Open AccessIntegration of genome‐wide association and extant brain expression QTL identifies candidate genes influencing prepulse inhibition in inbred F 1 mice
Author(s) -
Sittig L. J.,
Carbonetto P.,
Engel K. A.,
Krauss K. S.,
Palmer A. A.
Publication year - 2016
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.12262
Subject(s) - biology , genetics , quantitative trait locus , expression quantitative trait loci , single nucleotide polymorphism , inbred strain , locus (genetics) , gene , genetic association , candidate gene , genome wide association study , genotype , computational biology
Genetic association mapping in structured populations of model organisms can offer a fruitful complement to human genetic studies by generating new biological hypotheses about complex traits. Here we investigated prepulse inhibition ( PPI ), a measure of sensorimotor gating that is disrupted in a number of psychiatric disorders. To identify genes that influence PPI , we constructed a panel of half‐sibs by crossing 30 females from common inbred mouse strains with inbred C57BL / 6J males to create male and female F 1 offspring. We used publicly available single nucleotide polymorphism ( SNP ) genotype data from these inbred strains to perform a genome‐wide association scan using a dense panel of over 150 000 SNPs in a combined sample of 604 mice representing 30 distinct F 1 genotypes. We identified two independent PPI ‐associated loci on Chromosomes 2 and 7, each of which explained 12–14% of the variance in PPI . Searches of available databases did not identify any plausible causative coding polymorphisms within these loci. However, previously collected expression quantitative trait locus ( eQTL ) data from hippocampus and striatum indicated that the SNPs on Chromosomes 2 and 7 that showed the strongest association with PPI were also strongly associated with expression of several transcripts, some of which have been implicated in human psychiatric disorders. This integrative approach successfully identified a focused set of genes which can be prioritized for follow‐up studies. More broadly, our results show that F 1 crosses among common inbred strains can be used in combination with other informatics and expression datasets to identify candidate genes for complex behavioral traits.