Risk prediction of late-onset Alzheimer’s disease implies an oligogenic architecture

Zhang, Qian; Sidorenko, Julia; Couvy‐Duchesne, Baptiste; Marioni, Riccardo E.; Wright, Margaret J.; Goate, Alison M.; Marcora, Edoardo; Huang, Kuan‐lin; Porter, Tenielle; Laws, Simon M.; Masters, Colin L.; Bush, Ashley I.; Fowler, Christopher; Darby, David; Pertile, Kelly K.; Restrepo, Carolina; Roberts, Blaine R.; Robertson, Jo; Rumble, Rebecca; Ryan, Tim; Collins, Steven J.; Thai, Christine; Trounson, Brett; Lennon, Kate; Li, Qiao‐Xin; Ugarte, Fernanda Yevenes; Volitakis, Irene; Vovos, Michael; Williams, Rob; Baker, Jenalle E.; Russell, Alyce; Peretti, Madeline; Milicic, Lidija; Lim, Lucy; Rodrigues, Mark; Taddei, Kevin; Taddei, Tania; Hone, Eugene; Lim, Fabian; Fernandez, Shane; Rainey‐Smith, Stephanie R.; Pedrini, Steve; Martins, Ralph N.; Doecke, James D.; Bourgeat, Pierrick; Fripp, Jürgen; Gibson, Simon; Leroux, Hugo; Hanson, David G.; Doré, Vincent; Zhang, Ping; Burnham, Samantha; Rowe, Christopher C.; Villemagne, Victor L.; Yates, Paul; Pejoska, Sveltana Bozin; Jones, Gareth T.; Ames, David; Cyarto, Elizabeth; Lautenschlager, Nicola T.; Barnham, Kevin J.; Cheng, Lesley; Hill, Andy; Killeen, N. E. B.; Maruff, Paul; Silbert, Brendan; Brown, Belinda M.; Sohrabi, Harmid; Savage, Greg; Vacher, Michaël; Sachdev, Perminder S.; Mather, Karen A.; Armstrong, Nicola J.; Thalamuthu, Anbupalam; Brodaty, Henry; Yengo, Loïc; Yang, Jian; Wray, Naomi R.; McRae, Allan F.; Visscher, Peter M.

doi:10.1038/s41467-020-18534-1

Cited by 144 publications

(170 citation statements)

References 66 publications

(139 reference statements)

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“…These models are important for the cross sectional understanding of the aging-related changes that lead to the development of AD, which is not easily achieved using human brain samples that represent the end stage (and/or one time point) of the disease, and in order to develop and test therapeutics 16 with high translational value. The identification of risk-associated polymorphisms to late-onset AD over the past several decades is aiding our understanding of the disease, and directing new therapeutic avenues, for example against microglia 13, [17][18][19][20] . Given the pronounced differences between humans and mice, modeling this complex disease of aging has proven challenging, with salient differences in lifespan, and in the sequences and processing of the key proteins that define the prominent pathologies of the AD brain (such as plaques (APP) and tangles (tau)).…”

Section: Usage Notesmentioning

confidence: 99%

Systematic Phenotyping and Characterization of the 5xFAD mouse model of Alzheimer’s Disease

Forner

Kawauchi

Balderrama-Gutierrez

et al. 2021

Preprint

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Mouse models of human diseases are invaluable tools for studying pathogenic mechanisms and testing interventions and therapeutics. For disorders such as Alzheimer's disease in which numerous models are being generated, a challenging first step is to identify the most appropriate model and age to effectively evaluate new therapeutic approaches. Here we conducted a detailed phenotypic characterization of the 5xFAD model on a congenic C57BL/6J strain background, across its lifespan, including a seldomly analyzed 18-month old time point to provide temporally correlated phenotyping of this model and a template for characterization of new models of LOAD as they are generated. This comprehensive analysis included quantification of plaque burden, β biochemical levels, and neuropathology, neurophysiological measurements and behavioral and cognitive assessments, and evaluation of microglia, astrocytes, and neurons. Analysis of transcriptional changes was conducted using bulk-tissue generated RNA-seq data from microdissected cortices and hippocampi as a function of aging, which can be explored at the UCI Mouse Explorer and AD Knowledge Portal. This deep-phenotyping pipeline identified novel aspects of age-related pathology in the 5xFAD model.

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Section: Usage Notesmentioning

confidence: 99%

Systematic Phenotyping and Characterization of the 5xFAD mouse model of Alzheimer’s Disease

Forner

Kawauchi

Balderrama-Gutierrez

et al. 2021

Preprint

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show abstract

“…Currently, is the upper limit of predictive power (on the liability scale) of PRS for most complex diseases (Khera et al, 2018; Lambert et al, 2019), with the exception of a few disorders with large-effect common variants (such as Alzheimer’s disease or type 1 diabetes) (Sharp et al, 2019; Q. Zhang et al, 2020).…”

Section: Resultsmentioning

confidence: 99%

Utility of polygenic embryo screening for disease depends on the selection strategy

Lencz

Backenroth

Green

et al. 2020

Preprint

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As of 2019, polygenic risk scores have been utilized to screen in vitro fertilization embryos for genetic liability to adult diseases, despite a lack of comprehensive modeling of expected outcomes. In this short report, we demonstrate that a strong determinant of the potential utility of such screening is the selection strategy employed, a factor that has not been previously studied. Minimal risk reduction is expected if only extremely high-scoring embryos are excluded, whereas risk reductions are substantially greater if the lowest-scoring embryo (for a given disease) is selected. We systematically examined the relative contributions of the variance explained by the score, the number of embryos, the disease prevalence, and the parental scores on the utility of screening. We discuss the results in the context of relative vs absolute risk, as well as the potential ethical concerns raised by such procedures.

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“…The heritability of LOAD is spread across many genetic variants; however, Zhang et al . (2020) 2 suggested that LOAD is more of an oligogenic than polygenic disorder due to the large effects of APOE variants. According to Zhang et al .…”

Section: Main Textmentioning

confidence: 99%

“…Late-onset Alzheimer’s disease is caused by a combination of many genetic variants with small effect sizes and environmental influences. Currently, only a fraction of the genetic variants underlying Alzheimer’s disease have been identified 2,3 . Here we show that increased sample sizes allowed for identification of seven novel genetic loci contributing to Alzheimer’s disease.…”

mentioning

confidence: 99%

Largest GWAS (N=1,126,563) of Alzheimer’s Disease Implicates Microglia and Immune Cells

Dp¹,

Ie²,

Je³

et al. 2020

Preprint

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SummaryLate-onset Alzheimer’s disease is a prevalent age-related polygenic disease that accounts for 50-70% of dementia cases1. Late-onset Alzheimer’s disease is caused by a combination of many genetic variants with small effect sizes and environmental influences. Currently, only a fraction of the genetic variants underlying Alzheimer’s disease have been identified2,3. Here we show that increased sample sizes allowed for identification of seven novel genetic loci contributing to Alzheimer’s disease. We highlighted eight potentially causal genes where gene expression changes are likely to explain the association. Human microglia were found as the only cell type where the gene expression pattern was significantly associated with the Alzheimer’s disease association signal. Gene set analysis identified four independent pathways for associated variants to influence disease pathology. Our results support the importance of microglia, amyloid and tau aggregation, and immune response in Alzheimer’s disease. We anticipate that through collaboration the results from this study can be included in larger meta-analyses of Alzheimer’s disease to identify further genetic variants which contribute to Alzheimer’s pathology. Furthermore, the increased understanding of the mechanisms that mediate the effect of genetic variants on disease progression will help identify potential pathways and gene-sets as targets for drug development.

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Risk prediction of late-onset Alzheimer’s disease implies an oligogenic architecture

Cited by 144 publications

References 66 publications

Systematic Phenotyping and Characterization of the 5xFAD mouse model of Alzheimer’s Disease

Systematic Phenotyping and Characterization of the 5xFAD mouse model of Alzheimer’s Disease

Utility of polygenic embryo screening for disease depends on the selection strategy

Largest GWAS (N=1,126,563) of Alzheimer’s Disease Implicates Microglia and Immune Cells

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