Reference number : GC-2017-Q1-03529, amendment number : , agreement type : G, recipient legal name : French, Leon (University of Toronto), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : Decoding the human genome has allowed us to discover associations between specific genes and diseases. It has also enabled us to measure expression or usage of all genes in the brain. The brain is the most complex object known to man, this complexity is seen in differences in gene usage in the brain, with three-quarters of genes changing across brain regions or age. While many genes are known to directly affect brain structure, many genes used in the brain play unknown roles. This research seeks to find patterns of gene usage that can help understand three other measures of the human brain. The first is neuroanatomy, we seek to find genes that differ across different regions and layers of the brain by combining several large datasets of gene usage. Second, we will join gene usage information with the grayscale images produced from magnetic resonance scans (MRI). Lastly, we examine epigenetic information that influences gene usage by marking the genome with specific tags. We will test if the relationships between gene usage and these methylation tags are different across time or sex. This work aims to increase our understanding of the molecules underlying neuroimages, brain structure, and control of gene usage. These projects will be undertaken by students that will learn how to apply data science methods to handle the large and complex datasets. 
 
According to the Canadian Mental Health Association, one in five Canadians will experience mental illness in their lifetime. Reducing this high burden is limited by the complexity of the brain, challenging our efforts to find cures. Our above work that involves examining sets of genes is of key importance to understanding complex mental illness. For disorders like schizophrenia, depression, and intellectual disability we do not understand which components of the brain are disrupted. However, we do know that these disorders are associated with changes in many genes. Our work to characterize what genes are doing in the brain may help find common characteristics of mental health associated genes. These clues can help guide therapeutic treatments. In this regard, all of our tools will be open source and freely available online for others to see their genes of interest across the landscape of the brain., program purpose french : Decoding the human genome has allowed us to discover associations between specific genes and diseases. It has also enabled us to measure expression or usage of all genes in the brain. The brain is the most complex object known to man, this complexity is seen in differences in gene usage in the brain, with three-quarters of genes changing across brain regions or age. While many genes are known to directly affect brain structure, many genes used in the brain play unknown roles. This research seeks to find patterns of gene usage that can help understand three other measures of the human brain. The first is neuroanatomy, we seek to find genes that differ across different regions and layers of the brain by combining several large datasets of gene usage. Second, we will join gene usage information with the grayscale images produced from magnetic resonance scans (MRI). Lastly, we examine epigenetic information that influences gene usage by marking the genome with specific tags. We will test if the relationships between gene usage and these methylation tags are different across time or sex. This work aims to increase our understanding of the molecules underlying neuroimages, brain structure, and control of gene usage. These projects will be undertaken by students that will learn how to apply data science methods to handle the large and complex datasets. 
 
According to the Canadian Mental Health Association, one in five Canadians will experience mental illness in their lifetime. Reducing this high burden is limited by the complexity of the brain, challenging our efforts to find cures. Our above work that involves examining sets of genes is of key importance to understanding complex mental illness. For disorders like schizophrenia, depression, and intellectual disability we do not understand which components of the brain are disrupted. However, we do know that these disorders are associated with changes in many genes. Our work to characterize what genes are doing in the brain may help find common characteristics of mental health associated genes. These clues can help guide therapeutic treatments. In this regard, all of our tools will be open source and freely available online for others to see their genes of interest across the landscape of the brain., agreement title english : Integrative Neuroinformatics for Neuroanatomy, agreement title french : Integrative Neuroinformatics for Neuroanatomy, agreement number : RGPIN, agreement value : 155000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada