There is currently much debate regarding the best model for how heritability varies across the genome. The authors of GCTA recommend the GCTA-LDMS-I model, the authors of LD Score Regression recommend the Baseline LD model, and we have recommended the LDAK model. Here we provide a statistical framework for assessing heritability models using summary statistics from genome-wide association studies. Based on 31 studies of complex human traits (average sample size 136,000), we show that the Baseline LD model is more realistic than other existing heritability models, but that it can be improved by incorporating features from the LDAK model. Our framework also provides a method for estimating the selection-related parameter α from summary statistics. We find strong evidence (P < 1 × 10−6) of negative genome-wide selection for traits, including height, systolic blood pressure and college education, and that the impact of selection is stronger inside functional categories, such as coding SNPs and promoter regions.
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We performed the UKBb GWAS using data applied for and downloaded via the UK Biobank website (www.ukbiobank.ac.uk). We obtained summary statistics for the 17 public GWAS studies from the websites of the corresponding studies. We downloaded the 1000 Genome Project data from the LDSC website (www.github.com/bulik/ldsc).
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We thank B. Shaban for help with the LDAK website, and A. Price, S. Gazal and H. Finucane for helpful discussions. D.S. is funded by the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie grant agreement no. 754513, by Aarhus University Research Foundation and by the Independent Research Fund Denmark under project no. 7025-00094B. D.S. and D.J.B. are funded by the Australian Research Council under project no. DP190103188.
The authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Plots compare likelihood ratio test (LRT) statistics (twice the improvement in log likelihood relative to the null model) computed using the likelihood from restricted maximum likelihood (REML) with those from loglSS, our new approximate likelihood, and loglOld, the approximate likelihood we reported in the original version of SumHer (see Supplementary Note for details). We only analyze the 14 UKBb GWAS, because to perform REML requires individual-level data, and we only consider the GCTA, LDAK and LDAK-Thin Models, because REML is only feasible for simple heritability models. To ensure a fair comparison, when running SumHer we restrict the reference panel to the 4.7 M GWAS SNPs. The bottom plots are zoomed versions of the top plots (obtained by excluding height, the most heritable trait). We see that the LRT statistics from loglSS are highly concordant with those from REML, indicating that the weights used when calculating loglSS perform well. We observe lower concordance between the LRT statistics from loglOld and those from REML, reflecting that loglOld was based on the assumption that test statistics were Gaussian distributed, rather than Gamma distributed.
This is an expanded version of Fig. 1d, and shows that estimates of functional enrichments tend to converge as the heritability model becomes more complex. Plots report the estimated proportion of SNP heritability contributed by each category of SNPs, averaged across either the 14 UKBb or 17 Public GWAS (vertical segments indicate 95% confidence intervals). Bars indicate the heritability model used and are ordered by number of parameters (see Supplementary Table 13 for definitions): GCTA + 1Fun Model (two parameters, used by Gusev et al.12), LDAK + 1Fun Model (two parameters, Speed et al.1), LDAK + 24Fun Model (25 parameters, Speed et al.4), Baseline Model (53 parameters, Finucane et al.9), BLD-LDAK and BLD-LDAK + Alpha Models (66 and 67 parameters, this paper) and Baseline LD Model (75 parameters, Gazal et al.10). The estimated enrichment of a category is obtained by dividing its estimated proportion of SNP heritability by the proportion of SNPs it contains (horizontal dashed lines). Numerical values are provided in Supplementary Tables 5 & 6.
The seven-parameter BLD-LDAK-Lite is a reduced version of the BLD-LDAK Model, obtained by removing two of the nine continuous annotations and all 57 binary annotations (Supplementary Table 8 explains how we used forward stepwise selection to decide which of the continuous annotations to retain). The nine-parameter BLD-LDAK-Lite+1Fun Model adds to the BLD-LDAK-Lite Model one function indicator and the corresponding 500 base pair buffer, while the eight-parameter BLD-LDAK-Lite+Alpha Model is the same as the BLD-LDAK-Lite Model, except annotations are scaled by [fj(1-fj)]1+α. These plots show that estimates of SNP heritability and confounding bias from the BLD-LDAK-Lite Model, and average estimates of functional enrichments from the BLD-LDAK-Lite+1Fun Model are close to the those from the BLD-LDAK Model, while estimates of α from the BLD-LDAK-Lite+Alpha Model are close to those from the BLD-LDAK + Alpha Model. Numbers indicate how many of the pairs of estimates are inconsistent either nominally or after Bonferroni correction. Numerical values are provided in Supplementary Tables 3–7.
Hou et al.21 proposed GRE, a method for estimating SNP heritability without specifying a heritability model. GRE requires individual level data and that there are more individuals than the number of SNPs on the largest chromosome. Here we compare estimates from GRE to those from SumHer for the 14 UKBb GWAS. To run GRE, we follow the instructions at www.github.com/bogdanlab/h2-GRE; to satisfy the sample size requirement, we use only the 623k directly-genotyped SNPs (Hou et al. did likewise). For SumHer, we consider ten heritability models; to enable a fair comparison with GRE, we always restrict the reference panel to genotyped SNPs. The first three plots compare estimates of SNP heritability from GRE and SumHer. It is noticeable that when using only genotyped SNPs, changing the heritability model has a much smaller impact on estimates of SNP heritability than when using imputed SNPs (Supplementary Table 3); this reflects that with fewer SNPs, the impact of the prior assumptions is reduced. Nonetheless, if we consider GRE estimates to be the ‘gold standard’, then this analysis indicates that the LDAK-Thin, GCTA-LDMS-R, GCTA-LDMS-I, BLD-LDAK, BLD-LDAK + Alpha and Baseline LD Models produce more accurate estimates of SNP heritability than the GCTA, LDAK, LDAK + 24Fun and Baseline Models. In the fourth plot, the solid and dashed lines mark the point estimate and 95% confidence intervals for the gradient when regressing onto the Akaike Information Criterion (AIC) the absolute difference between estimates from SumHer and GRE (when performing this regression, we include an indicator for trait, to reflect that AIC will tend to be lower for more heritable traits). If we again consider GRE estimates to be the gold standard, then the fact that the gradient is significantly positive (P < 10−6) indicates that lower AIC implies more accurate estimates of SNP heritability.
The plots compare likelihood ratio test (LRT) statistics (twice the improvement in log likelihood relative to the null model), computed using loglSS, our approximate model likelihood. We consider six heritability models (see Supplementary Table 13 for definitions), estimating parameters using either maximum likelihood (our recommended approach) or weighted least-squares regression (the approach used by LDSC and previously by SumHer). Note that when we estimate parameters for the Baseline and Baseline LD Models using weighted least-squares regression, we frequently obtain negative E[Sj]; so that we can compute loglSS, we replace these with 10−6. These plots show that for the GCTA, GCTA-LDMS-I, LDAK and LDAK + 24Fun Models (the simpler models), the two solvers result in near-identical model fit. However, for the Baseline and Baseline LD Models (the more complex models), weighted least-squares regression often results in a worse fit, because it does not respect that test statistics are approximately Gamma distributed. Note that the reason we observe discordance between the weighted least-squares estimates from LDSC and SumHer (mainly evident for the Baseline Model), is because the SumHer weighted least-squares solver is always iterative, whereas the LDSC solver is iterative when provided with a single-parameter heritability model, but one-step when provided with a multi-parameter model.
For our main analysis of the UKBb GWAS, we first identified individuals with values for all 14 phenotypes, then filtered so that no pair remained with allelic correlation >0.02 (Supplementary Note 6). As a secondary analysis, we instead identified individuals with values for any of the 14 phenotypes, then filtered so that no pair remained with allelic correlation >0.03125. This increased the number of individuals from 130,080 to 246,655, with on average 236k phenotypic values per GWAS (range 201k to 247k). The first plot shows that increasing the sample size does not change the ranking of models based on the Akaike Information Criterion. The remaining three plots shows that it does not significantly change estimates of SNP heritability or average functional enrichments from the BLD-LDAK Model, nor estimates of the selection-related parameter α from the BLD-LDAK + Alpha Model (horizontal and vertical segments indicate 95% confidence intervals; numbers indicate how many of the pairs of estimates are inconsistent either nominally or after Bonferroni correction).
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Speed, D., Holmes, J. & Balding, D.J. Evaluating and improving heritability models using summary statistics. Nat Genet 52, 458–462 (2020). https://doi.org/10.1038/s41588-020-0600-y
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