Optimized phenotyping of complex morphological traits: enhancing discovery of common and rare genetic variants

Yuan, Meng, Goovaerts, Seppe, Lee, Myoung K., Devine, Jay, Richmond, Stephen ORCID: https://orcid.org/0000-0001-5449-5318, Walsh, Susan, Shriver, Mark D., Shaffer, John R., Marazita, Mary L., Peeters, Hilde, Weinberg, Seth M. and Claes, Peter 2025. Optimized phenotyping of complex morphological traits: enhancing discovery of common and rare genetic variants. Briefings in Bioinformatics 26 (2) , bbaf090. 10.1093/bib/bbaf090

PDF - Published Version Available under License Creative Commons Attribution Non-commercial. Download (1MB)

Abstract

Genotype–phenotype (G-P) analyses for complex morphological traits typically utilize simple, predetermined anatomical measures or features derived via unsupervised dimension reduction techniques (e.g. principal component analysis (PCA) or eigen-shapes). Despite the popularity of these approaches, they do not necessarily reveal axes of phenotypic variation that are genetically relevant. Therefore, we introduce a framework to optimize phenotyping for G-P analyses, such as genome-wide association studies (GWAS) of common variants or rare variant association studies (RVAS) of rare variants. Our strategy is two-fold: (i) we construct a multidimensional feature space spanning a wide range of phenotypic variation, and (ii) within this feature space, we use an optimization algorithm to search for directions or feature combinations that are genetically enriched. To test our approach, we examine human facial shape in the context of GWAS and RVAS. In GWAS, we optimize for phenotypes exhibiting high heritability, estimated from either family data or genomic relatedness measured in unrelated individuals. In RVAS, we optimize for the skewness of phenotype distributions, aiming to detect commingled distributions that suggest single or few genomic loci with major effects. We compare our approach with eigen-shapes as baseline in GWAS involving 8246 individuals of European ancestry and in gene-based tests of rare variants with a subset of 1906 individuals. After applying linkage disequilibrium score regression to our GWAS results, heritability-enriched phenotypes yielded the highest SNP heritability, followed by eigen-shapes, while commingling-based traits displayed the lowest SNP heritability. Heritability-enriched phenotypes also exhibited higher discovery rates, identifying the same number of independent genomic loci as eigen-shapes with a smaller effective number of traits. For RVAS, commingling-based traits resulted in more genes passing the exome-wide significance threshold than eigen-shapes, while heritability-enriched phenotypes lead to only a few associations. Overall, our results demonstrate that optimized phenotyping allows for the extraction of genetically relevant traits that can specifically enhance discovery efforts of common and rare variants, as evidenced by their increased power in facial GWAS and RVAS.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Dentistry
Publisher:	Oxford University Press
ISSN:	1467-5463
Date of First Compliant Deposit:	23 March 2025
Date of Acceptance:	18 February 2025
Last Modified:	25 Mar 2025 10:15
URI:	https://orca.cardiff.ac.uk/id/eprint/177099

Actions (repository staff only)

Edit Item