Research Biographies

back to contents


Biomedical Genomics

Programme Leader

Chris Haley

Contact Details

E-mail address:
Telephone: +44 (0)131 651 8695
Fax: +44 (0)131 651 8800
Address: MRC Human Genetics Unit MRC IGMM, University of Edinburgh Western General Hospital, Crewe Road, Edinburgh EH4 2XU
Research Programme 1: Research Programme 1: Genetics of Complex and Quantitative Traits
Research Programme 2: Quantitative Trait Locus (QTL) Identification in Isolate Populations



I hold joint appointments as a Group Leader in the MRC Human Genetics Unit in the MRC Institute of Genetics and Molecular Medicine and as a Principal Investigator in the Roslin Institute, both these organisations being part of the University of Edinburgh. I have published over 200 refereed journal articles and my ISI Web of Science h index is 41. My research interests lie in developing an understanding of the control complex traits, where inter-individual variation within and between populations is controlled by variation at a number of genes (often referred to as quantitative trait loci or QTL), by environmental factors and by the complex interactions of these components. Most variation between individuals within and between populations is in the form of complex traits. Consequently variation in complex traits is responsible for most inter-individual variation in susceptibility to disease (both infectious and metabolic) in humans, livestock and other species and underlies responses to selection, both artificial selection in livestock and natural selection in all species. We can only be really effective in understanding and treating many diseases, in predicting individual’s risk of developing particular conditions and in dissecting the causes and consequences of natural selection if we understand the genetic control of variation in complex traits.

My research focuses both on developing approaches to dissect complex traits and in applications of these approaches to specific biological models. My group has developed the most widely used approaches for linkage mapping of QTLs livestock. These have been made available to the community the through the software QTL Express and latterly through GridQTL. We have further developed and applied methods for the analysis of the high density of markers that are available in human populations and which are becoming available in various livestock breeds and developed, disseminated and applied analysis methods for detection of gene interactions (epistasis). I have has led and collaborated in a large number of projects focusing on the dissection of complex traits in a range of species. These have included the first genome-wide scan of QTLs in livestock and many subsequent studies as well as studies in humans, fish, model vertebrates and plant species.

Current research projects include:
Understanding and utilizing the genomic causes of complex trait variation. Using statistical approaches and computational analysis we attempt to obtain a high level view of the causes of variation between individuals. For example what proportion of the variation is due to genetic effects and what proportion to the family environment or to environment or lifestyle events particular to an individual? How important is genetic variation between different regions of the country? We explore these effects for different traits using data from large population studies and in collaboration with other research groups within the University. These studies inform our detailed genomic dissection of traits using tools such as GWAS and also our research on the use of genomic information in phenotypic prediction.

The development and application of statistical approaches to capture rare variants. It has become clear that much variation between individuals is due to rare genetic variants that may be restricted to a single population or even a single pedigree. Loci which contain common variants of relatively small effect often also contain rare variants of larger effect. We have developed methods based on regional heritability mapping that detects such loci and we are applying these to detect effect such loci in a number of different population samples.

Exploring the impact of epistatic variation and other genetic variation that escapes standard genome-wide association analysis. Interactions between different genes (epistasis) is clearly important within biological pathways and networks, however detecting the effects of epistasis on complex trait variation is challenging. We have developed methods high-throughput statistical methods to facilitate analysis of epistasis in human populations and explored the use of these methods by both simulation and in real data.

The use of genomic information to predict individual’s complex trait phenotypes and health outcomes. To be able to achieve the goals of precision medicine we need to be able to predict an individual’s phenotypes and features such as response to treatment using information from their genome, lifestyle and environmental circumstances. We are exploring alternative statistical approaches to such prediction using both machine learning tools and methods adapted from those used by animal breeders.



Academic Qualifications 

  • Bachelor
    • 1976, Bachelor of Science, 1st, University of Birmingham
  • Doctorate
    • 1980, Doctor of Philosophy, PhD, University of Birmingham


Research in a Nutshell

My research group is interested in understanding the control of complex trait variation in humans and other species. But what does this mean exactly? Well if we look within populations of humans or other species we see that individuals differ from one another in almost all characteristics or traits. For example height and weight, personality traits and even susceptibility to colds and flu or arthritis all differ between people. For such traits variation is controlled by a number of different genes and environmental events or lifestyle choices as well as interactions between these various factors. Thus there is no simple cause of variation between individuals and such traits are termed complex. Our research uses computational and statistical analysis of data where traits have been measured on large samples of humans or other species and data on genetic, environmental and other potentially causative factors have also been measured. We use these analyses to estimate the extent of genetic and environmental influences on variation and ultimately we hope to identify the most important environmental influences and genes with the biggest impact. This information can ultimately be used build models that can help identify individuals susceptible to disease or contribute to the design of treatments and drugs that may ameliorate disease.