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1 NAME: ANDREW PAUL MORRIS OMB No and (Rev. 09/17 Approved Through 03/31/2020) BIOGRAPHICAL SKETCH Provide the following information for the Senior/key personnel and other significant contributors. Follow this format for each person. DO NOT EXCEED FIVE PAGES. era COMMONS USER NAME (credential, e.g., agency login): MORRISA POSITION TITLE: PROFESSOR OF STATISTICAL GENETICS EDUCATION/TRAINING (Begin with baccalaureate or other initial professional education, such as nursing, include postdoctoral training and residency training if applicable. Add/delete rows as necessary.) INSTITUTION AND LOCATION DEGREE (if applicable) Completion Date MM/YYYY FIELD OF STUDY University of Reading, Reading, UK BSc 07/1994 Statistics University of Reading, Reading, UK MSc 09/1995 Biometry University of Reading, Reading, UK PhD 09/1998 Applied Statistics A. Personal Statement The primary aim of my research is the development and evaluation of novel statistical methodology for the analysis of the next-generation of genome-wide association studies (GWAS) of complex human traits. My research considers common and rare genetic variation from diverse ethnic groups, interrogated through traditional GWAS genotyping arrays, supplemented by imputation, and via whole-exome or whole-genome resequencing studies. I have focused on developing methods to enable discovery of novel loci through improved modelling of traits, and fine-mapping through aggregation of GWAS from multiple ethnic groups, and integration with genomic annotation in relevant tissues. The analytical tools that I have developed have been widely utilized in GWAS of a wide range of complex human traits. I have co-led analytical groups in international consortia efforts making use of cutting edge studies to understand the genetic basis of type 2 diabetes (DIAGRAM, T2D-GENES, DIAMANTE) and related metabolic phenotypes (MAGIC and AAGILE), kidney function (COGENT-Kidney), anthropometric measures (GIANT), blood pressure (ICBP), and women s health disorders (including endometriosis and polycystic ovary syndrome). I have previously contributed to ground-breaking studies of human genetic variation and its impact on common diseases including the International HapMap Consortium and the Wellcome Trust Case Control Consortium. I have contributed to the training of researchers for the analysis of GWAS, having supervised 8 students to the successful completion of their PhD (to date). I currently co-lead the Wellcome Trust Advanced Course on the Design and Analysis of Genetic-based Association Studies (since 2006), and previously acted as co-course director for the Wellcome Trust DPhil Program in Genomic Medicine and Statistics, at the University of Oxford. B. Positions and Honors Positions and Employment Postdoctoral Research Fellow, Department of Applied Statistics, University of Reading, UK Research Scientist, Wellcome Trust Centre for Human Genetics, University of Oxford, UK University Research Lecturer, University of Oxford, UK Group Leader, Wellcome Trust Centre for Human Genetics, University of Oxford, UK 2012-present Visiting Professor, Estonian Genome Centre, University of Tartu, Estonia 2014-present Professor of Statistical Genetics, Department of Biostatistics, University of Liverpool, UK 2014-present Visiting Professor, Nuffield Department of Medicine, University of Oxford, UK

2 Other Experience and Professional Memberships 2006-present Instructor, Wellcome Trust Advanced Course: Analysis of genetic-based association studies Co-director, Wellcome Trust DPhil Program in Genomic Medicine and Statistics 2011-present Member, Editorial Board, Genetic Epidemiology 2014-present Member, Editorial Board, Scientific Reports 2014-present Section Editor, Current Diabetes Reports 2015-present Member, Editorial Board, elife Science 2012-present Member, Board of Directors, International Genetic Epidemiology Society 2015-present Member, Program Committee, International Genetic Epidemiology Society 2016-present President Elect, President and Past President, International Genetic Epidemiology Society 2016-present Member of Genomics England Clinical Interpretation Partnership on Machine Learning, Quantitative Methods and Functional Genomics present Independent Scientific Advisory Committee on MHRA Database Research. Honors Leverhulme Trust Early Career Fellowship Wellcome Trust Senior Fellowship in Basic Biomedical Science Wellcome Trust Senior Fellowship in Basic Biomedical Science (renewal) 2010 Best paper in Genetic Epidemiology: A powerful approach to sub-phenotype analysis in population-based genetic association studies. C. Contributions to Science I have published more than 220 peer-reviewed papers, with an h-index of 65. My most significant contributions to science include: 1. Development of statistical methods for the analysis of genome-wide association and re-sequencing studies that have been widely applied to better understand the genetic basis of complex human traits. 2. Identification and characterization of loci implicated in susceptibility to type 2 diabetes. 3. Identification of loci associated with a wide range of other complex human traits. 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 genomewide 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, 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 genomewide association studies accounting for ancestry increases power for discovery and improves finemapping resolution. Hum Mol Genet 26: PMID: Syed H, Jorgensen AL, Morris AP (2017). SurvivalGWAS_SV: software for the analysis of genomewide association studies of imputed genotypes with "time-to-event" outcomes. BMC Bioinformatics 18: 265. PMID: 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: 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: PMID: Cook JP, Morris AP (2016). Multi-ethnic genome-wide association study identifies novel locus for type 2 diabetes susceptibility. Eur J Hum Genet 24: PMID: Asimit JL, Hatzikotoulas K, McCarthy M, Morris AP, Zeggini E (2016). Trans-ethnic study design approaches for fine-mapping. Eur J Hum Genet 24: PMID:

3 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: e PMID: 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: PMID: Morris AP (2011). Transethnic meta-analysis of genomewide association studies. Genet Epidemiol 35: PMID: Mägi R, Lindgren CM, Morris AP (2010). Meta-analysis of sex-specific genome-wide association studies. Genet Epidemiol 34: PMID: Mägi R, Morris AP (2010). GWAMA: software for genome-wide association meta-analysis. BMC Bioinformatics 11: 288. PMID: 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: PMID: Morris AP, Zeggini E (2010). An evaluation of statistical approaches to rare variant analysis in genetic association studies. Genet Epidemiol 34: PMID: Identification and characterization of loci implicated in susceptibility to type 2 diabetes. I have co-led 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 through integration with genomic annotation for the purposes of fine-mapping. Our research has identified more than 80 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. Mahajan A,, Saleheen D, Morris AP, Rotter JI, McCarthy MI (2018). Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes. Nat Genet (in press). PMID: in progress. 2. 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: PMID: 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: PMID: 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: PMID: 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: PMID: 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: PMID: 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: PMID: 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: PMID: Identification of loci associated with a wide range of other complex human traits. I have co-led analytical groups as part of large-scale international efforts to understand the genetic basis of a wide range of complex

4 human phenotypes, including glycemic traits and anthropometric measures. 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: PMID: 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: PMID: Mahajan A,.., Morris AP, Franceschini N (2016). Trans-ethnic fine-mapping highlights kidney function genes linked to salt sensitivity. Am J Hum Genet 99: PMID: 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: e PMID: 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: PMID: 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: PMID: Franceschini N *, van Rooij FJ *, Prins BP *, Feitosa MF *, Karakas M *,, Felix JF, Alizadeh BZ, Cupples LA, Perry JR, Morris AP (2012). Discovery and fine mapping of serum protein loci through transethnic meta-analysis. Am J Hum Genet 91: PMID: Ho JE *, Mahajan A *,, Morris AP, Wang TJ (2012). Clinical and genetic correlates of growth differentiation factor 15 in the community. Clin Chem 58: PMID: D. Additional Information: Research Support and/or Scholastic Performance Current grants MRC (R013519/1) 06/01/18-05/31/21 Development of methodology and computationally efficient software for analysis of PGx exome sequencing studies of complex "time-to-event" outcomes. 367,737. Role: co-applicant (PI Andrea Jorgensen, University of Liverpool, UK). NIH-NIMHD (R01 MD012765) 09/01/17-03/31/22 Leveraging ancestry to map kidney loci. $756,347. Role: Co-applicant (PI Nora Franceschini, University of North Carolina, USA). MRC (MR/P012965/1) 01/01/17-01/01/20 Functional and genomic analysis of novel epilepsy mutations. 390,206. Role: Co-applicant (PI Alan Morgan, University of Liverpool, UK). MRC-NIHR Efficacy and Mechanism Evaluation Programme (EME 15/20/01) 10/01/16-02/28/20 Phase IIb, randomised, double-blind, placebo-controlled, multi-centre trial of infliximab with transcriptomic biomarker and mechanism evaluation in patients with acute pancreatitis. 1,594,000 (intent to fund). Role: Co-applicant (PI Robert Sutton, University of Liverpool, UK). BBSRC Newcastle/Liverpool/Durham Doctoral Training Programme 10/01/16-09/30/20 Evaluation of methodology for transcriptomic imputation into genome-wide association studies of complex human traits to infer causal mechanisms. 50,000. Role: Co-applicant (PI Heather Cordell, University of Newcastle, UK).

5 NIH-NIDDK (U01 DK105535) 04/01/15-03/31/20 Integrating genome-scale data to reveal causal mechanisms in type 2 diabetes. $1,652,000. Role: Key personnel (PI Mark McCarthy, University of Oxford, UK) Previous key grants (completed in the last three years) Wellcome Trust (Senior Research Fellowship, WT098017) 01/07/12-06/30/17 Statistical methods for the analysis of genome-wide association and re-sequencing studies. 1,013,000. Role: PI