Fisher Memorial Trust

Window at Gonville & Caius College Cambridge

(Photograph by Denise Till)

The Fisher Memorial Trust was set up to promote interest in the life and work of the great statistician, evolutionary biologist and geneticist, Sir Ronald Aylmer Fisher (1890-1962) and to maintain his scientific legacy by encouraging discussion of the scientific fields in which he was active.

Charity Commission charity number 313536

Committee members

Society Representative Position
Biometric Society Prof Rosemary Bailey
Biometric Society Prof Vern Farewell**
Biometric Society Mr Andrew Mead *
Genetics Society Prof Brian Charlesworth, FRS
Genetics Society Prof Adam Eyre-Walker Treasurer
Royal Society Sir Walter Bodmer, FRSChairman
Royal Statistical Society Dr Andrew Sutherland ***
Royal Statistical Society Prof Stephen SennSecretary

* Replaced Prof John Gower 1 February 2015
** Replaced Prof Mike Kenward 1 February 2016
*** Replaced Prof Peter Diggle 15 February 2019

Annual reports

Report for 2015
Report for 2016
Report for 2017

The Fisher Memorial Lectures

IDr. F. Yates 23.3.66 Computers, the second revolution in statistics.
IIDr. R. R. Race6.3.68Blood groups in human genetics
IIIProf. E. A. Cornish3.9.69Developments from the Fisher-Cornish expansions.
IVProf. K. Mather18.12.70Biometrical genetics.
VProf. G. A. Barnard19.9.72Statistical inference and its historical development
VIProf. L. L. Cavalli-Sforza24.6.74Cultural versus biological evolution
VIIProf. R. Hide17.11.77Motions in planetry fluids
VIIIProf. D. J. Finney20.9.78Bioassay and the practice of statistical inference
IXProf. J. Maynard Smith19.3.81The evolution of the sex ratio
XProf. J. H. Bennett3.6.81R. A. Fisher and The Genetical Theory of Natural Selection
XIProf. S. Karlin20.4.83Kin selection and altruism
XIIProf. D. R. Cox5.4.84Regression and the design of experiments
XIIIProf. S. M. Stigler22.9.86Francis Galton and the unravelling of the normal world
XIVProf. G. E. Box23.3.88Quality improvement, an expanding domain for the application of scientific method
XVSir Walter Bodmer23.3.90Genetic sequences
XVIProf. D. Lindley17.9.92Statistics of the market place
XVIIProf. A. J. Jeffereys16.8.93Molecular sleuthing: the story of genetic fingerprinting
XVIIIDr. A. W. F. Edwards20.10.94Fiducial inference and the fundamental theorem of natural selection
XIXProf. M. J. R. Healy3.4.95The life and work of Frank Yates
XXProf. J. A. Nelder5.9.96Computers and statistics: the continuing revolution.
XXISir John Kingman16.11.98Mathematics of genetic diversity: before and after DNA.
XXIIProf. B Efron12.9.00The essential Fisher
XXIIISir Richard Doll29.10.01Proof of causality: Deductions from epidemiological evidence
XXIVDr. Oliver Mayo26.06.02The realisation of Fisher's research programme
XXVProf. Warren Ewens7.04.03Statistics and the transformation from genetics to genomics
XXVIProf. Adrian Smith8.09.04Towards an Evidence-Based Society: the Role of Statistical Thinking
XXVIIProf. E.A.Thompson4.12.061953: an unrecognized summit in Human Genetic Linkage Analysis
XXVIIIProf. R.A. Bailey15.07.08Design of dose-escalation trials
XXIX Prof B Charlesworth and
Prof D Charlesworth
6.01.10Fisher and Modern Evolutionary Genetics
XXXProf Philip Dawid10.11.11Causal inference from experimental data
XXXIProf Peter Donnelly14.11.12Genetic Variation in Human Health and Disease
XXXIIProf David Spiegelhalter3.07.13Putting life into numbers: the highs and lows of communicating statistics to the public
XXXIIIProf Bill Hill8.01.14Applying quantitative genetic and genomic information to animal improvement
XXXIVProf Peter McCullagh27.10.15Empirical phenomena and universal laws
XXXVProf Nancy Reid27.10.16 Statistical science and data science: where do we go from here?
XXXVIProf Stephen Senn04.09.17 And thereby hangs a tail: the strange history of P-values
XXXVIIProf Joe Felsenstein04.01.2018 Is there a more fundamental theorem of natural selection?
XXXVIIIProf Michael Goddard9.10.2018The Genetic Architecture of Complex Traits

Mike Goddard receiving the Fisher Memorial Lecture Award
from Walter Bodmer

Fisher grandson Richard Newsom with Walter Bodmer

Most recent lecture

The 38th Fisher Memorial Lecture was given by by Professor Michael Goddard in Edinburgh on 9 October 2018 as part of a centenary meeting sponsored by the Fisher Memorial Trust, the Genetics Society, the Galton Institute, the London Mathematical Society and the Royal Statistical Society, to celebrate the publication of Fisher's famous paper: "The correlation between relatives on the supposition of Mendelian inheritance. Transactions of the Royal Society of Edinburgh. 1918;52:339-433.

Mike delivered a fine lecture as a fitting end to an excellent day in which Fisher's legacy and more modern developments were presented by a number of research veterans and some early career researchers. This was followed by a reception open to all attendees at which the Fisher Memorial Trust were delighted to have the presence of Fisher's grandson Richard Newsom. This was in turn followed by the usual Fisher Memorial Dinner for the lecturer and guests.

Title: The Genetic Architecture of Complex Traits

Abstract Despite the complete lack of knowledge of the physical nature of genes, a century ago Fisher showed how Mendelian inheritance of many loci could explain the observed statistical properties of quantitative or complex traits, such as the resemblance between relatives. As the number of loci increases, we obtain the infinitesimal model, which has been the basis for our understanding of the genetics of complex traits and very successful practical applications in animal and plant breeding. However, there was little knowledge of the genes causing variation, or even of their number, and limited understanding of the evolutionary forces that controlled the ubiquitous genetic variation we observed. In the last decade our knowledge of the genetics of complex traits has been revolutionized by the availability of data on thousands to millions of single nucleotide polymorphisms (SNPs). The purpose of this lecture is to summarise what we have learnt about the architecture of complex traits, and the evolutionary forces that bring this about.

A simple but surprising result is that most quantitative genetic variation is due to very many polymorphisms, each with a tiny effect on the trait, and segregating in the population at moderate allele frequencies. For instance, there are approximately 10,000,000 sites in the genome where a mutation can affect a typical quantitative trait. Each generation, mutation generates new variation; some of the new mutant alleles have a large effect, but selection keeps them very rare. Most of the variation is caused by mutations of small effect that are almost neutral, and hence segregate at moderate allele frequencies. However, occasionally a mutation is favoured by selection and while it segregates generates a large variance. This is most common when the environment changes greatly so that the direction of selection on some mutations reverses. This new understanding explains many previously puzzling results, such as the linear response to artificial selection and the failure to find the genes causing variation in complex traits.

The ability to genotype thousands of SNPs at moderate cost has been utilised in methods of genomic selection or genomic prediction, which predict the genetic value of individuals for a trait based on SNP genotypes. This method is revolutionising animal and plant breeding and will be important in human medicine, for instance in personalised medicine.

Next Lecture: The 39th Fisher Memorial Lecture

This will be given on 12 July 2019 by Brian Cullis and Alison Smith at the , 7th IBS Channel Network Conference , Rothamsted.


The Past, Present and Future of Agricultural Statistics


One of our main research interests over the past 30 or more years has been the use of linear mixed models (LMM) for the analysis of data from crop improvement programmes. These data arise from comparative experiments in which the aim, typically, is to select the 'best' varieties. In order to maximise the accuracy of selection we have developed analytic procedures that involve LMM with complex variance and correlation structures. For example, we use separable auto-regressive models to mitigate the impact of spatial trend within experiments conducted in the field, and factor analytic models to extract key information about variety by environment interaction in the analysis of multi-environment trial data.

We were fortunate enough to have trained and worked as young biometricians when analysis of variance (ANOVA) techniques were the primary method of analysis for comparative experiments.

Our tool of trade was the GENSTAT package, so that the elegant notation of Wilkinson and Rogers and the framework of Block and Treatment structures became ingrained in our statistical thinking. So, despite the complexity of the LMM we now use, we appreciate the importance of maintaining these fundamental concepts, in particular the link between the analysis and the experimental design. We are concerned that this view is shared by only a few, as is evidenced by what we regard as a widespread mis-use of LMM for comparative experiments. This may either be due to an unintentional lapse in transitioning from ANOVA to LMM or a complete lack of exposure to traditional methods of analysis for comparative experiments.

Over recent years, we have made it a priority to fill in this gap for our young statistical colleagues at the University of Wollongong. In particular we have attempted to provide a link between ANOVA and LMM and to explain how to derive LMM that reflect the randomisation employed in the design of the experiment, no matter how complex. We found this to be a non-trivial task and tried numerous educational tools but without great success. A turning point was Brian's introduction of an Honours Statistics course on experimental design at the University of Wollongong. He based this course on Rosemary Bailey's book and found words of wisdom that have inspired us to develop an approach that we have termed Design Tableau (DT). The main aim of DT is to provide a simple, but general series of steps for specifying the LMM for a comparative experiment. It is founded on the seminal work of Sir Ronald Fisher, John Nelder, Rosemary Bailey and Robin Thompson. The motivation and concepts underlying Design Tableau will form the basis of our presentation. We will discuss the formal link between ANOVA and LMM, describe the steps that constitute DT, illustrate DT for simple cases in which the LMM may be used to re-produce an ANOVA and finally demonstrate how DT can be applied in a wide range of complex comparative experiments.


The RA Fisher Digital Archive University of Adelaide

Figures from the history of statistics Site maintained by John Aldrich

A Guide to R.A. Fisher Specific Fisher guide maintained by John Aldrich

Sir Ronald Aylmer Fisher MacTutor History of Mathematics Biography

Ronald Aylmer Fisher Monash University Fisher pages

COPSS Awards Fisher Lecture of the Committee of Presidents of Statistical Societies (Wikipedia page)

Chance, risk and healthOpen University series of 4 podcasts on RA Fisher and his legacy

Page of quotations from the works of Fisher collected by Anthony Edwards

Fisher memorabilia

The committee is interested to establish an index of Fisher memorabilia. If anybody has knowledge of the whereabouts of any Fisher memorabilia or is interested in learning of their whereabouts, please contact the secretary Stephen Senn or any member of the committee.

This page last updated 27 February 2019

Page maintained by Stephen Senn