Joint QTL mapping reveals patterns of pleiotropy across structured environments in budding yeast
- 1Harvard University
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.
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BibTeX
@misc{gopalakrishnan2026joint,
title = {{Joint QTL mapping reveals patterns of pleiotropy across structured environments in budding yeast}},
author = {S. Gopalakrishnan and E. F. Fenton and A. Rego-Costa and S. Petti and M. M. Desai},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.30.763211},
url = {https://www.biorxiv.org/content/10.64898/2026.10.30.763211v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - Joint QTL mapping reveals patterns of pleiotropy across structured environments in budding yeast
AU - S. Gopalakrishnan
AU - E. F. Fenton
AU - A. Rego-Costa
AU - S. Petti
AU - M. M. Desai
PY - 2026
DA - 2026/10/30
PB - bioRxiv
DO - 10.64898/2026.10.30.763211
UR - https://www.biorxiv.org/content/10.64898/2026.10.30.763211v1
AB - 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.
SN - 2692-8205
ER -