Dr Michael Morrissey:
Dr Michael Morrissey
University of St Andrews
tel: 01334 463738
Evolutionary Quantitiative Genetics
School of Biology
Institute of Behavioural and Neural Sciences
IBANS Behavioural Ecology
Biology Postgraduate Recruitment Committee
Centre for Biological Diversity
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lab webpage: Evolutionary Quantitative Genetics
Evolutionary statistical quantitative genetics, or, analysis of longitudinal data from populations of unmanipulated animals
Some of the most valuable data for understanding how evolution works in natural populations is individual-based longitudinal data from pedigreed populations. Longitudinal data on individuals provides possibilities to link aspects of phenotype to life histories and fitness. Pedigree data allows inference of the genetic basis of variation in phenotypic traits, based on patterns of similarity of relatives.
With collaborators at the University of Edinburgh and elsewhere, a portion of my research revolves around the study of the selection and genetics of a range of traits in Soay sheep from St Kilda (pictured) and other long-term animal datasets from around the world.
Evolutionary genetic theory
I use analytical and computational approaches to understanding what patterns of genetic variation are expected in nature, and also of how to interpret observed patterns in microevolutionary parameters, including both aspects of genetics and selection. I have an ongoing interest in the patterns of genetic variation that are generated by complex landscape arrangements, especially in dendritic systems, which characterize all freshwater landscapes. I have recently been working on the interpretation of relationships between phenotypic traits and fitness mean in terms “chains of causation” in the context of characterizing the form of natural selection.
Software for empirical microevolutionary studies in nature
Analysis of data from natural populations is often very challenging. Datasets are often incomplete due to practical realities such as limited molecular information to resolve pedigrees, and/or imperfect detection of individuals for recording of life history information. I work on developing statistical tools to link fundamental evolutionary genetic theory to real data from the field. R packages include pedantics, and gsg.
I am interested in how evolution works on generation-to-generation time scales. My work centres on phenotypic variation in natural populations. I investigate what the causes are of phenotypic variation, especially with respect to genetics, as the genetic variants underlying phenotypic differences among individuals are the raw material for evolutionary change. I am also interested in the consequences of phenotypic variation, especially with respect to fitness variation. When phenotypic variation causes fitness variation, natural selection occurs, which is the cause of adaptive evolution.
I apply cutting-edge statistical methodologies to study the selection and genetics of ecologically-important traits in natural populations. In addition to my primary focus on the empirical study of quantitative traits in natural populations, I do theoretical population genetic work, and sometimes focus on methodological development.