Genome-wide SNPs clarify a complex radiation and support recognition of an additional cat species.
Ontology highlight
ABSTRACT:
SUBMITTER: Trindade FJ
PROVIDER: S-EPMC8557425 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:This article is part of a series written for people responsible for making decisions about health policies and programmes and for those who support these decision makers. Policymakers and those supporting them often find themselves in situations that spur them on to work out how best to define a problem. These situations may range from being asked an awkward or challenging question in the legislature, through to finding a problem highlighted on the front page of a newspaper. The motivations for policymakers wanting to clarify a problem are diverse. These may range from deciding whether to pay serious attention to a particular problem that others claim is important, through to wondering how to convince others to agree that a problem is important. Debates and struggles over how to define a problem are a critically important part of the policymaking process. The outcome of these debates and struggles will influence whether and, in part, how policymakers take action to address a problem. Efforts at problem clarification that are informed by an appreciation of concurrent developments are more likely to generate actions. These concurrent developments can relate to policy and programme options (e.g. the publication of a report demonstrating the effectiveness of a particular option) or to political events (e.g. the appointment of a new Minister of Health with a personal interest in a particular issue). In this article, we suggest questions that can be used to guide those involved in identifying a problem and characterising its features. These are: 1. What is the problem? 2. How did the problem come to attention and has this process influenced the prospect of it being addressed? 3. What indicators can be used, or collected, to establish the magnitude of the problem and to measure progress in addressing it? 4. What comparisons can be made to establish the magnitude of the problem and to measure progress in addressing it? 5. How can the problem be framed (or described) in a way that will motivate different groups?
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Project description:BACKGROUND: Recent development of high-resolution single nucleotide polymorphism (SNP) arrays allows detailed assessment of genome-wide human genome variations. There is increasing recognition of the importance of SNPs for medicine and developmental biology. However, SNP data set typically has a large number of SNPs (e.g., 400 thousand SNPs in genome-wide Parkinson disease data set) and a few hundred of samples. Conventional classification methods may not be effective when applied to such genome-wide SNP data. RESULTS: In this paper, we use shrunken dissimilarity measure to analyze and select relevant SNPs for classification problems. Examples of HapMap data and Parkinson disease (PD) data are given to demonstrate the effectiveness of the proposed method, and illustrate it has a potential to become a useful analysis tool for SNP data sets. We use Parkinson disease data as an example, and perform a whole genome analysis. For the 367440 SNPs with less than 1% missing percentage from all 22 chromosomes, we can select 357 SNPs from this data set. For the unique genes that those SNPs are located in, a gene-gene similarity value is computed using GOSemSim and gene pairs that has a similarity value being greater than a threshold are selected to construct several groups of genes. For the SNPs that involved in these groups of genes, a statistical software PLINK is employed to compute the pair-wise SNP-SNP interactions, and SNPs with significance of P < 0.01 are chosen to identify SNPs networks based on their P values. Here SNPs networks are constructed based on Gene Ontology knowledge, and therefore each SNP network plays a role in the biological process. An analysis shows that such networks have relationships directly or indirectly to Parkinson disease. CONCLUSIONS: Experimental results show that our approach is suitable to handle genetic variations, and provide useful knowledge in a genome-wide SNP study.
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