483289 A C . PASS AF=0.7098 ES:SE:LP:AF:SS:ID -0.0018:0.0052:0.159455:0.7098:92298:rs2483289
1 3491107 rs4431782 T C . PASS AF=0.0752 ES:SE:LP:AF:SS:ID 0.005:0.0086:0.323489:0.0752:85727.2:rs4431782
1 3564818 rs3765690 A T . PASS AF=0.04222 ES:SE:LP:AF:SS:ID -0.0004:0.0114:0.0571985:0.04222:88619:rs3765690
1 3654747 rs7515164 C A . PASS AF=0.06464 ES:SE:LP:AF:SS:ID 0.0101:0.0101:0.459045:0.06464:92265.1:rs7515164
1 3793324 rs6668103 A G . PASS AF=0.2876 ES:SE:LP:AF:SS:ID 0.0043:0.0063:0.304343:0.2876:69498:rs6668103
1 3812610 rs12757243 G A . PASS AF=0.009235 ES:SE:LP:AF:SS:ID 0.0053:0.023:0.00353211:0.009235:84514.8:rs12757243
1 4010342 rs12046333 A G . PASS AF=0.06992 ES:SE:LP:AF:SS:ID 0.0222:0.0104:1.36241:0.06992:79406.3:rs12046333
1 4039896 rs11587331 C T . PASS AF=0.5897 ES:SE:LP:AF:SS:ID 0.0034:0.0049:0.269622:0.5897:92307:rs11587331
1 4080885 rs4378174 C T . PASS AF=0.1583 ES:SE:LP:AF:SS:ID -0.0038:0.007:0.264321:0.1583:92253.3:rs4378174
1 4085543 rs12026862 T C . PASS AF=0.01451 ES:SE:LP:AF:SS:ID 0.0151:0.0166:0.203564:0.01451:72381.7:rs12026862