Joint QTL mapping reveals patterns of pleiotropy across structured environments in budding yeast

Quantitative trait locus (QTL) mapping studies in model organisms have long been used to infer the structure of genotype-phenotype maps. However, prior work has largely focused on analyzing the genetic architecture of complex traits individually. Here, we sought to instead infer genotype-phenotype maps for a large set of related phenotypes simultaneously. To do so, we used bulk assays to measure the competitive fitness of each of 100,000 individuals from a large budding yeast QTL mapping panel across a set of 101 phenotypes related by a ``hub-and-perturbation'' pattern, and then used a forward search approach to jointly infer the genetic basis of all phenotypes together. Our results reveal complex patterns of pleiotropy in the genetic architecture of these traits. We find that these patterns of pleiotropy reflect the structure of the environmental relationships as well as functional information about individual variants and the interactions between them. This suggests that high-throughput QTL mapping across structured environments can help uncover biologically meaningful genetic interactions, along with their variation across related environmental contexts.

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Publication Details

Journal
bioRxiv (Cold Spring Harbor Laboratory)
Published
2026-10-31
DOI
https://doi.org/10.64898/2026.10.30.763211
Primary Topic
Genetic Mapping and Diversity in Plants and Animals
Type
preprint
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preprint

Joint QTL mapping reveals patterns of pleiotropy across structured environments in budding yeast

Eliot F. Fenton, Artur Rego‐Costa, Michael M. Desai, Samantha Petti et al.
bioRxiv (Cold Spring Harbor Laboratory)
Genetic Mapping and Diversity in Plants and Animals
preprint

Joint QTL mapping reveals patterns of pleiotropy across structured environments in budding yeast

Eliot F. Fenton, Artur Rego‐Costa, Michael M. Desai, Samantha Petti, Shreyas Gopalakrishnan
preprint en

Abstract

Quantitative trait locus (QTL) mapping studies in model organisms have long been used to infer the structure of genotype-phenotype maps. However, prior work has largely focused on analyzing the genetic architecture of complex traits individually. Here, we sought to instead infer genotype-phenotype maps for a large set of related phenotypes simultaneously. To do so, we used bulk assays to measure the competitive fitness of each of 100,000 individuals from a large budding yeast QTL mapping panel across a set of 101 phenotypes related by a ``hub-and-perturbation'' pattern, and then used a forward search approach to jointly infer the genetic basis of all phenotypes together. Our results reveal complex patterns of pleiotropy in the genetic architecture of these traits. We find that these patterns of pleiotropy reflect the structure of the environmental relationships as well as functional information about individual variants and the interactions between them. This suggests that high-throughput QTL mapping across structured environments can help uncover biologically meaningful genetic interactions, along with their variation across related environmental contexts.

bioRxiv (Cold Spring Harbor Laboratory)
Tufts University (US), Harvard University (US)
Genetic Mapping and Diversity in Plants and Animals
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