TY - JOUR

T1 - A Novel Framework for Analysis of the Shared Genetic Background of Correlated Traits

AU - Svishcheva, Gulnara R.

AU - Tiys, Evgeny S.

AU - Elgaeva, Elizaveta E.

AU - Feoktistova, Sofia G.

AU - Timmers, Paul R.H.J.

AU - Sharapov, Sodbo Zh

AU - Axenovich, Tatiana I.

AU - Tsepilov, Yakov A.

N1 - Funding Information: 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). Publisher Copyright: © 2022 by the authors.

PY - 2022/10

Y1 - 2022/10

N2 - 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.

AB - 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.

KW - GWAS

KW - linear combination of traits

KW - proportion of heritability explained by SGF

KW - shared genetic component

KW - shared heritability

KW - Polymorphism, Single Nucleotide/genetics

KW - Genome-Wide Association Study

KW - Phenotype

KW - Lipids

KW - Genetic Background

UR - http://www.scopus.com/inward/record.url?scp=85140762490&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/edfa5a28-bdf3-34fd-baac-038af32244d8/

U2 - 10.3390/genes13101694

DO - 10.3390/genes13101694

M3 - Article

C2 - 36292579

AN - SCOPUS:85140762490

VL - 13

JO - Genes

JF - Genes

SN - 2073-4425

IS - 10

M1 - 1694

ER -