Chapter 10 Some other analyses

Read more in this PowerPoint presentation.

Here we just mention some other types of analyses that haven’t really been covered in the course, but that are useful to know that they exist. We simply mention a few tools; you can find a list with more here.

• Estimation of SNP-based heritability: The proportion of phenotypic variance in a trait that can be explained by genetic variation captured by measured SNPs. A simple way to estimate it is with LD Score regression (LDSC) using GWAS summary statistics (B. K. Bulik-Sullivan et al., 2015). R packages bigsnpr and GenomicSEM also provide some implementation of LDSC. There exist many other tools to estimate SNP-heritability, such as LDpred2-auto (mentioned before).

• Genetic Correlation: LDSC can be also used to estimate the genome-wide genetic correlation between two traits using GWAS summary statistics (B. Bulik-Sullivan et al., 2015), and is also implemented in R package GenomicSEM. Local genetic correlations (e.g. within LD blocks) can also be estimated (Darlay et al., 2025; Zhang, Zhang, Zhang, & Zhao, 2023).

• Partitioned heritability: Heritability partitioned by functional annotation (which may be cell-type or state-specific), which can be estimated using S-LDSC (Finucane et al., 2015), SumHer (Speed & Balding, 2019) and SBayesRC (Zheng et al., 2024).

• Gene-based tests: These determine the genes associated with a trait from a GWAS by using proximity, such as mBAT-combo as an R package or a command-line software (Li et al., 2023).

• Summary statistic imputation: imputation of GWAS summary statistics help increase the SNP overlap between GWAS summary statistics and LD reference panels.

• Meta-analysis of GWAS:

  • same trait, different cohorts: METAL (Willer, Li, & Abecasis, 2010)
  • multiple traits: MTAG (Turley et al., 2018)
  • multiple ancestries: MR-MEGA (Mägi et al., 2017)

• Colocalisation: The same variant may be significant for two traits, but not necessarily be causal for both. Tools like COLOC + SuSiE directly assess this, yielding probabilities the signal is driven by the same variant, or two different variants (Wallace, 2021). SMR is a complementary approach designed for testing if a molQTL is also a causal variant for a trait, yielding an association statistic (Zhu et al., 2016).

• Gene expression signature of a trait: Use GWAS summary stats with eQTL/gene expression data. Early tools include FUSION (Gusev et al., 2016) and PrediXcan (Gamazon et al., 2015), although many more have been developed since.

• Structural Equation Modelling: GenomicSEM is a tool for structural equation modelling of GWAS summary statistics, allowing you to model the relationships between traits using their genetic architecture (Grotzinger et al., 2019).

References

Bulik-Sullivan, B., Finucane, H.K., Anttila, V., Gusev, A., Day, F.R., Loh, P.-R., et al. (2015). An atlas of genetic correlations across human diseases and traits. Nature Genetics, 47, 1236–1241.

Bulik-Sullivan, B.K., Loh, P.-R., Finucane, H.K., Ripke, S., Yang, J., Psychiatric Genomics Consortium, S.W.G. of the, et al. (2015). LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nature Genetics, 47, 291–295.

Darlay, R., Shah, R.L., Dodds, R.M., Nair, A.T., Pearson, E.R., Witham, M.D., et al. (2025). Exploring similarities and differences between methods that exploit patterns of local genetic correlation to identify shared causal loci through application to genome-wide association studies of multiple long term conditions. Genetic Epidemiology, 49, e70012.

Finucane, H.K., Bulik-Sullivan, B., Gusev, A., Trynka, G., Reshef, Y., Loh, P.-R., et al. (2015). Partitioning heritability by functional annotation using genome-wide association summary statistics. Nature Genetics, 47, 1228–1235.

Gamazon, E.R., Wheeler, H.E., Shah, K.P., Mozaffari, S.V., Aquino-Michaels, K., Carroll, R.J., et al. (2015). A gene-based association method for mapping traits using reference transcriptome data. Nature Genetics, 47, 1091–1098.

Grotzinger, A.D., Rhemtulla, M., Vlaming, R. de, Ritchie, S.J., Mallard, T.T., Hill, W.D., et al. (2019). Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. Nature Human Behaviour, 3, 513–525.

Gusev, A., Ko, A., Shi, H., Bhatia, G., Chung, W., Penninx, B.W., et al. (2016). Integrative approaches for large-scale transcriptome-wide association studies. Nature Genetics, 48, 245–252.

Li, A., Liu, S., Bakshi, A., Jiang, L., Chen, W., Zheng, Z., et al. (2023). mBAT-combo: A more powerful test to detect gene-trait associations from GWAS data. The American Journal of Human Genetics, 110, 30–43.

Mägi, R., Horikoshi, M., Sofer, T., Mahajan, A., Kitajima, H., Franceschini, N., et al. (2017). Trans-ethnic meta-regression of genome-wide association studies accounting for ancestry increases power for discovery and improves fine-mapping resolution. Human Molecular Genetics, 26, 3639–3650.

Speed, D., & Balding, D.J. (2019). SumHer better estimates the SNP heritability of complex traits from summary statistics. Nature Genetics, 51, 277–284.

Turley, P., Walters, R.K., Maghzian, O., Okbay, A., Lee, J.J., Fontana, M.A., et al. (2018). Multi-trait analysis of genome-wide association summary statistics using MTAG. Nature Genetics, 50, 229–237.

Wallace, C. (2021). A more accurate method for colocalisation analysis allowing for multiple causal variants. PLoS Genetics, 17, e1009440.

Willer, C.J., Li, Y., & Abecasis, G.R. (2010). METAL: Fast and efficient meta-analysis of genomewide association scans. Bioinformatics, 26, 2190–2191.

Zhang, C., Zhang, Y., Zhang, Y., & Zhao, H. (2023). Benchmarking of local genetic correlation estimation methods using summary statistics from genome-wide association studies. Briefings in Bioinformatics, 24, bbad407.

Zheng, Z., Liu, S., Sidorenko, J., Wang, Y., Lin, T., Yengo, L., et al. (2024). Leveraging functional genomic annotations and genome coverage to improve polygenic prediction of complex traits within and between ancestries. Nature Genetics, 56, 767–777.

Zhu, Z., Zhang, F., Hu, H., Bakshi, A., Robinson, M.R., Powell, J.E., et al. (2016). Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nature Genetics, 48, 481–487.