Role of Bioinformatics in Genome‐wide Association Studies

Abstract

A central goal of human genetics is to identify genetic variants that are associated with disease. Better understanding the role of genetic and environmental factors in disease risk will likely improve diagnosis, prevention and treatment. It is now technically and economically feasible to conduct genome‐wide association studies (GWAS) with over a million single‐nucleotide polymorphisms (SNPs) distributed across the genome. Although GWAS have led to many discoveries, the genetic underpinnings of most common diseases remain largely unexplained. One likely explanation for this “missing hereditability” is that traditional GWAS approaches have focused on one SNP at a time and have failed to account for the complexity of many genotype–phenotype relationships that are characterised by substantial heterogeneity, and gene–gene and gene–environment interactions. Such underlying genetic complexity creates bioinformatics challenges related to modelling, attribute selection and biological interpretation that must be addressed in order to realise the full potential of GWAS. The benefits of meeting these bioinformatics challenges will also extend to whole‐genome and whole‐exome sequence analysis.

Key Concepts:

  • Genetic complexity is likely to underlie many common diseases.

  • Bioinformatics tools will be necessary to uncover nonlinear genetic predictors of common diseases.

  • Data mining and machine learning methods can increase power to discover nonlinear genetic predictors of common disease.

  • Filter and wrapper algorithms are necessary to limit the number of attributes examined so modelling strategies are powerful and computationally practical.

  • Prior biological knowledge can improve the analysis and interpretation of GWAS data.

  • Powerful and intuitive software packages are necessary to enable collaboration between biologists, biostatisticians and bioinformaticists.

Keywords: GWAS; genome; genetics; bioinformatics; statistics; epistasis

Figure 1.

Overview of the random forest (RF) algorithm. Feature selection using a RF classifier for the integrated analysis of multiple data types. Adapted from Reif et al.. Reproduced from Proceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. Washington D.C., pp. 171–178. Copyright © 2006, IEEE.

Figure 2.

Summary of the constructive induction process for multifactor dimensionality reduction (MDR). The left bar and right bars represent the number of cases and controls, respectively. Dark‐shaded cells are high risk whereas light‐shaded cells are low risk. Prediction using any classifier can be carried out using the final constructed attribute. Image reproduced from Moore et al. (2010) Bioinformatics challenges for genome‐wide association studies. Bioinformatics26(4): 445–455. Copyright Oxford University Press.

Figure 3.

Summary of the neighbour selection process of Relief, ReliefF and spatially uniform ReliefF (SURF). Each panel shows cases and controls distributed by their genotypes for two continuous markers. When analysing real data, the process is similar, however, there are thousands of discrete valued markers (SNPs) that are each represented by one of thousands of dimensions. A randomly selected instance (R) is shown by the filled red circle. The neighbours that are used for weighting are highlighted in blue. The three shown algorithms differ in the selection of neighbours. Relief (a) selects the nearest individual of the same case/control status (blue circle) and the nearest neighbour of the opposite case/control status (blue cross). ReliefF (b) selects some user‐specified number of individuals (two in this example) to use for weighting. SURF (c) uses all individuals within a distance threshold (represented by the dotted line). Image reproduced from Moore et al. (2010) Bioinformatics challenges for genome‐wide association studies. Bioinformatics26(4): 445–455. Copyright Oxford University Press.

Figure 4.

Suggested flowchart for bioinformatics analysis of GWAS data. In addition to parametric statistical methods, filter and wrapper algorithms are used in conjunction with computational modelling approaches. Biological knowledge public databases play a very important role at all levels of analysis and interpretation. Image reproduced from Moore et al. (2010) Bioinformatics challenges for genome‐wide association studies. Bioinformatics26(4): 445–455. Copyright Oxford University Press.

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Gilbert‐Diamond, Diane, Asselbergs, Folkert W, Williams, Scott M, and Moore, Jason H(Oct 2011) Role of Bioinformatics in Genome‐wide Association Studies. In: eLS. John Wiley & Sons Ltd, Chichester. http://www.els.net [doi: 10.1002/9780470015902.a0023578]