Statistical Genetics Unit

The Statistical Genetics Unit (SGU) is a cross-school unit, comprising the Statistical Genetics research group in the Department of Medical and Molecular Genetics (MMG), part of the King's College London School of Medicine, and the Statistical Genetics group at the Social, Genetic and Developmental Psychiatry Centre (SGDP), part of the Institute of Psychiatry. Our aims are to develop and support methods in statistical genetics, with a particular focus on complex traits. Applications include behavioural genetics modelling in twin and family data and the identification and evaluation of causal genetic variation in complex diseases.

We have well-established and productive collaborations with molecular and clinical research groups working in such diverse fields as inflammatory bowel disease, unipolar depression, and transplantation. The very real questions arising from these projects, in the context of the constantly changing landscape of molecular data generation technologies, are what motivate our research into new statistical developments and data analysis approaches.

Cathryn Lewis’s research covers the development and application of statistical methods for complex genetic phenotypes. Major interests include (1) developing risk estimation models for complex genetic disorders (Graham Goddard, Evangelos Vassos), (2) identifying genetic susceptibility for unipolar depression and other complex disorders, (3) pharmacogenetic studies, (4) meta-analysis of genome-wide linkage studies. SGDP link | MMG link

Fruhling Rijsdijk develops Structural Equation Models (SEM) for analyzing genetically sensitive family and twin data, in particular where ascertainment correction is required. This is the case when working with hospital ascertained sample of affected probands (e.g. schizophrenia, bipolar disorder) or when investigating the relationship between a disorder and endophenotypes. She is working on genetic modelling of Latent Class membership, Gene x Environment interactions and correlations in SEM, and the application of genetic growth curve modelling to describe the genetic factors influencing dynamic systems. SGDP link

Mike Weale’s research interests broadly cover association studies and population genetics. His research group (Knight, Rebollo Mesa, Crouch, Mollon) investigates the integration of genomewide association study (GWAS) hits with bioinformatic data, GWAS QC, genome-genome interaction, inference of biogeographic origin based on DNA data, genomewide RNAseq-SNP association, rare variant association and RNA expression profiling and prediction. His collaborations include projects within the Division of Genetics and Molecular Medicine, the MRC Centre for Transplantation and the Institute of Neurology (UCL). MMG link

Tom Price’s research interests cover a wide-range of topics in statistical and quantitative genetics, including assessment of gene-environment interaction and correlation, pharmacogenetics and the analysis of genome-wide association studies for birth weight and for cognitive abilities. SGDP link | Personal webpage

Evangelos Vassos is developing models for risk of developing schizophrenia in high-risk populations, including both genetic factors (SNP, CNVs), and environmental factors (e.g. family history, cannabis use, urbanicity) to assess the potential of using such information in identifying individuals at high risk of developing schizophrenia. Models will be built using risk estimates based on literature and external data sets, then evaluated on local cohorts of schizophrenia and prodromal samples. SGDP link

Oliver Davis is a postdoctoral fellow funded by a Sir Henry Wellcome Fellowship from the Wellcome Trust to explore developmental patterns of genome-wide association in cognitive, behavioural and psychiatric disorders. His interests include computational biology and statistical techniques for the analysis and visualisation of genome-wide association studies in rich datasets, quantitative genetics and twin methods. SGDP link

Jo Knight is a postdoctoral fellow funded by the Biomedical Research Centre. Her research combines genomewide association studies (GWAS) with bioinformatics, developing empirically based methods for prioritisation of near-significant GWAS results using other functional genomic information. MMG link

Amy Butler is responsible for the depression studies database, integrating phenotype data from the DeCC, DeNt and GENDEP studies to enable their common analysis in the genome-wide association study, and in subsequent analyses to dissect the phenotype for unipolar depression, working with Peter McGuffin and Cathryn Lewis. SGDP link

Irene Rebollo is a research fellow within the MRC Centre for Transplantation. In addition to providing general biostatistical support for the Centre, she is working the analysis of donor-recipient genomewide association studies for renal transplant outcome and on RNA expression profiling for prediction of acute rejection in renal transplant recipients. MMG link

Desmond Campbell uses pedigrees of multifactorial diseases to estimate liability and risk using a Generalised Linear Mixed Model. The liability predictions generated can then be used in subsequent analyses searching for disease associations.

Daniel Crouch is developing methods for predicting components of biogeographical origin based purely on a sample’s DNA data (genomewide SNP data). MMG link

Graham Goddard develops methods for estimating disease risk conferred by genetic and environmental factors, evaluating the potential for genomic profiles to be used in identifying individuals at high risk of disease. MMG link

Jennifer Mollon is developing methods for the analysis of genomewide donor-recipient SNP-SNP interactions. Her research is motivated by genomewide association studies on renal transplant outcome being carried out by the MRC Centre for Transplantation, in particular a multicentre UK-Irish consortium project funded by the Wellcome Trust Case-Control Consortium, Phase 3. MMG link