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Sam Yeaman

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Open access Jul 2026

A conundrum of pleiotropy: GWAS-derived pleiotropy is weakly negatively associated with co-expression network centrality in Arabidopsis thaliana

Pleiotropy, the influence of a single gene on multiple phenotypic traits, is a fundamental feature of genetic systems but remains difficult to quantify at genome-wide scales. Genome-wide association studies (GWAS) provide one avenue to characterize pleiotropy through multi-trait genetic associations, while functional genomic approaches such as gene co-expression networks offer indirect predictions of gene importance and pleiotropic potential based on network centrality statistics. However, the relationship between GWAS-derived pleiotropy and network-based measures of gene importance remains unclear. Here, we quantified gene-level pleiotropy in Arabidopsis thaliana using gene-trait associations from the AraGWAS Catalog and AraPheno databases and compared them to previous estimates of pleiotropy based on gene co-expression networks. GWAS pleiotropy was measured using the Hill number of order q = 1, an entropy-based metric adapted from ecological diversity theory. Across 2,179 genes, pleiotropy exhibited a right-skewed distribution, with most genes influencing relatively few traits and a smaller subset exhibiting broad multi-trait effects. Contrary to expectations, pleiotropy was weakly negatively correlated with multiple measures of co-expression network centrality, including degree, strength, closeness, and betweenness. Additional analyses showed that GWAS-associated genes were enriched within the co-expression network, exhibited broader expression across tissues, and were modestly enriched among conserved BUSCO genes. Together, these findings reveal a conundrum: although genes identified by GWAS are broadly expressed and integrated into functional networks, the most pleiotropic genes occupy relatively peripheral positions within those networks. This unexpected pattern suggests that GWAS-derived pleiotropy captures a distinct aspect of gene function than co-expression network centrality.

Chatendeep Gill, Sam Yeaman · bioRxiv · 0 citations

Open access Aug 2026

A simulation-based method for genotype-environment association analysis

Genotype-environment association (GEA) analyses are widely used to identify loci underlying local adaptation by examining correlations between allele frequencies and environmental variables across a species’ range. A major challenge for this approach is distinguishing true adaptive signals from spurious associations arising from population structure. Several methods have been developed to account for population structure, but these methods can suffer from reduced statistical power or increased false positives under some conditions. To address this, we introduce a new GEA method, termed SimGEA. In essence, SimGEA infers a neutral evolutionary model that reproduces the population structure observed in empirical data and uses this model to simulate neutral alleles. By applying the same GEA statistic to both the empirical and simulated data, SimGEA evaluates the significance of observed associations against neutral expectations that account for population structure. We compared the performance of SimGEA with that of existing GEA methods, including LFMM2 and BayPass, using simulations of local adaptation in two-dimensional space. We found that SimGEA consistently controlled the false discovery rate without substantially sacrificing statistical power across the scenarios examined. These results suggest that calibrating statistics using neutral simulations provides a robust and flexible approach for accounting for population structure in GEA analyses.

Takahiro Sakamoto, Sam Yeaman · bioRxiv · 0 citations