Phenotypic traits can be discrete, i.e. they are either present or absent and are inherited in a Mendelian pattern. Quantitative genetics refers to continuous phenotypes that are not brought about by this textbook pattern of inheritance but rather by a number of genes that collectively and quantitatively influence the trait in interaction with the environment (Hill and Mulder 2011). Examples of such complex traits are the colour and shape of fruit, the height or size of organisms as well as diseases like diabetes (Donnelly 2008), schizophrenia (Purcell et al. 2009) and cancer (Vazquez et al. 2012). The function and activity of the genes that influence a given phenotype is further dependent on natural variation that introduces single nucleotide polymorphisms (SNPs) or small insertions or deletions (InDels) into genes or non-coding DNA. Genome-wide association studies (GWAS) aim at comparing the genomes of individuals to correlate the presence of SNPs within genes with a specific phenotype and in this manner define risk factors or susceptibility genes in disease (Illig et al. 2009; Soto-Ortolaza et al. 2013). Risk factors have been described and include the BRCA1 gene for breast cancer (Neuhausen et al. 1994; Williams et al. 2005), MLH1 in colorectal cancer (Papadopoulos et al. 1994), SNCA in Parkinson’s disease (Warner and Schapira 2003; Mueller et al. 2005) and MYO18B in schizo- phrenia (Purcell et al. 2009) to name only a few within this field of ongoing research (a detailed catalogue of GWAS studies is available from the National Human Genome Research Institute). With a better knowledge of risk factors, disease progress and outcome is more predictable and treatments can be designed more effectively.
1.7.1
Mapping of QTLs
Quantitative trait loci (QTLs) are genomic regions which contain susceptibility genes and are distributed over the genome (Collard et al. 2005). They can be identified because they segregate with a trait of interest. In GWAS, QTLs are described by the over-representation of the same genomic regions containing genetic variants, or in other words by the linkage to a phenotype
(Daniels et al. 1996; Chung et al. 2014). Since the sample number in genome-wide association studies is limiting (Almasy and Blangero 2008), model organisms with a relatively short life cycle are powerful tools to not only identify QTLs, but also to characterise the genes within a given QTL in respect to a genetically complex phenotype of interest. QTL mapping has been performed in plants (Young 1996), Drosophila melanogaster (Leips and Mackay 2000; Pasyukova et al. 2000; Edwards and Mackay 2009), yeast (Katou et al. 2009; Liti and Louis 2012) or C. elegans (Ayyadevara et al. 2003; Green et al. 2013; Andersen et al. 2015) and resulted in the descrip- tion of behavioural as well as disease related traits. Basically, wild isolates that are sufficiently diverse on a genomic level, are crossed, and recombinant inbred lines (RILs) are obtained via backcrossing (Takuno et al. 2012; Mulualem and Bekeko 2016). With the help of molecular markers such as fragment-length polymorphisms (FLPs), the parental sequence origin is de- termined and tracked (Zipperlen et al. 2005). The set of RILs is compared to the original isolate to establish genome-wide genotype-phenotype maps and identify linkage disequilibria, i.e. loci that are associated with the trait of interest. This straight-forward strategy has helped in gaining a deeper understanding in the complex nature of disease.
As of February 2017, the World Health Organisation describes cancer as the second leading cause of death globally, after ischaemic heart disease and stroke (http://www.who.int/cancer/). Hereby, the most common types of cancer concern the lung, liver, colon, stomach and breast. Cancer is a disease during which cells divide in an uncontrolled manner so that tissues grow abnormally to give rise to so called “tumours” that compromise organ function. Cells altered in such a way can spread to other parts of the body and build new colonies known as “meta- stases”, which are the major cause of death from cancer. The transformation of normal cells into tumour cells occurs through carcinogens, among which radiation (UV, ionising), tobacco smoke or aflatoxins (e.g. present in raw mushrooms) as well as viral and bacterial infections. Cell transformation is the process of DNA alterations (mutations) with consequent changes in protein activity or function. Progress in research has helped in gaining insight into the con- tribution of basic cellular signalling pathways involved in the normal development of higher organisms during cancer. Prominent examples comprise the highly conserved WNT, EGFR/ RAS/MAPK and NOTCH pathways (Reya and Clevers 2005; Fernandez-Medarde and Santos 2011; Takebe et al. 2013). However, while deviations in these signalling pathways can initiate cell transformation and cancer development, the combination and interaction of further genes or “risk factors” present within an individual genome (the entirety of genes) greatly determi- nes the speed of disease progression, response to treatment and thus prognosis (Stessman et al. 2014). Risk factors contribute to a disease through not necessarily obvious processes when they exhibit a specific DNA composition brought about by spontaneous mutations. Such spontaneous mutations (polymorphisms) occur naturally and are the origin of evolution, since they change gene sequences and affect protein function. Therefore, a deeper understanding of the effect of polymorphisms on the function of risk factors and thus disease outcome is of great importance. In genome-wide association studies (GWAS), the DNA sequence of disease patients is compared to discover risk factors, i.e. genes in which mutations occur with incre- ased frequency (Soto-Ortolaza et al. 2013). Although such studies have helped in describing risk factors, they suffer from a limited sample number and the lack of further functional studies. Therefore, the application of simple organisms like the nematode C. elegans is preferred, since basic signalling pathways are conserved (Spradling et al. 2006). During the development of the
types) (Sternberg 2005). Thus, it is a feasible system to identify risk factors (also called modi- fiers) and study their effect on these signalling pathways.
The aim was to simulate a simplified cancerous environment by employing mutated C. elegans lines that exhibit impaired function of ß-catenin, the master regulator of WNT signalling or an overactive form of RAS that is frequently found in cancer tissues (Fernandez-Medarde and Santos 2011; Clevers and Nusse 2012). Further, to mimic the natural variation in the human population, we employed the two worm strains N2 and CB4856 that are diverse on a genomic level, mixed their genomes to obtain a range of mutation included introgression lines (miILs) carrying different genomic parts of N2 and CB4856 (Doroszuk et al. 2009) and aimed at verifying previously predicted quantitative trait loci (QTLs) that contain modifiers of the signalling pathways (Schmid et al. 2015). By assessing the effect of defined CB4856 and N2 genomic regions or the knockdown of selected genes respectively on the outcome of the mutant phenotype, we expected to find a number of modifiers of the WNT and RAS/MAPK pathways, which we could then describe in more detail and in that contribute another tiny piece of understanding to the vast and incomplete field of cancer research.