Pathway-specific polygenic scores for Alzheimer’s disease are associated with changes in brain structure in younger and older adults

Abstract Genome-wide association studies have identified multiple Alzheimer’s disease risk loci with small effect sizes. Polygenic risk scores, which aggregate these variants, are associated with grey matter structural changes. However, genome-wide scores do not allow mechanistic interpretations. The present study explored associations between disease pathway-specific scores and grey matter structure in younger and older adults. Data from two separate population cohorts were used as follows: the Avon Longitudinal Study of Parents and Children, mean age 19.8, and UK Biobank, mean age 64.4 (combined n = 18 689). Alzheimer’s polygenic risk scores were computed using the largest genome-wide association study of clinically assessed Alzheimer’s to date. Relationships between subcortical volumes and cortical thickness, pathway-specific scores and genome-wide scores were examined. Increased pathway-specific scores were associated with reduced cortical thickness in both the younger and older cohorts. For example, the reverse cholesterol transport pathway score showed evidence of association with lower left middle temporal cortex thickness in the younger Avon participants (P = 0.034; beta = −0.013, CI −0.025, −0.001) and in the older UK Biobank participants (P = 0.019; beta = −0.003, CI −0.005, −4.56 × 10−4). Pathway scores were associated with smaller subcortical volumes, such as smaller hippocampal volume, in UK Biobank older adults. There was also evidence of positive association between subcortical volumes in Avon younger adults. For example, the tau protein-binding pathway score was negatively associated with left hippocampal volume in UK Biobank (P = 8.35 × 10−05; beta = −11.392, CI −17.066, −5.718) and positively associated with hippocampal volume in the Avon study (P = 0.040; beta = 51.952, CI 2.445, 101.460). The immune response score had a distinct pattern of association, being only associated with reduced thickness in the right posterior cingulate in older and younger adults (P = 0.011; beta = −0.003, CI −0.005, −0.001 in UK Biobank; P = 0.034; beta = −0.016, CI −0.031, −0.001 in the Avon study). The immune response score was associated with smaller subcortical volumes in the older adults, but not younger adults. The disease pathway scores showed greater evidence of association with imaging phenotypes than the genome-wide score. This suggests that pathway-specific polygenic methods may allow progress towards a mechanistic understanding of structural changes linked to polygenic risk in pre-clinical Alzheimer’s disease. Pathway-specific profiling could further define pathophysiology in individuals, moving towards precision medicine in Alzheimer’s disease.

[1]  Nick C Fox,et al.  New insights into the genetic etiology of Alzheimer’s disease and related dementias , 2022, Nature Genetics.

[2]  F. Cayuman,et al.  Changes in neuronal excitability and synaptic transmission in nucleus accumbens in a transgenic Alzheimer’s disease mouse model , 2020, Scientific Reports.

[3]  Blaine R. Roberts,et al.  Risk prediction of late-onset Alzheimer’s disease implies an oligogenic architecture , 2020, Nature Communications.

[4]  Derek K. Jones,et al.  Population neuroimaging: generation of a comprehensive data resource within the ALSPAC pregnancy and birth cohort , 2020, Wellcome open research.

[5]  Leon M. Aksman,et al.  A comprehensive analysis of methods for assessing polygenic burden on Alzheimer’s disease pathology and risk beyond APOE , 2019, Brain communications.

[6]  Katrin Amunts,et al.  Pathway-Specific Genetic Risk for Alzheimer's Disease Differentiates Regional Patterns of Cortical Atrophy in Older Adults. , 2019, Cerebral cortex.

[7]  Tom R. Gaunt,et al.  The Avon Longitudinal Study of Parents and Children (ALSPAC): an update on the enrolled sample of index children in 2019 , 2019, Wellcome open research.

[8]  Nick C Fox,et al.  Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing , 2019, Nature Genetics.

[9]  Timothy J. Hohman,et al.  Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk , 2019, Nature Genetics.

[10]  P. Holmans,et al.  Disentangling the biological pathways involved in early features of Alzheimer's disease in the Rotterdam Study , 2018, Alzheimer's & Dementia.

[11]  Paul M. Thompson,et al.  Systemic inflammation as a predictor of brain aging: Contributions of physical activity, metabolic risk, and genetic risk , 2018, NeuroImage.

[12]  R. Marioni,et al.  GWAS on family history of Alzheimer’s disease , 2018, bioRxiv.

[13]  Chunshui Yu,et al.  Polygenic risk for Alzheimer's disease influences precuneal volume in two independent general populations , 2017, Neurobiology of Aging.

[14]  J. Hort,et al.  Subregional Structural Alterations in Hippocampus and Nucleus Accumbens Correlate with the Clinical Impairment in Patients with Alzheimer’s Disease Clinical Spectrum: Parallel Combining Volume and Vertex-Based Approach , 2017, Front. Neurol..

[15]  Ravi S. Menon,et al.  Spontaneous low frequency BOLD signal variations from resting-state fMRI are decreased in Alzheimer disease , 2017, PloS one.

[16]  Ludovica Griffanti,et al.  Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank , 2017, NeuroImage.

[17]  D. Salat,et al.  Mild traumatic brain injury is associated with reduced cortical thickness in those at risk for Alzheimer’s disease , 2017, Brain : a journal of neurology.

[18]  Xavier Caseras,et al.  Multimodal Brain Imaging Reveals Structural Differences in Alzheimer’s Disease Polygenic Risk Carriers: A Study in Healthy Young Adults , 2017, Biological Psychiatry.

[19]  Linda Chang,et al.  Gray matter maturation and cognition in children with different APOE ε genotypes , 2016, Neurology.

[20]  Theresa M. Harrison,et al.  An Alzheimer’s Disease Genetic Risk Score Predicts Longitudinal Thinning of Hippocampal Complex Subregions in Healthy Older Adults , 2016, eNeuro.

[21]  Amelia A. Assareh,et al.  The effect of increased genetic risk for Alzheimer's disease on hippocampal and amygdala volume , 2016, Neurobiology of Aging.

[22]  M. Gill,et al.  Common polygenic variation enhances risk prediction for Alzheimer's disease. , 2015, Brain : a journal of neurology.

[23]  Gabor T. Marth,et al.  A global reference for human genetic variation , 2015, Nature.

[24]  Kewei Chen,et al.  Brain Imaging and Blood Biomarker Abnormalities in Children With Autosomal Dominant Alzheimer Disease: A Cross-Sectional Study. , 2015, JAMA neurology.

[25]  Joris M. Mooij,et al.  MAGMA: Generalized Gene-Set Analysis of GWAS Data , 2015, PLoS Comput. Biol..

[26]  P. Elliott,et al.  UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age , 2015, PLoS medicine.

[27]  J. Gallacher,et al.  Convergent genetic and expression data implicate immunity in Alzheimer's disease , 2014, Alzheimer's & Dementia.

[28]  N. Wray,et al.  Research review: Polygenic methods and their application to psychiatric traits. , 2014, Journal of child psychology and psychiatry, and allied disciplines.

[29]  Martin Styner,et al.  Common variants in psychiatric risk genes predict brain structure at birth. , 2014, Cerebral cortex.

[30]  Nick C Fox,et al.  Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease , 2013, Nature Genetics.

[31]  Marina Boccardi,et al.  Striatal morphology in early-onset and late-onset Alzheimer's disease: a preliminary study , 2013, Neurobiology of Aging.

[32]  A. Wimo,et al.  The global prevalence of dementia: A systematic review and metaanalysis , 2013, Alzheimer's & Dementia.

[33]  Eric M Reiman,et al.  Florbetapir PET analysis of amyloid-β deposition in the presenilin 1 E280A autosomal dominant Alzheimer's disease kindred: a cross-sectional study , 2012, The Lancet Neurology.

[34]  Kewei Chen,et al.  Brain imaging and fluid biomarker analysis in young adults at genetic risk for autosomal dominant Alzheimer's disease in the presenilin 1 E280A kindred: a case-control study , 2012, The Lancet Neurology.

[35]  Harald Hampel,et al.  Reduced Hippocampal Volume in Healthy Young ApoE4 Carriers: An MRI Study , 2012, PloS one.

[36]  Eric M Reiman,et al.  Cortical atrophy in presymptomatic Alzheimer's disease presenilin 1 mutation carriers , 2012, Journal of Neurology, Neurosurgery & Psychiatry.

[37]  R. Buckner,et al.  The association between a polygenic Alzheimer score and cortical thickness in clinically normal subjects. , 2012, Cerebral cortex.

[38]  R. Tanzi The genetics of Alzheimer disease. , 2012, Cold Spring Harbor perspectives in medicine.

[39]  Bruce Fischl,et al.  FreeSurfer , 2012, NeuroImage.

[40]  P. Holmans,et al.  Pathway analysis of IGAP GWAS data implicates endocytosis in the aetiology of late-onset Alzheimer's disease , 2012, Alzheimer's & Dementia.

[41]  D. Lawlor,et al.  Cohort Profile: The Avon Longitudinal Study of Parents and Children: ALSPAC mothers cohort , 2012, International journal of epidemiology.

[42]  D. Lawlor,et al.  Cohort Profile: The ‘Children of the 90s’—the index offspring of the Avon Longitudinal Study of Parents and Children , 2012, International journal of epidemiology.

[43]  O. Delaneau,et al.  A linear complexity phasing method for thousands of genomes , 2011, Nature Methods.

[44]  J. Marchini,et al.  Genotype Imputation with Thousands of Genomes , 2011, G3: Genes | Genomes | Genetics.

[45]  Nick C Fox,et al.  Acceleration of cortical thinning in familial Alzheimer's disease , 2011, Neurobiology of Aging.

[46]  Klaus P. Ebmeier,et al.  The APOE ɛ4 allele modulates brain white matter integrity in healthy adults , 2011, Molecular Psychiatry.

[47]  M. Weiner,et al.  The dynamics of cortical and hippocampal atrophy in Alzheimer disease. , 2011, Archives of neurology.

[48]  Timothy O. Laumann,et al.  Informatics and Data Mining Tools and Strategies for the Human Connectome Project , 2011, Front. Neuroinform..

[49]  Paul M. Thompson,et al.  Neuroimaging Measures as Endophenotypes in Alzheimer's Disease , 2011, International journal of Alzheimer's disease.

[50]  H. Braak,et al.  The pathological process underlying Alzheimer’s disease in individuals under thirty , 2011, Acta Neuropathologica.

[51]  Carole Dufouil,et al.  Effects of ApoE-ɛ4 allele load and age on the rates of grey matter and hippocampal volumes loss in a longitudinal cohort of 1186 healthy elderly persons , 2010, NeuroImage.

[52]  Tianzi Jiang,et al.  Cortical thickness is associated with different apolipoprotein E genotypes in healthy elderly adults , 2010, Neuroscience Letters.

[53]  Rachel L. Mistur,et al.  Subregional hippocampal atrophy predicts Alzheimer's dementia in the cognitively normal , 2010, Neurobiology of Aging.

[54]  Charles D. Smith,et al.  Evidence that volume of anterior medial temporal lobe is reduced in seniors destined for mild cognitive impairment , 2010, Neurobiology of Aging.

[55]  Nick C Fox,et al.  The clinical use of structural MRI in Alzheimer disease , 2010, Nature Reviews Neurology.

[56]  Chunshui Yu,et al.  Hippocampal volume and asymmetry in mild cognitive impairment and Alzheimer's disease: Meta‐analyses of MRI studies , 2009, Hippocampus.

[57]  P. Visscher,et al.  Common polygenic variation contributes to risk of schizophrenia and bipolar disorder , 2009, Nature.

[58]  P. Matthews,et al.  Distinct patterns of brain activity in young carriers of the APOE e4 allele , 2009, NeuroImage.

[59]  P. Sachdev,et al.  In Vivo Hippocampal Measurement and Memory: A Comparison of Manual Tracing and Automated Segmentation in a Large Community-Based Sample , 2009, PloS one.

[60]  J. Morris,et al.  Differential effects of aging and Alzheimer's disease on medial temporal lobe cortical thickness and surface area , 2009, Neurobiology of Aging.

[61]  R Hashimoto,et al.  Effect of the brain‐derived neurotrophic factor and the apolipoprotein E polymorphisms on disease progression in preclinical Alzheimer’s disease , 2009, Genes, brain, and behavior.

[62]  Manuel A. R. Ferreira,et al.  PLINK: a tool set for whole-genome association and population-based linkage analyses. , 2007, American journal of human genetics.

[63]  Deanna Greenstein,et al.  Cortical morphology in children and adolescents with different apolipoprotein E gene polymorphisms: an observational study , 2007, The Lancet Neurology.

[64]  F. Schmitt,et al.  Brain structural alterations before mild cognitive impairment , 2007, Neurology.

[65]  Anders M. Dale,et al.  An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest , 2006, NeuroImage.

[66]  Jonas Persson,et al.  Reduced hippocampal volume in non-demented carriers of the apolipoprotein E ɛ4: Relation to chronological age and recognition memory , 2006, Neuroscience Letters.

[67]  Carole Dufouil,et al.  No ɛ4 gene dose effect on hippocampal atrophy in a large MRI database of healthy elderly subjects , 2005, NeuroImage.

[68]  A. Dale,et al.  Thinning of the cerebral cortex in aging. , 2004, Cerebral cortex.

[69]  Nick C Fox,et al.  Assessing the onset of structural change in familial Alzheimer's disease , 2003, Annals of neurology.

[70]  Nick C Fox,et al.  Imaging of onset and progression of Alzheimer's disease with voxel-compression mapping of serial magnetic resonance images , 2001, The Lancet.

[71]  J. Kaye,et al.  Volume loss of the hippocampus and temporal lobe in healthy elderly persons destined to develop dementia , 1997, Neurology.

[72]  Nick C Fox,et al.  Presymptomatic hippocampal atrophy in Alzheimer's disease. A longitudinal MRI study. , 1996, Brain : a journal of neurology.

[73]  Cedric E. Ginestet,et al.  No differences in hippocampal volume between carriers and non-carriers of the ApoE ε4 and ε2 alleles in young healthy adolescents. , 2014, Journal of Alzheimer's disease : JAD.

[74]  Xue Hua,et al.  Brain differences in infants at differential genetic risk for late-onset Alzheimer disease: a cross-sectional imaging study. , 2014, JAMA neurology.

[75]  P. Visser,et al.  New MRI markers for Alzheimer's disease: a meta-analysis of diffusion tensor imaging and a comparison with medial temporal lobe measurements. , 2012, Journal of Alzheimer's disease : JAD.

[76]  P. Lewczuk,et al.  Hippocampal volume differences between healthy young apolipoprotein E ε2 and ε4 carriers. , 2011, Journal of Alzheimer's disease : JAD.

[77]  Emiliano Macaluso,et al.  Grey and white matter changes at different stages of Alzheimer's disease. , 2010, Journal of Alzheimer's disease : JAD.

[78]  Nick C Fox,et al.  Reduced cortical thickness in the posterior cingulate gyrus is characteristic of both typical and atypical Alzheimer's disease. , 2010, Journal of Alzheimer's disease : JAD.

[79]  A. Hofman,et al.  Use of hippocampal and amygdalar volumes on magnetic resonance imaging to predict dementia in cognitively intact elderly people. , 2006, Archives of general psychiatry.

[80]  H. Braak,et al.  Neuropathological stageing of Alzheimer-related changes , 2004, Acta Neuropathologica.