bioRxivGeneticsPreprint
Genome-wide association study of open fieldbehavior in outbred heterogeneous stock ratsidentifies multiple loci implicated inpsychiatric disorders
Mustafa Hakan GunturkuniD, Tengfei WangiD, Apurva S. ChitreiD, Angel Garcia Martinez, Katie HolliD, Celine L. St. PierreiD, Hannah V. BimschlegeriD, Jianjun GaoiD, Riyan ChengiD, Oksana PolesskayaiD, Leah Solberg‐Woods, Abraham A. PalmeriD, Hao CheniD
Abstract
Many personality traits are influenced by genetic factors. Rodents models provide an efficient system for analyzing genetic contribution to these traits. Using 1,246 adolescent heterogeneous stock (HS) male and female rats, we conducted a genome-wide association study (GWAS) of behaviors measured in an open field, including locomotion, novel object interaction, and social interaction. We identified 30 genome-wide significant quantitative trait loci (QTL). Using multiple criteria, including the presence of high impact genomic variants and co-localization of cis-eQTL, we identified 13 candidate genes (Adarb2, Ankrd26, Cacna1c, Clock, Crhr1, Ctu2, Cyp26b1, Eva1a, Fam114a1, Kcnj9, Mlf2, Rab27b, Sec11a) for these traits. Most of these genes have been implicated by human GWAS of various psychiatric traits. For example, Cacna1c, a gene known to be critical for social behavior in rodents and implicated in human schizophrenia and bipolar disorder, is a candidate gene for distance to the social zone. In addition, the QTL region for total distance to the novel object zone, on Chr1 at 144 Mb, is syntenic to a hotspot on human Chr15 (82.5-90.8 Mb) that contains 14 genes associated with psychiatric or substance abuse traits. Although some of the genes identified by this study appear to replicate findings from prior human GWAS, others likely represent novel findings that can be the catalyst for future molecular and genetic insights into human psychiatric diseases. Together, these findings provide strong support for the use of the HS population to study psychiatric disorders.
- organism: rat
- assay: whole-genome sequencing
- research focus: GWAS / association mapping
- approach: statistical model
- Citations
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