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

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

Gulnara R Svishcheva  1   2 , Evgeny S Tiys  1 , Elizaveta E Elgaeva  1   3 , Sofia G Feoktistova  1 , Paul R H J Timmers  4   5 , Sodbo Zh Sharapov  1   3 , Tatiana I Axenovich  1 , Yakov A Tsepilov  1   3
Affiliations

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

Gulnara R Svishcheva et al. Genes (Basel). .

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.

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Conflict of interest statement

P.R.H.J.T. is an employee of BioAge Labs. The remaining authors declare that they have no conflict of interest.

Figures

Figure 1
Figure 1
Flowchart of the SHAHER framework. Details are given in the text.
Figure 2
Figure 2
A schematic depicting the overall workflow of a simulation study. All details are given in the text.
Figure 3
Figure 3
The plot of the shared heritability of SGIT (the maxSH method) versus the shared heritability of GIP1 (the GIP method). For different h2 and different number of traits (Ntr) for s = 0.3.
Figure 4
Figure 4
Results of the application of SHAHER to anthropometric traits. (A) The heatmap of genetic correlations between the original, SGI and UGI traits. The number, color strength and size of the squares in the matrix show the values of the correlation coefficients between the traits. The diagonal elements represent heritabilities. Crossed out values indicate insignificant correlations. (B) Boxplots of −log10(p-value) for the SGIT with respect to the number of the original traits significantly associated with the locus. Two outliers for loci with −log10(p-value) > 40 are omitted. The number at the top of the boxplot corresponds to the number of significant SNPs. (C) The heatmap of the numbers of overlapping loci between traits. The numbers in the cells represent the absolute numbers of overlapping loci. The color strength and size of the squares in the cells show the relative scaled number of overlapping loci (on a scale from 0 to 1). The diagonal elements represent the number of loci found for every trait. (D) The heatmap of the numbers of overlapping gene sets between traits. The color strength and size of the squares in the cells show the relative scaled number of overlapping gene sets (on a scale from 0 to 1). The diagonal elements represent the number of gene sets found for every trait.
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