Licastro F, Chiapelli M, Caldarera CM, Caruso C, Lio D, Corder EH.
Acute myocardial infarction and proinflammatory gene variants.
Ann N Y Acad Sci. 2007 Nov;1119:227-42.
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This is exactly what needs to be done in AD genetics and in other complex disorders where multiple, physiologically plausible, gene candidates have been identified that are in many cases relevant to the complex multifactorial pathologies of these diseases (see Alzgene, SchizophreniaGene and Polygenic Signaling Pathways). Combarros et al. have reviewed the evidence for statistical epistasis between candidate genes in AD and illustrate that dozens of genes can influence the risk-promoting effects of several others. Such studies usually report on gene pair interactions (for example Apo4 + one other), but when so many genes are implicated in these polygenic disorders (over 200 in late-onset AD), it is clear that a more complex permutational approach is needed. Single candidate gene association studies are notoriously inconsistent in these types of polygenic disorders, but given the number of genes and pathological processes involved, and the multitude of potential interactions involved (epistatic and others), this is perhaps not too surprising.
Elizabeth Corder's pioneering work injects some hard statistics into the complex reality of multifactorial polygenic diseases and shows that risk is better matched to sets of relevant gene variants than to any particular gene polymorphism. A similar argument has been proposed for bladder cancer, another polygenic disorder plagued by inconsistency in single gene association studies (Wu et al., 2006). These observations surely simply reflect the complex multifactorial nature of such diseases, in which genetic polymorphisms are but one way to control the malfunction of several different pathological processes, and the efficiency of their counteracting networks.
This type of systems biology approach should be extremely useful in the analysis of the wave of whole-genome association studies currently underway.
Regarding the value of identifying all of the minor genetic risk factors for AD and the role of systems biology approaches in AD research, both are important endeavors and essential to elucidating the biological pathways involved in the etiology and pathogenesis of AD. Using a baseball analogy, if ApoE is our only "major league" gene, the next best AD genes would have to be considered “Little Leaguers,” i.e., those that have only minor effects on risk, but yield statistically significant p-values by meta-analyses on Alzgene (see "top Alzgene results"). The “top Alzgene results” (total of 29) are routinely updated and ranked by Lars Bertram and colleagues according to greatest effects on AD risk. ApoE is, predictably, number one. One copy of ApoE increases risk for AD by ~threefold, and two copies, by more than 10-fold. In contrast, the other 28 genes listed in the “top Alzgene results” increase risk by Some have questioned the value of determining the identity of these “Little Leaguers.” I would argue that although the “Little League” genes exert only minor effects on AD risk, if we can statistically confirm enough of them, e.g., by testing in multiple independent samples and employing meta-analyses, we will eventually be able to take this growing list and begin searching for biological pathways in which these genes functionally intersect. Bioinformatic and systems biology approaches on a large set of genes with minor, but statistically significant effects on AD risk should provide valuable clues to the etiology and pathogenesis of AD.
So, I believe that we need to establish the full list of “Little League” genes, including the vast majority with minor effects, by meta-analyses (as determined on Alzgene), and then determine the biological pathways in which they may functionally intersect. In this way, we should be able to discover novel biological pathways involved in the disease. The key is to perform the genetic studies first, so as to establish the full team of relevant players, even if they are mainly “Little Leaguers.” If we assemble a big enough team, we can then search for the biological pathways in which they functionally intersect.
I would also argue that systems biology approaches best follow genetic studies, not drive them. The full set of genetic risk factors for AD can be established by the “biased” approach of testing biologically plausible candidate genes, or by “unbiased” genetic analyses, e.g., genomewide association screens. The strongest hits emerging from these studies must be subjected to replication testing in multiple independent samples and meta-analyses, e.g., on Alzgene, to determine which ones carry the highest probability of being real risk factors. The statistically confirmed AD genes, the vast majority of which will exert only minor effects on risk, can then be compared to the "x, y, and z" variables that must be integrated into a large and complex algebraic equation. You cannot solve (or in this case, even formulate) the equation until you start to replace the many "variables" with "givens.” Testing multiple genes in multiple independent samples and then conducting meta-analyses across all samples to see which ones statistically pass muster can reveal these "givens.” Subsequently, systems biology can be employed to better formulate and ultimately solve the equation.
The history of the AD field has shown that genetics has best informed us as to the identity of the relevant biological factors involved in the etiology and pathogenesis of this disease. Today, it is difficult to carry out a relevant experiment in AD without working on one of the four established AD genes (APP, PSEN1, PSEN1, ApoE) revealed by genetics studies. These four genes have been estimated to account for only 30 percent of the genetic variance of AD. “Sporadic” AD is heavily influenced by genetic factors; twin studies have revealed that up to 80 percent of AD is caused by inherited factors. Dozens of labs around the world are attempting to identify novel AD genes. Most use the biased approach of testing biological candidate genes. A growing number are using the unbiased approach of genomewide association screens. For the past two years, our lab has been conducting the "Alzheimer's Genome Project" (supported by the Cure Alzheimer’s Fund), in which we are employing genomewide association screening of more than 400 late-onset AD families (NIMH sample) with replication testing in more than 900 additional late-onset AD families (NIA, NCRAD, and CAG samples) to search for population-based AD genetic risk factors for late-onset AD of all effect sizes, beyond ApoE.
We now realize that there are no more “major league” AD genes like ApoE. However, the field continues to obtain and publish novel genetic hits every week. Our manuscript presenting some of the strongest hits from our family-based genomewide association screen is currently under review. Ultimately, only meta-analyses of these hits in multiple independent samples will reveal whether they will ultimately join the growing list of “top Alzgene results” and be worthy of inclusion in bioinformatic and systems biology analyses. Every newly confirmed gene, even those with minor effects, will contribute to the elucidation of novel biological pathways, providing new clues for effectively treating and preventing AD.
What about the concept that AD, instead of being a common disease with a number of "risk factors,” might be a set of numerous rare diseases with a more or less common phenotype, but each with a different cause? That way, there would be no Little Leaguers within, say, the U.S., but only Major Leaguers, each one within its own small country. The problem is partly semantic: "risk factor" in a statistical, non-bayesian, context means a greater-than-chance association with, say, a disease, which does not entail a causal relationship, whereas "factor,” meaning etymologically "agent,” connotes a cause.
The Tanzi "Little League,” the early onset AD (EOAD) genes APP, PSEN1, and PSEN2, provide a ready-made model (Bruni et al., 1992) for the proposed concept of AD as a multigenic, as distinct from polygenic, entity. The only needed postulate is that late-onset AD (LOAD) genes/mutations are expressed stochastically like the EOAD ones, but much later. Death from other causes before expression of the mutation masks the familial transmission, giving the appearance of "sporadic" disease to LOAD.