Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease

Allen, Genevera I.; Amoroso, Nicola; Anghel, Catalina; Balagurusamy, Venkat; Bare, J. Christopher; Beaton, Derek; Bellotti, R.; Bennett, David A.; Boehme, Kevin L.; Boutros, Paul C.; Caberlotto, Laura; Caloian, Cristian; Campbell, Frederick; Neto, Elias Chaibub; Chang, Yu Chuan; Chen, Beibei; Chen, Chien‐Yu; Chien, Ting-Ying; Clark, Tim W.; Das, Sudeshna; Davatzikos, Christos; Deng, Jieyao; Dillenberger, Donna; Dobson, Richard; Dong, Qilin; Doshi, Jimit; Duma, Denise; Errico, Rosangela; Erus, Güray; Everett, Evan; Fardo, David W.; Friend, Stephen H.; Fröhlich, Holger; Gan, Jessica; George-Hyslop, Peter St; Ghosh, Satrajit; Glaab, Enrico; Green, Robert C.; Guan, Yuanfang; Hong, Ming; Huang, Chao; Hwang, Jinseub; Ibrahim, Joseph G.; Inglese, Paolo; Iyappan, Anandhi; Jiang, Qijia; Katsumata, Yuriko; Kauwe, John; Klein, Arno; Kong, Dehan; Krause, Roland; Lalonde, Emilie; Lauria, Mario; Lee, Eunjee; Lin, Xihui; Liu, Zhandong; Livingstone, Julie; Logsdon, Benjamin A.; Lovestone, Simon; Wei, Tsung; Malhotra, Ashutosh; Mangravite, Lara M.; Maxwell, Taylor J.; Merrill, Emily; Nagorski, John; Namasivayam, A; Narayan, Manjari; Naz, Mufassra; Newhouse, Stephen; Norman, Thea; Nurtdinov, Ramil; Oyang, Yen Jen; Pawitan, Yudi; Peng, Sijia; Peters, Mette A.; Piccolo, Stephen R.; Praveen, Paurush; Priami, Corrado; Sabelnykova, Veronica Y.; Senger, Philipp; Shen, Xia; Simmons, Andrew; Sotiras, Aristeidis; Stolovitzky, Gustavo; Tangaro, Sabina; Tateo, Andrea; Tung, Yi An; Tustison, Nicholas J.; Varol, Erdem; Vradenburg, George; Weiner, Michael W.; Xiao, Guanghua; Xie, Lei; Xie, Yang; Xu, Jia; Yang, Hojin; Zhan, Xiaowei; Zhou, Yunyun; Zhu, Fan; Zhu, Hongtu; Zhu, Shanfeng

doi:10.1016/j.jalz.2016.02.006

Cited by 80 publications

(63 citation statements)

References 33 publications

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“…Two Challenges have established community efforts to build predictive models based on single-nucleotide polymorphism (SNP) data, including the prediction of clinical non-response following anti-TNF (tumour necrosis factor) treatment in patients with rheumatoid arthritis 37 or the prediction of Alzheimer disease diagnosis 61 . The outcomes of these Challenges demonstrated that the genetic contribution to overall performance was minimal, suggesting that current methodologies are not able to identify and compile genetic signals given existing sample collections.…”

Section: What Have Challenges Taught Us?mentioning

confidence: 99%

“…Clinical measures of disease state that represent the complex interactions of human biology aggregated across multiple genetic and non-genetic factors tend to provide the greatest contribution to predictions. In the Alzheimer’s Disease Big Data DREAM Challenge 61 , cognitive measures of brain function greatly outperformed SNP genotypes for predicting disease status. In the Rheumatoid Arthritis Responder Challenge 37 , clinical measures of pretreatment disease severity had the greatest contribution to prediction of anti-TNF treatment response.…”

Section: What Have Challenges Taught Us?mentioning

confidence: 99%

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Crowdsourcing biomedical research: leveraging communities as innovation engines

et al. 2016

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The generation of large-scale biomedical data is creating unprecedented opportunities for basic and translational science. Typically, the data producers perform initial analyses, but it is very likely that the most informative methods may reside with other groups. Crowdsourcing the analysis of complex and massive data has emerged as a framework to find robust methodologies. When the crowdsourcing is done in the form of collaborative scientific competitions, known as Challenges, the validation of the methods is inherently addressed. Challenges also encourage open innovation, create collaborative communities to solve diverse and important biomedical problems, and foster the creation and dissemination of well-curated data repositories.

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Section: What Have Challenges Taught Us?mentioning

confidence: 99%

Section: What Have Challenges Taught Us?mentioning

confidence: 99%

Crowdsourcing biomedical research: leveraging communities as innovation engines

et al. 2016

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“…Although prior studies have shown that the synaptic density and brain expression levels of multiple proteins in various biochemical pathways are correlated with resilient cognition [14,15], the molecular and genetic underpinnings of the dissociation of cognition and pathology remain poorly understood, and currently there are no known genetic or epigenetic determinants of the cognition–pathology discordance [16]. …”

Section: Introductionmentioning

confidence: 99%

Identification of genes associated with dissociation of cognitive performance and neuropathological burden: Multistep analysis of genetic, epigenetic, and transcriptional data

White

Yang

et al. 2017

PLoS Med

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IntroductionThe molecular underpinnings of the dissociation of cognitive performance and neuropathological burden are poorly understood, and there are currently no known genetic or epigenetic determinants of the dissociation.Methods and findings“Residual cognition” was quantified by regressing out the effects of cerebral pathologies and demographic characteristics on global cognitive performance proximate to death. To identify genes influencing residual cognition, we leveraged neuropathological, genetic, epigenetic, and transcriptional data available for deceased participants of the Religious Orders Study (n = 492) and the Rush Memory and Aging Project (n = 487). Given that our sample size was underpowered to detect genome-wide significance, we applied a multistep approach to identify genes influencing residual cognition, based on our prior observation that independent genetic and epigenetic risk factors can converge on the same locus. In the first step (n = 979), we performed a genome-wide association study with a predefined suggestive p < 10−5, and nine independent loci met this threshold in eight distinct chromosomal regions. Three of the six genes within 100 kb of the lead SNP are expressed in the dorsolateral prefrontal cortex (DLPFC): UNC5C, ENC1, and TMEM106B. In the second step, in the subset of participants with DLPFC DNA methylation data (n = 648), we found that residual cognition was related to differential DNA methylation of UNC5C and ENC1 (false discovery rate < 0.05). In the third step, in the subset of participants with DLPFC RNA sequencing data (n = 469), brain transcription levels of UNC5C and ENC1 were evaluated for their association with residual cognition: RNA levels of both UNC5C (estimated effect = −0.40, 95% CI −0.69 to −0.10, p = 0.0089) and ENC1 (estimated effect = 0.0064, 95% CI 0.0033 to 0.0096, p = 5.7 × 10−5) were associated with residual cognition. In secondary analyses, we explored the mechanism of these associations and found that ENC1 may be related to the previously documented effect of depression on cognitive decline, while UNC5C may alter the composition of presynaptic terminals. Of note, the TMEM106B allele identified in the first step as being associated with better residual cognition is in strong linkage disequilibrium with rs1990622A (r2 = 0.66), a previously identified protective allele for TDP-43 proteinopathy. Limitations include the small sample size for the genetic analysis, which was underpowered to detect genome-wide significance, the evaluation being limited to a single cortical region for epigenetic and transcriptomic data, and the use of categorical measures for certain non-amyloid-plaque, non-neurofibrillary-tangle neuropathologies.ConclusionsThrough a multistep analysis of cognitive, neuropathological, genomic, epigenomic, and transcriptomic data, we identified ENC1 and UNC5C as genes with convergent genetic, epigenetic, and transcriptomic evidence supporting a potential role in the dissociation of cognition and neuropathology in an aging population, and we expa...

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“…These data underwent extensive QC as part of the AD Big Data DREAM Challenge (Allen et al, 2016) (https://www.synapse.org/#!Synapse:syn2290704/wiki/60828). Briefly, samples from Illumina Human610-Quad BeadChip and Illumina HumanOmniExpress BeadChip arrays were mapped to hg19, converted to the positive strand and filtered for minor allele frequency (removed MAF<0.05), SNP call rate (<0.98), sample call rate (<0.98), Hardy-Weinberg equilibrium (p<0.001) and relatedness.…”

Section: Methodsmentioning

confidence: 99%

CSF protein changes associated with hippocampal sclerosis risk gene variants highlight impact of GRN/PGRN

Fardo

Katsumata

Kauwe

et al. 2017

Experimental Gerontology

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Objective Hippocampal sclerosis of aging (HS-Aging) is a common cause of dementia in older adults. We tested the variability in cerebrospinal fluid (CSF) proteins associated with previously identified HS-Aging risk single nucleotide polymorphisms (SNPs). Methods Alzheimer’s Disease Neuroimaging Initiative cohort (ADNI; n=237) data, combining both multiplexed proteomics CSF and genotype data, were used to assess the association between CSF analytes and risk SNPs in four genes (SNPs): GRN (rs5848), TMEM106B (rs1990622), ABCC9 (rs704180), and KCNMB2 (rs9637454). For controls, non-HS-Aging SNPs in APOE (rs429358/rs7412) and MAPT (rs8070723) were also analyzed against Aβ1-42 and total tau CSF analytes. Results The GRN risk SNP (rs5848) status correlated with variation in CSF proteins, with the risk allele (T) associated with increased levels of AXL Receptor Tyrosine Kinase (AXL), TNF-Related Apoptosis-Inducing Ligand Receptor 3 (TRAIL-R3), Vascular Cell Adhesion Molecule-1 (VCAM-1) and clusterin (CLU) (all p<0.05 after Bonferroni correction). The TRAIL-R3 correlation was significant in meta-analysis with an additional dataset (p=5.05×10−5). Further, the rs5848 SNP status was associated with increased CSF tau protein – a marker of neurodegeneration (p=0.015). These data are remarkable since this GRN SNP has been found to be a risk factor for multiple types of dementia-related brain pathologies.

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Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease

Cited by 80 publications

References 33 publications

Crowdsourcing biomedical research: leveraging communities as innovation engines

Crowdsourcing biomedical research: leveraging communities as innovation engines

Identification of genes associated with dissociation of cognitive performance and neuropathological burden: Multistep analysis of genetic, epigenetic, and transcriptional data

CSF protein changes associated with hippocampal sclerosis risk gene variants highlight impact of GRN/PGRN

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