Methods and bioinformatic tools to study polymorphic inversions in complex diseases
Loading...
Document Type
Document Version
Author
Director
González Ruiz, Juan Ramón
Tutor
Other authors
Publication Date
Pages
294 p.
Embargo date
Citation
Ruiz Arenas, C. Methods and bioinformatic tools to study polymorphic inversions in complex diseases. Universitat Pompeu Fabra; 2019. handle: http://hdl.handle.net/10803/666582
This citation was generated automatically.
Citation
Doctoral program
Programa de doctorat en Biomedicina
Abstract
Las inversiones cromosómicas son variantes estructurales donde un segmento de ADN cambia su orientación. Las inversiones cromosómicas reducen la recombinación homóloga y producen diferentes haplotipos en los cromosomas estándar e invertidos. Como resultado, influyen en la adaptación y la selección y desempeñan un papel en la susceptibilidad a las enfermedades humanas. Las inversiones se pueden estudiar con métodos experimentales y bioinformáticos. Los datos de SNP array se pueden usar para determinar genotipos de inversión mediante el uso de diferencias de haplotipos entre cromosomas invertidos y estándares. Sin embargo, estos métodos no están optimizados para grandes cohortes (con miles de individuos, como dbGaP o UK Biobank). Además, los métodos actuales solo pueden genotipar las inversiones con dos haplotipos y la clasificación es difícil de armonizar entre cohortes. Finalmente, se conoce que las inversiones cromosómicas afectan la expresión génica y la metilación del ADN. Sin embargo, no existen métodos precisos para evaluar globalmente el efecto de las inversiones en la expresión génica local o la metilación del ADN. El objetivo principal de esta tesis es desarrollar nuevos métodos robustos y escalables así como herramientas bionformáticas para estudiar los efectos fenotípicos y funcionales de las inversiones cromosómicas, superando las limitaciones existentes. Con este fin, he desarrollado un nuevo método para genotipar las inversiones cromosómicas que se puede usar en grandes cohortes, con inversiones con múltiples haplotipos y que utiliza haplotipos de referencia que permite el análisis conjunto de múltiples cohortes. En segundo lugar, he implementado un método multivariante basado en el análisis de la redundancia para estudiar los efectos de las inversiones cromosómicas en la metilación del ADN y la expresión génica locales. A continuación, he aplicado ambos métodos para estudiar el papel de las inversiones cromosómicas en dos grupos de enfermedades complejas: trastornos del neurodesarrollo y cáncer. Finalmente, he desarrollado un nuevo método para estudiar cómo las inversiones cromosómicas afectan los patrones de recombinación. Este método es aplicable a cualquier región genómica que contenga subpoblaciones con diferentes patrones de recombinación, lo que permite asociar estas subpoblaciones a rasgos fenotípicos.
Chromosomal inversions are structural variants where a segment changes its orientation. Chromosomal inversions reduce homologous recombination, producing different haplotypes in standard and inverted chromosomes. As a result, they influence adaptation and selection and play a role in susceptibility to human diseases. Inversions can be studied using experimental and bioinformatic methods. SNP array data can be used to call inversion genotypes by using haplotype differences between inverted and standard chromosomes. However, these methods are not optimized for large cohorts (thousands of individuals from existing databases such as dbGaP or UK Biobank). Also, current methods can only genotype inversions with two haplotypes and the inversion calling is difficult to be harmonized among cohorts. Finally, it is recognized that chromosomal inversions affect gene expression and DNA methylation. However, there are no accurate methods to globally assess the effect of inversions on local gene expression or DNA methylation. The main aim of this thesis is to develop new robust and scalable methods and bioinformatic tools to study the phenotypic and functional effects of chromosomal inversions by overcoming the existing limitations. To this end, I have developed a new method to genotype chromosomal inversions that can be used in large cohorts, inversions with multiple haplotypes and that uses reference haplotypes allowing the integrative analysis of multiple cohorts. Second, I have implemented a multivariate method based on redundancy analysis to study the effects of chromosomal inversions on local DNA methylation and gene expression. Then, I applied both methods to study the role of chromosomal inversions in two groups of complex diseases: neurodevelopmental disorders and cancer. Finally, I developed a new method to study how chromosomal inversions affect recombination patterns. This method is extendable to any genomic regions containing subpopulations with different recombination patterns, allowing associating these subpopulations to phenotypic traits.
Chromosomal inversions are structural variants where a segment changes its orientation. Chromosomal inversions reduce homologous recombination, producing different haplotypes in standard and inverted chromosomes. As a result, they influence adaptation and selection and play a role in susceptibility to human diseases. Inversions can be studied using experimental and bioinformatic methods. SNP array data can be used to call inversion genotypes by using haplotype differences between inverted and standard chromosomes. However, these methods are not optimized for large cohorts (thousands of individuals from existing databases such as dbGaP or UK Biobank). Also, current methods can only genotype inversions with two haplotypes and the inversion calling is difficult to be harmonized among cohorts. Finally, it is recognized that chromosomal inversions affect gene expression and DNA methylation. However, there are no accurate methods to globally assess the effect of inversions on local gene expression or DNA methylation. The main aim of this thesis is to develop new robust and scalable methods and bioinformatic tools to study the phenotypic and functional effects of chromosomal inversions by overcoming the existing limitations. To this end, I have developed a new method to genotype chromosomal inversions that can be used in large cohorts, inversions with multiple haplotypes and that uses reference haplotypes allowing the integrative analysis of multiple cohorts. Second, I have implemented a multivariate method based on redundancy analysis to study the effects of chromosomal inversions on local DNA methylation and gene expression. Then, I applied both methods to study the role of chromosomal inversions in two groups of complex diseases: neurodevelopmental disorders and cancer. Finally, I developed a new method to study how chromosomal inversions affect recombination patterns. This method is extendable to any genomic regions containing subpopulations with different recombination patterns, allowing associating these subpopulations to phenotypic traits.
Keywords
Chromosomal inversions, Genetic epidemiology, Methods development, Genotyping, Inversiones cromosómicas, Epidemiología genética, Desarrollo de métodos, Genotipado
Subjects
575 - General genetics. General cytogenetics. Immunogenetics. Evolution. Phylogeny
Publisher
Universitat Pompeu Fabra
