Te Tari Pāngarau me te Tatauranga
Department of Mathematics & Statistics

Quantitative Genetics Group


Members of the group

Michael Lee

Michael’s main background in Science is in animal breeding particularly in sheep. He has a PhD from the University of Otago and done Postdocs at the Uppsala University (Sweden) and Durham University (UK). Previously, he has worked in plant breeding/biotech in Sweden (Svalof Weibull AB, BASF Plant Sciences & ScanBi AB.) and genomics (AgResearch @ Invermay). After retraining in Economics, Michael worked in Health Economics (London, UK), Animal Breeding (quantitative genetics; genomic selection & economic evaluation) for Pfizer Animal Genetics (largely in beef cattle) and Animal Genomics (Invermay). Currently, he is a Scientist based at the Department of Mathematics and Statistics with research interests in genomic predictions and animal genetic improvement (in particular in maternal traits for sheep).


Benoit Auvray

Benoit is a statistician and quantitative geneticist specialising in researching and implementing genetic evaluation systems for farm animals, integrating individual performance, pedigree and genomic information. His genetics work is mostly applied in sheep at the moment but he has working knowledge of genetic evaluation in other production systems, including dairy and beef cattle.

Originally from Belgium, Benoit obtained his MSc from Gembloux University in quantitative genetics in 1999. During his Master he helped develop software at the University of Georgia (USA) used for genetic evaluation of bivariate threshold-linear traits.

After graduating, Benoit worked in agricultural mechanisation and statistical analysis of clinical trial data before coming back to quantitative genetics and helping develop the current dairy cattle evaluation system of the Walloon region in Belgium.

In 2003, he moved to New Zealand to work at AgResearch Ltd on projects to map QTL in sheep and integrate genomic information into genetic evaluation systems for Ovita Ltd. Since then he has helped to create a new 2-stage genomic evaluation system for the dual purpose NZ sheep industry using genotypes from low density SNP arrays, being mostly responsible for the theoretical development and first practical implementation of the current system (‘SHEEP5K’, commercialised by Zoetis, Inc.).

In 2014 he moved to the Mathematics and Statistics department at the University of Otago where he is now leading a project aiming to develop a new genomic evaluation system for NZ sheep, through Beef+Lamb Genetics Ltd, integrating in a single-step evaluation animal performance, pedigree and genomic information to estimate the breeding potential of animals. The new system is expected to be in place for commercial use in 2017 and will replace the current Sheep Improvement Limited national evaluation system for dual purpose sheep.

Benoit is also a director and data scientist at Iris Data Science, a small data science company based in Dunedin. In his spare time, he enjoys looking after his 4 children, practicing and teaching combat sports, travelling, playing board games, cooking, reading, writing statistical models for sport predictions, juggling and the occasional hike.


Phillip Wilcox

Phillip Wilcox has a background in molecular and quantitative genetics, primarily in forest trees and human medical genetics. Prior to joining the Department of Mathematics and Statistics in August 2015 he was a senior scientist at the forestry-focused Crown Research Institute, Scion, and also a part-time senior research fellow with the University of Otago’s Department of Biochemistry. Phillip’s main interests are in applications of genomic sciences for primary sector breeding and medical applications, in particular linkage mapping, association genetics and genomic selection. He also has interests in developing new statistical methods and workflows for analysing genomic data, and has led the pan-organisation Virtual Institute of Statistical Genetics. Previously Phillip also led molecular breeding research at Scion, and is the Convenor of MapNet, a New Zealand-wide collective of researchers from primary, conservation and health sectors involved in applying genomic sciences (

Phillip is of Ngāti Rakaipaaka, Rongomaihane, Ngati Kahungunu ki Wairoa and Pākeha descent and has a strong interest in working with Māori communities regarding applications of genomic technologies. In 2001 he started the Te Arotūruki initiative while at FRI, which developed processes for scientists to engage with Māori communities regarding controversial technologies. He has been involved in multiple projects including the Mata Ira: He Tangata Kei Tua project which has developed ethically informed guidelines for medical genomics. Phillip also works with Te Iwi o Ngāti Rakaipaaka on the Rakaipaaka Health and Ancestry Study.

Phillip’s main focus at the University is establishing a Master of Applied Sciences in Quantitative Genetics programme, with financial support from Beef +Lamb NZ Genetics. He is undertaking research in livestock, humans and trees.

Whenever he gets spare time, Phillip enjoys fishing and hunting, reading Māori tribal histories, and spending time with whānau.


John Holmes

John is a PhD candidate in Statistics working in genomics and animal breeding from a statistical perspective. His interests include walking, DIY and Sudoku. John has a first class honours degree in Mathematics and a degree in Statistics. He has been awarded the University of Otago Prestige Scholarship in Art, has held a Beverley Bursary in Mathematics and shared the Gopi Jain Memorial Prize in Statistics. He has tutored Statistics and Mathematics for the department and taught part of the first year Statistics course at Summer School 2016.


Mohammad A. Nilforooshan

Mohammad is a Postdoctoral Fellow in Quantitative Genetics. He earned his PhD from the Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences. He worked as a Postdoc for the University of Nebraska-Lincoln, and as a Geneticist for the Interbull Centre. He joined the Quantitative Genetics group at the University of Otago, in November 2015. Mohammad’s main interests are Quantitative Genetics and Computer Programming, and his main responsibilities are developing and implementing the single-step GBLUP method for Beef+Lamb NZ Genetics.

He enjoys Indian and Asian restaurants in Dunedin.


Diana Giraldo

Diana joined the Quantitative genetics group as a Research Assistant Fellow. With a background in Statistics, Ecology and Public Health, Diana’s roles are: working and managing large data sets (e.g. genotype); assisting with data analysis and helping interpreting results; and, contributing with the development and testing existent code.

Diana has also worked as Statistician in the financial sector, national household surveys, salmon parasites, and completed her Masters in human energy behaviour at the University of Otago.