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}
*********************************************************************
* 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/dev/ieu-a-import/processed/ieu-a-111/ieu-a-111.vcf.gz \
--ref-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-111/ldsc.txt \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/
Beginning analysis at Tue Feb 4 19:39:29 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-111/ieu-a-111.vcf.gz ...
Read summary statistics for 2735816 SNPs.
Reading reference panel LD Score from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/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 /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/[1-22] ...
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 1271805 SNPs remain.
After merging with regression SNP LD, 1271805 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.09 (0.0139)
Lambda GC: 1.0078
Mean Chi^2: 1.0306
Intercept: 0.9646 (0.005)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Tue Feb 4 19:39:55 2020
Total time elapsed: 26.62s
{
"af_correlation": 0.9172,
"inflation_factor": 1,
"mean_EFFECT": -0.0003,
"n": 42737.2,
"n_snps": 2735816,
"n_clumped_hits": 10,
"n_p_sig": 282,
"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": 476254,
"n_miss_AF_reference": 26825,
"n_est": 42278.1972,
"ratio_se_n": 0.9946,
"mean_diff": -8.2376e-06,
"ratio_diff": 0.8407,
"sd_y_est1": 0.9854,
"sd_y_est2": 0.9801,
"r2_sum1": 0.0085,
"r2_sum2": 0.0087,
"r2_sum3": 0.0088,
"r2_sum4": 0.0091,
"ldsc_nsnp_merge_refpanel_ld": 1271805,
"ldsc_nsnp_merge_regression_ld": 1271805,
"ldsc_observed_scale_h2_beta": 0.09,
"ldsc_observed_scale_h2_se": 0.0139,
"ldsc_intercept_beta": 0.9646,
"ldsc_intercept_se": 0.005,
"ldsc_lambda_gc": 1.0078,
"ldsc_mean_chisq": 1.0306,
"ldsc_ratio": -1.1569
}
name | value |
---|---|
af_correlation | FALSE |
inflation_factor | FALSE |
n | FALSE |
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 | numeric.mean | numeric.sd | numeric.p0 | numeric.p25 | numeric.p50 | numeric.p75 | numeric.p100 | numeric.hist |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
character | ID | 0 | 1.0000000 | 3 | 42 | 0 | 2735816 | 0 | 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 |
character | ALT | 0 | 1.0000000 | 1 | 1 | 0 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA | NA |
numeric | CHROM | 0 | 1.0000000 | NA | NA | NA | NA | NA | 8.613925e+00 | 5.688296e+00 | 1.0000 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.300000e+01 | ▇▅▅▂▂ |
numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | 7.881549e+07 | 5.590162e+07 | 6689.0000 | 3.245602e+07 | 7.000384e+07 | 1.144381e+08 | 2.492114e+08 | ▇▆▅▂▁ |
numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | -3.009000e-04 | 5.440687e-01 | -410.0000 | -6.900000e-03 | 0.000000e+00 | 7.000000e-03 | 1.800000e+02 | ▁▁▁▇▁ |
numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | 2.259780e-02 | 9.919579e-01 | 0.0065 | 7.200000e-03 | 8.800000e-03 | 1.400000e-02 | 6.000000e+02 | ▇▁▁▁▁ |
numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | 4.989985e-01 | 2.895481e-01 | 0.0000 | 2.500000e-01 | 5.000000e-01 | 7.499995e-01 | 1.000000e+00 | ▇▇▇▇▇ |
numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | 4.990132e-01 | 2.894479e-01 | 0.0000 | 2.481193e-01 | 4.990049e-01 | 7.498474e-01 | 1.000000e+00 | ▇▇▇▇▇ |
numeric | AF | 476254 | 0.8259188 | NA | NA | NA | NA | NA | 3.565770e-01 | 2.754926e-01 | 0.0083 | 1.167000e-01 | 2.917000e-01 | 5.583000e-01 | 9.917000e-01 | ▇▅▃▂▂ |
numeric | AF_reference | 26825 | 0.9901949 | NA | NA | NA | NA | NA | 3.543869e-01 | 2.612540e-01 | 0.0000 | 1.343850e-01 | 2.889380e-01 | 5.411340e-01 | 1.000000e+00 | ▇▆▃▃▂ |
numeric | N | 0 | 1.0000000 | NA | NA | NA | NA | NA | 3.803275e+04 | 1.127059e+04 | 108.0000 | 4.079050e+04 | 4.273020e+04 | 4.273470e+04 | 4.273720e+04 | ▁▁▁▁▇ |
CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
---|---|---|---|---|---|---|---|---|---|---|---|
1 | 564477 | rs6650104 | A | G | -0.0660 | 0.200 | 0.7300002 | 0.7414000 | NA | 0.0003994 | 345.85 |
1 | 567753 | rs532073948 | A | G | -0.2200 | 0.200 | 0.2700001 | 0.2713321 | NA | 0.0005990 | 2070.00 |
1 | 721290 | rs12565286 | G | C | -0.0160 | 0.028 | 0.5600000 | 0.5677092 | NA | 0.0371406 | 19821.00 |
1 | 723819 | rs11804171 | T | A | -0.0190 | 0.028 | 0.5000000 | 0.4974095 | NA | 0.1345850 | 19839.00 |
1 | 723891 | rs2977670 | G | C | -0.0140 | 0.040 | 0.7300002 | 0.7263387 | NA | 0.7799520 | 8713.97 |
1 | 752566 | rs3094315 | G | A | 0.0073 | 0.012 | 0.5300002 | 0.5429664 | NA | 0.7182510 | 28452.80 |
1 | 752721 | rs3131972 | A | G | 0.0300 | 0.089 | 0.7300002 | 0.7360576 | NA | 0.6533550 | 345.93 |
1 | 753541 | rs2073813 | G | A | -0.0100 | 0.015 | 0.5199996 | 0.5049851 | NA | 0.3019170 | 20081.00 |
1 | 754182 | rs3131969 | A | G | -0.0064 | 0.092 | 0.9400001 | 0.9445397 | NA | 0.6785140 | 345.99 |
1 | 754192 | rs3131968 | A | G | -0.0046 | 0.021 | 0.8300000 | 0.8266130 | NA | 0.6785140 | 10723.40 |
CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
---|---|---|---|---|---|---|---|---|---|---|---|
22 | 51211392 | rs3888396 | T | C | -0.0840 | 0.0410 | 0.0400000 | 0.0404836 | NA | 0.1641370 | 11261.0 |
22 | 51212875 | rs2238837 | A | C | 0.0230 | 0.0120 | 0.0420001 | 0.0552803 | 0.3917 | 0.3724040 | 25240.3 |
22 | 51213613 | rs34726907 | C | T | 0.0310 | 0.0410 | 0.4500005 | 0.4495907 | NA | 0.1727240 | 1889.0 |
22 | 51219006 | rs28729663 | G | A | 0.0180 | 0.0370 | 0.6300007 | 0.6266223 | NA | 0.2052720 | 2052.0 |
22 | 51229805 | rs9616985 | T | C | 0.0620 | 0.0240 | 0.0094999 | 0.0097851 | 0.0917 | 0.0730831 | 21961.9 |
23 | 35921591 | rs2204667 | C | G | -0.0030 | 0.0090 | 0.7400005 | 0.7388827 | 0.1667 | NA | 42731.7 |
23 | 51666786 | rs14115 | A | G | -0.0012 | 0.0150 | 0.9299999 | 0.9362373 | 0.0250 | NA | 42643.9 |
23 | 70163799 | rs1626496 | A | C | 0.0036 | 0.0130 | 0.7800007 | 0.7818392 | 0.0750 | NA | 42628.2 |
23 | 91415872 | rs6562597 | G | A | -0.0320 | 0.0250 | 0.1900002 | 0.2005451 | 0.0083 | 0.0021192 | 40457.6 |
23 | 118495837 | rs12882977 | G | A | -0.0061 | 0.0067 | 0.3599996 | 0.3625864 | 0.5167 | 0.2307280 | 42733.6 |
1 564477 rs6650104 A G . PASS . ES:SE:LP:SS:ID -0.066:0.2:0.136677:345.85:rs6650104
1 567753 rs11510103 A G . PASS . ES:SE:LP:SS:ID -0.22:0.2:0.568636:2070:rs11510103
1 721290 rs12565286 G C . PASS . ES:SE:LP:SS:ID -0.016:0.028:0.251812:19821:rs12565286
1 723819 rs11804171 T A . PASS . ES:SE:LP:SS:ID -0.019:0.028:0.30103:19839:rs11804171
1 723891 rs2977670 G C . PASS . ES:SE:LP:SS:ID -0.014:0.04:0.136677:8713.97:rs2977670
1 752566 rs3094315 G A . PASS . ES:SE:LP:SS:ID 0.0073:0.012:0.275724:28452.8:rs3094315
1 752721 rs3131972 A G . PASS . ES:SE:LP:SS:ID 0.03:0.089:0.136677:345.93:rs3131972
1 753541 rs2073813 G A . PASS . ES:SE:LP:SS:ID -0.01:0.015:0.283997:20081:rs2073813
1 754182 rs3131969 A G . PASS . ES:SE:LP:SS:ID -0.0064:0.092:0.0268721:345.99:rs3131969
1 754192 rs3131968 A G . PASS . ES:SE:LP:SS:ID -0.0046:0.021:0.0809219:10723.4:rs3131968
1 760912 rs1048488 C T . PASS . ES:SE:LP:SS:ID 0.031:0.089:0.136677:346:rs1048488
1 761732 rs2286139 C T . PASS . ES:SE:LP:SS:ID 0.059:0.035:1.03152:2043:rs2286139
1 768448 rs12562034 G A . PASS . ES:SE:LP:SS:ID -0.034:0.022:0.920819:16193.4:rs12562034
1 775659 rs2905035 A G . PASS . ES:SE:LP:SS:ID 0.0048:0.012:0.154902:30803.7:rs2905035
1 776546 rs12124819 A G . PASS . ES:SE:LP:SS:ID -0.022:0.021:0.522879:5504.12:rs12124819
1 777122 rs2980319 A T . PASS . ES:SE:LP:SS:ID 0.0052:0.012:0.167491:30804.7:rs2980319
1 779322 rs4040617 A G . PASS . ES:SE:LP:SS:ID -0.0051:0.012:0.167491:30761.8:rs4040617
1 780785 rs2977612 T A . PASS . ES:SE:LP:SS:ID 0.0049:0.012:0.161151:30803.7:rs2977612
1 784023 rs17160939 A G . PASS . ES:SE:LP:SS:ID 0.059:0.097:0.267606:2590.97:rs17160939
1 785050 rs2905062 G A . PASS . ES:SE:LP:SS:ID 0.005:0.012:0.161151:30803.7:rs2905062
1 785989 rs2980300 T C . PASS . ES:SE:LP:SS:ID 0.0037:0.012:0.119186:30732.7:rs2980300
1 798026 rs4951864 C T . PASS . ES:SE:LP:SS:ID 0.018:0.022:0.39794:17294.4:rs4951864
1 798801 rs12132517 G A . PASS . ES:SE:LP:SS:ID -0.017:0.021:0.376751:17294.1:rs12132517
1 798959 rs11240777 G A . PASS . ES:SE:LP:SS:ID -0.0051:0.014:0.148742:19231.2:rs11240777
1 838555 rs4970383 C A . PASS . ES:SE:LP:SS:ID -0.068:0.082:0.387216:346:rs4970383
1 846808 rs4475691 C T . PASS . ES:SE:LP:SS:ID -0.035:0.031:0.585027:2818.97:rs4475691
1 846864 rs950122 G C . PASS . ES:SE:LP:SS:ID -0.25:0.16:0.958607:345.44:rs950122
1 854250 rs7537756 A G . PASS . ES:SE:LP:SS:ID -0.058:0.092:0.275724:346:rs7537756
1 861808 rs13302982 A G . PASS . ES:SE:LP:SS:ID -0.2:0.18:0.552842:346:rs13302982
1 863124 rs4040604 G T . PASS . ES:SE:LP:SS:ID -0.2:0.18:0.552842:346:rs4040604
1 870645 rs28576697 T C . PASS . ES:SE:LP:SS:ID -0.14:0.079:1.14874:345.96:rs28576697
1 879317 rs7523549 C T . PASS . ES:SE:LP:SS:ID 0.27:0.21:0.721246:345.98:rs7523549
1 880238 rs3748592 A G . PASS . ES:SE:LP:SS:ID -0.067:0.16:0.173925:346:rs3748592
1 880390 rs3748593 C A . PASS . ES:SE:LP:SS:ID 0.22:0.22:0.481486:346:rs3748593
1 882033 rs2272756 G A . PASS . ES:SE:LP:SS:ID -0.028:0.052:0.229148:1977:rs2272756
1 882803 rs2340582 A G . PASS . ES:SE:LP:SS:ID -0.067:0.16:0.173925:346:rs2340582
1 884815 rs4246503 A G . PASS . ES:SE:LP:SS:ID -0.067:0.16:0.173925:346:rs4246503
1 886384 rs3748594 G A . PASS . ES:SE:LP:SS:ID 0.25:0.2:0.657577:346:rs3748594
1 887560 rs3748595 A C . PASS . ES:SE:LP:SS:ID -0.1:0.15:0.318759:346:rs3748595
1 888659 rs3748597 T C . PASS . ES:SE:LP:SS:ID -0.038:0.052:0.327902:2287:rs3748597
1 891945 rs13303106 A G . PASS . ES:SE:LP:SS:ID 0.019:0.072:0.102373:346:rs13303106
1 894573 rs13303010 G A . PASS . ES:SE:LP:SS:ID -0.21:0.12:1.06048:346:rs13303010
1 900505 rs28705211 G C . PASS . ES:SE:LP:SS:ID 0.0037:0.027:0.05061:2818.82:rs28705211
1 900730 rs3935066 G A . PASS . ES:SE:LP:SS:ID -0.21:0.13:0.920819:346:rs3935066
1 903104 rs6696281 C T . PASS . ES:SE:LP:SS:ID 0.2:0.2:0.508638:346:rs6696281
1 908414 rs28504611 C T . PASS . ES:SE:LP:SS:ID -0.28:0.23:0.638272:345.97:rs28504611
1 916834 rs6694632 G A . PASS . ES:SE:LP:SS:ID -0.018:0.069:0.09691:346:rs6694632
1 918384 rs13303118 G T . PASS . ES:SE:LP:SS:ID -0.02:0.025:0.387216:2590.12:rs13303118
1 918573 rs2341354 A G . PASS . ES:SE:LP:SS:ID 0.011:0.027:0.173925:2072:rs2341354
1 924898 rs6665000 C A . PASS . ES:SE:LP:SS:ID -0.093:0.21:0.180456:346:rs6665000