Introduction

1. A Comparative Analysis of Genome-Wide Epistatic Genetic Variation for Circadian Locomotor Activity in C57BL x Balb Mice and Two Drosophila Recombinant Inbred Strains Fleri A, Ullrich B, Possidente D, Sheppard A, Possidente B Skidmore College, Saratoga Springs, NY Dept. of Biology USA SUNY Albany Albany, NY Dept. of Biology USA CIRCADIAN BEHAVIOR ASSAY FLIES: Approximately 25 male flies for each of the seven genotypes, aged 1-7 days post-eclosion were assayed for circadian locomotor behavior for 3 days in 12:12 LD followed by 9 days in DD in Trikinetics Drosophila Activity Monitors (Fig 1). TauDD was estimated by chi-square periodogram and sleep score (SLP) by the mean number of 10-minute intervals per day with no activity. Mean activity in 12:12 LD (XLD), mean activity in the light phase in LD (XL), mean activity in the dark phase in LD, mean activity in DD were measured per ten-minute interval in each condition. MICE: Ten males per cross were assayed for 14 days in 12:12 LD and 14 days in DD in Minimitter running wheels. Data were collected at 10 minute intervals and tauDD estimated using chi-square periodogram. Activity counts per 10min interval were estimated for 12:12 LD (XLD), the light phase of LD (XL), the dark phase of LD (XD) and constant dark. Free-running period in DD (tauDD) was estimated by chi-square periodogram. RESULTS DROSOPHILA: Significant additive genetic variance was observed for XL, XD, DD and tauDD and SLP score. Significant dominance variance was observed for XLD, XD, XDD and SLP score. No significant epistatic genetic variance was observed (Tables 1,2). TABLE 1. VARIANCE COMPONENTS XLD XL XD XDD TauDD SLP Ve 3.1 3.5 4.9 3.5 0.32 498 Va 0.1 0.7* 1.7* 1.5* 0.06* 83* Vd 0.6* 0.3 1.0* 0.8 0.00 162* Vi 1.2 1.9 - 3.6 0.24 - Total 4.9 6.5 6.4 9.3 0.63 484 *p<0.05 TABLE 2. GENOTYPE MEANS P1 4.5 4.4 4.6 4.8 24.35 68 P2 4.8 3.3 6.4 6.5 23.64 55 F1(p1xp2) 2.7 2.8 2.7 3.0 24.06 95 F1(p2xp1) 3.5 2.6 4.4 4.6 24.05 78 BC(F1xp1) 5.1 5.2 5.1 7.0 24.72 61 BC(F1xp2) 5.1 4.5 5.6 5.7 23.84 59 F2 3.9 3.8 4.0 5.4 24.11 78 Introduction Conclusions OBJECTIVE: To estimate genome-wide components of additive, dominance, epistatic and non-genetic variance components for circadian locomotor activity in fruit flies and mice. PREVALENCE OF EPISTASIS: No significant components of epistatic genetic variance were observed. All traits, however, displayed significant components of additive or dominance genetic variance, or both. These results suggest that epistatic genetic variance is, overall, a relatively minor component of genetic variance for circadian behavior. STRENGTHS OF BIOMETRICAL GENETIC ANALYSIS: The biometrical genetic assay used here provides a relatively simple method for estimating genome-wide sources of genetic variance, including epistatic, that contribute to a complex trait. This may be useful as a screen for inbred strains in studies where the objective is to either investigate, or avoid significant epistatic genetic mechanisms. WEAKNESSES OF BIOMETRICAL GENETIC ANALYSIS: The approach used here has several significant limitations. One is that the results only apply to the two parental inbred strain genotypes tested so generalizations can’t be made beyond those genotypes unless additional strains are tested. A second limitation is that individual loci can not be identified. Quantitative Trait Locus (QTL) analysis, however, can be applied to the F2 generation to screen identified QTL for epistatic interactions. This approach has been used successfully by Shimomura et al. (2002) to identify significant epistatic interactions among QTL for phase, period, amplitude and fragmentation of circadian activity rhythms in the inbred strains of mice analyzed here. Their epistatic interactions explained approximately 5-15% of the variance for these traits. This discrepancy between the present results and Shimomura et al. (2002) highlights a third limitation of the biometrical method: there is more statistical power for detecting additive and dominance than there is for epistasis in this design. This is evident from the relatively large residual variance components attributable to epistasis (by subtraction) that were not detected as significant, even though much smaller variances attributable to additive or dominance effects were statistically. One modification that might be reasonable, given that the other variance components can be estimated directly and with reasonable precision (and given the results of Shimomura et al.) might be to assume that any epistatic variance component equal to or greater in magnitude to a statistically significant additive or dominance component is likely to represent meaningful epistasis detectable by QTL analysis. OVERALL CONCLUSION: In both studies, epistatic contributions were relatively low to non-significant, but the QTL analysis has more statistical power to detect relatively minor epistatic contributions and map them to identified regions of a chromosome. The biometrical technique, however, is much simpler and can be done relatively easily as a screen for likely epistatic interactions in specific inbred strain genotypes. MICE: Significant additive genetic variance was observed for XL and Taudd. Significant genetic variance occurred for XLD, XD, XDD and Taudd. No significant epistatic genetic variance was observed (Tables 3,4). TABLE 3. VARIANCE COMPONENTS XLD XL XD XDD Taudd Ve 27.6 35.6 866 489 0.11 Va 25.8 7.1* 56 21 0.09* Vd 111* 0.9 403* 123* 0.02* Vi 187 - 129 26 - Total: 358 39.0 1455 658 0.18 *p<0.05 TABLE 4. GENOTYPE MEANS BALB 52 17 88 68 22.82 C57 38 9 66 57 23.69 F1 66 15 128 84 23.54 BC(F1xB) 65 16 113 76 23.40 BC(F1xC) 53 12 94 67 23.52 F2 40 12 70 63 23.49 Genetic variance in quantitative traits results from the combined additive (direct), dominant (interactive within loci) and epistatic (interactive among loci) effects of allelic variation.  Generally, it is more difficult to estimate components of epistatic genetic variation than additive and dominance components, so the relative importance of epistasis as a contributor to genetic variation in behavior is uncertain. Epistatic interactions have been identified for some circadian quantitative trait loci (QTL) (1), and are evident when effects of major mutations or transgenic constructs are altered in new genetic backgrounds. Classic biometrical genetic analysis permits the partitioning of genome-wide additive, dominance, epistatic and non-genetic sources of phenotypic variation in certain experimental designs. One of the simplest, the derived generations from two inbred strains, is used here to partition genetic variance components for circadian behavior in mice and Drosophila. Figure 1. Drosophila Activity Monitor (TriKinetics Inc., Waltham, MA). Each monitor records activity from 32 individual flies. XLD XL XD XDD TauDD Materials and methods SUBJECTS: Balb/cJ and C57BL/6J inbred mouse strains, and inbred fruit fly strains number 11 and 70 from the recombinant inbred “roo” lines (2) were each bred to simultaneously produce the following Mendelian generations: CROSSES: Mice Flies P1: C57BL 11 P2: Balb 70 F1: C57 x Balb 11 x 70 and 70 x 11 (reciprocals) BackCross To P1: F1 x C57 F1 (11x70) x 11 To P2: F1 x Balb F1 (11x70) x 70 F2: F1 x F1 F1 (11x70) x F1 (11x70) GENETIC VARIANCE ANALYSIS: Additive, dominance, epistatic and non-genetic variance components were estimated as follows according to Mather and Jinks (3): Additive= VA: from inbred strain difference (P1 vs. P1) Dominance= VD: from F1 vs. P1+ P2 strain midpoint Non-Genetic=VE: From variance within isogenic strains (P1, P2, F1) Epistasis=VI: Mather & Jinks A,B,C scalar tests for epistasis as deviation of cross means from linear model predicted from observed additive and dominance effects, estimated by subtracting Va, Vd and Ve from Total Variance. ACTOGRAMS FOR Drosophila: Composite actograms for each genotype, with three days in 12:12 LD followed by nine days in DD, are shown. ACTOGRAMS FOR MICE: Representative actograms for each genotype, with 14 days in 12:12 LD followed By 14 days in DD are shown. • LITERATURE CITED • Carlborg O, Haley CS (2004) Epistasis: Too often neglected on complex trait • studies? Nature Reviews Genetics 5:619-623. • 2. Nuzhdin SV, Pasyukova EG, Dilda CI, Zeng ZB, Mackay TF (1997) Sex-specific quantitative trait loci affecting longevity in Drosophila melanogaster. • PNAS 94:9734-9739. • Mather K, Jinks JL (1977 Introduction to Biometrical Genetics. Cornell • University Press, Ithaca NY, USA. • 4. Shimomura K, Low-Zeddies SS, King DP, Steeves TD, Whitely A, Kushla J, • Zemenides PD, Lin A, Vitaterna MH, Churchill GA, Takahashi JS (2001) • Genome-wide epistatic interaction analysis reveals complex genetic determinants of circadian behavior in mice. Genome Res 11(6):959-980. XLD XL XD XDD TauDD SLP