<div dir="ltr"><p style="line-height:150%;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial;margin-right:0cm;margin-left:0cm;font-size:12pt;font-family:"Times New Roman",serif">Prezados, <p style="line-height:150%;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial;margin-right:0cm;margin-left:0cm;font-size:12pt;font-family:"Times New Roman",serif">Temos o prazer de convidar a todos para participação no nono seminário do Ciclo de Seminários 2020 do Programa de Pós-Graduação em Estatística da UFRGS (PPGEst-UFRGS). Informações abaixo: Evento: Ciclo de Seminários 2020 do PPGEst-UFRGS Palestrante: Dr. Kevin L. Keys (Ambys Medicines) Título: Cross-population generalizability of predictive models for gene expression Resumo: Gene expression is a core component of human physiology. However, studies of genetically controlled gene expression and its effect on complex disease phenotypes have remained prohibitively expensive until recently. The advent of transcriptome-wide association studies (TWAS) arose concurrently with large datasets of linked genotype and RNA sequencing data, such as the Genotype-Tissue Expression Repository (GTEx) or the Depression Genes Network (DGN) dataset. TWAS leverages this paired genotype-expression information to create a powerful gene-based test of association. While TWAS has successfully identified dozens of novel genetic disease associations, recent interest has turned to possible sample biases in the reference data, which are overwhelmingly of European descent. Little is known about the generalizability of these models to other populations. We test for cross-population generalizability of gene expression prediction models using a dataset of African American individuals with whole blood expression data. We find that the default models trained in large datasets such as GTEx and DGN fare poorly in African Americans, with a notable reduction in prediction accuracy when compared to European Americans. We replicate these limitations in cross-population generalizability using the five populations in the GEUVADIS dataset. Via realistic simulations of both populations and gene expression, we show that accurate cross-population generalizability of transcriptome prediction only arises when eQTL architecture is substantially shared across populations. In contrast, models with non-identical eQTLs showed patterns similar to real-world data. These results point toward a pressing need to generate RNA-Seq data in multiple global human populations as a critical step towards multi-ethnic utility of gene expression prediction. Bio: Kevin L. Keys is a computational biologist at Ambys Medicines, Inc. Previously, he was a postdoctoral scholar at the UCSF Department of Medicine and a Data Science Fellow at the Berkeley Institute for Data Sciences at UC Berkeley. His biological research interests span computational genomics, bioinformatics, and statistical genetics, and his mathematical interests span scientific computing, high-dimensional statistical inference, and mathematical optimization. Kevin completed his M.S. and Ph.D in Biomathematics at UCLA with Kenneth Lange, where he developed open-source penalized regression methods for genetic association studies. He received his undergraduate degrees in mathematics and linguistics from The University of Arizona. Data: 24 de agosto de 2020 (segunda-feira) Horário: 14h00 às 15:20 (1 hora de apresentação e 20 min para perguntas) Local: Ambiente virtual Mconf (Acesso pelo link <a href="https://mconf.ufrgs.br/webconf/00040710" target="_blank" style="color:blue"><span style="font-size:12pt;line-height:150%;color:rgb(17,85,204);background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial">https://mconf.ufrgs.br/webconf/00040710</a><span style="font-size:12pt;line-height:150%;color:rgb(51,51,51);background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial">) Moderador: Prof. Dra. Gabriela Bettella Cybis OBS 1: Após o acesso pelo link acima a participação na reunião será autorizada pelo moderador por ordem de acesso. O sistema permite um limite de 60 participantes. Os participantes externos à UFRGS deverão acessar a reunião através da opção “convidados”. OBS 2: A agenda de seminários pode ser acessada em <a href="https://www.ufrgs.br/ppgest/seminarios/" style="color:blue">https://www.ufrgs.br/ppgest/seminarios/</a> OBS 3: Abaixo os links para acessar as palestras anteriores autorizadas: Seminário 1 (Profa. Dra. Taiane Schaedler Prass, UFRGS) Link: <a href="https://youtu.be/_-DGDNepTno" target="_blank" style="color:blue">https://youtu.be/_-DGDNepTno</a> Seminário 3 (Prof. Dr. Marcelo Bourguignon Pereira, UFRN) Link: <a href="https://youtu.be/lYqEw_VZqV4" target="_blank" style="color:blue">https://youtu.be/lYqEw_VZqV4</a> Seminário 4 (Prof. Dr. Hedibert Freitas Lopes, Insper) Link: <a href="https://youtu.be/8PYG89fD8jQ" target="_blank" style="color:blue">https://youtu.be/8PYG89fD8jQ</a> Seminário 5 (Prof. Dr. Marcelo Fernandes, FGV) Link: <a href="https://youtu.be/hzJu8D9qc2I" style="color:blue">https://youtu.be/hzJu8D9qc2I</a> Seminário 6 (Prof. Dr. Francisco Cribari, FGV) Link: <a href="https://youtu.be/AM-NBUrADLg" target="_blank" style="color:blue">https://youtu.be/AM-NBUrADLg</a> Seminario 8 <a href="https://youtu.be/UHql_Uu1L3Y" target="_blank" style="color:blue">https://youtu.be/UHql_Uu1L3Y</a> Contamos com a presença de todos. PPGEst-UFRGS<div> </div>-- <div dir="ltr" class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div> <div><div dir="ltr"><div dir="ltr">Danilo Marcondes Filho<div style="font-size:small">Department of Statistics Universidade Federal do Rio Grande do Sul (UFRGS)</div><div style="font-size:small"> Av. 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