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Multiple genetic loci affect place learning and memory performance in Drosophila melanogaster
Author(s) -
WilliamsSimon Patricka A.,
Posey Christopher,
Mitchell Samuel,
Ng'oma Enoch,
Mrkvicka James A.,
Zars Troy,
King Elizabeth G.
Publication year - 2019
Publication title -
genes, brain and behavior
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.315
H-Index - 91
eISSN - 1601-183X
pISSN - 1601-1848
DOI - 10.1111/gbb.12581
Subject(s) - drosophila melanogaster , biology , genetics , inbred strain , transcriptome , candidate gene , population , drosophila (subgenus) , set (abstract data type) , gene , evolutionary biology , computer science , gene expression , demography , sociology , programming language
Learning and memory are critical functions for all animals, giving individuals the ability to respond to changes in their environment. Within populations, individuals vary, however the mechanisms underlying this variation in performance are largely unknown. Thus, it remains to be determined what genetic factors cause an individual to have high learning ability and what factors determine how well an individual will remember what they have learned. To genetically dissect learning and memory performance, we used the Drosophila synthetic population resource (DSPR), a multiparent mapping resource in the model system Drosophila melanogaster , consisting of a large set of recombinant inbred lines (RILs) that naturally vary in these and other traits. Fruit flies can be trained in a “heat box” to learn to remain on one side of a chamber (place learning) and can remember this (place memory) over short timescales. Using this paradigm, we measured place learning and memory for ~49 000 individual flies from over 700 DSPR RILs. We identified 16 different loci across the genome that significantly affect place learning and/or memory performance, with 5 of these loci affecting both traits. To identify transcriptomic differences associated with performance, we performed RNA‐Seq on pooled samples of seven high performing and seven low performing RILs for both learning and memory and identified hundreds of genes with differences in expression in the two sets. Integrating our transcriptomic results with the mapping results allowed us to identify nine promising candidate genes, advancing our understanding of the genetic basis underlying natural variation in learning and memory performance.

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