Large genome-wide association studies have implicated fewer than two dozen genes in AD, but some scientists believe thousands of other variants may influence the course of the disease, even if only by a smidgen. When considered en masse, these genetic weenies may punch above their weight, according to a study published in the July 6 Neurology. Researchers led by Elizabeth Mormino of Massachusetts General Hospital in Charlestown report that thousands of genetic variants, which by themselves fail to pass significance thresholds imposed by GWAS, nevertheless associate with cognitive and structural brain changes that precede the onset of dementia. They propose using polygenic risk scores derived from these GWAS runners-up to more completely assess genetic risk for AD, rather than focusing on the few strongest loci.

“If we restrict ourselves to the top hits, we may be missing some useful genetic information that is buried beneath the surface of GWAS data,” Mormino told Alzforum. Commenters agreed that the most important implication of the study was that more genetic associations exist below the statistical thresholds used for AD GWAS.

Some studies estimate that genetic variation explains more than half the risk of developing AD (see Gatz et al., 2006). However, the 21 variants thus far found in GWAS together account for only 2 percent of the AD risk attributable to genetics (see Oct 2013 newsJul 2013 conference coverage). The ApoE4 gene alone takes care of another 6 percent. That leaves a sizable portion of genetic underpinnings of AD unaccounted for.

To unearth the hidden associations, researchers have started lumping together genetic polymorphisms that fall below the statistical GWAS criteria. They generate polygenic risk scores (PGRS) based on how many of these polymorphisms a person has (see Maher 2015). Some studies have revealed that many weakly associated loci cumulate to strengthen the genetic contribution underlying a given disease, while others, such as a recent polygenic study on diabetes, have found that not to be the case (see Fuchsberger et al., 2016). One recent polygenic study analyzed data from the International Genomics of Alzheimer’s Project (IGAP) to find that the collective association of thousands of variants accounted for far more of the AD heritability than did the 21 GWAS hits (see Escott-Price et al., 2015). A more recent study by Sonya Foley and colleagues at Cardiff University, Wales, correlated polygenic risk scores with low hippocampal volume in healthy young adults, suggesting a genetic basis for changes in the brain that precede dementia (see Foley et al., 2016). 

Mormino and colleagues more broadly explored the question if polygenic risk scores associate with pathological changes that precede the onset of dementia. The researchers started by revisiting the GWAS data from IGAP, which included more than 17,000 people with AD and 37,000 healthy controls. They lowered the significance threshold from a stringent p value of 5x10-8 to a more liberal cut hoping to uncover variants that together might associate with AD. When they chose a p value of 1x10-2, more than 16,000 polymorphisms emerged. Using this expanded set of SNPs, the researchers generated polygenic risk scores for individual people in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, based on how many of these SNPs each person harbored. Compared with just considering the GWAS hits alone, those scores increased the researchers’ ability to distinguish 166 patients with AD dementia from 194 older cognitively normal people in ADNI by about fourfold, Mormino et al. report. Lowering the significance threshold further (i.e., 10-1 or more), and thus including even more polymorphisms, brought no further improvement in the ability to distinguish between AD patients and controls.

Armed with this expanded set of SNPs, the researchers searched for association between PGRS and early biomarkers of AD in 526 ADNI participants who at baseline were cognitively normal or had mild cognitive impairment but no dementia. The researchers found that higher PGRS associated with lower baseline performance on memory tests and with smaller hippocampal volumes. Over a nearly three-year follow-up period, higher PGRS scores associated with faster decline in memory and executive function, but curiously not with decreases in hippocampal volume. The higher scores also associated with progression: People with high PGRS were more likely to progress to MCI, or from MCI to AD.

What about polygenic risk and amyloid burden? In 505 participants without dementia from ADNI2, higher PGRS correlated with having a positive amyloid-PET scan. In 272 ADNI1 participants with available CSF data, higher PGRS trended toward lower CSF Aβ levels though this association missed statistical significance. The researchers attributed this to the smaller number of participants.

The researchers next tried to relate PGRS to changes that occur decades prior to disease onset. To do this, they measured PGRS in more than 1,300 healthy volunteers, aged 18 to 35, from the Brain Genomics Superstruct Project, an open-access neuroimaging dataset run by Randy Buckner at Massachusetts General Hospital in Boston. As with the Welsh study, high polygenic risk scores marginally associated with small hippocampal volume, indicating that polygenic risk influences brain structure at an early age, even five decades prior to the typical onset of AD in people who ultimately get it. Furthermore, given that most people at this age have no amyloid deposition yet, this polygenic influence on brain structure is likely amyloid-independent, Mormino told Alzforum. “The potential implications of this result are that changes associated with AD may begin earlier than we thought, and also have a genetic basis,” she said.

The polygenic risk score accounted for a modest amount of variance in disease factors. For example, the score explained 2.3 percent of the variance in baseline memory, 3.2 percent of the variance in longitudinal memory, 1 percent of the variance in Aβ deposition, and 2 percent of the variance in baseline hippocampal volume in the ADNI cohort, and just 0.2 percent of the variance of hippocampal volume in the young cohort. Mormino said this is likely due to the presence of unknown rare variants with large effect sizes, or to synergistic relationships between variants, for which the polygenic scores at present cannot account. Furthermore, Mormino said she was measuring association with intermediate phenotypes, such as Aβ deposition and hippocampal volume, rather than the final outcome of AD that had been used to select the polymorphisms in the first place. Because having a small hippocampus, for example, does not always lead to AD, the strength of the associations is inherently limited.

Could polygenic risk scores be used to identify candidates for prevention trials? Mormino considers this use a long way off. Gerard Schellenberg of the University of Pennsylvania in Philadelphia agreed, saying that polygenic risk scores are unlikely to predict who will develop a given phenotype because they account for such a small percentage of the variance. Instead he sees value in combined scores of genetic and non-genetic risk, such as cardiovascular health and lifestyle factors. Schellenberg pointed out a fundamental implication of this paper. “The most important aspect of the study was not the potential application of PGRS, but rather the implication that unknown genes associated with AD still exist,” he said. “But just because they exist doesn’t mean we’ll find them.”

Nick Martin of QIMR Berghofer Medical Research Institute in Queensland, Australia, saw the findings as a motivation to do even larger GWAS to uncover these hidden risk variants. “The lesson of this paper is that even larger sample sizes will find new loci, which may lead us to new pathways and new biology,” he wrote. “This is certainly the experience with the recent success of GWAS for schizophrenia, where 108 separate loci have now been identified, leading us to previously unsuspected biology and strong leads for new therapies.”—Jessica Shugart


  1. This is a very interesting paper indeed. It shows, perhaps not surprisingly, that genetic risk loci for AD also predict endophenotypes associated with AD, such as memory loss, lower executive functioning, reduced hippocampal volume, etc., in younger, so far unaffected subjects. More interestingly, it shows how much stronger the associations are if you calculate a polygenic risk score (PGRS) including loci below the threshold of formal genome wide significance of 5 x 10-8 commonly accepted in genome-wide association studies (GWAS), implying that there are many more loci yet to be found influencing AD risk and associated phenotypes. Some people in the research community are saying, “That’s enough GWAS; we already have 19 significant loci and those are the biggest ones.” But the lesson of this paper is that even larger sample sizes will find new loci that may lead us to new pathways and new biology. This is certainly the experience with the recent success of GWAS for schizophrenia, where 108 separate loci now have been identified, leading us to previously unsuspected biology and strong leads for new therapies. And it is important to remember that there is no connection between the effect size of a risk locus and its importance as a potential drug target, for example, the extremely successful cholesterol-lowering statin drugs target HMGCR, a rather small GWAS hit for circulating lipid levels.

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News Citations

  1. Paper Alert: New Alzheimer’s Genes Published
  2. Pooled GWAS Reveals New Alzheimer’s Genes and Pathways

Paper Citations

  1. . Role of genes and environments for explaining Alzheimer disease. Arch Gen Psychiatry. 2006 Feb;63(2):168-74. PubMed.
  2. . Polygenic Scores in Epidemiology: Risk Prediction, Etiology, and Clinical Utility. Curr Epidemiol Rep. 2015 Dec;2(4):239-244. Epub 2015 Sep 28 PubMed.
  3. . The genetic architecture of type 2 diabetes. Nature. 2016 Jul 11; PubMed.
  4. . Common polygenic variation enhances risk prediction for Alzheimer's disease. Brain. 2015 Dec;138(Pt 12):3673-84. Epub 2015 Oct 21 PubMed.
  5. . Multimodal Brain Imaging Reveals Structural Differences in Alzheimer's Disease Polygenic Risk Carriers: A Study in Healthy Young Adults. Biol Psychiatry. 2016 Mar 16; PubMed.

Further Reading


  1. . Polygenic Scores in Epidemiology: Risk Prediction, Etiology, and Clinical Utility. Curr Epidemiol Rep. 2015 Dec;2(4):239-244. Epub 2015 Sep 28 PubMed.

Primary Papers

  1. . Polygenic risk of Alzheimer disease is associated with early- and late-life processes. Neurology. 2016 Aug 2;87(5):481-8. Epub 2016 Jul 6 PubMed.