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PGRN Polygenic Modeling Network-­‐wide Project Annual Progress Report for March 30 2012 Summary The PGRN Polygenic Modeling network-­‐wide project was initiated Summer 2011, with the goal of elucidating the polygenic architecture underlying pharmacogenetic traits. We have a distributed organizational structure designed (1) to engage participating PGRN groups in polygenic analyses of their own pharmacological genome-­‐wide association study (GWAS) data, and (2) to synergize and integrate with the PGRN Statistical Resource (P-­‐
STAR) in project administration, interpretation of results and follow-­‐up in the area of pathway/network discovery [an established P-­‐STAR goal]. The PI’s group has now published a new polygenic analysis method that has impelled this project, with application to several complex diseases (Stahl et al. Nature Genetics 2012). All participating groups have conducted a formal analysis plan implementing one of two primary analyses for the project, and secondary analyses are in progress. Additional activities have included working group meetings at semi-­‐annual PGRN meetings and the annual P-­‐STAR workshop, teleconferences/webinars, a web site, and a wiki forum. The polygenic modeling project analyses are contributing to one GWAS publication of a specific participating group (PhRAT), in preparation, and contributions to other group-­‐specific publications are expected. An invited review/perspective, including all project participants as co-­‐authors, is also planned for the journal Pharmacogenomics. Personnel and leadership PI Robert Plenge (PhRAT), Lead Investigator Eli Stahl (PhRAT) P-­‐STAR PI Marylyn Ritchie, Investigator Sarah Pendergrass Participating PGRN groups: PAAR (Nancy Cox and Eric Gamazon)* PAAR4kids (Mary Relling and Cheng Cheng) PAPI (Alan Shuldiner and Jeff O’Connell) PARC (Jerry Rotter and Xiaohui Li) PEAR (Julie Johnson, Yan Gong and Jeff O’Connell) PhAT (Kelan Tantisira and Michael McGeachie) PhRAT (Plenge, Stahl and Jing Cui) PMT (Deanna Kroetz and Aparna Chibber, John Witte and Joel Meford) *-­‐ Not in the funded proposal, later joined [without funding support] The PGRN Polygenic Modeling project has the goals of detecting polygenic contributions to pharmacological traits through polygenic analysis of GWAS data, and of elucidating polygenic models that underlie them. This is achieved through a distributed organization in which project key personnel work with P-­‐STAR (PGRN-­‐Statistical Resource) for administration and dissemination of relevant results, software and analysis plans, and analysts within the participating groups carry out the analyses. Our hope is to facilitate and guide the project analyses within the groups, to demonstrate the value of the analyses and results within and between groups, and to integrate polygenic analyses with genomic and other integrative analyses within the groups and coordinated by P-­‐STAR. Page 1 of 3 Polygenic Modeling Project, Annual Progress Report for March 30 2012 Activities We have developed polygenic analysis methodology that spurred the development of this project, and that we present with application to disease risk in rheumatoid arthritis, celiac disease, myocardial infarction and type 2 diabetes, in an upcoming paper in Nature Genetics (online publication date March 25, doi 10.1038/ng.2232). Numerous follow-­‐up projects to this paper with methodological relevance to PGRN are in progress, with hopes for application to pharmacological traits, including (1) to detect and estimate genetic variance due to common versus rare variants genome-­‐wide, and (2) to develop polygenic predictors of phenotype and assess their clinical utility. All participating groups have successfully completed a first analysis plan designed to detect and estimate the total additive genetic variance due to common SNPs in the GWAS data. Compelling results have been achieved by several groups: 1. PhRAT has detected significant genetic variance in common SNP data for response to anti-­‐TNF biologic therapy for rheumatoid arthritis treatment, and interestingly, we have detected greater common-­‐SNP heritability for each of two anti-­‐TNF biologic drug types (soluble TNF receptor etanercept, TNF monoclonal antibodies adalimumab/infliximab) than is shared between the two. This will be part of the publication describing the anti-­‐TNF GWAS (in preparation for PLoS Genetics), which has also identified a highly suggestive SNP associated with response specifically to etanercept treatment. 2. PAAR4kids group has found significant common-­‐SNP heritability for minimal residual disease in response to antileukemic therapy in childhood acute lymphoblastic leukemia (ALL). Secondary analyses are ongoing, including analysis of positive control phenotypes based on ethnicity, and of an alternative phenotype of methotrexate clearance for which GWAS is also in progress. 3. PAAR has analyzed cytotoxicity of several cancer chemotherapies in lymphoblastoid cell lines of the CEPH extended kindred, for which a range heritabilities have been estimated. As part of the Polygenic Modeling project, Eric Gamazon has estimated common-­‐SNP heritabilities based on common SNP GWAS data, with and without related individuals. 4. PAPI has estimated heritability of response to clopidogrel from the Old Order Amish extended kindred, and has repeated the analyses using common SNP GWAS data. Other groups have faced limitations of small sample size. We have worked with each of these groups individually to increase sample size using multiple approaches including analyses of imputed data allowing GWAS datasets typed on different platforms to be merged, expanding datasets to include multi-­‐ethnic sample collections, and defining alternative phenotypes to increase sample sizes. Further work is being pursued to address how best to use some of the complex pharmacological phenotypes (including repeated measures, ordinal and times-­‐to-­‐event as well as binary) in the linear mixed model framework, and how their coding may affect results and their interpretations. The project has been enriched by the synergistic contributions of its members. This has included those of Jeff O’Connell, who has a background in animal breeding and extended kindred analysis, has been indispensible in guiding analyses, and has developed software (MMAP) that independently implements a primary methodology of the project in an extensible way. Mary Relling was an initiator of involving PAAR in this project, as a way to compare trait heritabilities with pedigree data (as a positive control). Group input from Cheng Cheng, Jerry Rotter, Jeff O’Connell and others, and resulting discussions, contributed to ongoing consideration of alternative phenotypes in the context of polygenic analysis and genetic analysis more broadly. These interactions have taken place primarily in two venues: working meetings within the Spring PGRN (Berkeley 2011, planned for Philadelphia Apr 2012) and P-­‐STAR (Chicago 2011) meetings, and a monthly webinar series (November 2011, January and February 2012). The Page 2 of 3 Polygenic Modeling Project, Annual Progress Report for March 30 2012 webinar has focused on polygenic analyses conducted by participating groups including Eli Stahl (PhRAT), Jeff O’Connell (PAPI) and Eric Gamazon (PAAR). We have also created a Web site (https://www.pgrn-­‐star.org/polygenic_modeling) in which the project is described publicly, and relevant publications, analysis plans, software, etcetera are distributed behind password-­‐
protection. Plans Publications. As stated above, we published an analysis paper in Nature Genetics this month detailing the methodology that underlies much of the Polygenic Modeling Project. The primary publications contributed to by the Polygenic Modeling Project will focus on the traits of interest to individual PGRN groups. These include primary GWAS publications, including that of PhRAT (manuscript in preparation). We also hope to bring together project results (and personnel as co-­‐
authors) in two publications. One is an invited (through Marylyn Ritchie) review/perspective to be published in the November 2012 issue of Pharmacogenomics. A second, at the end of the project, will bring together results of the participating groups and comprehensive simulation analyses, to disseminate results and insights from the project to the pharmacogenetics and broader human genetics communities. Analysis plans. As stated above, all groups have conducted a primary analysis aimed at estimating the genetic contribution of common SNPs in the GWAS data to pharmacological phenotype variance. Secondary analyses facilitating the interpretation of these results (negative or positive) will be designed and conducted where the participating groups plan to publish the analysis. Moving forward, the second primary analysis methodology, polygenic risk score analysis, will be conducted by groups with sufficient sample sizes for a cross-­‐validation experimental design. Both methodologies will be carried out for gene sets and networks/pathways, based on (1) biology of the disease, drug target and/or pharmacology, as well as (2) machine-­‐learning methods based on the genetic data, to assess their pharmacogenetic contributions. We see opportunities in (1) continuing to support the participating groups in detecting genetic signal in PGRN GWAS data, including facilitating the development and dissemination of Jeff O’Connell’s MMAP software, (2) considering the development of polygenic prediction both for predicting pharmacogenetic phenotypes, and for understanding the related pharmacological traits such as efficacy and adverse events, and (3) understanding the genetic architectures of treatment efficacy and adverse events vis a vis common SNP variation versus identity by decent among relatives, and in comparison with common, complex disease, particularly in cancer drugs with Nancy Cox, PAAR, and other groups. Timeline Activities Linear mixed model (LMM) polygenic analyses Polygenic risk score analyses Integrating pathways/gene sets into polygenic analyses Publications Group-­‐specific GWAS Pharmacogenomics review/perspective Summation/synthesis paper -­‐Spring Summer 2012 2012 Winter 2013 Spring 2013 Summer 2013 X X X X X X X X X X X X X X X X X Fall 2012 Page 3 of 3