A Novel Framework for Analysis of the Shared Genetic Background of Correlated Traits

Gulnara R Svishcheva; Evgeny S Tiys; Elizaveta E Elgaeva; Sofia G Feoktistova; Paul R H J Timmers; Sodbo Zh Sharapov; Tatiana I Axenovich; Yakov A Tsepilov

doi:10.3390/genes13101694

A Novel Framework for Analysis of the Shared Genetic Background of Correlated Traits

Genes (Basel). 2022 Sep 21;13(10):1694. doi: 10.3390/genes13101694.

Authors

Gulnara R Svishcheva^{1

2}, Evgeny S Tiys¹, Elizaveta E Elgaeva^{1

3}, Sofia G Feoktistova¹, Paul R H J Timmers^{4

5}, Sodbo Zh Sharapov^{1

3}, Tatiana I Axenovich¹, Yakov A Tsepilov^{1

3}

Affiliations

¹ Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia.
² Vavilov Institute of General Genetics, Russian Academy of Sciences, 117971 Moscow, Russia.
³ Novosibirsk State University, 630090 Novosibirsk, Russia.
⁴ MRC Human Genetics Unit, MRC Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH8 9YL, UK.
⁵ Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh EH8 9YL, UK.

Abstract

We propose a novel effective framework for the analysis of the shared genetic background for a set of genetically correlated traits using SNP-level GWAS summary statistics. This framework called SHAHER is based on the construction of a linear combination of traits by maximizing the proportion of its genetic variance explained by the shared genetic factors. SHAHER requires only full GWAS summary statistics and matrices of genetic and phenotypic correlations between traits as inputs. Our framework allows both shared and unshared genetic factors to be effectively analyzed. We tested our framework using simulation studies, compared it with previous developments, and assessed its performance using three real datasets: anthropometric traits, psychiatric conditions and lipid concentrations. SHAHER is versatile and applicable to summary statistics from GWASs with arbitrary sample sizes and sample overlaps, allows for the incorporation of different GWAS models (Cox, linear and logistic), and is computationally fast.

Keywords: GWAS; linear combination of traits; proportion of heritability explained by SGF; shared genetic component; shared heritability.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Genetic Background
Genome-Wide Association Study*
Lipids
Phenotype
Polymorphism, Single Nucleotide* / genetics

Substances

Lipids

Grants and funding

The work of GRS was supported by the Russian Foundation for Basic Research (project 20-04-00464). The work of Y.A.T. and T.I.A. was supported by the Russian Science Foundation (RSF) grant and Government of the Novosibirsk region No. 22-15-20037. The work of E.E.E. was supported by the grant for the implementation of the strategic academic leadership program “Priority 2030” in Novosibirsk State University. The work of S.Z.S. was supported by budget project No. FWNR-2022-0020. The work of PRHJT was supported by the Medical Research Council Human Genetics Unit (MC_UU_00007/10).