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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_41204_I252.vcf.gz --id UKB-b:16662 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41204_I252.txt.gz --cohort_cases 3340 --cohort_controls 459670 --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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"bcftools_viewCommand": "view -T ^/mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-16662/mac_discard.txt -Oz /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-16662/UKB-b-16662_raw.vcf.gz; Date=Thu Oct 17 12:33:54 2019",
"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-16662/ukb-b-16662.vcf.gz; Date=Sun May 10 09:31:18 2020"
}
*********************************************************************
* 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-16662/UKB-b-16662_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-16662/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:45:13 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-16662/UKB-b-16662_data.vcf.gz ...
Read summary statistics for 4224081 SNPs.
Dropped 863 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, 993514 SNPs remain.
After merging with regression SNP LD, 993514 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0064 (0.0013)
Lambda GC: 1.0822
Mean Chi^2: 1.0885
Intercept: 1.0247 (0.0084)
Ratio: 0.2793 (0.0955)
Analysis finished at Thu Oct 17 14:46:06 2019
Total time elapsed: 52.54s
{
"af_correlation": 0.8838,
"inflation_factor": 1.0966,
"mean_EFFECT": 1.4422e-06,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 3,
"n_p_sig": 111,
"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": 34714,
"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": 993514,
"ldsc_nsnp_merge_regression_ld": 993514,
"ldsc_observed_scale_h2_beta": 0.0064,
"ldsc_observed_scale_h2_se": 0.0013,
"ldsc_intercept_beta": 1.0247,
"ldsc_intercept_se": 0.0084,
"ldsc_lambda_gc": 1.0822,
"ldsc_mean_chisq": 1.0885,
"ldsc_ratio": 0.2791
}
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 |
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 SNPsn_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:
A, C, G or T
.< 0
or > 1
.<= 0
or = Infinity
).< 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.
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.
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.
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.
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
> 0.5ldsc_intercept_beta
: ldsc_intercept_beta
> 1.5n_clumped_hits
: n_clumped_hits
> 1000r2_sum<*>
: r2_sum<*>
> 0.5Plots
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 | 4223223 | 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 | 4224081 | 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 | 8.654916e+00 | 5.765296e+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 | 7.859688e+07 | 5.673600e+07 | 828.0000000 | 3.167563e+07 | 6.896943e+07 | 1.146802e+08 | 2.492385e+08 | ▇▆▅▂▁ |
numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.400000e-06 | 2.209000e-04 | -0.0013074 | -1.436000e-04 | 1.600000e-06 | 1.467000e-04 | 1.879000e-03 | ▁▆▇▁▁ |
numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.096000e-04 | 3.230000e-05 | 0.0001695 | 1.817000e-04 | 1.988000e-04 | 2.314000e-04 | 6.198000e-04 | ▇▁▁▁▁ |
numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.865761e-01 | 2.922276e-01 | 0.0000000 | 2.300001e-01 | 4.799997e-01 | 7.400005e-01 | 1.000000e+00 | ▇▇▇▇▇ |
numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.865781e-01 | 2.922007e-01 | 0.0000000 | 2.300637e-01 | 4.819529e-01 | 7.400730e-01 | 9.999999e-01 | ▇▇▇▇▇ |
numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.846482e-01 | 2.195092e-01 | 0.1047910 | 1.937820e-01 | 3.326660e-01 | 5.453050e-01 | 8.952090e-01 | ▇▅▃▂▂ |
numeric | AF_reference | 34714 | 0.9917819 | NA | NA | NA | NA | NA | NA | NA | 3.747524e-01 | 2.215004e-01 | 0.0000000 | 1.922920e-01 | 3.306710e-01 | 5.335460e-01 | 1.000000e+00 | ▆▇▅▃▁ |
CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
---|---|---|---|---|---|---|---|---|---|---|---|
1 | 49298 | rs200943160 | T | C | 0.0001711 | 0.0003119 | 0.5800000 | 0.5833580 | 0.623765 | 0.782149 | NA |
1 | 54676 | rs2462492 | C | T | 0.0002727 | 0.0003090 | 0.3800004 | 0.3774824 | 0.400401 | NA | NA |
1 | 91536 | rs6702460 | G | T | -0.0000347 | 0.0003042 | 0.9100000 | 0.9091829 | 0.456846 | 0.420727 | NA |
1 | 534192 | rs6680723 | C | T | 0.0001668 | 0.0003475 | 0.6300007 | 0.6312542 | 0.240959 | NA | NA |
1 | 693731 | rs12238997 | A | G | -0.0002443 | 0.0002912 | 0.4000000 | 0.4014691 | 0.116329 | 0.141773 | NA |
1 | 706368 | rs55727773 | A | G | -0.0000253 | 0.0002157 | 0.9100000 | 0.9068009 | 0.515645 | 0.275160 | NA |
1 | 729679 | rs4951859 | C | G | 0.0000434 | 0.0002524 | 0.8600001 | 0.8635953 | 0.843204 | 0.639976 | NA |
1 | 731718 | rs142557973 | T | C | -0.0001935 | 0.0002762 | 0.4799997 | 0.4835833 | 0.122312 | 0.154353 | NA |
1 | 734349 | rs141242758 | T | C | -0.0001982 | 0.0002764 | 0.4700002 | 0.4731891 | 0.121554 | 0.152556 | NA |
1 | 736289 | rs79010578 | T | A | -0.0000133 | 0.0002724 | 0.9599999 | 0.9611821 | 0.132335 | 0.139577 | NA |
CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
---|---|---|---|---|---|---|---|---|---|---|---|
22 | 51198027 | rs34939255 | A | G | -0.0001529 | 0.0002151 | 0.4799997 | 0.4771965 | 0.254562 | 0.0984425 | NA |
22 | 51208537 | rs72619593 | G | A | 0.0000095 | 0.0002876 | 0.9699999 | 0.9735238 | 0.120735 | 0.1142170 | NA |
22 | 51210289 | rs112565862 | C | T | -0.0000551 | 0.0002864 | 0.8499999 | 0.8475253 | 0.129958 | 0.1018370 | NA |
22 | 51211106 | rs9628250 | T | C | -0.0000497 | 0.0002133 | 0.8200001 | 0.8158710 | 0.271547 | 0.1671330 | NA |
22 | 51211392 | rs3888396 | T | C | -0.0000622 | 0.0002838 | 0.8300000 | 0.8265505 | 0.132638 | 0.1641370 | NA |
22 | 51212875 | rs2238837 | A | C | 0.0000273 | 0.0002027 | 0.8900000 | 0.8929941 | 0.331457 | 0.3724040 | NA |
22 | 51213613 | rs34726907 | C | T | 0.0002340 | 0.0002670 | 0.3800004 | 0.3809094 | 0.127814 | 0.1727240 | NA |
22 | 51216564 | rs9616970 | T | C | 0.0002118 | 0.0002659 | 0.4299995 | 0.4257298 | 0.128328 | 0.1563500 | NA |
22 | 51219006 | rs28729663 | G | A | 0.0002232 | 0.0002602 | 0.3900004 | 0.3910775 | 0.137950 | 0.2052720 | NA |
22 | 51237063 | rs3896457 | T | C | 0.0000326 | 0.0002075 | 0.8800001 | 0.8750130 | 0.297974 | 0.2050720 | NA |
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