Sex, gender diversity, and brain structure in early adolescence.

There remains little consensus about the relationship between sex and brain structure, particularly in early adolescence. Moreover, few pediatric neuroimaging studies have analyzed both sex and gender as variables of interest-many of which included small sample sizes and relied on binary definitions of gender. The current study examined gender diversity with a continuous felt-gender score and categorized sex based on X and Y allele frequency in a large sample of children ages 9-11 years old (N = 7195). Then, a statistical model-building approach was employed to determine whether gender diversity and sex independently or jointly relate to brain morphology, including subcortical volume, cortical thickness, gyrification, and white matter microstructure. Additional sensitivity analyses found that male versus female differences in gyrification and white matter were largely accounted for by total brain volume, rather than sex per se. The model with sex, but not gender diversity, was the best-fitting model in 60.1% of gray matter regions and 61.9% of white matter regions after adjusting for brain volume. The proportion of variance accounted for by sex was negligible to small in all cases. While models including felt-gender explained a greater amount of variance in a few regions, the felt-gender score alone was not a significant predictor on its own for any white or gray matter regions examined. Overall, these findings demonstrate that at ages 9-11 years old, sex accounts for a small proportion of variance in brain structure, while gender diversity is not directly associated with neurostructural diversity.

FEMA: Fast and efficient mixed-effects algorithm for large sample whole-brain imaging data.

The linear mixed-effects model (LME) is a versatile approach to account for dependence among observations. Many large-scale neuroimaging datasets with complex designs have increased the need for LME; however LME has seldom been used in whole-brain imaging analyses due to its heavy computational requirements. In this paper, we introduce a fast and efficient mixed-effects algorithm (FEMA) that makes whole-brain vertex-wise, voxel-wise, and connectome-wide LME analyses in large samples possible. We validate FEMA with extensive simulations, showing that the estimates of the fixed effects are equivalent to standard maximum likelihood estimates but obtained with orders of magnitude improvement in computational speed. We demonstrate the applicability of FEMA by studying the cross-sectional and longitudinal effects of age on region-of-interest level and vertex-wise cortical thickness, as well as connectome-wide functional connectivity values derived from resting state functional MRI, using longitudinal imaging data from the Adolescent Brain Cognitive DevelopmentSM Study release 4.0. Our analyses reveal distinct spatial patterns for the annualized changes in vertex-wise cortical thickness and connectome-wide connectivity values in early adolescence, highlighting a critical time of brain maturation. The simulations and application to real data show that FEMA enables advanced investigation of the relationships between large numbers of neuroimaging metrics and variables of interest while considering complex study designs, including repeated measures and family structures, in a fast and efficient manner. The source code for FEMA is available via: https://github.com/cmig-research-group/cmig_tools/.

The complexities of suicide: a multilevel survival analysis examining individual, familial and neighbourhood determinants of suicide risk using Danish register-based data.

BACKGROUND: Suicide risk is complex and nuanced, and how place impacts suicide risk when considered alongside detailed individual risk factors remains uncertain. We aimed to examine suicide risk in Denmark with both individual and neighbourhood level risk factors. METHODS: We used Danish register-based data to identify individuals born in Denmark from 1972, with full parental information and psychiatric diagnosis history. We fitted a two-level survival model to estimate individual and neighbourhood determinants on suicide risk. RESULTS: We identified 1723 cases of suicide in Denmark during the follow-up period from 1982 to 2015. Suicide risk was explained mainly by individual determinants. Parental comorbidities, particularly maternal schizophrenia [incidence rate ratio (IRR): 2.29, 95% CI 1.56-3.16] and paternal death (2.29, 95% CI 1.31-3.72) partly explained suicide risk when adjusted for all other determinants. The general contextual effect of suicide risk across neighbourhoods showed a median incidence rate ratio (MRR) of 1.13 (1.01-1.28), which was further reduced with full adjustment. Suicide risk increased in neighbourhoods with a higher proportion of manual workers (IRR: 1.08; 1.03-1.14), and decreased with a higher population density (IRR: 0.89; 0.83-0.96). CONCLUSION: Suicide risk varies mainly between individuals, with parental comorbidities having the largest effect on suicide risk. Suicide risk was less impacted by neighbourhood, though, albeit to a lesser extent than individual determinants, some characteristics were associated with suicide risk. Suicide prevention policies might consider targeting interventions towards individuals more vulnerable due to particular parental comorbidities, whilst taking into account that some neighbourhood characteristics might exacerbate this risk further.

Genotype Data and Derived Genetic Instruments of Adolescent Brain Cognitive Development Study® for Better Understanding of Human Brain Development.

The data release of Adolescent Brain Cognitive Development® (ABCD) Study represents an extensive resource for investigating factors relating to child development and mental wellbeing. The genotype data of ABCD has been used extensively in the context of genetic analysis, including genome-wide association studies and polygenic score predictions. However, there are unique opportunities provided by ABCD genetic data that have not yet been fully tapped. The diverse genomic variability, the enriched relatedness among ABCD subsets, and the longitudinal design of the ABCD challenge researchers to perform novel analyses to gain deeper insight into human brain development. Genetic instruments derived from the ABCD genetic data, such as genetic principal components, can help to better control confounds beyond the context of genetic analyses. To facilitate the use genomic information in the ABCD for inference, we here detail the processing procedures, quality controls, general characteristics, and the corresponding resources in the ABCD genotype data of release 4.0.

Comparing Pruning and Thresholding with Continuous Shrinkage Polygenic Score Methods in a Large Sample of Ancestrally Diverse Adolescents from the ABCD Study®.

Using individuals' genetic data researchers can generate Polygenic Scores (PS) that are able to predict risk for diseases, variability in different behaviors as well as anthropomorphic measures. This is achieved by leveraging models learned from previously published large Genome-Wide Association Studies (GWASs) associating locations in the genome with a phenotype of interest. Previous GWASs have predominantly been performed in European ancestry individuals. This is of concern as PS generated in samples with a different ancestry to the original training GWAS have been shown to have lower performance and limited portability, and many efforts are now underway to collect genetic databases on individuals of diverse ancestries. In this study, we compare multiple methods of generating PS, including pruning and thresholding and Bayesian continuous shrinkage models, to determine which of them is best able to overcome these limitations. To do this we use the ABCD Study, a longitudinal cohort with deep phenotyping on individuals of diverse ancestry. We generate PS for anthropometric and psychiatric phenotypes using previously published GWAS summary statistics and examine their performance in three subsamples of ABCD: African ancestry individuals (n = 811), European ancestry Individuals (n = 6703), and admixed ancestry individuals (n = 3664). We find that the single ancestry continuous shrinkage method, PRScs (CS), and the multi ancestry meta method, PRScsx Meta (CSx Meta), show the best performance across ancestries and phenotypes.

Paternal age and 13 psychiatric disorders in the offspring: a population-based cohort study of 7 million children in Taiwan.

Although paternal age has been linked to certain psychiatric disorders, the nature of any causal relationship remains elusive. Here, we aimed to comprehensively assess the magnitude of a wide range of offspring's psychiatric risk conferred by paternal age, leveraging a pedigree inferred from covered-insurance relationship (accuracy >98%) in Taiwan's single-payer compulsory insurance program. We also examined whether there is an independent role of paternal age and explored the potential effect of parental age difference. A total cohort of 7,264,788 individuals born between 1980 and 2018 were included; 5,572,232 with sibling(s) were selected for sibling-comparison analyses and 1,368,942 and 1,044,420 children with information of paternal-grandparents and maternal-grandparents, respectively, were selected for multi-generation analyses. Using inpatient/outpatient claims data (1997-2018), we identified schizophrenia, autism, bipolar disorder (BPD), attention deficit-hyperactivity disorder (ADHD), major depressive disorder (MDD), eating disorder (ED), substance use disorder (SUD), mental retardation (MR), tic disorder, obsessive-compulsive disorder (OCD), anxiety, and somatoform disorder. We identified suicides using death certificates. Logistic regression analysis was used to estimate the paternal/maternal/grand-paternal age association with psychiatric risk in the offspring. The total cohort and sibling-comparison cohort resulted in similar estimates. Paternal age had a U-shaped relationship with offspring's MDD, ED, SUD, and anxiety. A very young maternal age (<20 years) was associated with markedly higher risk in offspring's SUD, MR, and suicide. Older paternal age (>25 years) was linearly associated with offspring's schizophrenia, autism, BPD, ADHD, MDD, ED, SUD, MR, OCD, anxiety, and suicide. Older grand-paternal age was linearly associated with offspring's schizophrenia, autism, ADHD, and MR. Dissimilar parental age was positively associated with offspring's ADHD, MDD, SUD, MR, anxiety, and suicide, and negatively associated with offspring's OCD. This comprehensive assessment provides solid evidence for the independent role of paternal age in psychiatric risk in the offspring and clarifies the significance of both early parenthood and delayed paternity.

Shared genetic architecture between schizophrenia and subcortical brain volumes implicates early neurodevelopmental processes and brain development in childhood.

Patients with schizophrenia have consistently shown brain volumetric abnormalities, implicating both etiological and pathological processes. However, the genetic relationship between schizophrenia and brain volumetric abnormalities remains poorly understood. Here, we applied novel statistical genetic approaches (MiXeR and conjunctional false discovery rate analysis) to investigate genetic overlap with mixed effect directions using independent genome-wide association studies of schizophrenia (n = 130,644) and brain volumetric phenotypes, including subcortical brain and intracranial volumes (n = 33,735). We found brain volumetric phenotypes share substantial genetic variants (74-96%) with schizophrenia, and observed 107 distinct shared loci with sign consistency in independent samples. Genes mapped by shared loci revealed (1) significant enrichment in neurodevelopmental biological processes, (2) three co-expression clusters with peak expression at the prenatal stage, and (3) genetically imputed thalamic expression of CRHR1 and ARL17A was associated with the thalamic volume as early as in childhood. Together, our findings provide evidence of shared genetic architecture between schizophrenia and brain volumetric phenotypes and suggest that altered early neurodevelopmental processes and brain development in childhood may be involved in schizophrenia development.

Association between neighborhood availability of physical activity facilities and cognitive performance in older adults.

The existing evidence on the contextual influence of the availability of local facilities for physical activity on the cognitive health of elderly residents is sparse. This study examined the association between neighborhood physical activity facilities and cognitive health in older individuals. A cohort study of community-dwelling older adults was performed using baseline data and follow-up data from the Taiwan Biobank. Cognitive health was measured in 32,396 individuals aged 60-70 years using the Mini-Mental State Examination (MMSE) with follow-up information on 8025 participants. The district was used as the proxy for local neighborhood. To determine neighborhood physical activity facilities, school campuses, parks, activity centers, gyms, swimming pools, and stadiums were included. Multilevel linear regression models were applied to examine the associations of neighborhood physical activity facilities with baseline MMSE and MMSE decline during follow-up, with adjustment for individual factors and neighborhood socioeconomic characteristics. Multilevel analyses revealed that there was a neighborhood-level effect on cognitive health among older adults. After adjusting for compositional and neighborhood socioeconomic characteristics, baseline MMSE was higher in individuals living in the middle- (beta = 0.12, p-value = 0.140) and high-density facility (beta = 0.22, p-value = 0.025) groups than in the low-density group (p-value for trend-test = 0.031). MMSE decline during follow-up was slower in the middle- (beta = 0.15, p-value = 0.114) and high-density facility (beta = 0.27, p-value = 0.052) groups than in the low-density group (p-value for trend-test = 0.032). Greater neighborhood availability of physical activity facilities was associated with better cognitive health among older residents. These findings have implications for designing communities and developing strategies to support cognitive health of an aging population.

Beyond SNP heritability: Polygenicity and discoverability of phenotypes estimated with a univariate Gaussian mixture model.

Estimating the polygenicity (proportion of causally associated single nucleotide polymorphisms (SNPs)) and discoverability (effect size variance) of causal SNPs for human traits is currently of considerable interest. SNP-heritability is proportional to the product of these quantities. We present a basic model, using detailed linkage disequilibrium structure from a reference panel of 11 million SNPs, to estimate these quantities from genome-wide association studies (GWAS) summary statistics. We apply the model to diverse phenotypes and validate the implementation with simulations. We find model polygenicities (as a fraction of the reference panel) ranging from ≃ 2 × 10-5 to ≃ 4 × 10-3, with discoverabilities similarly ranging over two orders of magnitude. A power analysis allows us to estimate the proportions of phenotypic variance explained additively by causal SNPs reaching genome-wide significance at current sample sizes, and map out sample sizes required to explain larger portions of additive SNP heritability. The model also allows for estimating residual inflation (or deflation from over-correcting of z-scores), and assessing compatibility of replication and discovery GWAS summary statistics.

A simple genetic stratification method for lower cost, more expedient clinical trials in early Alzheimer's disease: A preliminary study of tau PET and cognitive outcomes.

INTRODUCTION: Identifying individuals who are most likely to accumulate tau and exhibit cognitive decline is critical for Alzheimer's disease (AD) clinical trials. METHODS: Participants (N = 235) who were cognitively normal or with mild cognitive impairment from the Alzheimer's Disease Neuroimaging Initiative were stratified by a cutoff on the polygenic hazard score (PHS) at 65th percentile (above as high-risk group and below as low-risk group). We evaluated the associations between the PHS risk groups and tau positron emission tomography and cognitive decline, respectively. Power analyses estimated the sample size needed for clinical trials to detect differences in tau accumulation or cognitive change. RESULTS: The high-risk group showed faster tau accumulation and cognitive decline. Clinical trials using the high-risk group would require a fraction of the sample size as trials without this inclusion criterion. DISCUSSION: Incorporating a PHS inclusion criterion represents a low-cost and accessible way to identify potential participants for AD clinical trials.

Generalization of cortical MOSTest genome-wide associations within and across samples.

Genome-Wide Association studies have typically been limited to univariate analysis in which a single outcome measure is tested against millions of variants. Recent work demonstrates that a Multivariate Omnibus Statistic Test (MOSTest) is well powered to discover genomic effects distributed across multiple phenotypes. Applied to cortical brain MRI morphology measures, MOSTest has resulted in a drastic improvement in power to discover loci when compared to established approaches (min-P). One question that arises is how well these discovered loci replicate in independent data. Here we perform 10 times cross validation within 34,973 individuals from UK Biobank for imaging measures of cortical area, thickness and sulcal depth (>1,000 dimensionality for each). By deploying a replication method that aggregates discovered effects distributed across multiple phenotypes, termed PolyVertex Score (MOSTest-PVS), we demonstrate a higher replication yield and comparable replication rate of discovered loci for MOSTest (# replicated loci: 242-496, replication rate: 96-97%) in independent data when compared with the established min-P approach (# replicated loci: 26-55, replication rate: 91-93%). An out-of-sample replication of discovered loci was conducted with a sample of 4,069 individuals from the Adolescent Brain Cognitive Development® (ABCD) study, who are on average 50 years younger than UK Biobank individuals. We observe a higher replication yield and comparable replication rate of MOSTest-PVS compared to min-P. This finding underscores the importance of using well-powered multivariate techniques for both discovery and replication of high dimensional phenotypes in Genome-Wide Association studies.

Unique prediction of developmental psychopathology from genetic and familial risk.

BACKGROUND: Early detection is critical for easing the rising burden of psychiatric disorders. However, the specificity of psychopathological measurements and genetic predictors is unclear among youth. METHODS: We measured associations between genetic risk for psychopathology (polygenic risk scores (PRS) and family history (FH) measures) and a wide range of behavioral measures in a large sample (n = 5,204) of early adolescent participants (9-11 years) from the Adolescent Brain and Cognitive Development StudySM . Associations were measured both with and without accounting for shared variance across measures of genetic risk. RESULTS: When controlling for genetic risk for other psychiatric disorders, polygenic risk for problematic opioid use (POU) is uniquely associated with lower behavioral inhibition. Attention deficit hyperactivity disorder (ADHD), depression (DEP), and attempted suicide (SUIC) PRS shared many significant associations with externalizing, internalizing, and psychosis-related behaviors. However, when accounting for all measures of genetic and familial risk, these PRS also showed clear, unique patterns of association. Polygenic risk for ASD, BIP, and SCZ, and attempted suicide uniquely predicted variability in cognitive performance. FH accounted for unique variability in behavior above and beyond PRS and vice versa, with FH measures explaining a greater proportion of unique variability compared to the PRS. CONCLUSION: Our results indicate that, among youth, many behaviors show shared genetic influences; however, there is also specificity in the profile of emerging psychopathologies for individuals with high genetic risk for particular disorders. This may be useful for quantifying early, differential risk for psychopathology in development.

A semi-parametric Bayesian model for semi-continuous longitudinal data.

Semi-continuous data present challenges in both model fitting and interpretation. Parametric distributions may be inappropriate for extreme long right tails of the data. Mean effects of covariates, susceptible to extreme values, may fail to capture relevant information for most of the sample. We propose a two-component semi-parametric Bayesian mixture model, with the discrete component captured by a probability mass (typically at zero) and the continuous component of the density modeled by a mixture of B-spline densities that can be flexibly fit to any data distribution. The model includes random effects of subjects to allow for application to longitudinal data. We specify prior distributions on parameters and perform model inference using a Markov chain Monte Carlo (MCMC) Gibbs-sampling algorithm programmed in R. Statistical inference can be made for multiple quantiles of the covariate effects simultaneously providing a comprehensive view. Various MCMC sampling techniques are used to facilitate convergence. We demonstrate the performance and the interpretability of the model via simulations and analyses on the National Consortium on Alcohol and Neurodevelopment in Adolescence study (NCANDA) data on alcohol binge drinking.

Individual Differences in Cognitive Performance Are Better Predicted by Global Rather Than Localized BOLD Activity Patterns Across the Cortex.

Despite its central role in revealing the neurobiological mechanisms of behavior, neuroimaging research faces the challenge of producing reliable biomarkers for cognitive processes and clinical outcomes. Statistically significant brain regions, identified by mass univariate statistical models commonly used in neuroimaging studies, explain minimal phenotypic variation, limiting the translational utility of neuroimaging phenotypes. This is potentially due to the observation that behavioral traits are influenced by variations in neuroimaging phenotypes that are globally distributed across the cortex and are therefore not captured by thresholded, statistical parametric maps commonly reported in neuroimaging studies. Here, we developed a novel multivariate prediction method, the Bayesian polyvertex score, that turns a unthresholded statistical parametric map into a summary score that aggregates the many but small effects across the cortex for behavioral prediction. By explicitly assuming a globally distributed effect size pattern and operating on the mass univariate summary statistics, it was able to achieve higher out-of-sample variance explained than mass univariate and popular multivariate methods while still preserving the interpretability of a generative model. Our findings suggest that similar to the polygenicity observed in the field of genetics, the neural basis of complex behaviors may rest in the global patterning of effect size variation of neuroimaging phenotypes, rather than in localized, candidate brain regions and networks.

Meaningful associations in the adolescent brain cognitive development study.

The Adolescent Brain Cognitive Development (ABCD) Study is the largest single-cohort prospective longitudinal study of neurodevelopment and children's health in the United States. A cohort of n = 11,880 children aged 9-10 years (and their parents/guardians) were recruited across 22 sites and are being followed with in-person visits on an annual basis for at least 10 years. The study approximates the US population on several key sociodemographic variables, including sex, race, ethnicity, household income, and parental education. Data collected include assessments of health, mental health, substance use, culture and environment and neurocognition, as well as geocoded exposures, structural and functional magnetic resonance imaging (MRI), and whole-genome genotyping. Here, we describe the ABCD Study aims and design, as well as issues surrounding estimation of meaningful associations using its data, including population inferences, hypothesis testing, power and precision, control of covariates, interpretation of associations, and recommended best practices for reproducible research, analytical procedures and reporting of results.

RICOPILI: Rapid Imputation for COnsortias PIpeLIne.

SUMMARY: Genome-wide association study (GWAS) analyses, at sufficient sample sizes and power, have successfully revealed biological insights for several complex traits. RICOPILI, an open-sourced Perl-based pipeline was developed to address the challenges of rapidly processing large-scale multi-cohort GWAS studies including quality control (QC), imputation and downstream analyses. The pipeline is computationally efficient with portability to a wide range of high-performance computing environments. RICOPILI was created as the Psychiatric Genomics Consortium pipeline for GWAS and adopted by other users. The pipeline features (i) technical and genomic QC in case-control and trio cohorts, (ii) genome-wide phasing and imputation, (iv) association analysis, (v) meta-analysis, (vi) polygenic risk scoring and (vii) replication analysis. Notably, a major differentiator from other GWAS pipelines, RICOPILI leverages on automated parallelization and cluster job management approaches for rapid production of imputed genome-wide data. A comprehensive meta-analysis of simulated GWAS data has been incorporated demonstrating each step of the pipeline. This includes all the associated visualization plots, to allow ease of data interpretation and manuscript preparation. Simulated GWAS datasets are also packaged with the pipeline for user training tutorials and developer work. AVAILABILITY AND IMPLEMENTATION: RICOPILI has a flexible architecture to allow for ongoing development and incorporation of newer available algorithms and is adaptable to various HPC environments (QSUB, BSUB, SLURM and others). Specific links for genomic resources are either directly provided in this paper or via tutorials and external links. The central location hosting scripts and tutorials is found at this URL: https://sites.google.com/a/broadinstitute.org/RICOPILI/home. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Sex-dependent autosomal effects on clinical progression of Alzheimer's disease.

Sex differences in the manifestations of Alzheimer's disease are under intense investigation. Despite the emerging importance of polygenic predictions for Alzheimer's disease, sex-dependent polygenic effects have not been demonstrated. Here, using a sex crossover analysis, we show that sex-dependent autosomal genetic effects on Alzheimer's disease can be revealed by characterizing disease progress via the hazard function. We first performed sex-stratified genome-wide associations, and then applied derived sex-dependent weights to two independent cohorts. Relative to sex-mismatched scores, sex-matched polygenic hazard scores showed significantly stronger associations with age-at-disease-onset, clinical progression, amyloid deposition, neurofibrillary tangles, and composite neuropathological scores, independent of apolipoprotein E. Models without using hazard weights, i.e. polygenic risk scores, showed lower predictive power than polygenic hazard scores with no evidence for sex differences. Our results indicate that revealing sex-dependent genetic architecture requires the consideration of temporal processes of Alzheimer's disease. This has strong implications not only for the genetic underpinning of Alzheimer's disease but also for how we estimate sex-dependent polygenic effects for clinical use.

Beyond heritability: improving discoverability in imaging genetics.

Structural neuroimaging measures based on magnetic resonance imaging have been at the forefront of imaging genetics. Global efforts to ensure homogeneity of measurements across study sites have enabled large-scale imaging genetic projects, accumulating nearly 50K samples for genome-wide association studies (GWAS). However, not many novel genetic variants have been identified by these GWAS, despite the high heritability of structural neuroimaging measures. Here, we discuss the limitations of using heritability as a guidance for assessing statistical power of GWAS, and highlight the importance of discoverability-which is the power to detect genetic variants for a given phenotype depending on its unique genomic architecture and GWAS sample size. Further, we present newly developed methods that boost genetic discovery in imaging genetics. By redefining imaging measures independent of traditional anatomical conventions, it is possible to improve discoverability, enabling identification of more genetic effects. Moreover, by leveraging enrichment priors from genomic annotations and independent GWAS of pleiotropic traits, we can better characterize effect size distributions, and identify reliable and replicable loci associated with structural neuroimaging measures. Statistical tools leveraging novel insights into the genetic discoverability of human traits, promises to accelerate the identification of genetic underpinnings underlying brain structural variation.

Discovery of shared genomic loci using the conditional false discovery rate approach.

In recent years, genome-wide association study (GWAS) sample sizes have become larger, the statistical power has improved and thousands of trait-associated variants have been uncovered, offering new insights into the genetic etiology of complex human traits and disorders. However, a large fraction of the polygenic architecture underlying most complex phenotypes still remains undetected. We here review the conditional false discovery rate (condFDR) method, a model-free strategy for analysis of GWAS summary data, which has improved yield of existing GWAS and provided novel findings of genetic overlap between a wide range of complex human phenotypes, including psychiatric, cardiovascular, and neurological disorders, as well as psychological and cognitive traits. The condFDR method was inspired by Empirical Bayes approaches and leverages auxiliary genetic information to improve statistical power for discovery of single-nucleotide polymorphisms (SNPs). The cross-trait condFDR strategy analyses separate GWAS data, and leverages overlapping SNP associations, i.e., cross-trait enrichment, to increase discovery of trait-associated SNPs. The extension of the condFDR approach to conjunctional FDR (conjFDR) identifies shared genomic loci between two phenotypes. The conjFDR approach allows for detection of shared genomic associations irrespective of the genetic correlation between the phenotypes, often revealing a mixture of antagonistic and agonistic directional effects among the shared loci. This review provides a methodological comparison between condFDR and other relevant cross-trait analytical tools and demonstrates how condFDR analysis may provide novel insights into the genetic relationship between complex phenotypes.

Polygenic hazard score, amyloid deposition and Alzheimer's neurodegeneration.

Mounting evidence indicates that the polygenic basis of late-onset Alzheimer's disease can be harnessed to identify individuals at greatest risk for cognitive decline. We have previously developed and validated a polygenic hazard score comprising of 31 single nucleotide polymorphisms for predicting Alzheimer's disease dementia age of onset. In this study, we examined whether polygenic hazard scores are associated with: (i) regional tracer uptake using amyloid PET; (ii) regional volume loss using longitudinal MRI; (iii) post-mortem regional amyloid-ß protein and tau associated neurofibrillary tangles; and (iv) four common non-Alzheimer's pathologies. Even after accounting for APOE, we found a strong association between polygenic hazard scores and amyloid PET standard uptake volume ratio with the largest effects within frontal cortical regions in 980 older individuals across the disease spectrum, and longitudinal MRI volume loss within the entorhinal cortex in 607 older individuals across the disease spectrum. We also found that higher polygenic hazard scores were associated with greater rates of cognitive and clinical decline in 632 non-demented older individuals, even after controlling for APOE status, frontal amyloid PET and entorhinal cortex volume. In addition, the combined model that included polygenic hazard scores, frontal amyloid PET and entorhinal cortex volume resulted in a better fit compared to a model with only imaging markers. Neuropathologically, we found that polygenic hazard scores were associated with regional post-mortem amyloid load and neuronal neurofibrillary tangles, even after accounting for APOE, validating our imaging findings. Lastly, polygenic hazard scores were associated with Lewy body and cerebrovascular pathology. Beyond APOE, we show that in living subjects, polygenic hazard scores were associated with amyloid deposition and neurodegeneration in susceptible brain regions. Polygenic hazard scores may also be useful for the identification of individuals at the highest risk for developing multi-aetiological dementia.