Factores de Riesgo

The investigation of the occurrence and causes of disease in free ranging wild populations of animals presents logistical difficulties. Identifying related animals in order to study the effects of inbreeding or to identify familial traits can be particularly challenging. Animals may be found in environments that are difficult to access, have a nocturnal existence or be particularly elusive (Witmer, 2005). The utilization of genetic microsatellite markers to identify related individuals has made keeping track of the dynamics of a population possible (Webster and Reichart, 2005, Jarne and Lagoda, 1996). Microsatellites are highly polymorphic repeat units of nucleotides of up to six base pairs (bp) and are reviewed by Ellegren, 2004 and Selkoe and Toonen, 2006. The repeats can occur between five to forty times at any one site in the genome and

thousands of times at different sites throughout the genome (Ellegren, 2004, Selkoe and Toonen, 2006). It is estimated that microsatellites account for 3% of the human genome (Ellegren, 2004, Lander et al., 2001).

Polymorphism of microsatellite alleles are commonly due to variations in the length of the allele caused by variations in the number of repeat units, rather than as a result of variations in the actual repeated sequence (Ellegren, 2004). Their presumed neutrality, along with their polymorphic existence have resulted in their frequent use as markers in population genetic studies involving the investigation of population structure, paternity and gene flow in a number of species ranging from Zebra finches (Taeniopygia guttata) and Black-faced Lion tamarins (Leontopithecus caissara) to Blacktip Reef sharks (Carcharhinus melanopterus) and Caribbean star corals (Montastraea faveolata) (Davies et al., 2013, Vignaud et al., 2013, Martins and Galetti, 2011, Webster and Reichart, 2005, Dawson et al., 2013). The field of marine mammal research poses additional logistical difficulties due to the aquatic environment of the animals

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sources including skin and blood (Bean et al., 2004, Yu et al., 2011, Torres-Florez et al., 2012). Samples such as these however require direct access to the animal which is not always possible, so additional non-invasive sampling methods have been employed such as extracting DNA from collected faeces or carcasses (Reed et al., 1997, Bean et al., 2004, Kretzmann et al., 2006). More recently studies into the merits of

environmental DNA sampling using sea water have been investigated (Foote et al., 2012). These various methods of obtaining DNA have allowed population genetic studies using microsatellite markers on a variety of marine mammals, enabling a greater understanding of their populations (Buchanan et al., 1998, Graves et al., 2009, Bean et al., 2004, Torres-Florez et al., 2012).

Microsatellites are found in both coding and non-coding regions of DNA, however the majority are found in non-coding DNA such as intergenic sequences or introns

(Ellegren, 2004) which are removed by RNA splicing prior to translation (Faustino and Cooper, 2003, Jaillon et al., 2008). In spite of this, polymorphisms within these

microsatellites have increasingly been found to be important in genetic function (Li et al., 2004, Zhang et al., 2009). An example of this is the microsatellite found within the Epidermal Growth Factor Receptor gene (EGFR), where polymorphisms of the CA repeat in intron one is associated with differing clinical outcomes in non-small cell lung cancer (Shitara et al., 2012) and oesophageal cancer patients (Vashist et al., 2013). Additionally the presence of shorter CA repeats at this locus was identified as more common in osteosarcoma patients (Kersting et al., 2008). Similarly longer CA repeats in intron five of the oestrogen receptor gene ESR2 have been identified as a risk factor in breast cancer in Nigerian women (Zheng et al., 2012). This challenges the argument of microsatellite neutrality (Li et al., 2002, Kashi and King, 2006).

Further to this microsatellites have been useful in detecting significant genetic

associations to common diseases in humans and domestic animals (Gulcher, 2012). One such example is that of type 2 diabetes in humans where, along with obesity and life style, genetic factors have been highlighted as additional risk factors. Individuals with a history of the disease in their family have been identified as being at greater risk

(Reynisdottir et al., 2003). Positional cloning through a genome-wide linkage study of Icelandic families led to the identification of the Transcription Factor 7-Like 2 gene

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(TCF7L2), variants of which are seen to be associated with the risk of developing type 2 diabetes (Reynisdottir et al., 2003, Grant et al., 2006). Associations have also been identified in neoplastic conditions. Certain alleles of the ZuBeCa3 microsatellite have been found to be associated with the presence of mammary tumours in various breeds of dog. The candidate gene of interest in this case is believed to be the Breast cancer- associated gene 1 (BRCA1) located in close proximity on chromosome 9 in domestic dogs. However, the relationship is yet to be confirmed (Bhattacharya et al., 2007). The consequences of inbreeding in animal populations have also been assessed using microsatellites. Increased homozygosity of microsatellite alleles in inbred animals have been found to be associated with decreased fitness traits including reduced sperm quality, increased parasite load and presence of skeletal deformities (Rijks et al., 2008, Gage et al., 2006, Fitzpatrick and Evans, 2009, Lacy and Horner, 1996). Inbreeding depression (reduced fitness in a population) has important implications, as the tendency towards homozygosity due to mating of related individuals has the potential to reveal deleterious alleles along with the loss of heterozygous advantage (Lacy and Horner, 1996, Hansson and Westerberg, 2002, Charlesworth and Willis, 2009). However, with regards to studying inbreeding, the relationship between measures of microsatellite heterozygosity and fitness known as heterozygosity-fitness correlations are only found to be fulfilled under specific circumstances. For instance when populations are small or when mating systems include behaviours such as polygyny (Balloux et al., 2004,

Fitzpatrick and Evans, 2009).

In the CSL microsatellite markers have been used to evaluate variation in the

susceptibility to a number of diseases (Acevedo-Whitehouse et al., 2003) leading to the conclusion that morbidity in thespecies may not be a random event. The study

incorporated 11 polymorphic microsatellite markers, which enabled the measurement of “internal relatedness” (Amos et al., 2001, Balloux et al., 2004) of 371 animals, 13 of which had a diagnosis of carcinoma of unspecified type. The animals had been admitted to the Marine Mammal Center in Sausalito, California, due to stranding. Internal relatedness (IR) is a measure of heterozygosity and can reveal inbreeding of an individual. The equation used IR=2H-Ʃfi / 2N-Ʃfi, where H is the number of loci that the individual is homozygote at and N is the number of loci genotyped, includes every

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allele genotyped in an individual as well as in the study population, as Ʃfi is the sum of the population frequencies of all the (i) alleles (Amos et al., 2001, Balloux et al., 2004). Therefore the IR calculation takes into account rare alleles in the population studied (Balloux et al., 2004, Amos et al., 2001). If the result of the calculation gives a positive number inbreeding is indicated, whereas a negative value suggests outbreeding and a zero result indicates that the parents were unrelated (Valimaki et al., 2007, Amos et al., 2001, Balloux et al., 2004).

The results of the study by Acevedo-Whitehouse et al., (2003) suggested that inbreeding may play a part in increased susceptibility to disease. An independent study confirmed that the CSL dataset published by Acevedo-Whitehouse and collaborators (2003) did indeed contain inbred individuals (Balloux et al., 2004), a phenomenon most likely explained by the species’ strong polygyny and philopatry (Gerber et al., 2010, Miller, 2009, Young and Gerber, 2008, Heath and Perrin, 2009). Interestingly, the condition most highly associated with the internal relatedness measure was that of carcinoma. However the role of “in-breeding” per se is uncertain as further statistical analysis by Acevedo-Whitehouse (unpublished) found that the strength of the measure was driven by particular microsatellites in the animals with neoplasia, namely Pv11 (Goodman, 1997) and M11a (Hoelzel et al., 2001).

Therefore the aim of the present study was to (i) investigate the relationship between the Pv11 and M11a loci with the occurrence of UGC in a new sample population. The new sample set only consisted of adult female animals in order to remove the confounding factors of sex and age. The study also incorporated a third microsatellite, Hg8.10 (Allen et al., 1995) not previously associated with cancer, that was used as a control.

In addition to the dataset generated by this study, a second separate dataset was provided for analysis, the second dataset was genotyped by Dr Acevedo-Whitehouse and consisted of 270 adult and sub-adult animals of both sexes, 66 of which were suffering from UGC specifically.

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