General Practice and Primary Care - Research Publications
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ItemRuns of homozygosity in killer whale genomes provide a global record of demographic histories.(Wiley, 2021-12)Runs of homozygosity (ROH) occur when offspring inherit haplotypes that are identical by descent from each parent. Length distributions of ROH are informative about population history; specifically, the probability of inbreeding mediated by mating system and/or population demography. Here, we investigated whether variation in killer whale (Orcinus orca) demographic history is reflected in genome-wide heterozygosity and ROH length distributions, using a global data set of 26 genomes representative of geographic and ecotypic variation in this species, and two F1 admixed individuals with Pacific-Atlantic parentage. We first reconstructed demographic history for each population as changes in effective population size through time using the pairwise sequential Markovian coalescent (PSMC) method. We found a subset of populations declined in effective population size during the Late Pleistocene, while others had more stable demography. Genomes inferred to have undergone ancestral declines in effective population size, were autozygous at hundreds of short ROH (<1 Mb), reflecting high background relatedness due to coalescence of haplotypes deep within the pedigree. In contrast, longer and therefore younger ROH (>1.5 Mb) were found in low latitude populations, and populations of known conservation concern. These include a Scottish killer whale, for which 37.8% of the autosomes were comprised of ROH >1.5 Mb in length. The fate of this population, in which only two adult males have been sighted in the past five years, and zero fecundity over the last two decades, may be inextricably linked to its demographic history and consequential inbreeding depression.
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ItemHandling uncertainty using features from pathology: Opportunities in primary care data for developing high risk cancer survival methods(ACM, 2021)More than 144 000 Australians were diagnosed with cancer in 2019. Diagnosing cancer in primary care is challenging due to the non-specific nature of cancer symptoms and its low prevalence. Understanding the epidemiology of cancer symptoms and patterns of presentation in patient's medical history from primary care data could be important to improve earlier detection and cancer outcomes. As past medical data about a patient can be incomplete, irregular or missing, this creates additional challenges when attempting to use the patient's history for any new diagnosis. Our research aims to investigate the opportunities in a patient's pathology history available to a GP, initially focused on the results within the frequently ordered full blood count to determine relevance to a future high-risk cancer prognosis, and treatment outcome. We investigated how past pathology test results can lead to deriving features that can be used to predict cancer outcomes, with emphasis on patients at risk of not surviving the cancer within 2-year period. This initial work focuses on patients with lung cancer, although the methodology can be applied to other types of cancer and other data within the medical record. Our findings indicate that even in cases of incomplete or obscure patient history, hematological measures can be useful in generating features relevant for predicting cancer risk and survival. The results strongly indicate to add the use of pathology test data for potential high-risk cancer diagnosis, and utilize additional pathology metrics or other primary care datasets even more for similar purposes.