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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"gwas_harmonisation_command": "--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/vcf_09_19b/bgzip_vcf/data.batch_5208.vcf.gz --id UKB-b:4535 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_5208.txt.gz --cohort_controls 100983 --ref /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/human_g1k_v37.fasta --json /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/ukb_gwas.json; 1.1.1",
"file_date": "2019-09-12T23:16:13.815639",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-4535/ukb-b-4535.vcf.gz; Date=Sat May 9 14:25:04 2020"
}
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/public/UKB-b-4535/UKB-b-4535_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4535/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:44:26 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4535/UKB-b-4535_data.vcf.gz ...
Read summary statistics for 9035029 SNPs.
Dropped 8866 SNPs with duplicated rs numbers.
Reading reference panel LD Score from ../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 ../reference/eur_w_ld_chr/[1-22] ...
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 1287310 SNPs remain.
After merging with regression SNP LD, 1287310 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.034 (0.0051)
Lambda GC: 1.0771
Mean Chi^2: 1.0808
Intercept: 1.0142 (0.006)
Ratio: 0.1753 (0.074)
Analysis finished at Thu Oct 17 14:46:06 2019
Total time elapsed: 1.0m:39.48sQC metrics
Metrics
Metrics
{
"af_correlation": 0.948,
"inflation_factor": 1.0475,
"mean_EFFECT": 0,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 2,
"n_p_sig": 167,
"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": 0,
"n_miss_AF_reference": 94588,
"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": 1287310,
"ldsc_nsnp_merge_regression_ld": 1287310,
"ldsc_observed_scale_h2_beta": 0.034,
"ldsc_observed_scale_h2_se": 0.0051,
"ldsc_intercept_beta": 1.0142,
"ldsc_intercept_se": 0.006,
"ldsc_lambda_gc": 1.0771,
"ldsc_mean_chisq": 1.0808,
"ldsc_ratio": 0.1757
}
Flags
| name | value |
|---|---|
| af_correlation | FALSE |
| inflation_factor | FALSE |
| n | TRUE |
| is_snpid_non_unique | FALSE |
| 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 | 0 | 1.000000 | 3 | 58 | 0 | 9026204 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | REF | 0 | 1.000000 | 1 | 1 | 0 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | ALT | 0 | 1.000000 | 1 | 1 | 0 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | N | 9035029 | 0.000000 | NA | NA | NA | NA | NA | NaN | : | NA | NA | NA | NA | NA | NA | NA | NA |
| numeric | CHROM | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 8.643024e+00 | 5.757961e+00 | 1.0000000 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.200000e+01 | ▇▅▃▂▂ |
| numeric | POS | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 7.878462e+07 | 5.633536e+07 | 828.0000000 | 3.243388e+07 | 6.934929e+07 | 1.145317e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 1.470000e-05 | 1.489420e-02 | -0.1721550 | -5.814600e-03 | 3.350000e-05 | 5.864800e-03 | 1.995330e-01 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 1.187600e-02 | 8.753400e-03 | 0.0042872 | 5.115200e-03 | 7.854300e-03 | 1.629470e-02 | 9.946820e-02 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 4.915581e-01 | 2.910936e-01 | 0.0000000 | 2.399999e-01 | 4.899999e-01 | 7.400005e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 4.915568e-01 | 2.910680e-01 | 0.0000000 | 2.375701e-01 | 4.888821e-01 | 7.436667e-01 | 9.999996e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 2.196087e-01 | 2.584668e-01 | 0.0034660 | 2.021200e-02 | 1.007010e-01 | 3.462280e-01 | 9.965340e-01 | ▇▂▁▁▁ |
| numeric | AF_reference | 94588 | 0.989531 | NA | NA | NA | NA | NA | NA | NA | 2.199718e-01 | 2.503651e-01 | 0.0000000 | 1.757190e-02 | 1.182110e-01 | 3.446490e-01 | 1.000000e+00 | ▇▂▁▁▁ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 49298 | rs200943160 | T | C | 0.0059403 | 0.0079197 | 0.4500005 | 0.4532100 | 0.623789 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0006599 | 0.0078666 | 0.9299999 | 0.9331487 | 0.398815 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | -0.0012142 | 0.0124836 | 0.9199999 | 0.9225158 | 0.104017 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0048036 | 0.0077374 | 0.5300002 | 0.5347157 | 0.455694 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | -0.0011698 | 0.0151534 | 0.9400001 | 0.9384646 | 0.074783 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0052983 | 0.0088444 | 0.5500004 | 0.5491326 | 0.240593 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | 0.0263006 | 0.0109633 | 0.0160000 | 0.0164414 | 0.912740 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0008120 | 0.0073570 | 0.9100000 | 0.9121188 | 0.117745 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | -0.0235337 | 0.0107553 | 0.0290001 | 0.0286621 | 0.067789 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0023008 | 0.0054545 | 0.6700003 | 0.6731646 | 0.514405 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219006 | rs28729663 | G | A | -0.0012770 | 0.0065995 | 0.8499999 | 0.8465716 | 0.137175 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | 0.0063206 | 0.0085932 | 0.4600002 | 0.4620150 | 0.072723 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | 0.0100846 | 0.0114323 | 0.3800004 | 0.3777148 | 0.041873 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | 0.0004557 | 0.0115056 | 0.9699999 | 0.9684069 | 0.049131 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | 0.0060126 | 0.0085960 | 0.4799997 | 0.4842610 | 0.072249 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | -0.0087853 | 0.0100681 | 0.3800004 | 0.3828879 | 0.054415 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | -0.0087722 | 0.0101211 | 0.3900004 | 0.3860933 | 0.054020 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0069052 | 0.0086272 | 0.4199997 | 0.4234816 | 0.072116 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | -0.0053880 | 0.0203901 | 0.7899998 | 0.7915901 | 0.019980 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0008039 | 0.0052471 | 0.8800001 | 0.8782336 | 0.298181 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623789 ES:SE:LP:AF:ID 0.00594034:0.00791967:0.346787:0.623789:rs10399793
1 54676 rs2462492 C T . PASS AF=0.398815 ES:SE:LP:AF:ID -0.000659877:0.00786655:0.0315171:0.398815:rs2462492
1 86028 rs114608975 T C . PASS AF=0.104017 ES:SE:LP:AF:ID -0.00121422:0.0124836:0.0362122:0.104017:rs114608975
1 91536 rs6702460 G T . PASS AF=0.455694 ES:SE:LP:AF:ID -0.00480356:0.0077374:0.275724:0.455694:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074783 ES:SE:LP:AF:ID -0.00116984:0.0151534:0.0268721:0.074783:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240593 ES:SE:LP:AF:ID 0.00529833:0.0088444:0.259637:0.240593:rs6680723
1 546697 rs12025928 A G . PASS AF=0.91274 ES:SE:LP:AF:ID 0.0263006:0.0109633:1.79588:0.91274:rs12025928
1 693731 rs12238997 A G . PASS AF=0.117745 ES:SE:LP:AF:ID -0.000811968:0.00735702:0.0409586:0.117745:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067789 ES:SE:LP:AF:ID -0.0235337:0.0107553:1.5376:0.067789:rs72631875
1 706368 rs12029736 A G . PASS AF=0.514405 ES:SE:LP:AF:ID 0.00230076:0.00545451:0.173925:0.514405:rs12029736
1 714596 rs149887893 T C . PASS AF=0.033688 ES:SE:LP:AF:ID -0.00406367:0.0136032:0.113509:0.033688:rs149887893
1 715265 rs12184267 C T . PASS AF=0.037345 ES:SE:LP:AF:ID -0.00398457:0.0123737:0.124939:0.037345:rs12184267
1 715367 rs12184277 A G . PASS AF=0.037425 ES:SE:LP:AF:ID -0.00330929:0.0123344:0.102373:0.037425:rs12184277
1 717485 rs12184279 C A . PASS AF=0.03711 ES:SE:LP:AF:ID -0.00488251:0.0124211:0.161151:0.03711:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016675 ES:SE:LP:AF:ID 0.0279285:0.0191944:0.823909:0.016675:rs144155419
1 720381 rs116801199 G T . PASS AF=0.037686 ES:SE:LP:AF:ID -0.00359647:0.0122808:0.113509:0.037686:rs116801199
1 721290 rs12565286 G C . PASS AF=0.03777 ES:SE:LP:AF:ID -0.00439443:0.0122431:0.142668:0.03777:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101223 ES:SE:LP:AF:ID -0.00269672:0.00902593:0.113509:0.101223:rs116030099
1 723891 rs2977670 G C . PASS AF=0.958024 ES:SE:LP:AF:ID 0.00519543:0.0117772:0.180456:0.958024:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031836 ES:SE:LP:AF:ID -0.0488021:0.0215348:1.63827:0.031836:rs12126395
1 725060 rs865924913 A T . PASS AF=0.052555 ES:SE:LP:AF:ID 0.00492305:0.0174211:0.107905:0.052555:rs865924913
1 726794 rs28454925 C G . PASS AF=0.037236 ES:SE:LP:AF:ID -0.00267005:0.0123286:0.0809219:0.037236:rs28454925
1 729632 rs116720794 C T . PASS AF=0.037557 ES:SE:LP:AF:ID -0.00108254:0.012223:0.0315171:0.037557:rs116720794
1 729679 rs4951859 C G . PASS AF=0.840864 ES:SE:LP:AF:ID -0.00109053:0.00637149:0.0655015:0.840864:rs4951859
1 730087 rs148120343 T C . PASS AF=0.056197 ES:SE:LP:AF:ID -0.0133853:0.0103676:0.69897:0.056197:rs148120343
1 731718 rs58276399 T C . PASS AF=0.123726 ES:SE:LP:AF:ID 6.24892e-05:0.00698338:0.00436481:0.123726:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025879 ES:SE:LP:AF:ID 0.0340246:0.017148:1.3279:0.025879:rs369030935
1 734349 rs141242758 T C . PASS AF=0.122922 ES:SE:LP:AF:ID -0.000360076:0.00698734:0.0177288:0.122922:rs141242758
1 736289 rs79010578 T A . PASS AF=0.13374 ES:SE:LP:AF:ID -0.00110637:0.00688599:0.0604807:0.13374:rs79010578
1 736689 rs181876450 T C . PASS AF=0.011213 ES:SE:LP:AF:ID -0.00642828:0.024973:0.09691:0.011213:rs181876450
1 740284 rs61770167 C T . PASS AF=0.005981 ES:SE:LP:AF:ID 0.0643363:0.0314777:1.38722:0.005981:rs61770167
1 752478 rs146277091 G A . PASS AF=0.037508 ES:SE:LP:AF:ID 0.000310261:0.0120918:0.00877392:0.037508:rs146277091
1 752566 rs3094315 G A . PASS AF=0.836664 ES:SE:LP:AF:ID -0.000184577:0.00616531:0.00877392:0.836664:rs3094315
1 752721 rs3131972 A G . PASS AF=0.836215 ES:SE:LP:AF:ID -0.000243068:0.00615801:0.0132283:0.836215:rs3131972
1 753405 rs3115860 C A . PASS AF=0.867985 ES:SE:LP:AF:ID 0.00104018:0.00660629:0.0604807:0.867985:rs3115860
1 753541 rs2073813 G A . PASS AF=0.131706 ES:SE:LP:AF:ID -0.000282367:0.00662003:0.0132283:0.131706:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037948 ES:SE:LP:AF:ID -0.00208141:0.0119024:0.0655015:0.037948:rs12184312
1 754105 rs12184325 C T . PASS AF=0.038195 ES:SE:LP:AF:ID -0.0025349:0.011829:0.0809219:0.038195:rs12184325
1 754182 rs3131969 A G . PASS AF=0.867267 ES:SE:LP:AF:ID 0.00112756:0.0065928:0.0655015:0.867267:rs3131969
1 754192 rs3131968 A G . PASS AF=0.86737 ES:SE:LP:AF:ID 0.00143237:0.00659588:0.0809219:0.86737:rs3131968
1 754211 rs12184313 G A . PASS AF=0.038158 ES:SE:LP:AF:ID -0.00213896:0.0118746:0.0655015:0.038158:rs12184313
1 754334 rs3131967 T C . PASS AF=0.867265 ES:SE:LP:AF:ID 0.00119338:0.0065924:0.0655015:0.867265:rs3131967
1 754433 rs150578204 G A . PASS AF=0.005078 ES:SE:LP:AF:ID -0.0166449:0.0342032:0.200659:0.005078:rs150578204
1 754458 rs142682604 G T . PASS AF=0.005046 ES:SE:LP:AF:ID -0.0166253:0.0342982:0.200659:0.005046:rs142682604
1 754503 rs3115859 G A . PASS AF=0.835764 ES:SE:LP:AF:ID -4.11421e-05:0.00614504:0.00436481:0.835764:rs3115859
1 754629 rs10454459 A G . PASS AF=0.038174 ES:SE:LP:AF:ID -0.00244999:0.0118908:0.0757207:0.038174:rs10454459
1 754964 rs3131966 C T . PASS AF=0.836369 ES:SE:LP:AF:ID -0.000454625:0.00616137:0.0268721:0.836369:rs3131966
1 755240 rs181660517 T G . PASS AF=0.013184 ES:SE:LP:AF:ID 0.00727193:0.0221009:0.130768:0.013184:rs181660517
1 755435 rs184270342 T G . PASS AF=0.005522 ES:SE:LP:AF:ID 0.0257641:0.0333497:0.356547:0.005522:rs184270342
1 755775 rs3131965 A G . PASS AF=0.837677 ES:SE:LP:AF:ID -1.95248e-05:0.00624568:-0:0.837677:rs3131965