Genetic and environmental influences on human responses to odors

Olfaction, the sense of smell, has many important functions in humans. Human responses to odors show substantial individual variation. Olfactory receptor genes have been identified and other genes may also influence olfaction. However, the proportion of phenotypic variation in odor response due to genetic variation remains largely unknown. Little is also known about which genes modify specific responses to odors. This study aimed to elucidate genetic and environmental influences on human responses to odors. Individuals from Finnish families (n=146) and Australian (n=413), British (n=163), Danish (n=336), and Finnish (n=399) twins rated intensity and pleasantness of a set of 12 (families) or 6 (twins) odors and tried to identify the odors. In addition, the participants rated their own sense of smell and annoyance experienced with different environmental odors. The odor stimuli of a commercial smell test (The Brief Smell Identification Test; banana, chocolate, cinnamon, gasoline, lemon, onion, paint thinner, pineapple, rose, smoke, soap, and turpentine) were presented in the family study. Based on the results of the family study and a literature survey, a new set of odor stimuli (androstenone, chocolate, cinnamon, isovaleric acid, lemon, and turpentine) was designed for the twin studies. In the family sample, heritabilities of the traits were estimated and underlying genomic regions were searched using a genome-wide linkage scan. In the pooled twin sample, variation in the measured traits was decomposed into genetic and environmental components using quantitative genetic modeling. In addition, associations between nongenetic factors (e.g., sex, age, and smoking) and olfactory-related traits were explored. Suggestive evidence for a genetic linkage for pleasantness of cinnamon at a locus on chromosome 4q32.3 emerged from the family sample. High heritability for the pleasantness of cinnamon was found in the family but not the twin study. Heritability of perceived intensity of androstenone odor was determined to be ~30% in the twin sample. A strong genetic correlation between perceived intensity and pleasantness of androstenone, in the absence of any environmental correlation, indicated that only the genetic correlation explained the phenotypic correlation between the traits (r=-0.27) and that the traits were influenced by an overlapping set of genes. Self-rated olfactory function appeared to reflect the odor annoyance experienced rather than actual olfactory acuity or genetic involvement. Results from nongenetic analyses supported the speculated superiority of females' olfactory abilities, the age-related diminishing of olfactory acuity, and the influences of experience-dependent factors on odor responses. This was the first study to estimate heritabilities and perform linkage screens for individual odors. A genetic effect was detected for only a few responses to specific odors, suggesting the predominance of environmental effects in odor perceptions.

Modeling ecological and evolutionary processes in spatially structured populations

Many species inhabit fragmented landscapes, resulting either from anthropogenic or from natural processes. The ecological and evolutionary dynamics of spatially structured populations are affected by a complex interplay between endogenous and exogenous factors. The metapopulation approach, simplifying the landscape to a discrete set of patches of breeding habitat surrounded by unsuitable matrix, has become a widely applied paradigm for the study of species inhabiting highly fragmented landscapes. In this thesis, I focus on the construction of biologically realistic models and their parameterization with empirical data, with the general objective of understanding how the interactions between individuals and their spatially structured environment affect ecological and evolutionary processes in fragmented landscapes. I study two hierarchically structured model systems, which are the Glanville fritillary butterfly in the Åland Islands, and a system of two interacting aphid species in the Tvärminne archipelago, both being located in South-Western Finland. The interesting and challenging feature of both study systems is that the population dynamics occur over multiple spatial scales that are linked by various processes. My main emphasis is in the development of mathematical and statistical methodologies. For the Glanville fritillary case study, I first build a Bayesian framework for the estimation of death rates and capture probabilities from mark-recapture data, with the novelty of accounting for variation among individuals in capture probabilities and survival. I then characterize the dispersal phase of the butterflies by deriving a mathematical approximation of a diffusion-based movement model applied to a network of patches. I use the movement model as a building block to construct an individual-based evolutionary model for the Glanville fritillary butterfly metapopulation. I parameterize the evolutionary model using a pattern-oriented approach, and use it to study how the landscape structure affects the evolution of dispersal. For the aphid case study, I develop a Bayesian model of hierarchical multi-scale metapopulation dynamics, where the observed extinction and colonization rates are decomposed into intrinsic rates operating specifically at each spatial scale. In summary, I show how analytical approaches, hierarchical Bayesian methods and individual-based simulations can be used individually or in combination to tackle complex problems from many different viewpoints. In particular, hierarchical Bayesian methods provide a useful tool for decomposing ecological complexity into more tractable components.