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Connecting thermal performance curve variation to the genotype: a multivariate QTL approach
Author(s) -
Latimer C. A. L.,
Foley B. R.,
Chenoweth S. F.
Publication year - 2015
Publication title -
journal of evolutionary biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.289
H-Index - 128
eISSN - 1420-9101
pISSN - 1010-061X
DOI - 10.1111/jeb.12552
Subject(s) - biology , quantitative trait locus , adaptation (eye) , multivariate statistics , genetic variation , population , trait , generalist and specialist species , evolutionary biology , genotype , selection (genetic algorithm) , genetics , statistics , ecology , gene , mathematics , machine learning , habitat , demography , neuroscience , sociology , computer science , programming language
Thermal performance curves ( TPC s) are continuous reaction norms that describe the relationship between organismal performance and temperature and are useful for understanding trade‐offs involved in thermal adaptation. Although thermal trade‐offs such as those between generalists and specialists or between hot‐ and cold‐adapted phenotypes are known to be genetically variable and evolve during thermal adaptation, little is known of the genetic basis to TPC s – specifically, the loci involved and the directionality of their effects across different temperatures. To address this, we took a multivariate approach, mapping quantitative trait loci ( QTL ) for locomotor activity TPC s in the fly, D rosophila serrata, using a panel of 76 recombinant inbred lines. The distribution of additive genetic (co)variance in the mapping population was remarkably similar to the distribution of mutational (co)variance for these traits. We detected 11 TPC QTL in females and 4 in males. Multivariate QTL effects were closely aligned with the major axes genetic (co)variation between temperatures; most QTL effects corresponded to variation for either overall increases or decreases in activity with a smaller number indicating possible trade‐offs between activity at high and low temperatures. QTL representing changes in curve shape such as the ‘generalist–specialist’ trade‐off, thought key to thermal adaptation, were poorly represented in the data. We discuss these results in the light of genetic constraints on thermal adaptation.