Summary

Summary {data-width=650}

Manhattan plot

manhattan_plot

manhattan_plot

QQ plot

qq_plot

qq_plot

AF plot

af_plot

af_plot

P-Z plot

pz_plot

pz_plot

beta_std plot

beta_std_plot

beta_std_plot

Metadata

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    "FORMAT.1": "<ID=SE,Number=A,Type=Float,Description=\"Standard error of effect size estimate\">",
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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_4935.vcf.gz --id UKB-b:14516 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_4935.txt.gz --cohort_controls 146831 --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",
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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/public/UKB-b-14516/UKB-b-14516_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-14516/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:43:42 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-14516/UKB-b-14516_data.vcf.gz ...
Read summary statistics for 9302418 SNPs.
Dropped 10296 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, 1287945 SNPs remain.
After merging with regression SNP LD, 1287945 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0032 (0.0031)
Lambda GC: 1.0092
Mean Chi^2: 1.0132
Intercept: 1.0036 (0.0063)
Ratio: 0.2752 (0.4808)
Analysis finished at Thu Oct 17 14:45:26 2019
Total time elapsed: 1.0m:43.76s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9487,
    "inflation_factor": 1,
    "mean_EFFECT": -0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 0,
    "n_p_sig": 0,
    "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": 111935,
    "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": 1287945,
    "ldsc_nsnp_merge_regression_ld": 1287945,
    "ldsc_observed_scale_h2_beta": 0.0032,
    "ldsc_observed_scale_h2_se": 0.0031,
    "ldsc_intercept_beta": 1.0036,
    "ldsc_intercept_se": 0.0063,
    "ldsc_lambda_gc": 1.0092,
    "ldsc_mean_chisq": 1.0132,
    "ldsc_ratio": 0.2727
}

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 between AF and AF_reference.
  • inflation_factor (lambda): Genomic inflation factor.
  • mean_EFFECT: Mean of EFFECT size.
  • n: Maximum value of reported sample size across all SNPs, \(n\).
  • n_clumped_hits: Number of clumped hits.
  • n_snps: Number of SNPs
  • n_p_sig: Number of SNPs with pvalue below 5e-8.
  • n_mono: Number of monomorphic (MAF == 1 or MAF == 0) SNPs.
  • n_ns: Number of SNPs with nonsense values:
    • alleles other than A, C, G or T.
    • P-values < 0 or > 1.
    • negative or infinite standard errors (<= 0 or = Infinity).
    • infinite beta estimates or allele frequencies < 0 or > 1.
  • n_mac: Number of cases where MAC (\(2 \times N \times MAF\)) is less than 6.
  • is_snpid_unique: true if the combination of ID REF ALT is unique and therefore no duplication in snpid.
  • n_miss_<*>: Number of NA observations 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 expect ratio_se_n to 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 of diff and the mean of diff2 (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<*>: r2 statistics under various assumptions
    • 1: \(r^2 = \sum_j{\frac{2 \times \beta_j^2 \times {MAF}_j \times (1 - {MAF}_j)}{\texttt{var1}}}\), \(\texttt{var1} = 1\).
    • 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: NOT is_snpid_unique.
  • mean_EFFECT_nonfinite: mean(EFFECT) is NA, NaN, or Inf.
  • mean_EFFECT_05: abs(mean(EFFECT)) > 0.5.
  • mean_EFFECT_01: abs(mean(EFFECT)) > 0.1.
  • mean_chisq: ldsc_mean_chisq > 1.3 or ldsc_mean_chisq < 0.7.
  • n_p_sig: n_p_sig > 1000.
  • miss_<*>: n_miss_<*> / n_snps > 0.01.
  • ldsc_ratio: ldsc_ratio > 0.5
  • ldsc_intercept_beta: ldsc_intercept_beta > 1.5
  • n_clumped_hits: n_clumped_hits > 1000
  • r2_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 9292174 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 9302418 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 8.635250e+00 5.754094e+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 7.881105e+07 5.630811e+07 828.0000000 3.250150e+07 6.939390e+07 1.145417e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -2.090000e-05 3.223300e-03 -0.0389789 -1.174200e-03 -1.360000e-05 1.145400e-03 3.929380e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 2.516500e-03 1.984800e-03 0.0008452 1.016500e-03 1.605100e-03 3.449800e-03 2.918020e-02 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.984611e-01 2.891691e-01 0.0000003 2.500000e-01 5.000000e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.984624e-01 2.891419e-01 0.0000003 2.474741e-01 4.984119e-01 7.488511e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.138521e-01 2.578204e-01 0.0023840 1.753100e-02 9.262600e-02 3.358020e-01 9.976160e-01 ▇▂▁▁▁
numeric AF_reference 111935 0.9879671 NA NA NA NA NA NA NA 2.148452e-01 2.496158e-01 0.0000000 1.477640e-02 1.110220e-01 3.356630e-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 0.0015399 0.0015573 0.3200000 0.3227478 0.623794 0.7821490 NA
1 54676 rs2462492 C T -0.0005899 0.0015479 0.6999999 0.7031388 0.399225 NA NA
1 86028 rs114608975 T C 0.0043878 0.0024658 0.0749998 0.0751611 0.103836 0.0277556 NA
1 91536 rs6702460 G T -0.0003885 0.0015247 0.8000000 0.7988918 0.456163 0.4207270 NA
1 234313 rs8179466 C T -0.0074690 0.0030094 0.0129999 0.0130694 0.074553 NA NA
1 534192 rs6680723 C T 0.0023618 0.0017426 0.1800002 0.1753147 0.241368 NA NA
1 546697 rs12025928 A G -0.0007630 0.0021635 0.7199992 0.7243293 0.913123 NA NA
1 693731 rs12238997 A G -0.0001777 0.0014500 0.9000000 0.9024805 0.117322 0.1417730 NA
1 705882 rs72631875 G A 0.0024497 0.0021240 0.2500000 0.2487828 0.067625 0.0315495 NA
1 706368 rs55727773 A G 0.0019546 0.0010754 0.0690001 0.0691428 0.514747 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0003327 0.0013041 0.8000000 0.7986076 0.136863 0.2052720 NA
22 51219387 rs9616832 T C 0.0011932 0.0016956 0.4799997 0.4816229 0.072640 0.0654952 NA
22 51219704 rs147475742 G A 0.0011326 0.0022645 0.6200004 0.6169678 0.041569 0.0473243 NA
22 51221190 rs369304721 G A 0.0008381 0.0022714 0.7099994 0.7121255 0.048983 NA NA
22 51221731 rs115055839 T C 0.0012955 0.0016969 0.4500005 0.4452060 0.072113 0.0625000 NA
22 51222100 rs114553188 G T -0.0001497 0.0019874 0.9400001 0.9399554 0.054357 0.0880591 NA
22 51223637 rs375798137 G A -0.0002023 0.0019975 0.9199999 0.9193428 0.053969 0.0788738 NA
22 51229805 rs9616985 T C 0.0012996 0.0017032 0.4500005 0.4454489 0.071964 0.0730831 NA
22 51232488 rs376461333 A G -0.0018769 0.0039896 0.6400000 0.6380322 0.020121 NA NA
22 51237063 rs3896457 T C 0.0010053 0.0010343 0.3300000 0.3310549 0.297811 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623794 ES:SE:LP:AF:ID  0.00153992:0.00155732:0.49485:0.623794:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399225 ES:SE:LP:AF:ID  -0.000589892:0.00154792:0.154902:0.399225:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103836 ES:SE:LP:AF:ID  0.00438777:0.00246576:1.12494:0.103836:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456163 ES:SE:LP:AF:ID  -0.000388463:0.00152469:0.09691:0.456163:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074553 ES:SE:LP:AF:ID  -0.00746905:0.00300944:1.88606:0.074553:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241368 ES:SE:LP:AF:ID  0.00236184:0.00174264:0.744727:0.241368:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913123 ES:SE:LP:AF:ID  -0.000763031:0.00216353:0.142668:0.913123:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117322 ES:SE:LP:AF:ID  -0.00017767:0.00145003:0.0457575:0.117322:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067625 ES:SE:LP:AF:ID  0.00244969:0.00212405:0.60206:0.067625:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.514747 ES:SE:LP:AF:ID  0.00195456:0.00107542:1.16115:0.514747:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033429 ES:SE:LP:AF:ID  0.000239647:0.00269371:0.0315171:0.033429:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037123 ES:SE:LP:AF:ID  -0.000420562:0.00244587:0.0655015:0.037123:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037251 ES:SE:LP:AF:ID  -0.000445808:0.00243622:0.0705811:0.037251:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036907 ES:SE:LP:AF:ID  -0.000176515:0.00245475:0.0268721:0.036907:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016411 ES:SE:LP:AF:ID  0.00382472:0.00381894:0.49485:0.016411:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037483 ES:SE:LP:AF:ID  -0.000198725:0.00242683:0.0315171:0.037483:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037583 ES:SE:LP:AF:ID  -1.93495e-05:0.00241875:0.00436481:0.037583:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101402 ES:SE:LP:AF:ID  -0.00197345:0.00177174:0.568636:0.101402:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958507 ES:SE:LP:AF:ID  0.00050277:0.0023353:0.0809219:0.958507:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031683 ES:SE:LP:AF:ID  -0.00204919:0.00425829:0.200659:0.031683:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052738 ES:SE:LP:AF:ID  0.00347776:0.00342867:0.508638:0.052738:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037061 ES:SE:LP:AF:ID  0.000150975:0.00243542:0.0222764:0.037061:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.0374   ES:SE:LP:AF:ID  0.000253222:0.00241331:0.0362122:0.0374:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841783 ES:SE:LP:AF:ID  -0.000194007:0.00125643:0.0555173:0.841783:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056318 ES:SE:LP:AF:ID  -0.00024882:0.002038:0.0457575:0.056318:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123218 ES:SE:LP:AF:ID  -3.79084e-05:0.00137667:0.00877392:0.123218:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025715 ES:SE:LP:AF:ID  0.00441734:0.00339206:0.721246:0.025715:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122434 ES:SE:LP:AF:ID  -1.77683e-05:0.00137733:0.00436481:0.122434:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.133521 ES:SE:LP:AF:ID  5.67101e-05:0.00135572:0.0132283:0.133521:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.01125  ES:SE:LP:AF:ID  -0.00871058:0.00492032:1.11351:0.01125:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005852 ES:SE:LP:AF:ID  0.00426904:0.00628527:0.30103:0.005852:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037319 ES:SE:LP:AF:ID  0.000303214:0.00238933:0.0457575:0.037319:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.8375   ES:SE:LP:AF:ID  -0.000430988:0.00121648:0.142668:0.8375:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.837128 ES:SE:LP:AF:ID  -0.00039561:0.00121522:0.130768:0.837128:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868651 ES:SE:LP:AF:ID  -0.000194532:0.0013035:0.0555173:0.868651:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131036 ES:SE:LP:AF:ID  -1.92152e-05:0.00130626:0.00436481:0.131036:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037816 ES:SE:LP:AF:ID  0.000459993:0.00235:0.0757207:0.037816:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038068 ES:SE:LP:AF:ID  0.00011076:0.00233497:0.0177288:0.038068:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867986 ES:SE:LP:AF:ID  -7.7477e-05:0.00130101:0.0222764:0.867986:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868093 ES:SE:LP:AF:ID  -8.01256e-05:0.00130157:0.0222764:0.868093:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037998 ES:SE:LP:AF:ID  0.000396679:0.00234528:0.0604807:0.037998:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.867988 ES:SE:LP:AF:ID  -0.000108196:0.00130095:0.0315171:0.867988:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005162 ES:SE:LP:AF:ID  0.00373737:0.00668442:0.236572:0.005162:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005132 ES:SE:LP:AF:ID  0.00353857:0.00669919:0.221849:0.005132:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.836609 ES:SE:LP:AF:ID  -0.000435931:0.00121204:0.142668:0.836609:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038004 ES:SE:LP:AF:ID  0.000551171:0.00234863:0.091515:0.038004:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.837238 ES:SE:LP:AF:ID  -0.000468169:0.00121537:0.154902:0.837238:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013315 ES:SE:LP:AF:ID  -0.0029505:0.00434249:0.30103:0.013315:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005441 ES:SE:LP:AF:ID  -0.00588365:0.0066166:0.431798:0.005441:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838426 ES:SE:LP:AF:ID  -0.000294262:0.00123181:0.091515:0.838426:rs3131965