Genetic variations in connection Citation for published version (APA): Cirillo, E. (2019). Genetic variations in connection: understanding the effects of Single Nucleotide Polymorphisms in their biological context. ProefschriftMaken Maastricht. https://doi.org/10.26481/dis.20190118ec Document status and date: Published: 01/01/2019 DOI: 10.26481/dis.20190118ec Document Version: Publisher's PDF, also known as Version of record Please check the document version of this publication: • A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. 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Cover design: Remco Wetzels (Proefschriftmaken.nl) Layout by: Elisa Cirillo Printed by: Proefschriftmaken.nl Published by: Proefschritfmaken.nl ISBN: 978-90-829118-4-8 1 Genetic variations in connection Understanding the effects of Single Nucleotide Polymorphisms in their biological context DISSERTATION to obtain the degree of Doctor at the Maastricht University, on the authority of the Rector Magnificus, Prof.dr. Rianne M. Letschert in accordance with the decision of the Board of Deans, to be defended in public on Friday 18th January 2019, 14.00 hours by Elisa Cirillo Supervisor Prof. Dr. Chris T. A. Evelo Co-supervisors Dr. Susan L.M. Coort Dr. Laurence D. Parnell (Tufts University) Assessment Comittee Prof. Dr. Maurice P.A. Zeegers (Chair) Prof. Dr. Ellen E. Blaak Prof. Dr. Annemie M.W.J. Schols Prof. Dr. Peter-Bram ’t Hoen (Radboud University) Dr. Marco Roos (Leiden University Medical Center) 1 TABLE OF CONTENTS 1 General introduction 2 2 A review of pathway-based analysis tools that visualize genetic variants 16 3 Providing gene-to-variant and variant-to-gene database identifier map- pings to use with BridgeDb mapping services 40 4 From SNPs to Pathways: Biological interpretation of Type 2 Diabetes (T2DM) Genome Wide Association Study (GWAS) results 52 5 A genetic reference network to better understand the role of non- coding variants in obesity 78 6 Biological pathways leading from ANGPTL8 to Diabetes Mellitus - A co-expression network based analysis 100 7 General discussion 132 Summary . 140 Riassunto . 144 Valorization . 147 Acknowledgements . 152 About the author . 156 List of publications . 158 2 1 CHAPTER 1 General introduction 2 The beauty and the tragedy of a single nucleotide polymor- phism The beauty Humans (Homo sapiens) have 99.5% to 99.8% of their genome essentially identical in everyone [19]. However, there is a large variety of different types of humans in the world. For example, it is generally not common to find two people that look exactly alike. These differences are even greater in the molecular and physiological mecha- nisms of the inner body. The actual human DNA variability is estimated from 0.2% to 0.5% of 3 billion nucleotides, but this small percentage reflects a very large number of variations in the DNA, from 6 to 15 million nucleotides. Part of these variations, both common and rare ones, are in the category of the Single Nucleotide Variants (SNVs) [19], in which are included the Single Nucleotide Polymorphisms (SNPs). A SNP is a DNA sequence variant, occurring when a single nucleotide (adenine, guanine, thymine, or cytosine) differs between members of a species or paired chromosomes in an indi- vidual. Thus, the beauty (Figure 1.1) of roughly 10 million variants is that they can potentially occur in numerous different combinations in various individuals. This is vastly more than enough to ensure individual uniqueness at the DNA level, while still representing a very small fraction of the total genome. Even in monozygotic twins, that essentially have the same chromosomal DNA sequence, it is possible to find differences in their physical characteristics. These differences reflect a DNA variability originating from somatic mutations, caused by small errors in DNA replication after the four- to eight-cell zygote stage; and from the action of the epigenetic mechanisms that play a role in the DNA plasticity and availability for the transcription process [6]. The tragedy In general, the majority of the human SNPs are located between genes and often they do not have any deleterious effects on the functionality of the gene or its encoded proteins. The tragedy (Figure 1.1) occurs when the SNPs located in the coding area or those placed in the regulatory regions of a gene, such as promoter or splicing sites, show a deleterious impact on the transcription of the gene or the protein activity, for instance. When this happens in a gene that plays a key role in a biological pathway, serious dis- eases such as the Rett syndrome for the monogenic X-dominant MECP2 gene [23] can occur. The scenario becomes convoluted in complex diseases such as: diabetes, obesity, and chronic obstructive pulmonary disease (COPD), to name a few, where the genetic background of the disease is most likely characterized by a combination of variations occurring in an unknown number of genes, usually interacting with various environ- 3 Fig. 1.1 Representation of the beauty and the tragedy of SNPs consequence. mental factors [9]. Although complex disorders often cluster in families, they do not have a clear pattern of inheritance. For this reason, it is difficult to determine a person’s risk of inheriting or passing on these disorders. Since 2005, population genetics has accelerated with the use of Genome Wide Association Study (GWAS) and the ability to examine the genotype-phenotype relationship with at first 100,000 genetic variants and now routinely 1 million (or more). Population genetic studies have been ongoing for decades but with much slower and less precise methods. These studies have been used to investigate the genetic background of individuals with complex diseases, and they are performed comparing the DNA sequences of genetically similar individuals (from the same population origin) with (cases) and without (controls) specific pheno- typic traits [10]. In particular, the comparison is performed between the alleles of cases and controls that have been detected through the whole genome, resulting in a list of SNPs significantly associated with the specific trait or disease. The core data analyzed in the different chapters of this thesis are SNPs detected with GWAS studies and as- sociated with different types of complex diseases and traits such as: Type 2 diabetes mellitus (T2DM) and obesity. 4 Genome-wide association studies as a tool to unravel com- plex diseases GWAS studies are the first step, followed by functional genomics studies, that can be used either to identify causal or predictive factors for a given trait or to investigate the genetic architecture of that trait. For example, the genetic risk factors identified from the list of SNPs associated with a complex disease, allows development of susceptibil- ity testing for disease prediction [16]. For clinicians, knowing the genetic susceptibility of a patient especially for complex diseases can improve the diagnosis and inform the choice of treatment [22]. Because these diseases have a strong environmental com- ponent, the correction of the lifestyle is considered both a form of prevention and a treatment. Indeed, the environmental changes could help to balance the genetic predis- position. In this regard, obtaining genetic information can launch a warning message to take action. On the other hand, from the researchers perspective GWAS studies are extensively used to identify the genetic factors contributing to disease phenotypes and to elucidate the extent to which those genetic factors affect the pathophysiology of a disease. This type of fundamental knowledge is at the base of drug design, especially in relation of pharmacogenetics. Inherited genetic variations present in drug targets or in enzymes that metabolize a drug [4] can affect the individual responses both in terms of adverse effects and therapeutic effects. Knowing the existence and the role of these variations (Figure 1.2) for specific diseases enables the design or selection of drugs for a personalized treatment [25]. After more than a decade of GWAS experience, re- searchers have recognized that the power of GWAS to identify within a population a true association between a SNP and a trait is dependent on the phenotypic variance ex- plained by the SNP [24].