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Abstract
Genome-wide association studies (GWAS) try to decipher how variations in the genome associate with human traits. One of the overarching aims of such analysis is to elucidate disease etiology. To this date, GWAS have successfully identified thousands of variants associated with various traits. However, these variants only explain a small proportion of trait variance observed between individuals, a phenomenon known as “the missing heritability.” Many have speculated on possible causes for this phenomenon, which have lead scientists down the path of genetic interactions, known as epistasis, as a potential explanation for the missing heritability. Few examples of epistasis have been found to influence human traits. However, studies in model organisms provide biological and statistical evidence of epistasis, suggesting that epistasis is a ubiquitous component in the human genome. The few identified examples of epistasis have been discovered by examining molecular traits, such as metabolomics. However, these associations are not connected to disease and are without clinical impact.
Many complex traits consist of underlying intermediate trait subtypes, where each subtype may be more influenced by specific genetic variations. Childhood asthma represents one of these complex diseases, composed of several functionally different subtypes. Focusing on particular childhood asthma subtypes may be closer linked to specific genetic mechanisms, facilitating the detection of epistasis effects.
This Ph.D. project aimed at exploring whether epistasis signals are involved in childhood asthma disease development. Towards that aim, I have contributed with two scientific papers. The first paper presents one of the largest GWAS on severe childhood asthma, characterized by hospitalizations due to asthma exacerbations. The results revealed a novel asthma locus near the FUT2 gene. FUT2 determines the "secretor-status" of the individual, controlling whether AB antigens are secreted in body fluids. Individuals with secretor-status had an increased risk of asthma. Based on this biological knowledge, we searched for interaction between the FUT2 variant and a variant located in the ABO gene. We observed statistical evidence of epistasis between these two genes, pointing towards a biological mechanism related to secretion of AB antigens. We subsequently show that the epistasis signal is associated with respiratory illnesses with the bacteria Streptococcus pneumonia.
Based on this finding, we conducted an exhaustive search for interactions, presented in the second paper. For this purpose, we developed a penalized logistic regression model founded in the Bayesian probabilistic modeling paradigm. The model provided evidence of interaction between two well-known childhood asthma genes in CDHR3 and GSDMB, where the interaction signal replicated in two larger independent data sets. We were able to provide evidence that the interaction between CDHR3 and GSDMB is related to an increased IL-17A response during viral infections.
The findings of two examples of epistasis associated with childhood asthma illustrate that it is possible to identify these elusive effects by focusing on specific asthma subtypes, which may be applicable for other complex traits. I hope that these findings will reignite the belief in epistasis, ultimately leading to an improved understanding of the human genome and its impact on disease.
Many complex traits consist of underlying intermediate trait subtypes, where each subtype may be more influenced by specific genetic variations. Childhood asthma represents one of these complex diseases, composed of several functionally different subtypes. Focusing on particular childhood asthma subtypes may be closer linked to specific genetic mechanisms, facilitating the detection of epistasis effects.
This Ph.D. project aimed at exploring whether epistasis signals are involved in childhood asthma disease development. Towards that aim, I have contributed with two scientific papers. The first paper presents one of the largest GWAS on severe childhood asthma, characterized by hospitalizations due to asthma exacerbations. The results revealed a novel asthma locus near the FUT2 gene. FUT2 determines the "secretor-status" of the individual, controlling whether AB antigens are secreted in body fluids. Individuals with secretor-status had an increased risk of asthma. Based on this biological knowledge, we searched for interaction between the FUT2 variant and a variant located in the ABO gene. We observed statistical evidence of epistasis between these two genes, pointing towards a biological mechanism related to secretion of AB antigens. We subsequently show that the epistasis signal is associated with respiratory illnesses with the bacteria Streptococcus pneumonia.
Based on this finding, we conducted an exhaustive search for interactions, presented in the second paper. For this purpose, we developed a penalized logistic regression model founded in the Bayesian probabilistic modeling paradigm. The model provided evidence of interaction between two well-known childhood asthma genes in CDHR3 and GSDMB, where the interaction signal replicated in two larger independent data sets. We were able to provide evidence that the interaction between CDHR3 and GSDMB is related to an increased IL-17A response during viral infections.
The findings of two examples of epistasis associated with childhood asthma illustrate that it is possible to identify these elusive effects by focusing on specific asthma subtypes, which may be applicable for other complex traits. I hope that these findings will reignite the belief in epistasis, ultimately leading to an improved understanding of the human genome and its impact on disease.
Original language | English |
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Publisher | DTU Health Technology |
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Number of pages | 211 |
Publication status | Published - 2021 |
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Analysis of epistasis in childhood asthma
Eliasen, A. U. (PhD Student), Albrechtsen, A. (Examiner), Standl, M. (Examiner), Papaleo, E. (Examiner), Pedersen, A. G. (Main Supervisor), Ahluwalia, T. S. (Supervisor), Bisgaard, H. (Supervisor), Rasmussen, M. A. (Supervisor) & Bønnelykke, K. (Supervisor)
15/10/2017 → 17/06/2021
Project: PhD