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An integrated framework for local genetic correlation analysis

Nature Genetics volume 54, pages 274–282 (2022)Cite this article

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Abstract

Genetic correlation (rg) analysis is used to identify phenotypes that may have a shared genetic basis. Traditionally, rg is studied globally, considering only the average of the shared signal across the genome, although this approach may fail when the rg is confined to particular genomic regions or in opposing directions at different loci. Current tools for local rg analysis are restricted to analysis of two phenotypes. Here we introduce LAVA, an integrated framework for local rg analysis that, in addition to testing the standard bivariate local rgs between two phenotypes, can evaluate local heritabilities and analyze conditional genetic relations between several phenotypes using partial correlation and multiple regression. Applied to 25 behavioral and health phenotypes, we show considerable heterogeneity in the bivariate local rgs across the genome, which is often masked by the global rg patterns, and demonstrate how our conditional approaches can elucidate more complex, multivariate genetic relations.

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Fig. 1: Overview of the number of significant bivariate local rgs between all 25 phenotype pairs.
Fig. 2: Comparison between the global genetic correlations estimated using LDSC and the mean local genetic correlations from LAVA across tested loci.
Fig. 3: Local genetic correlation hotspots within the MHC.

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Data availability

All analyses in this study relied on publicly available summary statistics that, in all but three cases, were downloaded from the GWAS Atlas2 (https://atlas.ctglab.nl). Original sources and Atlas IDs (when applicable) are referenced in Table 1. As a reference for the estimation of LD, we used the European subset of the 1000 Genomes26 data as downloaded from https://ctg.cncr.nl/software/magma. The locus file used for all the LAVA analyses can be accessed at https://github.com/josefin-werme/lava-scripts2021 (ref. 61).

Code availability

LAVA is implemented as an R package, which is publicly available at the LAVA website (https://ctg.cncr.nl/software/lava) and LAVA GitHub repository (https://github.com/josefin-werme/LAVA). Analysis scripts and the exact package version (v0.0.6) used for the generation of the main results can be downloaded from https://github.com/josefin-werme/lava-scripts2021 (ref. 61). The method used for genome partitioning is available at https://github.com/cadeleeuw/lava-partitioning (ref. 62).

References

  1. Chesmore, K., Bartlett, J. & Williams, S. M. The ubiquity of pleiotropy in human disease. Hum. Genet. 137, 39–44 (2018).

    Article  CAS  PubMed  Google Scholar 

  2. Watanabe, K. et al. A global overview of pleiotropy and genetic architecture in complex traits. Nat. Genet. 51, 1339–1348 (2019).

    Article  CAS  PubMed  Google Scholar 

  3. Visscher, P. M. et al. 10 years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101, 5–22 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Shikov, A. E., Skitchenko, R. K., Predeus, A. V. & Barbitoff, Y. A. Phenome-wide functional dissection of pleiotropic effects highlights key molecular pathways for human complex traits. Sci. Rep. 10, 1037 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Hackinger, S. & Zeggini, E. Statistical methods to detect pleiotropy in human complex traits. Open Biol. 7, 170125 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Zhao, W. et al. Identification of new susceptibility loci for type 2 diabetes and shared etiological pathways with coronary heart disease. Nat. Genet. 49, 1450–1457 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Giambartolomei, C. et al. A Bayesian framework for multiple trait colocalization from summary association statistics. Bioinformatics 34, 2538–2545 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Hormozdiari, F. et al. Colocalization of GWAS and eQTL signals detects target genes. Am. J. Hum. Genet. 99, 1245–1260 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Porcu, E. et al. Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits. Nat. Commun. 10, 3300 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Solovieff, N., Cotsapas, C., Lee, P. H., Purcell, S. M. & Smoller, J. W. Pleiotropy in complex traits: challenges and strategies. Nat. Rev. Genet. 14, 483–495 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Pickrell, J. K. et al. Detection and interpretation of shared genetic influences on 42 human traits. Nat. Genet. 48, 709–717 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Zeng, P., Hao, X. & Zhou, X. Pleiotropic mapping and annotation selection in genome-wide association studies with penalized Gaussian mixture models. Bioinformatics 34, 2797–2807 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Grotzinger, A. D. et al. Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. Nat. Hum. Behav. 3, 513–525 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. van Rheenen, W., Peyrot, W. J., Schork, A. J., Lee, S. H. & Wray, N. R. Genetic correlations of polygenic disease traits: from theory to practice. Nat. Rev. Genet. 20, 567–581 (2019).

    Article  PubMed  Google Scholar 

  18. Zheng, J. et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics 33, 272–279 (2017).

    Article  CAS  PubMed  Google Scholar 

  19. Shi, H., Mancuso, N., Spendlove, S. & Pasaniuc, B. Local genetic correlation gives insights into the shared genetic architecture of complex traits. Am. J. Hum. Genet. 101, 737–751 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Lu, Q. et al. A powerful approach to estimating annotation-stratified genetic covariance via GWAS summary statistics. Am. J. Hum. Genet. 101, 939–964 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Frei, O. et al. Bivariate causal mixture model quantifies polygenic overlap between complex traits beyond genetic correlation. Nat. Commun. 10, 2417 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Zhang, Y. et al. SUPERGNOVA: local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits. Genome Biol. 22, 262 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Guo, H., Li, J. J., Lu, Q. & Hou, L. Detecting local genetic correlations with scan statistics. Nat. Commun. 12, 2033 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Cichonska, A. et al. metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis. Bioinformatics 32, 1981–1989 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Bolormaa, S. et al. A multi-trait, meta-analysis for detecting pleiotropic polymorphisms for stature, fatness and reproduction in beef cattle. PLoS Genet. 10, e1004198 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  26. The 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article  Google Scholar 

  27. Newey, W. K. & West, K. D. Hypothesis testing with efficient method of moments estimation. Int. Econ. Rev. 28, 777 (1987).

    Article  Google Scholar 

  28. Canela-Xandri, O., Rawlik, K. & Tenesa, A. An atlas of genetic associations in UK Biobank. Nat. Genet. 50, 1593–1599 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Flajnik, M. F. & Kasahara, M. Comparative genomics of the MHC: glimpses into the evolution of the adaptive immune system. Immunity 15, 351–362 (2001).

    Article  CAS  PubMed  Google Scholar 

  30. Matzaraki, V., Kumar, V., Wijmenga, C. & Zhernakova, A. The MHC locus and genetic susceptibility to autoimmune and infectious diseases. Genome Biol. 18, 76 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Howard, D. M. et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat. Neurosci. 22, 343–352 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Zhou, H. et al. Genome-wide meta-analysis of problematic alcohol use in 435,563 individuals yields insights into biology and relationships with other traits. Nat. Neurosci. 23, 809–818 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Pasman, J. A. et al. GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal influence of schizophrenia. Nat. Neurosci. 21, 1161–1170 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Nagel, M. et al. Meta-analysis of genome-wide association studies for neuroticism in 449,484 individuals identifies novel genetic loci and pathways. Nat. Genet. 50, 920–927 (2018).

    Article  CAS  PubMed  Google Scholar 

  35. Lane, J. M. et al. Genome-wide association analyses of sleep disturbance traits identify new loci and highlight shared genetics with neuropsychiatric and metabolic traits. Nat. Genet. 49, 274–281 (2017).

    Article  CAS  PubMed  Google Scholar 

  36. Mota, N. R. et al. NCAM1-TTC12-ANKK1-DRD2 gene cluster and the clinical and genetic heterogeneity of adults with ADHD. Am. J. Med. Genet. B 168, 433–444 (2015).

    Article  CAS  Google Scholar 

  37. Chauhdary, Z., Rehman, K. & Akash, M. S. H. The composite alliance of FTO locus with obesity-related genetic variants. Clin. Exp. Pharmacol. Physiol. 48, 954–965 (2021).

    Article  CAS  PubMed  Google Scholar 

  38. Smemo, S. et al. Obesity-associated variants within FTO form long-range functional connections with IRX3. Nature 507, 371–375 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Doaei, S. et al. Changes in FTO and IRX3 gene expression in obese and overweight male adolescents undergoing an intensive lifestyle intervention and the role of FTO genotype in this interaction. J. Transl. Med. 17, 1–8 (2019).

    Article  CAS  Google Scholar 

  40. Bego, T. et al. Association of FTO gene variant (rs8050136) with type 2 diabetes and markers of obesity, glycaemic control and inflammation. J. Med. Biochem. 38, 153–163 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Wang, Y., Rimm, E. B., Stampfer, M. J., Willett, W. C. & Hu, F. B. Comparison of abdominal adiposity and overall obesity in predicting risk of type 2 diabetes among men. Am. J. Clin. Nutr. 81, 555–563 (2005).

    Article  CAS  PubMed  Google Scholar 

  42. Lee, D. H. et al. Comparison of the association of predicted fat mass, body mass index, and other obesity indicators with type 2 diabetes risk: two large prospective studies in US men and women. Eur. J. Epidemiol. 33, 1113–1123 (2018).

    Article  CAS  PubMed  Google Scholar 

  43. Afroze, D., Yousuf, A., Tramboo, N. A., Shah, Z. A. & Ahmad, A. ApoE gene polymorphism and its relationship with coronary artery disease in ethnic Kashmiri population. Clin. Exp. Med. 16, 551–556 (2016).

    Article  CAS  PubMed  Google Scholar 

  44. Karjalainen, J. P. et al. New evidence from plasma ceramides links apoE polymorphism to greater risk of coronary artery disease in Finnish adults. J. Lipid Res. 60, 1622–1629 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Nelson, C. P. et al. Association analyses based on false discovery rate implicate new loci for coronary artery disease. Nat. Genet. 49, 1385–1391 (2017).

    Article  CAS  PubMed  Google Scholar 

  46. Willer, C. J. et al. Discovery and refinement of loci associated with lipid levels. Nat. Genet. 45, 1274–1285 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Pulit, S. L. et al. Meta-analysis of genome-wide association studies for body fat distribution in 694,649 individuals of European ancestry. Hum. Mol. Genet. 28, 166–174 (2019).

    Article  CAS  PubMed  Google Scholar 

  48. Forgetta, V. et al. Rare genetic variants of large effect influence risk of type 1 diabetes. Diabetes 69, 784–795 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Xue, A. et al. Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes.Nat. Commun. 9, 2941 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Okada, Y. et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 506, 376–381 (2014).

    Article  CAS  PubMed  Google Scholar 

  51. Julià, A. et al. Genome-wide association study meta-analysis identifies five new loci for systemic lupus erythematosus. Arthritis Res. Ther. 20, 100 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Liu, J. Z. et al. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat. Genet. 47, 979–986 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Folkersen, L. et al. Genomic and drug target evaluation of 90 cardiovascular proteins in 30,931 individuals. Nat. Metab. 2, 1135–1148 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Nagel, M., Watanabe, K., Stringer, S., Posthuma, D. & Van Der Sluis, S. Item-level analyses reveal genetic heterogeneity in neuroticism. Nat. Commun. 9, 905 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50, 1112–1121 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Bulik-Sullivan, B. K. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–375 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Agresti, A. An Introduction to Categorical Data Analysis. Statistics in Medicine (Wiley, 2007).

  60. Lee, S. H., Wray, N. R., Goddard, M. E. & Visscher, P. M. Estimating missing heritability for disease from genome-wide association studies. Am. J. Hum. Genet. 88, 294–305 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Werme, J. & de Leeuw, C. A. LAVA analysis scripts and package (v0.0.6). Zenodo https://doi.org/10.5281/ZENODO.5795207 (2021).

  62. de Leeuw, C. A. LAVA partitioning algorithm (v1.0.0). Zenodo https://doi.org/10.5281/ZENODO.5583779 (2021).

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Acknowledgements

This work was funded by COSYN (Comorbidity and Synapse Biology in Clinically Overlapping Psychiatric Disorders: Horizon 2020 Program of the European Union under RIA grant agreement 667301, D.P.) and the Netherlands Organization for Scientific Research (VICI 435-14-005, D.P.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. The analyses were carried out on the Genetic Cluster Computer, which is financed by the Netherlands Organization for Scientific Research (480-05-003, D.P.), VU University (Amsterdam, the Netherlands) and the Dutch Brain Foundation, hosted by the Dutch National Computing and Networking Services SurfSARA.

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Authors and Affiliations

  1. Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research, VU University, Amsterdam, the Netherlands

    Josefin Werme, Danielle Posthuma & Christiaan A. de Leeuw

  2. Section Complex Trait Genetics, Department of Child and Adolescent Psychology and Psychiatry, Amsterdam Neuroscience, VU University Medical Centre, Amsterdam, the Netherlands

    Sophie van der Sluis & Danielle Posthuma

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  1. Josefin Werme
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Contributions

J.W., S.v.d.S., D.P. and C.A.d.L. conceived of the study. J.W. and C.A.d.L. developed the statistical framework and implemented the software. J.W. performed the analyses, simulations and wrote the manuscript with contributions from C.A.d.L. J.W., S.v.d.S., D.P. and C.A.d.L. participated in the interpretation of the results and revision of the manuscript. All authors provided meaningful contributions at each stage of the project.

Corresponding authors

Correspondence to Josefin Werme or Christiaan A. de Leeuw.

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C.A.d.L. is funded by Hoffman-La Roche. The other authors declare no competing interests.

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Nature Genetics thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–18, Tables 1–3, 157 and 158, Methods and Note.

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Supplementary Tables

Tables containing the results for all genetic correlation hotspots with accompanying network plots.

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Werme, J., van der Sluis, S., Posthuma, D. et al. An integrated framework for local genetic correlation analysis. Nat Genet 54, 274–282 (2022). https://doi.org/10.1038/s41588-022-01017-y

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