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Behavioral variation within and between populations and species of the genus Papio has been studied extensively, but little is known about the genetic causes of individual- or population-level differences. This study investigates the influence of genetic variation on personality (sometimes referred to as temperament) in baboons and identifies a candidate gene partially responsible for the variation in that phenotype. To accomplish these goals, we examined individual variation in response to both novel objects and an apparent novel social partner (using a mirror test) among pedigreed baboons (n = 578) from the Southwest National Primate Research Center. We investigated the frequency and duration of individual behaviors in response to novel objects and used multivariate factor analysis to identify trait-like dimensions of personality. Exploratory factor analysis identified two distinct dimensions of personality within this population. Factor 1 accounts for 46.8 % of the variance within the behavioral matrix, and consists primarily of behaviors related to the "boldness" of the subject. Factor 2 accounts for 18.8 % of the variation, and contains several "anxiety" like behaviors. Several specific behaviors, and the two personality factors, were significantly heritable, with the factors showing higher heritability than most individual behaviors. Subsequent analyses show that the behavioral reactions observed in the test protocol are associated with animals' social behavior observed later in their home social groups. Finally we used linkage analysis to map quantitative trait loci for the measured phenotypes. Single nucleotide polymorphisms in a positional candidate gene (SNAP25) are associated with variation in one of the personality factors, and CSF levels of homovanillic acid and 3-methoxy-4-hydroxyphenylglycol. This study documents heritable variation in personality among baboons and suggests that sequence variation in SNAP25 may influence differences in behavior and neurochemistry in these nonhuman primates.

Anxious temperament (AT) in human and non-human primates is a trait-like phenotype evident early in life that is characterized by increased behavioural and physiological reactivity to mildly threatening stimuli. Studies in children demonstrate that AT is an important risk factor for the later development of anxiety disorders, depression and comorbid substance abuse. Despite its importance as an early predictor of psychopathology, little is known about the factors that predispose vulnerable children to develop AT and the brain systems that underlie its expression. To characterize the neural circuitry associated with AT and the extent to which the function of this circuit is heritable, we studied a large sample of rhesus monkeys phenotyped for AT. Using 238 young monkeys from a multigenerational single-family pedigree, we simultaneously assessed brain metabolic activity and AT while monkeys were exposed to the relevant ethological condition that elicits the phenotype. High-resolution (18)F-labelled deoxyglucose positron-emission tomography (FDG-PET) was selected as the imaging modality because it provides semi-quantitative indices of absolute glucose metabolic rate, allows for simultaneous measurement of behaviour and brain activity, and has a time course suited for assessing temperament-associated sustained brain responses. Here we demonstrate that the central nucleus region of the amygdala and the anterior hippocampus are key components of the neural circuit predictive of AT. We also show significant heritability of the AT phenotype by using quantitative genetic analysis. Additionally, using voxelwise analyses, we reveal significant heritability of metabolic activity in AT-associated hippocampal regions. However, activity in the amygdala region predictive of AT is not significantly heritable. Furthermore, the heritabilities of the hippocampal and amygdala regions significantly differ from each other. Even though these structures are closely linked, the results suggest differential influences of genes and environment on how these brain regions mediate AT and the ongoing risk of developing anxiety and depression.

To evaluate baboons (Papio hamadryas) as a primate model for the study of the genetic control of brain size and internal structure, we performed high resolution (<500 microm) magnetic resonance imaging on 109 pedigreed baboons. Quantitative genetic analysis of these MR images using a variance components approach indicates that native (untransformed) brain volume exhibits significant heritability among these baboons (h(2) = 0.52, P = 0.0049), with age and sex also accounting for substantial variation. Using global spatial normalization, we transformed all images to a standard population-specific reference, and recalculated the heritability of brain volume. The transformed images generated heritability estimates of h(2) = 0.82 (P = 0.00022) for total brain volume, h(2) = 0.86 (P = 0.0006) for cerebral volume, h(2) = 0.73 (P = 0.0069) for exposed surface area of the cerebrum and h(2) = 0.67 (P = 0.01) for gray matter volume. Regional differences in the genetic effects on brain structure were calculated using a voxel-based morphometry (VBM) approach. This analysis of regional variation shows that some areas of motor cortex and the superior temporal gyrus show relatively high heritability while other regions (e.g. superior parietal cortex) exhibit lower heritability. The general pattern of regional differences is similar to that observed in previous studies of humans. The present study demonstrates that there is substantial genetic variation underlying individual variation in brain size and structure among Papio baboons, and that broad patterns of genetic influence on variation in brain structure may be similar in baboons and humans., ((c) 2007 Wiley-Liss, Inc.)

Identification of polymorphic microsatellite loci in nonhuman primates is useful for various biomedical and evolutionary studies of these species. Prior methods for identifying microsatellites in nonhuman primates are inefficient. We describe a new strategy for marker development that uses the available whole genome sequence for rhesus macaques. Fifty-four novel rhesus-derived microsatellites were genotyped in large pedigrees of rhesus monkeys. Linkage analysis was used to place 51 of these loci into the existing rhesus linkage map. In addition, we find that microsatellites identified this way are polymorphic in other Old World monkeys such as baboons. This approach to marker development is more efficient than previous methods and produces polymorphisms with known locations in the rhesus genome assembly. Finally, we propose a nomenclature system that can be used for rhesus-derived microsatellites genotyped in any species or for novel loci derived from the genome sequence of any nonhuman primate.

Rhesus macaques (Macaca mulatta) are the most widely used nonhuman primate species in biomedical research. To create new opportunities for genetic and genomic studies using rhesus monkeys, we constructed a genetic linkage map of the rhesus genome. This map consists of 241 microsatellite loci, all previously mapped in the human genome. These polymorphisms were genotyped in five pedigrees of rhesus monkeys totaling 865 animals. The resulting linkage map covers 2048 cM including all 20 rhesus autosomes, with average spacing between markers of 9.3 cM. Average heterozygosity among those markers is 0.73. This linkage map provides new comparative information concerning locus order and interlocus distances in humans and rhesus monkeys. The map will facilitate whole-genome linkage screens to locate quantitative trait loci (QTLs) that influence individual variation in phenotypic traits related to basic primate anatomy, physiology, and behavior, as well as QTLs relevant to risk factors for human disease.

Genetic and dietary effects on LDL phenotypes, including predominant LDL particle diameter, LDL size distribution, and non-HDL cholesterol and apoB concentrations, were investigated in 150 pedigreed baboons that are members of 19 sire groups. Baboons were fed a sequence of three defined diets differing in levels of fat and cholesterol. Increasing dietary fat had relatively little effect on two measures of LDL particle size. However, increasing the level of cholesterol in the diet resulted in larger increases of the predominant LDL particle diameters and in the proportion of stain on LDLs > 28 nm. As expected, apoB and non-HDL cholesterol concentrations significantly increased when levels of dietary fat and cholesterol were increased. Correlations among the LDL phenotypes suggested that several different aspects of the LDL phenotype were captured by the four LDL measures across the three diets. Genetic effects indicated by sire group membership were significant both for expression of the LDL phenotypes and for response to changes in diet.

The effects of diabetes on lipoprotein particle sizes were assessed using samples from 94 subjects with non-insulin-dependent diabetes mellitus. From a larger population of nondiabetic subjects who showed normal glucose tolerance, we selected an exact match in terms of age, sex, and menopausal status. We designed a protocol to make nondenaturing gradient gels for the resolution of LDL subfractions and generated two measures of LDL size: diameter of the predominant LDL species and proportion of LDL cholesterol (LDL-C) in particles larger than 25.5 nm (large LDL-C). Similarly, we made two measures of HDL size, large HDL cholesterol (HDL-C) and large HDL-apoAI, which represents the proportion of HDL-C and apoAI, respectively, occurring on particles larger than HDL-3. In pairwise comparisons, diabetes was associated with significantly (P < .004) smaller lipoprotein particles for all measures except large HDL-C. Each of the size measures was significantly and positively correlated with each of the others, suggesting that common metabolic mechanisms influence lipoprotein particle sizes across classes of lipoproteins. In addition, each of the size measures was correlated with a variety of measures of HDL and beta-lipoprotein concentrations, which included HDL-C, LDL-C, triglycerides, and apoAI, apoB, and apoE. We used stepwise regression analyses to select from the measures of lipoprotein concentrations those independently correlated with each of the lipoprotein size measures. After adjusting for these metabolic correlates of lipoprotein size measures, we found the effect of diabetes on lipoprotein size measures was no longer significant except for a modest effect (P = .027) on large HDL-apoAI.