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genome-wide association studies

Largest-Ever Alzheimer’s Gene Study Brings New Answers

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Alzheimer's Risk Genes

Predicting whether someone will get Alzheimer’s disease (AD) late in life, and how to use that information for prevention, has been an intense focus of biomedical research. The goal of this work is to learn not only about the genes involved in AD, but how they work together and with other complex biological, environmental, and lifestyle factors to drive this devastating neurological disease.

It’s good news to be able to report that an international team of researchers, partly funded by NIH, has made more progress in explaining the genetic component of AD. Their analysis, involving data from more than 35,000 individuals with late-onset AD, has identified variants in five new genes that put people at greater risk of AD [1]. It also points to molecular pathways involved in AD as possible avenues for prevention, and offers further confirmation of 20 other genes that had been implicated previously in AD.

The results of this largest-ever genomic study of AD suggests key roles for genes involved in the processing of beta-amyloid peptides, which form plaques in the brain recognized as an important early indicator of AD. They also offer the first evidence for a genetic link to proteins that bind tau, the protein responsible for telltale tangles in the AD brain that track closely with a person’s cognitive decline.

The new findings are the latest from the International Genomics of Alzheimer’s Project (IGAP) consortium, led by a large, collaborative team including Brian Kunkle and Margaret Pericak-Vance, University of Miami Miller School of Medicine, Miami, FL. The effort, spanning four consortia focused on AD in the United States and Europe, was launched in 2011 with the aim of discovering and mapping all the genes that contribute to AD.

An earlier IGAP study including about 25,500 people with late-onset AD identified 20 common gene variants that influence a person’s risk for developing AD late in life [2]. While that was terrific progress to be sure, the analysis also showed that those gene variants could explain only a third of the genetic component of AD. It was clear more genes with ties to AD were yet to be found.

So, in the study reported in Nature Genetics, the researchers expanded the search. While so-called genome-wide association studies (GWAS) are generally useful in identifying gene variants that turn up often in association with particular diseases or other traits, the ones that arise more rarely require much larger sample sizes to find.

To increase their odds of finding additional variants, the researchers analyzed genomic data for more than 94,000 individuals, including more than 35,000 with a diagnosis of late-onset AD and another 60,000 older people without AD. Their search led them to variants in five additional genes, named IQCK, ACE, ADAM10, ADAMTS1, and WWOX, associated with late-onset AD that hadn’t turned up in the previous study.

Further analysis of those genes supports a view of AD in which groups of genes work together to influence risk and disease progression. In addition to some genes influencing the processing of beta-amyloid peptides and accumulation of tau proteins, others appear to contribute to AD via certain aspects of the immune system and lipid metabolism.

Each of these newly discovered variants contributes only a small amount of increased risk, and therefore probably have limited value in predicting an average person’s risk of developing AD later in life. But they are invaluable when it comes to advancing our understanding of AD’s biological underpinnings and pointing the way to potentially new treatment approaches. For instance, these new data highlight intriguing similarities between early-onset and late-onset AD, suggesting that treatments developed for people with the early-onset form also might prove beneficial for people with the more common late-onset disease.

It’s worth noting that the new findings continue to suggest that the search is not yet over—many more as-yet undiscovered rare variants likely play a role in AD. The search for answers to AD and so many other complex health conditions—assisted through collaborative data sharing efforts such as this one—continues at an accelerating pace.

References:

[1] Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Kunkle BW, Grenier-Boley B, Sims R, Bis JC, et. al. Nat Genet. 2019 Mar;51(3):414-430.

[2] Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Lambert JC, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R, Bellenguez C, DeStafano AL, Bis JC, et al. Nat Genet. 2013 Dec;45(12):1452-8.

Links:

Alzheimer’s Disease Genetics Fact Sheet (National Institute on Aging/NIH)

Genome-Wide Association Studies (NIH)

Margaret Pericak-Vance (University of Miami Health System, FL)

NIH Support: National Institute on Aging; National Heart, Lung, and Blood Institute; National Human Genome Research Institute; National Institute of Allergy and Infectious Diseases; Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Diabetes and Digestive and Kidney Disease; National Institute of Neurological Disorders and Stroke


Creative Minds: Building Better Computational Models of Common Disease

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Hilary Finucane

Hilary Finucane

Not so long ago, Hilary Finucane was a talented young mathematician about to complete a master’s degree in theoretical computer science. As much as she enjoyed exploring pure mathematics, Finucane had begun having second thoughts about her career choice. She wanted to use her gift for numbers in a way that would have more real-world impact.

The solution to her dilemma was, literally, standing right by her side. Her husband Yakir Reshef, also a mathematician, was developing a new algorithm at the Broad Institute of MIT and Harvard, Cambridge, MA, to improve detection of unexpected associations in large data sets. So, Finucane helped the Broad team with modeling biomedical topics ranging from the gut microbiome to global health. That work led to her co-authoring a paper in the journal Science [1], providing a strong start to what’s shaping up to be a rewarding career in computational biology.


Flipping a Genetic Switch on Obesity?

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Illustration of a DNA switchWhen weight loss is the goal, the equation seems simple enough: consume fewer calories and burn more of them exercising. But for some people, losing and keeping off the weight is much more difficult for reasons that can include a genetic component. While there are rare genetic causes of extreme obesity, the strongest common genetic contributor discovered so far is a variant found in an intron of the FTO gene. Variations in this untranslated region of the gene have been tied to differences in body mass and a risk of obesity [1]. For the one in six people of European descent born with two copies of the risk variant, the consequence is carrying around an average of an extra 7 pounds [2].

Now, NIH-funded researchers reporting in The New England Journal of Medicine [3] have figured out how this gene influences body weight. The answer is not, as many had suspected, in regions of the brain that control appetite, but in the progenitor cells that produce white and beige fat. The researchers found that the risk variant is part of a larger genetic circuit that determines whether our bodies burn or store fat. This discovery may yield new approaches to intervene in obesity with treatments designed to change the way fat cells handle calories.


Mining the Big Data Mountain

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Cartoon of three men mining mountains of data

Credit: Chris Jones, NIH

Biomedical researchers and clinicians are generating an enormous, ever-expanding trove of digital data through DNA sequencing, biomedical imaging, and by replacing a patient’s medical chart with a lifelong electronic medical record. What can be done with all of this “Big Data”?

Besides being handy for patients and doctors, Big Data may provide priceless raw material for the next era of biomedical research. Today, I want to share an example of research that is leveraging the power of Big Data.


Exploring the Complex Genetics of Schizophrenia

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Illustration of a human head showing a brain and DNA

Credit: Jonathan Bailey, National Human Genome Research Institute, NIH

Schizophrenia is one of the most prevalent, tragic, and frustrating of all human illnesses, affecting about 1% of the human population, or 2.4 million Americans [1]. Decades of research have failed to provide a clear cause in most cases, but family clustering has suggested that inheritance must play some role. Over the last five years, multiple research projects known as genome-wide association studies (GWAS) have identified dozens of common variations in the human genome associated with increased risk of schizophrenia [2]. However, the individual effects of these variants are weak, and it’s often not been clear which genes were actually affected by the variations. Now, advances in DNA sequencing technology have made it possible to move beyond these association studies to study the actual DNA sequence of the protein-coding region of the entire genome for thousands of individuals with schizophrenia. Reports just published have revealed a complex constellation of rare mutations that point to specific genes—at least in certain cases.