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X-WR-CALNAME:CANSSI Ontario STAGE
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DTSTART:20240607T160000Z
DTEND:20240607T170000Z
DTSTAMP:20230411T180900Z
CREATED:20230411
LAST-MODIFIED:20260316
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SEQUENCE:22
TRANSP:OPAQUE
SUMMARY:STAGE ISSS: Jonathan Marchini
DESCRIPTION:\nJoin us for the next instalment of the STAGE International Speaker Seminar Series (ISSS) with\n\n\n\nJonathan Marchini, PhD\n\n\n\nExecutive Director, Head of Statistical Genetics and Machine Learning Regeneron Genetics Center Free Online (by Zoom) Event | Registration Required\n\n\n\nTalk Title:\n\n\n\nStatistical methods for large scale genetic association studies \n\n\n\nAbstract:\n\n\n\nThe study of rare genetic variation, which can be important in the development of complex diseases, has been increasingly carried out thanks to advances in sequencing technologies. The inherent challenges posed by the rarity of these variants and the need for large sample sizes have required the development of gene-based tests. These tests, offering enhanced statistical power over single variant tests, aggregate information across multiple variants and can integrate external functional annotations to improve power of rare variant analysis. We will describe several existing and novel features in the association tool REGENIE and related programs which have been developed at the Regeneron Genetic Center (RGC) to carry out analyses of over 2 million exome-sequenced and genotyped individuals across a diverse set of cohorts with many thousands of phenotypes. We highlight the power of meta-analysis in genetic studies; this involves combining information across studies without requiring access to individual level data. It can be performed using summary statistics from a genome-wide scan of individual variants, or from gene-based tests that aggregate variants within a gene. The former approach is effective at identifying common variants with modest effects, while the latter boosts power for detecting rare variant associations. In this vein, we showcase REMETA, a tool designed for the efficient meta-analysis of gene-based tests in rare variant studies suitable for biobank-scale data sets. REMETA amalgamates results from multiple studies, enhancing the statistical power and reliability of the findings. We demonstrate the usefulness of these approaches for rare variant association testing through large-scale data applications. \n\n\n\nSpeaker Profile:\n\n\n\nJonathan Marchini is Head of Statistical Genetics and Machine Learning at the Regeneron Genetics Center. His\nresearch spans statistical and population genomics and machine learning. He has pioneered the statistical method of\ngenotype imputation, in which correlations between genetic variants (known as linkage disequilibrium) are used to\npredict/impute genotypes at genetic variants not directed typed. This approach has been used in all almost genome-\nwide association studies (GWAS) since its inception in 2006, and is basis upon which many international meta-analysis\nconsortia have been built.\n\n\n\n\nHe has published methodological papers on SNP and CNV genotype calling, Bayesian association tests, population\nstructure inference, gene-gene interactions, gene-environment interactions, haplotype estimation, genotype imputation,\nvariant calling from sequencing, multi-phenotype analysis, tensor decomposition for rna-sequence analysis and multi-\nomics data integration. More recently, the REGENIE machine learning method has revolutionised the use of whole\ngenome wide regression and linear mixed models in GWAS.\n\n\n\n\nHis software and methods (such as imputation) were used to analyze the first ever large GWAS in 2007 as part of the\nWellcome Trust Case Control Consortium (WTCCC) and set the standard for the development of this field. His work on\nphasing and imputation from sequence data were central methodological developments used by the 1000 Genomes\nProject and the UK Biobank Projects. His research group carried out the first large scale analysis of thousands of MRI\nbrain imaging derived phenotypes from the UK Biobank. He co-led the Haplotype Reference Consortium, which brought\ntogether the largest collection of human whole genome sequence data to create an imputation panel that has been\nwidely used in by the human genetics community. His has received funding from the MRC, Wellcome Trust and the\nEuropean Research Council. In 2012 Jonathan was awarded a Philip Leverhulme Research Prize.\n\n\n\n\nSponsors:\n\n\n\nCANSSI Ontario STAGE (STAGE) is a training program in genetic epidemiology and statistical genetics housed at the University of Toronto Dalla Lana School of Public Health. It operates with financial and in-kind support from CANSSI Ontario, an extra-departmental unit in the Faculty of Arts & Science at U of T.\n\n\n\nSTAGE would like to thank our generous seminar sponsors!For a complete list of sponsors, please see here ( https://stage.utoronto.ca/sponsors/ ).\n\n\n\nPhotography Disclosure:\n\n\n\nPhotographs and/or video may be taken of participants at STAGE events. These photos/videos are for the Program’s use only and may appear on its website, in printed brochures, or in other promotional or reporting materials. By attending STAGE events, you accept the possibility that you may be videotaped or photographed. If you have any concerns, please inform us by sending an e-mail to esther.berzunza@utoronto.ca\n
URL:https://stage.utoronto.ca/events/stage-isss-jonathan-marchini/
CATEGORIES:CANSSI Ontario STAGE ISSS
LOCATION:Zoom (Live Stream)
ATTACH;FMTTYPE=image/png:https://stage.utoronto.ca/wp-content/uploads/2023/04/640px-Jonathan_Marchini-e1733778449926.png
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