Using biological networks to search for interacting loci in genome-wide association studies

Emily, Mathieu; Mailund, Thomas; Hein, Jotun; Schauser, Leif; Schierup, Mikkel H.

doi:10.1038/ejhg.2009.15

Cited by 129 publications

(131 citation statements)

References 41 publications

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“…[59][60][61][62] Similar inclusive approaches can help GWAS results identify gene networks that contribute to a tested trait. 25,32,[63][64][65] This approach has been applied to human stature. As noted earlier, statistically significant stature-mapping hits account for only about 10% of the heritability.…”

Section: Significance Beyond ''Significance''mentioning

confidence: 99%

Seeing the forest through the gene‐trees

Weiss

2010

Evolutionary Anthropology

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Section: Significance Beyond ''Significance''mentioning

confidence: 99%

Seeing the forest through the gene‐trees

Weiss

2010

Evolutionary Anthropology

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“…Because there is no guarantee that a case in an unrelated data set is not also a case with respect to the current phenotype, this too is a C + analysis, using additional population samples. Both analyses in Burton et al (2007) are univariate, but several other authors have since performed interaction testing on these data (e.g., Emily et al 2009;Liu et al 2011;Prabhu and Pe'er 2012;Lippert et al 2013).…”

Section: Application To the Wtccc Studymentioning

confidence: 99%

Exploiting Population Samples to Enhance Genome-Wide Association Studies of Disease

Kaufman

2014

Genetics

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It is widely acknowledged that genome-wide association studies (GWAS) of complex human disease fail to explain a large portion of heritability, primarily due to lack of statistical power-a problem that is exacerbated when seeking detection of interactions of multiple genomic loci. An untapped source of information that is already widely available, and that is expected to grow in coming years, is population samples. Such samples contain genetic marker data for additional individuals, but not their relevant phenotypes. In this article we develop a highly efficient testing framework based on a constrained maximum-likelihood estimate in a case-controlpopulation setting. We leverage the available population data and optional modeling assumptions, such as Hardy-Weinberg equilibrium (HWE) in the population and linkage equilibrium (LE) between distal loci, to substantially improve power of association and interaction tests. We demonstrate, via simulation and application to actual GWAS data sets, that our approach is substantially more powerful and robust than standard testing approaches that ignore or make naive use of the population sample. We report several novel and credible pairwise interactions, in bipolar disorder, coronary artery disease, Crohn's disease, and rheumatoid arthritis. G ENOME-WIDE association studies (GWAS) have implicated thousands of single-nucleotide polymorphisms (SNPs) in the human genome as associated with hundreds of phenotypes (Johnson and O'Donnell 2009). However, as many researchers have pointed out (Manolio et al. 2009;Eichler et al. 2010), the results from GWAS fail to explain the observed heritability of many phenotypes, including complex human diseases, whose genetic architectures remain largely unknown. One often-cited reason for this problem is that the high multiple-testing burden requires an exceedingly stringent statistical significance level. Furthermore, while most studies have employed univariate (locusby-locus) testing approaches, complex diseases are likely to be affected by interactions between loci (Eichler et al. 2010). Such interactions arise when there is a dependence of genotypic effects of one locus on genotypes at other loci (Cordell 2009).In the case of interactions, due to the overwhelming number of locus subsets, the multiple-testing problem becomes a serious computational and statistical challenge. Even when limiting exploration to pairwise SNP-SNP interactions, a modest study including 300,000 usable loci requires testing 45 billion SNP pairs, and associations must have P-values , 10 212 (the 0.05 Bonferroni-corrected significance level) to be declared statistically significant genome-wide. Recently, several authors have suggested sophisticated approximate and exhaustive methods for detecting pairwise interactions (Brinza et al. 2010;Liu et al. 2011;Prabhu and Pe'er 2012). These methods constitute a major step in dealing with the computational issue of carrying out the large number of tests, but their application to actual studies has led to surprisingly ...

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“…Networks are not discrete causal units after all. Systems thinking may provide sets of genes to use in a kind of Bayesian way to modify or help interpret mapping (e.g., Emily et al 2009) and to help account explicitly for how evolution works to generate complex traits in ways that have been long understood in principle (Bard 2010). But even experimentally controlled approaches have not simplified matters (Chen et al 2008;Emilsson et al 2008;Keller et al 2008;Eleftherohorinou et al 2009;Li et al 2010) in what might appear to be simple classically adaptive traits in flies Harbison et al 2009;Mackay et al 2009) or even in yeast .…”

Section: "Unless Profitable Variations Do Occur" (Darwin 1859): Life mentioning

confidence: 99%

Is Life Law-Like?

Weiss

Buchanan

2011

Genetics

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Genes are generally assumed to be primary biological causes of biological phenotypes and their evolution. In just over a century, a research agenda that has built on Mendel's experiments and on Darwin's theory of natural selection as a law of nature has had unprecedented scientific success in isolating and characterizing many aspects of genetic causation. We revel in these successes, and yet the story is not quite so simple. The complex cooperative nature of genetic architecture and its evolution include teasingly tractable components, but much remains elusive. The proliferation of data generated in our "omics" age raises the question of whether we even have (or need) a unified theory or "law" of life, or even clear standards of inference by which to answer the question. If not, this not only has implications for the widely promulgated belief that we will soon be able to predict phenotypes like disease risk from genes, but also speaks to the limitations in the underlying science itself. Much of life seems to be characterized by ad hoc, ephemeral, contextual probabilism without proper underlying distributions. To the extent that this is true, causal effects are not asymptotically predictable, and new ways of understanding life may be required.Perhaps the correct way of viewing the whole subject would be to look at the inheritance of every character whatever as the rule, and the non-inheritance as the anomaly.Charles Darwin (1859)

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Using biological networks to search for interacting loci in genome-wide association studies

Cited by 129 publications

References 41 publications

Seeing the forest through the gene‐trees

Seeing the forest through the gene‐trees

Exploiting Population Samples to Enhance Genome-Wide Association Studies of Disease

Is Life Law-Like?

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