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_1687.vcf.gz --id UKB-b:4650 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_1687.txt.gz --cohort_controls 454718 --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": "2019-09-13T01:34:27.074185",
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"bcftools_viewCommand": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-4650/ukb-b-4650.vcf.gz; Date=Sun May 10 09:55:28 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-4650/UKB-b-4650_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4650/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:44:33 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4650/UKB-b-4650_data.vcf.gz ...
Read summary statistics for 9851866 SNPs.
Dropped 14738 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, 1289166 SNPs remain.
After merging with regression SNP LD, 1289166 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.1333 (0.0068)
Lambda GC: 1.7829
Mean Chi^2: 2.29
Intercept: 1.0866 (0.0124)
Ratio: 0.0671 (0.0096)
Analysis finished at Thu Oct 17 14:46:21 2019
Total time elapsed: 1.0m:47.27sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9499,
"inflation_factor": 1.4921,
"mean_EFFECT": -0,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 223,
"n_p_sig": 28191,
"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": 184849,
"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": 1289166,
"ldsc_nsnp_merge_regression_ld": 1289166,
"ldsc_observed_scale_h2_beta": 0.1333,
"ldsc_observed_scale_h2_se": 0.0068,
"ldsc_intercept_beta": 1.0866,
"ldsc_intercept_se": 0.0124,
"ldsc_lambda_gc": 1.7829,
"ldsc_mean_chisq": 2.29,
"ldsc_ratio": 0.0671
}
Flags
| name | value |
|---|---|
| af_correlation | FALSE |
| inflation_factor | TRUE |
| n | TRUE |
| 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 | 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 | 3 | 58 | 0 | 9837196 | 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 | N | 9851866 | 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 | 8.622825e+00 | 5.748290e+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 | 7.886027e+07 | 5.628334e+07 | 828.0000000 | 3.259061e+07 | 6.948835e+07 | 1.145912e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -3.160000e-05 | 7.209400e-03 | -0.1503420 | -2.573100e-03 | -1.630000e-05 | 2.548100e-03 | 1.774370e-01 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.826500e-03 | 4.570700e-03 | 0.0013498 | 1.653000e-03 | 2.771900e-03 | 6.395800e-03 | 7.088240e-02 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.319545e-01 | 3.062510e-01 | 0.0000000 | 1.499999e-01 | 4.100001e-01 | 6.999999e-01 | 1.000000e+00 | ▇▅▅▅▅ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.319554e-01 | 3.062276e-01 | 0.0000000 | 1.492831e-01 | 4.064603e-01 | 6.967186e-01 | 1.000000e+00 | ▇▅▅▅▅ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.035069e-01 | 2.568641e-01 | 0.0009760 | 1.316800e-02 | 7.790900e-02 | 3.164468e-01 | 9.990180e-01 | ▇▂▁▁▁ |
| numeric | AF_reference | 184849 | 0.9812372 | NA | NA | NA | NA | NA | NA | NA | 2.068392e-01 | 2.482924e-01 | 0.0000000 | 1.198080e-02 | 9.984030e-02 | 3.202880e-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 | -0.0030757 | 0.0024812 | 0.2200002 | 0.2151271 | 0.623726 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0013692 | 0.0024579 | 0.5800000 | 0.5775028 | 0.400461 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0041702 | 0.0039317 | 0.2900000 | 0.2888512 | 0.103548 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0019020 | 0.0024204 | 0.4299995 | 0.4319567 | 0.456916 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0017274 | 0.0047708 | 0.7199992 | 0.7172903 | 0.074517 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0004397 | 0.0027648 | 0.8700001 | 0.8736494 | 0.240993 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | 0.0008168 | 0.0034493 | 0.8100000 | 0.8128067 | 0.913492 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | 0.0016112 | 0.0023165 | 0.4899999 | 0.4867303 | 0.116345 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | 0.0048511 | 0.0033957 | 0.1499999 | 0.1531209 | 0.067277 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0017612 | 0.0017164 | 0.2999998 | 0.3048415 | 0.515660 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219704 | rs147475742 | G | A | -0.0027601 | 0.0036135 | 0.4400003 | 0.4449581 | 0.041915 | 0.0473243 | NA |
| 22 | 51219766 | rs182321900 | C | T | -0.0160211 | 0.0168368 | 0.3400001 | 0.3413240 | 0.001934 | NA | NA |
| 22 | 51220146 | rs868950473 | C | T | -0.0184906 | 0.0166784 | 0.2700001 | 0.2675788 | 0.001983 | NA | NA |
| 22 | 51221190 | rs369304721 | G | A | -0.0049866 | 0.0036063 | 0.1700000 | 0.1667418 | 0.049708 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | -0.0028389 | 0.0026978 | 0.2900000 | 0.2926612 | 0.073192 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | 0.0008046 | 0.0031734 | 0.8000000 | 0.7998530 | 0.054513 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | 0.0007258 | 0.0031888 | 0.8200001 | 0.8199610 | 0.054142 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | -0.0028359 | 0.0027075 | 0.2900000 | 0.2949093 | 0.073032 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | 0.0002286 | 0.0063726 | 0.9699999 | 0.9713842 | 0.020064 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0010070 | 0.0016554 | 0.5400003 | 0.5429877 | 0.297997 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623726 ES:SE:LP:AF:ID -0.00307572:0.00248124:0.657577:0.623726:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400461 ES:SE:LP:AF:ID -0.00136915:0.00245792:0.236572:0.400461:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103548 ES:SE:LP:AF:ID 0.00417016:0.00393172:0.537602:0.103548:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456916 ES:SE:LP:AF:ID -0.00190205:0.00242038:0.366532:0.456916:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074517 ES:SE:LP:AF:ID 0.00172742:0.00477079:0.142668:0.074517:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240993 ES:SE:LP:AF:ID -0.00043967:0.00276479:0.0604807:0.240993:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913492 ES:SE:LP:AF:ID 0.00081682:0.00344931:0.091515:0.913492:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116345 ES:SE:LP:AF:ID 0.00161119:0.00231653:0.309804:0.116345:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067277 ES:SE:LP:AF:ID 0.00485112:0.00339573:0.823909:0.067277:rs72631875
1 706368 rs12029736 A G . PASS AF=0.51566 ES:SE:LP:AF:ID 0.00176125:0.00171644:0.522879:0.51566:rs12029736
1 714596 rs149887893 T C . PASS AF=0.032967 ES:SE:LP:AF:ID -0.000527479:0.00432878:0.0457575:0.032967:rs149887893
1 715265 rs12184267 C T . PASS AF=0.036585 ES:SE:LP:AF:ID 0.000324841:0.00393166:0.0315171:0.036585:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036701 ES:SE:LP:AF:ID 0.000841892:0.00391682:0.0809219:0.036701:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036403 ES:SE:LP:AF:ID 7.46378e-05:0.00394488:0.00877392:0.036403:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016408 ES:SE:LP:AF:ID 0.00855768:0.00607194:0.79588:0.016408:rs144155419
1 720381 rs116801199 G T . PASS AF=0.036939 ES:SE:LP:AF:ID 0.000374549:0.00390136:0.0362122:0.036939:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037034 ES:SE:LP:AF:ID 0.000678694:0.00388811:0.0655015:0.037034:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101199 ES:SE:LP:AF:ID -0.000957134:0.00283177:0.130768:0.101199:rs116030099
1 723891 rs2977670 G C . PASS AF=0.959141 ES:SE:LP:AF:ID 0.00112273:0.00375034:0.119186:0.959141:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031452 ES:SE:LP:AF:ID -0.00212466:0.00680508:0.124939:0.031452:rs12126395
1 725060 rs865924913 A T . PASS AF=0.053271 ES:SE:LP:AF:ID -0.0052605:0.00541069:0.481486:0.053271:rs865924913
1 726794 rs28454925 C G . PASS AF=0.036554 ES:SE:LP:AF:ID 0.00069313:0.00391318:0.0655015:0.036554:rs28454925
1 729632 rs116720794 C T . PASS AF=0.036867 ES:SE:LP:AF:ID -0.000321505:0.00387764:0.0315171:0.036867:rs116720794
1 729679 rs4951859 C G . PASS AF=0.843243 ES:SE:LP:AF:ID -0.00117401:0.00200789:0.251812:0.843243:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055952 ES:SE:LP:AF:ID -0.00230549:0.00324951:0.318759:0.055952:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122308 ES:SE:LP:AF:ID 0.00167613:0.00219767:0.346787:0.122308:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025695 ES:SE:LP:AF:ID -0.010224:0.00540791:1.22915:0.025695:rs369030935
1 734349 rs141242758 T C . PASS AF=0.12155 ES:SE:LP:AF:ID 0.00164763:0.00219858:0.346787:0.12155:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132313 ES:SE:LP:AF:ID 0.000974214:0.00216706:0.187087:0.132313:rs79010578
1 736689 rs181876450 T C . PASS AF=0.011124 ES:SE:LP:AF:ID -0.00662442:0.00788257:0.39794:0.011124:rs181876450
1 740284 rs61770167 C T . PASS AF=0.005686 ES:SE:LP:AF:ID 0.0188928:0.0101834:1.19382:0.005686:rs61770167
1 742813 rs112573343 C T . PASS AF=0.002265 ES:SE:LP:AF:ID -0.0206681:0.0171275:0.638272:0.002265:rs112573343
1 746189 rs139221807 A G . PASS AF=0.001035 ES:SE:LP:AF:ID -0.0276523:0.0278858:0.49485:0.001035:rs139221807
1 752478 rs146277091 G A . PASS AF=0.036782 ES:SE:LP:AF:ID -0.000145191:0.00383837:0.0132283:0.036782:rs146277091
1 752566 rs3094315 G A . PASS AF=0.838968 ES:SE:LP:AF:ID -0.00165764:0.00194442:0.408935:0.838968:rs3094315
1 752721 rs3131972 A G . PASS AF=0.8386 ES:SE:LP:AF:ID -0.00174783:0.00194235:0.431798:0.8386:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869763 ES:SE:LP:AF:ID -0.00144357:0.00208415:0.309804:0.869763:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129879 ES:SE:LP:AF:ID 0.00166708:0.00208846:0.376751:0.129879:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037291 ES:SE:LP:AF:ID -0.000341498:0.00377351:0.0315171:0.037291:rs12184312
1 754105 rs12184325 C T . PASS AF=0.037536 ES:SE:LP:AF:ID -0.000232791:0.00374959:0.0222764:0.037536:rs12184325
1 754182 rs3131969 A G . PASS AF=0.86911 ES:SE:LP:AF:ID -0.00153701:0.00208009:0.337242:0.86911:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869209 ES:SE:LP:AF:ID -0.00157468:0.00208092:0.346787:0.869209:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037494 ES:SE:LP:AF:ID -0.000514232:0.00376579:0.05061:0.037494:rs12184313
1 754334 rs3131967 T C . PASS AF=0.869112 ES:SE:LP:AF:ID -0.0015506:0.00208004:0.337242:0.869112:rs3131967
1 754433 rs150578204 G A . PASS AF=0.005134 ES:SE:LP:AF:ID -0.00618939:0.0106664:0.251812:0.005134:rs150578204
1 754458 rs142682604 G T . PASS AF=0.0051 ES:SE:LP:AF:ID -0.00606633:0.0106945:0.244125:0.0051:rs142682604
1 754503 rs3115859 G A . PASS AF=0.83805 ES:SE:LP:AF:ID -0.00165855:0.00193696:0.408935:0.83805:rs3115859
1 754629 rs10454459 A G . PASS AF=0.037507 ES:SE:LP:AF:ID -0.000738479:0.0037711:0.0757207:0.037507:rs10454459
1 754964 rs3131966 C T . PASS AF=0.838681 ES:SE:LP:AF:ID -0.0016902:0.0019424:0.420216:0.838681:rs3131966
1 755240 rs181660517 T G . PASS AF=0.013783 ES:SE:LP:AF:ID -0.00185643:0.00677763:0.107905:0.013783:rs181660517