Development of statistical methods for the analysis of genome-wide association and re-sequencing studies.
The primary aim of my research is the development of powerful statistical methods for the analysis of genome-wide association and re-sequencing studies, and their implementation in user-friendly software that can be distributed to the wider research community. Most recently, my research has focused on development of methodology for genome-wide meta-analysis, including aggregation of studies from diverse populations for fine-mapping, to enable improved modelling of complex traits (including "time-to-event" outcomes in pharmacogenetics), and powerful analysis of rare genetic variation in “gene-based” tests. Some of my recent publications include:
1. Mägi R, Horikoshi M, Sofer T, Mahajan A, Kitajima H, Franceschini N, McCarthy MI, COGENT-Kidney Consortium, T2D-GENES Consortium, Morris AP (2017). Trans-ethnic meta-regression of genome-wide association studies accounting for ancestry increases power for discovery and improves fine-mapping resolution. Hum Mol Genet 26: 3639-50. PMID: 28911207.
2. Syed H, Jorgensen AL, Morris AP (2017). SurvivalGWAS_SV: software for the analysis of genome-wide association studies of imputed genotypes with "time-to-event" outcomes. BMC Bioinformatics 18: 265. PMID: 28525968.
3. Mägi R, Suleimanov YV, Clarke GM, Kaakinen M, Fischer K, Prokopenko I, Morris AP (2017). SCOPA and META-SCOPA: software for the analysis and aggregation of genome-wide association studies of multiple correlated phenotypes. BMC Bioinformatics 18: 25. PMID: 28077070.
4. Cook JP, Mahajan A, Morris AP (2017). Guidance for the utility of linear models in meta-analysis of genetic association studies of binary phenotypes. Eur J Hum Genet 25: 240-5. PMID: 27848946.
5. Cook JP, Morris AP (2016). Multi-ethnic genome-wide association study identifies novel locus for type 2 diabetes susceptibility. Eur J Hum Genet 24: 1175-80. PMID: 27189021.
6. Asimit JL, Hatzikotoulas K, McCarthy M, Morris AP, Zeggini E (2016). Trans-ethnic study design approaches for fine-mapping. Eur J Hum Genet 24: 1330-6. PMID: 26839038.
7. Clarke GM, Rivas MA, Morris AP (2013). A flexible approach for the analysis of rare variants allowing for a mixture of effects on binary or quantitative traits. PLoS Genet 9: e1003694. PMID: 23966874.
8. Mägi R, Asimit JL, Day-Williams AG, Zeggini E, Morris AP (2012). Genome-wide association analysis of imputed rare variants: application to seven common complex diseases. Genet Epidemiol 36: 785-96. PMID: 22951892.
9. Morris AP (2011). Transethnic meta-analysis of genomewide association studies. Genet Epidemiol 35: 809-22. PMID: 22125221.
10. Mägi R, Lindgren CM, Morris AP (2010). Meta-analysis of sex-specific genome-wide association studies. Genet Epidemiol 34: 846-53. PMID: 21104887.
11. Mägi R, Morris AP (2010). GWAMA: software for genome-wide association meta-analysis. BMC Bioinformatics 11: 288. PMID: 20509871.
12. Morris AP, Lindgren CM, Zeggini E, Timpson NJ, Frayling TM, Hattersley AT, McCarthy MI (2010). A powerful approach to sub-phenotype analysis in population-based genetic association studies. Genet Epidemiol 34: 335-43. PMID: 20039379.
13. Morris AP, Zeggini E (2010). An evaluation of statistical approaches to rare variant analysis in genetic association studies. Genet Epidemiol 34: 188-93. PMID: 19810025.
Identification and characterization of loci implicated in susceptibility to type 2 diabetes.
I co-lead analytical groups as part of large-scale international efforts to understand the genetic basis of susceptibility to type 2 diabetes. These efforts have focused on aggregating genome-wide association studies of European ancestry (as part of the DIAGRAM Consortium), from diverse ethnicities (as part of the T2D-GENES Consortium and DIAMANTE Consortium), and through integration with genomic annotation for the purposes of fine-mapping. Our research has identified more than 100 loci associated with type 2 diabetes, have demonstrated that the effects of these loci are typically homogenous across ethnic groups, and have highlighted molecular mechanisms through which variants underlying association signals exert their effects on disease. Some of my recent publications include:
1. Scott RA*, Scott LJ*, Mägi R*, Marullo L*, Gaulton KJ*, Kaakinen M*, …, Morris AP†, Boehnke M†, McCarthy MI†, Prokopenko I†; DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium (2017). An expanded genome-wide association study of type 2 diabetes in Europeans. Diabetes 66: 2888-902. PMID: 28566273.
2. Fuchsberger C*, Flannick J*, Teslovich TM*, Mahajan A*, Agarwala V*, Gaulton KJ*, …, Morris AP, Kang HM, Boehnke M†, Altshuler D†, McCarthy MI† (2016). The genetic architecture of type 2 diabetes. Nature 536: 41-7. PMID: 27398621.
3. Horikoshi M, …, Boehnke M†, McCarthy MI†, Morris AP† (2016). Transancestral fine-mapping of four type 2 diabetes susceptibility loci highlights potential causal regulatory mechanisms. Hum Mol Genet 25: 2070-81. PMID: 26911676.
4. Gaulton KJ*, Ferreira T*, Lee Y*, Raimondo A*, Mägi R*, Reschen ME*, …, Gloyn AL†, Altshuler D†, Boehnke M†, Teslovich TM†, McCarthy MI†, Morris AP† (2015). Genetic fine-mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci. Nat Genet 47: 1415-25. PMID: 26551672.
5. Mahajan A*, Go MJ*, Zhang W*, Below JE*, …, Altshuler D†, Bowden DW†, Cho YS†, Cox NJ†, Cruz M†, Hanis CL†, Kooner J†, Lee J-Y†, Seielstad M†, Teo YY†, Boehnke M†, Parra EJ†, Chambers JC†, Tai ES†, McCarthy MI†, Morris AP† (2014). Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat Genet 46: 234-44. PMID: 24509480.
6. Morris AP*, Voight BF*, Teslovich TM*, Ferreira T*, Segrè AV*, Steinthorsdottir V*, …, Altshuler D†, Boehnke M†, McCarthy MI† (2012). Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet 44: 981-90. PMID: 22885922.
7. Voight BF*, Scott LJ*, Steinthorsdottir V*, Morris AP*, Dina C*, …, Altshuler D, Boehnke M, McCarthy MI (2010). Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat Genet 42: 579-89. PMID: 20581827.
Identification of loci associated with a wide range of complex human traits.
I co-lead analytical groups as part of large-scale international efforts to understand the genetic basis of a wide range of complex human phenotypes, including gestational diabetes, glycemic traits, kidney function and endometriosis. These studies have provided insight into the biological pathways underlying these phenotypes, and provided initial evidence as to the relevant causal mechanisms through which the effects of association signals are mediated. Some of my recent publications include:
1. Horikoshi M*, Beaumont RN*, Day FR*, Warrington NM*, Kooijman MN*, Fernandez-Tajes J*, …, Morris AP†, Ong KK†, Felix JF†, Timpson NJ†, Perry JR†, Evans DM†, McCarthy MI†, Freathy RM† (2016). Genome-wide associations for birth weight and correlations with adult disease. Nature 538: 248-52. PMID: 27680694.
2. Ehret GB*, Ferreira T*, …, Morris AP†, Newton-Cheh C†, Munroe PB† (2016). The genetics of blood pressure regulation and its target organs from association studies in 342,415 individuals. Nat Genet 48: 1171-84. PMID: 27618452.
3. Mahajan A, .., Morris AP†, Franceschini N† (2016). Trans-ethnic fine-mapping highlights kidney function genes linked to salt sensitivity. Am J Hum Genet 99: 636-46. PMID: 27588450.
4. Horikoshi M*, Mӓgi R*, …, Ripatti S†, Prokopenko I†, McCarthy MI†, Morris AP† (2015). Discovery and fine-mapping of glycaemic and obesity-related trait loci using high-density imputation. PLoS Genet 11: e1005230. PMID: 26132169.
5. Surakka I, Horikoshi M, Mägi R, Sarin AP, …, McCarthy MI, Morris AP, Prokopenko I, Ripatti S; ENGAGE Consortium (2015). The impact of low-frequency and rare variants on lipid levels. Nat Genet 47: 589-97. PMID: 25961943.
6. Shungin D*, Winkler TW*, Croteau-Chonka DC*, Ferreira T*, Locke AE*, Mägi R*, …, Heid IM†, Loos RJ†, Cupples LA†, Morris AP†, Lindgren CM†, Mohlke KL† (2015). New genetic loci link adipose and insulin biology to body fat distribution. Nature 518: 187-96. PMID: 25673412.
- Functional and genomic analyses of novel epilepsy mutations
- Integrating genome-scale data to reveal causal mechanisms in type 2 diabetes
- Statistical methods for the analysis of genome-wide association and re-sequencing studies
Prof Ines Barroso
Project: Genetics of glycemic traits
External: Wellcome Trust Sanger Institute
Collaboration to understand the genetic contribution to glycemic traits.
Prof Heather Cordell
Project: Methodology for transcriptomic imputation
External: University of Newcastle
Development and evaluation of methodology for transcriptomic imputation in genome-wide association studies.
Prof Steve Eyre
Project: Methodology for omics data integration
External: University of Manchester
Development of methodology for integration of genetic fine-mapping and genomic annotation, with application to rheumatoid arthritis.
Dr Nora Franceschini
Project: Genetics of kidney function
External: University of North Carolina, USA
Collaboration to understand the genetic contribution to kidney function and chronic kidney disease in diverse populations.
Prof Cecilia Lindgren
Project: Genetics of anthropometric traits
External: University of Oxford
Collaboration to understand the genetic contribution to obesity and fat distribution.
Dr Reedik Magi
Project: Genetics of gestational diabates and reproductive traits
External: University of Tartu, Estonia
Collaboration to understand the genetic contribution to gestational diabetes and reproductive traits.
Prof Mark McCarthy
Project: Genetics of type 2 diabetes
External: University of Oxford
Collaboration to understand the genetic contribution to type 2 diabetes susceptibility.
Prof Martin Tobin, Prof Louise Wain
Project: Genetics of lung function
External: University of Leicester
Collaboration to understand the genetic contribution to lung function.
Prof Krina Zondervan
Project: Genetics of endometriosis
External: University of Oxford
Collaboration to understand the genetic contribution to endometriosis.
Prof Patricia Munroe
Project: Genetics of blood pressure
External: Queen Mary University London
Collaboration to understand the genetic contribution to blood pressure and hypertension.