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IntroductionThere is a pressing need for non-invasive, cost-effective tools for early detection of Alzheimer's disease (AD).MethodsUsing data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), Cox proportional models were conducted to develop a multimodal hazard score (MHS) combining age, a polygenic hazard score (PHS), brain atrophy, and memory to predict conversion from mild cognitive impairment (MCI) to dementia. Power calculations estimated required clinical trial sample sizes after hypothetical enrichment using the MHS. Cox regression determined predicted age of onset for AD pathology from the PHS.ResultsThe MHS predicted conversion from MCI to dementia (hazard ratio for 80th versus 20th percentile: 27.03). Models suggest that application of the MHS could reduce clinical trial sample sizes by 67%. The PHS alone predicted age of onset of amyloid and tau.DiscussionThe MHS may improve early detection of AD for use in memory clinics or for clinical trial enrichment.HighlightsA multimodal hazard score (MHS) combined age, genetics, brain atrophy, and memory. The MHS predicted time to conversion from mild cognitive impairment to dementia. MHS reduced hypothetical Alzheimer's disease (AD) clinical trial sample sizes by 67%. A polygenic hazard score predicted age of onset of AD neuropathology.

BackgroundPolygenic hazard scores (PHS) estimate age-dependent genetic risk of late-onset Alzheimer's disease (AD), but there is limited information about the performance of PHS on real-world data where the population of interest differs from the model development population and part of the model genotypes are missing or need to be imputed.ObjectiveThe aim of this study was to estimate age-dependent risk of late-onset AD using polygenic predictors in Nordic populations.MethodsWe used Desikan PHS model, based on Cox proportional hazards assumption, to obtain age-dependent hazard scores for AD from individual genotypes in the Norwegian DemGene cohort (n = 2,772). We assessed the risk discrimination and calibration of Desikan model and extended it by adding new genotype markers (the Desikan Nordic model). Finally, we evaluated both Desikan and Desikan Nordic models in two independent Danish cohorts: The Copenhagen City Heart Study (CCHS) cohort (n = 7,643) and The Copenhagen General Population Study (CGPS) cohort (n = 10,886).ResultsWe showed a robust prediction efficiency of Desikan model in stratifying AD risk groups in Nordic populations, even when some of the model SNPs were missing or imputed. We attempted to improve Desikan PHS model by adding new SNPs to it, but we still achieved similar risk discrimination and calibration with the extended model.ConclusionPHS modeling has the potential to guide the timing of treatment initiation based on individual risk profiles and can help enrich clinical trials with people at high risk to AD in Nordic populations.

Brain morphology has been shown to be highly heritable, yet only a small portion of the heritability is explained by the genetic variants discovered so far. Here we extended the Multivariate Omnibus Statistical Test (MOSTest) and applied it to genome-wide association studies (GWAS) of vertex-wise structural magnetic resonance imaging (MRI) cortical measures from N=35,657 participants in the UK Biobank. We identified 695 loci for cortical surface area and 539 for cortical thickness, in total 780 unique genetic loci associated with cortical morphology robustly replicated in 8,060 children of mixed ethnicity from the Adolescent Brain Cognitive Development (ABCD) Study®. This reflects more than 8-fold increase in genetic discovery at no cost to generalizability compared to the commonly used univariate GWAS methods applied to region of interest (ROI) data. Functional follow up including gene-based analyses implicated 10% of all protein-coding genes and pointed towards pathways involved in neurogenesis and cell differentiation. Power analysis indicated that applying the MOSTest to vertex-wise structural MRI data triples the effective sample size compared to conventional univariate GWAS approaches. The large boost in power obtained with the vertex-wise MOSTest together with pronounced replication rates and highlighted biologically meaningful pathways underscores the advantage of multivariate approaches in the context of highly distributed polygenic architecture of the human brain.

Background: To better understand physical activity behavior and its health benefits in people living with health conditions, we studied people with and without 20 different self-reported health conditions with regard to (1) their physical activity levels, (2) factors correlated with these physical activity levels, and (3) the association between physical activity and all-cause mortality. Methods: We used a subsample (n = 88,659) of the Lifelines cohort study from the Netherlands. For people living with and without 20 different self-reported health conditions, we studied the aforementioned factors in relation to physical activity. Physical activity was assessed with the Short Questionnaire to Assess Health-Enhancing Physical Activity Questionnaire, and mortality data were obtained from the Dutch death register. Results: People with a reported health condition were less likely to meet physical activity guidelines than people without a reported health condition (odds ratios ranging from 0.55 to 0.89). Higher body mass index and sitting time, and lower self-rated health, physical functioning, and education levels were associated with lower odds of meeting physical activity guidelines across most health conditions. Finally, we found a protective association between physical activity and all-cause mortality in both people living with and without different health conditions. Conclusion: People living with different health conditions are generally less physically active compared with people living without a health condition. Both people living with and without self-reported health conditions share a number of key factors associated with physical activity levels. We also observed the expected protective association between physical activity and all-cause mortality. [ABSTRACT FROM AUTHOR]

Aims: Many people with a long-standing spinal cord injury have an inactive lifestyle. Although exercise self-efficacy is considered a key determinant of engaging in exercise, the relationship between exercise self-efficacy and physical activity remains unclear. Therefore, this study examines the relationship between exercise self-efficacy and the amount of physical activity in persons with long-standing spinal cord injury. Methods: This cross-sectional study included 268 individuals (aged 28–65 years) with spinal cord injury ≥ 10 years and using a wheelchair. Physical activity was measured with the Physical Activity Scale for Individuals with Physical Disabilities. Exercise self-efficacy was assessed with the Spinal cord injury Exercise Self-Efficacy Scale. Univariate and multivariable regression analyses were performed to test for the association between exercise self-efficacy and physical activity, controlling for supposed confounders. Results: Univariate regression analysis revealed that exercise self-efficacy was significantly related to the level of daily physical activity (β = 0.05; 95% CI 0.04–0.07; 15% explained variance; p < 0.001). In multivariable regression analysis exercise self-efficacy remained, explaining a significant additional amount of the variance (2%; p < 0.001) of physical activity. Conclusion: Exercise-self efficacy is a weak but independent explanatory factor of the level of physical activity among persons with long-standing spinal cord injury. Longitudinal trials are needed to study the impact of interventions targeting an increase of exercise self-efficacy on the amount of physical activity performed. Pre-intervention levels of exercise-self-efficacy might mediate the effectiveness of interventions that aim at increasing physical activities in people with a long-standing spinal cord injury. Enhancing exercise-self efficacy may improve levels of physical activity, even in people with a long-standing spinal cord injury. When it comes to enhancing physical activity, efforts to enhance non-structured daily physical activities such as household activities and gardening might be as important as efforts to enhance sports and other physical exercise. [ABSTRACT FROM AUTHOR]

Haplotypes are the units of inheritance in an organism, and many genetic analyses depend on their precise determination. Methods for haplotyping single individuals use the phasing information available in next-generation sequencing reads, by matching overlapping single-nucleotide polymorphisms while penalizing post hoc nucleotide corrections made. Haplotyping diploids is relatively easy, but the complexity of the problem increases drastically for polyploid genomes, which are found in both model organisms and in economically relevant plant and animal species. Although a number of tools are available for haplotyping polyploids, the effects of the genomic makeup and the sequencing strategy followed on the accuracy of these methods have hitherto not been thoroughly evaluated. We developed the simulation pipeline haplosim to evaluate the performance of three haplotype estimation algorithms for polyploids: HapCompass, HapTree and SDhaP, in settings varying in sequencing approach, ploidy levels and genomic diversity, using tetraploid potato as the model. Our results show that sequencing depth is the major determinant of haplotype estimation quality, that 1 kb PacBio circular consensus sequencing reads and Illumina reads with large insert-sizes are competitive and that all methods fail to produce good haplotypes when ploidy levels increase. Comparing the three methods, HapTree produces the most accurate estimates, but also consumes the most resources. There is clearly room for improvement in polyploid haplotyping algorithms. [ABSTRACT FROM AUTHOR]

Individual differences in aggressive behavior emerge in early childhood and predict persisting behavioral problems and disorders. Studies of antisocial and severe aggression in adulthood indicate substantial underlying biology. However, little attention has been given to genome-wide approaches of aggressive behavior in children. We analyzed data from nine population-based studies and assessed aggressive behavior using well-validated parent-reported questionnaires. This is the largest sample exploring children's aggressive behavior to date (N = 18,988), with measures in two developmental stages (N = 15,668 early childhood and N = 16,311 middle childhood/early adolescence). First, we estimated the additive genetic variance of children's aggressive behavior based on genome-wide SNP information, using genome-wide complex trait analysis (GCTA). Second, genetic associations within each study were assessed using a quasi-Poisson regression approach, capturing the highly right-skewed distribution of aggressive behavior. Third, we performed meta-analyses of genome-wide associations for both the total age-mixed sample and the two developmental stages. Finally, we performed a gene-based test using the summary statistics of the total sample. GCTA quantified variance tagged by common SNPs (10-54%). The meta-analysis of the total sample identified one region in chromosome 2 (2p12) at near genome-wide significance (top SNP rs11126630, P = 5.30 × 10(-8) ). The separate meta-analyses of the two developmental stages revealed suggestive evidence of association at the same locus. The gene-based analysis indicated association of variation within AVPR1A with aggressive behavior. We conclude that common variants at 2p12 show suggestive evidence for association with childhood aggression. Replication of these initial findings is needed, and further studies should clarify its biological meaning. © 2015 Wiley Periodicals, Inc., (© 2015 Wiley Periodicals, Inc.)