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 /data/cromwell-executions/qc/1d909277-5569-4c99-bf55-62e6e0f0ff68/call-ldsc/inputs/562856347/ieu-b-85.vcf.gz \
--ref-ld-chr /data/ref/eur_w_ld_chr/ \
--out /data/igd/ieu-b-85/ldsc.txt \
--w-ld-chr /data/ref/eur_w_ld_chr/ 

Beginning analysis at Wed Dec  2 20:28:20 2020
Reading summary statistics from /data/cromwell-executions/qc/1d909277-5569-4c99-bf55-62e6e0f0ff68/call-ldsc/inputs/562856347/ieu-b-85.vcf.gz ...
Traceback (most recent call last):
  File "./ldsc/ldsc.py", line 647, in <module>
    sumstats.estimate_h2(args, log)
  File "/ldsc/ldscore/sumstats.py", line 330, in estimate_h2
    args, log, args.h2)
  File "/ldsc/ldscore/sumstats.py", line 246, in _read_ld_sumstats
    sumstats = _read_sumstats(args, log, fh, alleles=alleles, dropna=dropna)
  File "/ldsc/ldscore/sumstats.py", line 167, in _read_sumstats
    sumstats = ps.sumstats(fh, alleles=alleles, dropna=dropna)
  File "/ldsc/ldscore/parse.py", line 97, in sumstats
    x = read_vcf(fh, alleles, slh)
  File "/ldsc/ldscore/parse.py", line 152, in read_vcf
    o = [[rec.id, rec.samples[sample]['ES'][0]/rec.samples[sample]['SE'][0]] for rec in vcf_in.fetch()]
ZeroDivisionError: float division by zero

Analysis finished at Wed Dec  2 20:28:20 2020
Total time elapsed: 0.46s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9645,
    "inflation_factor": 1.1407,
    "mean_EFFECT": 0.0322,
    "n": "-Inf",
    "n_snps": 20345125,
    "n_clumped_hits": 138,
    "n_p_sig": 20586,
    "n_mono": 493237,
    "n_ns": 1859469,
    "n_mac": 0,
    "is_snpid_unique": false,
    "n_miss_EFFECT": 0,
    "n_miss_SE": 0,
    "n_miss_PVAL": 0,
    "n_miss_AF": 0,
    "n_miss_AF_reference": 1014129,
    "n_est": "NA",
    "ratio_se_n": "NA",
    "mean_diff": "NaN",
    "ratio_diff": "NaN",
    "sd_y_est1": "NA",
    "sd_y_est2": "NA",
    "r2_sum1": 0,
    "r2_sum2": 0,
    "r2_sum3": 0,
    "r2_sum4": 0,
    "ldsc_nsnp_merge_refpanel_ld": "NA",
    "ldsc_nsnp_merge_regression_ld": "NA",
    "ldsc_observed_scale_h2_beta": "NA",
    "ldsc_observed_scale_h2_se": "NA",
    "ldsc_intercept_beta": "NA",
    "ldsc_intercept_se": "NA",
    "ldsc_lambda_gc": "NA",
    "ldsc_mean_chisq": "NA",
    "ldsc_ratio": "NA"
}

Flags

name value
af_correlation FALSE
inflation_factor FALSE
n TRUE
is_snpid_non_unique TRUE
mean_EFFECT_nonfinite FALSE
mean_EFFECT_05 FALSE
mean_EFFECT_01 FALSE
mean_chisq NA
n_p_sig TRUE
miss_EFFECT FALSE
miss_SE FALSE
miss_PVAL FALSE
ldsc_ratio NA
ldsc_intercept_beta NA
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 623 0.9999694 3 83 0 20216467 0 NA NA NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 103 0 79316 0 NA NA NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 662 0 47605 0 NA NA NA NA NA NA NA NA NA NA
logical N 20345125 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 9.140618e+00 6.231939e+00 1.000 4.000000e+00 8.000000e+00 1.400000e+01 2.300000e+01 ▇▅▅▂▂
numeric POS 0 1.0000000 NA NA NA NA NA NA NA 7.866279e+07 5.613253e+07 52.000 3.245028e+07 6.948664e+07 1.147475e+08 2.492405e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 3.224910e-02 1.602496e+00 -20.407 -2.710000e-02 0.000000e+00 2.770000e-02 2.041850e+01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 3.199034e-01 1.171082e+00 0.000 1.230000e-02 4.650000e-02 1.789000e-01 3.520550e+01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.787770e-01 2.943525e-01 0.000 2.197000e-01 4.712995e-01 7.334998e-01 1.000000e+00 ▇▇▇▆▆
numeric PVAL_ztest 18 0.9999991 NA NA NA NA NA NA NA 4.787771e-01 2.943557e-01 0.000 2.197201e-01 4.712602e-01 7.334318e-01 1.000000e+00 ▇▇▇▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 1.576294e-01 2.644071e-01 0.000 1.400000e-03 1.260000e-02 1.971000e-01 1.000000e+00 ▇▁▁▁▁
numeric AF_reference 1014129 0.9501537 NA NA NA NA NA NA NA 1.346296e-01 2.235728e-01 0.000 1.397800e-03 1.397760e-02 1.699280e-01 1.000000e+00 ▇▁▁▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 10177 rs1264289758 AC A -0.0211 0.0136 0.1217001 0.1207890 0.6104 NA NA
1 10352 rs1557426776 TA T -0.0094 0.0128 0.4627996 0.4627202 0.5753 NA NA
1 10616 rs1557426951 C CCGCCGTTGCAAAGGCGCGCCG 0.0572 0.0696 0.4107003 0.4111685 0.0066 NA NA
1 10642 rs558604819 G A 0.5592 0.3514 0.1115000 0.1115311 0.0004 0.0041933 NA
1 11008 rs575272151 C G 0.0204 0.0196 0.2978997 0.2979608 0.0942 0.0880591 NA
1 11012 rs544419019 C G 0.0204 0.0196 0.2978997 0.2979608 0.0942 0.0880591 NA
1 13110 rs540538026 G A -0.0377 0.0332 0.2564998 0.2561482 0.0589 0.0267572 NA
1 13116 rs62635286 T G 0.0035 0.0178 0.8447000 0.8441177 0.1745 0.0970447 NA
1 13118 rs200579949 A G 0.0035 0.0178 0.8447000 0.8441177 0.1745 0.0970447 NA
1 13273 rs531730856 G C -0.0090 0.0201 0.6535999 0.6543256 0.1399 0.0950479 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154927199 rs645904 C T -0.0106 0.0066 0.1070999 0.1082606 0.2426 0.3674170 NA
23 154927201 rs778081536 C T -0.1261 0.0908 0.1650000 0.1649038 0.0036 0.0068874 NA
23 154927581 rs644138 G A -0.0083 0.0063 0.1914000 0.1876844 0.2906 0.4635760 NA
23 154928151 rs144607509 C T -0.0170 0.0463 0.7137005 0.7134917 0.0223 0.0084768 NA
23 154928909 rs148015245 C T -2.1415 1.5664 0.1715999 0.1715790 0.0035 0.0007947 NA
23 154929412 rs557132 C T -0.0109 0.0066 0.0968100 0.0986334 0.2419 0.3568210 NA
23 154929637 rs35185538 C CT 0.0053 0.0072 0.4611997 0.4616631 0.7917 NA NA
23 154929952 rs4012982 C CAA 0.0123 0.0101 0.2247000 0.2232917 0.8061 NA NA
23 154930046 rs757303759 T A 0.1556 0.1282 0.2251003 0.2248514 0.0010 0.0023841 NA
23 154930230 rs781880 A G -0.0108 0.0066 0.1021001 0.1017635 0.2417 0.3618540 NA

bcf preview

1   10177   rs1264289758    AC  A   .   PASS    AF=0.6104   ES:SE:LP:AF:ID  -0.0211:0.0136:0.914709:0.6104:rs1264289758
1   10352   rs1557426776    TA  T   .   PASS    AF=0.5753   ES:SE:LP:AF:ID  -0.0094:0.0128:0.334607:0.5753:rs1557426776
1   10616   rs1557426951    C   CCGCCGTTGCAAAGGCGCGCCG  .   PASS    AF=0.0066   ES:SE:LP:AF:ID  0.0572:0.0696:0.386475:0.0066:rs1557426951
1   10642   rs1201957060    G   A   .   PASS    AF=0.0004   ES:SE:LP:AF:ID  0.5592:0.3514:0.952725:0.0004:rs1201957060
1   11008   rs575272151 C   G   .   PASS    AF=0.0942   ES:SE:LP:AF:ID  0.0204:0.0196:0.52593:0.0942:rs575272151
1   11012   rs544419019 C   G   .   PASS    AF=0.0942   ES:SE:LP:AF:ID  0.0204:0.0196:0.52593:0.0942:rs544419019
1   13110   rs540538026 G   A   .   PASS    AF=0.0589   ES:SE:LP:AF:ID  -0.0377:0.0332:0.590913:0.0589:rs540538026
1   13116   rs62635286  T   G   .   PASS    AF=0.1745   ES:SE:LP:AF:ID  0.0035:0.0178:0.0732975:0.1745:rs62635286
1   13118   rs62028691  A   G   .   PASS    AF=0.1745   ES:SE:LP:AF:ID  0.0035:0.0178:0.0732975:0.1745:rs62028691
1   13273   rs531730856 G   C   .   PASS    AF=0.1399   ES:SE:LP:AF:ID  -0.009:0.0201:0.184688:0.1399:rs531730856
1   13289   rs538791886 C   T   .   PASS    AF=0.0003   ES:SE:LP:AF:ID  -8.9682:7.0131:0.696804:0.0003:rs538791886
1   13494   rs1272445563    A   G   .   PASS    AF=0.0021   ES:SE:LP:AF:ID  -0.0058:0.1461:0.0140349:0.0021:rs1272445563
1   13543   rs540466151 T   G   .   PASS    AF=0.0001   ES:SE:LP:AF:ID  0.0268:1.0907:0.0085967:0.0001:rs540466151
1   13550   rs554008981 G   A   .   PASS    AF=0.0064   ES:SE:LP:AF:ID  0.0525:0.1158:0.186686:0.0064:rs554008981
1   14464   rs546169444 A   T   .   PASS    AF=0.1628   ES:SE:LP:AF:ID  0.0103:0.0188:0.233587:0.1628:rs546169444
1   14599   rs707680    T   A   .   PASS    AF=0.175    ES:SE:LP:AF:ID  0.0146:0.0203:0.324497:0.175:rs707680
1   14604   rs1418508701    A   G   .   PASS    AF=0.175    ES:SE:LP:AF:ID  0.0146:0.0203:0.324497:0.175:rs1418508701
1   14728   rs547701710 C   A   .   PASS    AF=0    ES:SE:LP:AF:ID  3.3949:2.5659:0.730954:0:rs547701710
1   14930   rs6682385   A   G   .   PASS    AF=0.5228   ES:SE:LP:AF:ID  0.0065:0.0131:0.20845:0.5228:rs6682385
1   14933   rs199856693 G   A   .   PASS    AF=0.0407   ES:SE:LP:AF:ID  -0.0794:0.036:1.56527:0.0407:rs199856693
1   14975   rs11803341  C   T   .   PASS    AF=0.0001   ES:SE:LP:AF:ID  1.3927:1.0013:0.784362:0.0001:rs11803341
1   15211   rs3982632   T   G   .   PASS    AF=0.71 ES:SE:LP:AF:ID  0.0447:0.015:2.54775:0.71:rs3982632
1   15274   rs1328799397    A   T   .   PASS    AF=0.7084   ES:SE:LP:AF:ID  0.004:0.0141:0.109355:0.7084:rs1328799397
1   15274   rs1328799397    A   G   .   PASS    AF=0.2886   ES:SE:LP:AF:ID  -0.0049:0.0141:0.136915:0.2886:rs1328799397
1   15585   rs533630043 G   A   .   PASS    AF=0.0041   ES:SE:LP:AF:ID  -0.0962:0.0951:0.505845:0.0041:rs533630043
1   15644   rs564003018 G   A   .   PASS    AF=0.0068   ES:SE:LP:AF:ID  -0.2087:0.1106:1.2279:0.0068:rs564003018
1   15774   rs374029747 G   A   .   PASS    AF=0.008    ES:SE:LP:AF:ID  -0.2582:0.1021:1.94006:0.008:rs374029747
1   15777   rs2691317   A   G   .   PASS    AF=0.018    ES:SE:LP:AF:ID  -0.085:0.0542:0.932929:0.018:rs2691317
1   15820   rs1316988498    G   T   .   PASS    AF=0.2928   ES:SE:LP:AF:ID  -0.0205:0.0157:0.715795:0.2928:rs1316988498
1   15850   rs575961614 G   A   .   PASS    AF=0.0001   ES:SE:LP:AF:ID  1.1654:1.26:0.449772:0.0001:rs575961614
1   15903   rs557514207 GC  G   .   PASS    AF=0.55 ES:SE:LP:AF:ID  0.0007:0.0131:0.0194967:0.55:rs557514207
1   16042   rs572465511 G   C   .   PASS    AF=0.0013   ES:SE:LP:AF:ID  -0.3897:0.2451:0.951558:0.0013:rs572465511
1   16141   rs529651976 C   T   .   PASS    AF=0.0013   ES:SE:LP:AF:ID  0.036:0.1815:0.0742239:0.0013:rs529651976
1   16142   rs548165136 G   A   .   PASS    AF=0.0016   ES:SE:LP:AF:ID  -0.0688:0.2065:0.131238:0.0016:rs548165136
1   16949   rs199745162 A   C   .   PASS    AF=0.0198   ES:SE:LP:AF:ID  -0.0568:0.0584:0.481223:0.0198:rs199745162
1   18643   rs564023708 G   A   .   PASS    AF=0.0095   ES:SE:LP:AF:ID  -0.097:0.0919:0.535659:0.0095:rs564023708
1   18849   rs533090414 C   G   .   PASS    AF=0.9826   ES:SE:LP:AF:ID  0.0194:0.0525:0.14752:0.9826:rs533090414
1   30923   rs1165072081    G   T   .   PASS    AF=0.9278   ES:SE:LP:AF:ID  0.0691:0.0329:1.44758:0.9278:rs1165072081
1   46285   rs545414834 ATAT    A   .   PASS    AF=0.0028   ES:SE:LP:AF:ID  -0.07:0.1291:0.230992:0.0028:rs545414834
1   46650   rs574298978 T   C   .   PASS    AF=0.0003   ES:SE:LP:AF:ID  -1.2168:2.1516:0.242832:0.0003:rs574298978
1   47159   rs540662756 T   C   .   PASS    AF=0.0538   ES:SE:LP:AF:ID  0.0159:0.0306:0.219323:0.0538:rs540662756
1   48327   rs565824523 C   A   .   PASS    AF=0.0006   ES:SE:LP:AF:ID  -0.9356:0.3779:1.87615:0.0006:rs565824523
1   48328   rs528394432 A   T   .   PASS    AF=0.0006   ES:SE:LP:AF:ID  -0.9356:0.3779:1.87615:0.0006:rs528394432
1   49298   rs10399793  T   C   .   PASS    AF=0.8502   ES:SE:LP:AF:ID  0.0404:0.0241:1.03297:0.8502:rs10399793
1   49554   rs539322794 A   G   .   PASS    AF=0.0889   ES:SE:LP:AF:ID  -0.0055:0.0307:0.0667152:0.0889:rs539322794
1   49988   rs543052237 T   A   .   PASS    AF=0.0005   ES:SE:LP:AF:ID  -0.3085:0.3683:0.395558:0.0005:rs543052237
1   49989   rs563024962 A   G   .   PASS    AF=0.0005   ES:SE:LP:AF:ID  -0.3085:0.3683:0.395558:0.0005:rs563024962
1   50891   rs542415070 T   C   .   PASS    AF=0.0038   ES:SE:LP:AF:ID  -0.1748:0.1483:0.622512:0.0038:rs542415070
1   51047   rs559500163 A   T   .   PASS    AF=0.0025   ES:SE:LP:AF:ID  -0.0855:0.1802:0.197021:0.0025:rs559500163
1   51049   rs528344458 A   C   .   PASS    AF=0.0025   ES:SE:LP:AF:ID  -0.0855:0.1802:0.197021:0.0025:rs528344458