Our first objective was to test whether the aggression phenotype of NC900 mice is controlled primarily by a locus with large effect using a small NC900 × B6 F2 cross well powered to detect QTL with large effect. While no such result was identified, we were able to map QTL for social behavior traits despite a relative lack of power to detect loci with modest effect. A single QTL for aggression (percent duration count and attack count) was detected on MMU19 but had a paradoxical genetic effect. The B6 parental line, which rarely exhibits aggression in our social interaction test, contributed the high aggression allele. While QTL describing transgressive variation (i.e. in this case where the allele from the C57BL/6J, rather than the NC900 increased attack levels) are relatively common (Rieseberg, 1999), they are usually detected within a broad architecture dominated by QTL allelic effects that are in the expected direction based on parental phenotypic divergence [21
]. Our results suggest that the NC900 mouse inter-male aggression phenotype is likely controlled by many additional QTL, each having effects too small to detect in the present F2 population.
Consideration of NC900 aggression as a complex trait is consistent with the genetic architecture of aggression in Drosophila where a minimum of 5 aggression QTL and extensive epistasis were detected [13
]. A cross of NC900 and NC100, while technically more difficult to evaluate (e.g. allele sharing), would be required to rule out the possibility that a genetic locus with a large effect was segregating in the ICR base population and contributed to the NC900 selection response, but is fixed in the same direction in the B6 line and thus would not be detected in this cross. The aggression QTL we detected do not correspond to those previously reported in mice [10
], which is not surprising for several reasons: 1) different methods of aggression behavior measurement; 2) different pre-aggression test housing environments; 3) laboratory-to-laboratory variation, and 4) different genetic background. It is particularly important to note that the aggression QTL we detected are potentially unique to the type of open field, social novelty aggression testing we used vs. resident intruder test [10
]. As an example, Roubertoux and colleagues found that differences in rearing and testing conditions produced stark differences in the genetic correlates of aggressive behavior [11
Extensive dominance gene action was evident for aggression traits, and significant over/under-dominance effects for other social behavior traits were also evident. Strong heterosis may imply that these traits are relevant to overall fitness [26
]. From an evolutionary perspective, social behaviors like intermale aggression are likely important components of fitness, because they affect the ability to produce offspring. In a laboratory setting, dominant males sire > 90% of the offspring [27
], but it is not known to what degree social behavior like aggression impacts fitness in wild mice. We have observed that overdominant gene action is common for other traits that affect overall fitness in mice such as litter size [22
We observed qualitative differences in individual attack styles. Highly aggressive mice were noted for higher attack speed and more attacks described as front attacks. We did not attempt to assess individual differences in counts of front, side, and rear attacks in this study, because these types of attacks were realized retrospectively. We also lacked means to measure attack speed in the present study. Given the degree of the striking qualitative differences in attack speed and style we observed, such measures are warranted in the future. Future studies should also consider the possibility that the genotypes of the standard opponents indirectly interact with the genotypes of the subjects [28
]. We selected B6 standard opponents to in an effort to avoid confounding aggressive subject vs. aggressive social partner interactions, but did not account for F2 phenotypic variation that may be attributable to indirect genetic interactions between B6 standard opponent genotypes and various mixtures of NC900 × B6 F2 genotypes.
The peaks of QTL for attack latency and approach on MMU7 fall ~10 Mb from the tyrosinase
). This is noteworthy because it has long been known that the tyr
locus is associated with behavioral differences [29
]. Since NC900 mice are albino, and albinism is caused by a recessive point mutation in tyr
], resulting in the absence of melanin production in the hair, skin, and eyes, it is conceivable that coat color represents a marker for attack latency and approach. Even though attack latency was never a NC900 high-aggression selection criterion, mean attack latencies significantly decreased across generations of selection for high levels of attack (unpublished data). We have reported effects of coat color on wheel running speed and also detected a QTL controlling mouse voluntary wheel running speed linked to tyr
]. In addition, effects of coat color on open-field activity have been reported [32
] and a contextual fear conditioning QTL has also been associated with the tyr
]. It is tempting to speculate that attack latency, wheel running speed, and fear conditioning QTL linked to the tyr
locus share common functional variation manifested by control over the pace with which animals perform motivated behaviors like attack, fear conditioning, and wheel running. Alternatively, the attack latency QTL could relate to visual defects caused by the absence of melanin. For example, it is feasible that a lack of melanin could augment sensitivity to light in albino mice [35
] resulting in an enhanced light-induced stress response. Thus, the faster attack latencies observed by F2 albino mice may be attributable to indirect effects of the tyr
locus on the stress response rather than direct effects of tyr
attack latency and approach tendencies per se.
High-density genotyping can be used to identify regions that are identical by descent (IBD) between strains used in mapping populations to refine QTL candidate intervals. Regions of IBD within QTL confidence intervals should be excluded as candidate QTL regions while regions with more than one haplotype remain. Based on our analysis, we can exclude 22% of the QTL confidence intervals indentified in this study.
QTL mapping was performed using methods that assume that our mapping population is analogous to a standard F2 population derived from two inbred strains. In contrast with these assumptions, our high-density genotype analysis shows that one-third of the genome is, in fact, segregating in a more complex pattern. This complex pattern poses challenges in QTL mapping especially in crosses with widely variable MAF distributions across the genome (Additional File 2
, Figure S1). We suggest that crosses involving selection lines use a combination of markers that distinguish genetic variation both between and within the selection lines. Ignoring segregating regions is likely to result in overlooking loci that have been otherwise identified. This should be particularly relevant to populations in which heterozygosity has been selected for a particular phenotype. The size of the segregating regions, the presence of a very narrow bottleneck of only 3 breeding pairs (see Materials and Methods), and the distribution of MAF in the six NC900 females used in this study suggest that maintenance of heterozygosity may have been under selection during creation of the high-aggression NC900 line. Furthermore, the size of the segregating regions is smaller than in other selection lines derived from similar ICR stock and genotyped with the same high-density platform (FPMV and DP unpublished).