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The role of complementary bipartite visual analytical representations in the analysis of SNPs: a case study in ancestral informative markers.

Authors
Bhavnani-SK; Bellala-G; Victor-S; Bassler-KE; Visweswaran-S
Source
J Am Med Inform Assoc 2012 Jun; 19(e1):e5-e12
NIOSHTIC No.
20043031
Abstract
OBJECTIVE: Several studies have shown how sets of single-nucleotide polymorphisms (SNPs) can help to classify subjects on the basis of their continental origins, with applications to case-control studies and population genetics. However, most of these studies use dimensionality-reduction methods, such as principal component analysis, or clustering methods that result in unipartite (either subjects or SNPs) representations of the data. Such analyses conceal important bipartite relationships, such as how subject and SNP clusters relate to each other, and the genotypes that determine their cluster memberships. METHODS: To overcome the limitations of current methods of analyzing SNP data, the authors used three bipartite analytical representations (bipartite network, heat map with dendrograms, and Circos ideogram) that enable the simultaneous visualization and analysis of subjects, SNPs, and subject attributes. RESULTS: The results demonstrate (1) novel insights into SNP data that are difficult to derive from purely unipartite views of the data, (2) the strengths and limitations of each method, revealing the role that each play in revealing novel insights, and (3) implications for how the methods can be used for the analysis of SNPs in genomic studies associated with disease. CONCLUSION: The results suggest that bipartite representations can reveal new patterns in SNP data compared with existing unipartite representations. However, the novel insights require multiple representations to discover, verify, and comprehend the complex relationships. The results therefore motivate the need for a complementary visual analytical framework that guides the use of multiple bipartite representations to analyze complex relationships in SNP data.
Keywords
Humans; Genes; Genetics; Genetic-factors; Nucleotides; Morphology; Case-studies; Analytical-instruments; Analytical-processes; Analytical-methods; Data-processing; Visual-images; Mathematical-models; Racial-factors; Biomarkers
Contact
Suresh K. Bhavnani, Institute for Translational Sciences, University of Texas Medical Branch, 301 University Blvd., Galveston, TX 77555-0129, USA
CODEN
JAMAFU
Publication Date
20120601
Document Type
Journal Article
Email Address
skbhavnani@gmail.com
Funding Type
Grant
Fiscal Year
2012
NTIS Accession No.
NTIS Price
Identifying No.
Grant-Number-R21-OH-009441
Issue of Publication
e1
ISSN
1067-5027
Source Name
Journal of the American Medical Informatics Association
State
TX; MI; PA
Performing Organization
University of Texas Medical Branch, Galveston
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