22 May 2008. Like baseball coaches selecting top prospects for their team, geneticists studying Alzheimer disease face a formidable challenge: how to identify the true stars on a playing field now sprawling with hundreds of genes that might contribute to AD risk. At present, ApoE tops the roster, with early-onset AD genes APP, PSEN1, and PSEN2 rounding out the short list of established AD genetic risk factors. However, since only 1 to 5 percent of Alzheimer cases are early-onset, ApoE is the field’s only “major league” gene. At least that’s how baseball fan Rudy Tanzi, Massachusetts General Hospital, Boston, sees it. “If ApoE is a major leaguer, the other genes on the list would be Little Leaguers,” wrote Tanzi, who spoke at the Alzheimer disease conference held 24-29 March in Keystone, Colorado, and corresponded with this reporter via e-mail after the meeting.
So what can be said about these so-called Little League genes? At this point, not much. According to Alzgene, a publicly available database that keeps a running scorecard of more than 500 genes implicated thus far as contributing to AD risk, all but ApoE increase disease risk just 1.1- to 1.4-fold. By comparison, the ApoE4 allele boosts AD risk about threefold when one copy is inherited, more than 10-fold if two copies are inherited (Corder et al., 1993).
It’s unlikely that any Little League genes will even come close to ApoE in their impact on AD risk. Most have relative risk ratios so small that researchers cannot reliably replicate them. Scaling up sample sizes from several hundred to tens of thousands of cases should help overcome this challenge, as it has in recent studies in other fields such as diabetes and cancer, and in an AD study of more than 17,000 gene variants in 4,000 volunteers conducted by researchers at Cardiff University, United Kingdom (Grupe et al., 2007).
In the meantime, a growing number of scientists in the AD field are looking toward integrative approaches that don’t consider genes individually but rather as groups of factors that together shape disease risk. “If you combine information on many gene variants, you get a better grasp on risk,” said Elizabeth Corder, Duke University, Durham, North Carolina, lead author on the 1993 Science paper reporting the discovery of ApoE4 as an AD risk factor (Corder et al., 1993). In a phone conversation with this reporter, Corder likened the identification of AD genes to the process by which physicians diagnose disease. Fever is a symptom common to many disorders and, though important, is not that informative in and of itself, Corder explained. “But when it is used in combination with other symptoms, doctors can make a more accurate diagnosis.”
An ongoing challenge in the study of complex, polygenic disorders such as AD concerns the development of informative case definitions. “What we're trying to do is look at alternate ways of clustering genes and phenotypes,” said Deborah Blacker, a geriatric psychiatrist and epidemiologist working on Alzheimer’s genetics at Harvard Medical School, Boston. “If you consider 20,000 ways to define things, you have to account for the fact you could find something by chance by massaging the data. People are slogging along to find a better way to do this. The field is somewhat stuck now. What [Corder] is doing is one thing that could be brought to bear.”
At the Alzheimer disease Keystone meeting in March, Corder presented data from a paper about an alternative approach to analyzing AD genetic data (Licastro et al., 2007). With colleagues at the University of Bologna and the University of Palermo in Italy, she applied a statistical method called Grade-of-membership (GoM) to analyze 260 AD patients and 190 controls. Combining a slew of information—including genetic profiles for ApoE and a handful of genes involved in inflammation (IL-6, IL-10, IL-1α, IL-1β, TNFα), along with other factors such as gender and age of disease onset—first author Federico Licastro and colleagues grouped the subjects into four risk sets that differ according to their likelihood of developing AD. For each risk set, each person received a membership score (i.e., a value between 0 and 1) reflecting his or her probability of belonging to that set. According to this analysis, variations among the genes related to inflammation—especially IL-10 and IL-1β—turned out to be more informative in identifying the risk sets than was ApoE.
Corder and colleagues (Licastro et al., 2007) have used the same GoM method to identify genetic risk sets for heart attack. As it turns out, the risk factors for AD and heart attack are quite similar. A manuscript in preparation will describe these findings, Corder said. “While these models are not perfect or complete, I think they represent a major improvement by being inclusive,” she told Alzforum, noting constraints of single-gene studies. “They're more biologically realistic.”
As it has been used to identify overlapping risk profiles for AD and heart attack, Corder said, GoM could be similarly applied in future studies involving comparisons of risk sets—for example, those that would distinguish AD from dementia with Lewy bodies (DLB).
Developed in the early 1990s by Duke mathematician Max Woodbury for social science applications, GoM has rarely been applied in disease research, let alone AD genetics. Many AD geneticists who corresponded with this reporter for the story were not familiar with the statistical approach nor with the findings Corder presented at Keystone earlier this year. Chris Carter, a UK-based systems biologist who formerly led the neuroscience genomics group at a European pharmaceutical company, was among the few who knew of and was impressed by Corder’s work. It “injects some hard statistics into the complex reality of multi-factorial polygenic diseases and shows that risk is better matched to sets of relevant gene variants than to any particular gene polymorphism,” he wrote via e-mail (see also Carter comment below).
Hilkka Soininen and colleagues at the University of Kuopio, Finland, have used the GoM approach to analyze single nucleotide polymorphisms of the apolipoprotein D gene in AD patients. In this study of 394 Finnish AD patients and 470 controls (Helisalmi et al., 2004), traditional analyses of SNP data and GoM analysis “provided comparable results, suggesting that GoM might be useful in profiling AD subpopulations,” Soininen wrote in an e-mail to ARF.
Rosalind Neuman, a mathematician at Washington University, St. Louis, has compared GoM with a related statistical approach called the latent class model in analyses of other conditions such as attention deficit hyperactivity disorder and alcoholism. “These methods may be very useful for trying to refine phenotypes in biological systems that are complicated,” Neuman told Alzforum.
Separate work published last month in the journal BMC Medical Genetics strengthens the push for more AD studies that look at gene-gene interactions rather than single-locus interactions. In this study of 200 late-onset AD patients and controls, researchers led by Craig Atwood, University of Wisconsin, Madison, uncovered a surprising reversal of risk: males with an ApoE4 allele who also carried a luteinizing hormone receptor intronic variant had almost no risk for AD (Haasl et al., 2008). “While the study is small,” wrote Atwood via e-mail to ARF, “it may help explain why some studies see an interaction with one gene, but then another study does not (i.e., all the hundreds of studies in AlzGene).” Despite ApoE4’s strong overall effect on AD risk, far from every person who inherits even two copies of this allele develops AD.
Amid growing recognition that new statistical approaches could more effectively cull top prospects from the swarms of minor genes, other researchers instead place their bets on genome-wide association studies. The typical AD human genetics study involves hundreds of cases, which doesn’t have enough statistical power, said Lars Bertram of Massachusetts General Hospital, who also is the lead investigator and scientific coordinator for Alzgene and related databases. But with concerted efforts to pool samples for systematic analyses of specific gene interactions, he said, even genes with very small effects on AD risk will come to the forefront. (For more about how data sharing can increase the power of human genetics studies, see ARF Live Discussion).
Citing genome-wide association studies in cancer and diabetes involving tens of thousands of samples (Sladek et al., 2007; Hung et al., 2008), Alison Goate, Washington University, St. Louis, Missouri, agreed, “At that point, you really can determine whether something was a real effect. That is where AD needs to go.”—Esther Landhuis.
Licastro F, Porcellini E, Caruso C, Lio D, Corder EH. Genetic risk profiles for Alzheimer's disease: integration of APOE genotype and variants that up-regulate inflammation. Neurobiol Aging. 2007 Nov;28(11):1637-43. Epub 2006 Aug 22. Abstract
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. Abstract
Haasl RJ, Ahmadi MR, Vadakkadath Meethal S, Gleason CE, Johnson SC, Bowen RL, Asthana S, Atwood CS. A luteinizing hormone receptor intronic variant is significantly associated with decreased risk of Alzheimer's disease in males carrying an apolipoprotein E epsilon4 allele. BMC Med Genet. 2008 Apr 25;9(1):37. [Epub ahead of print] Abstract