Progress in Polygenic Composite Scores in Alzheimer's and Other Complex Diseases

Walking time and genetic predisposition for Alzheimer's disease: Results from the HELIAD study

Objective: Our study aimed to explore whether physical condition might affect the association between genetic predisposition for Alzheimer's Disease (AD) and AD incidence. Methods: The sample of participants consisted of 561 community-dwelling adults over 64 years old, without baseline dementia (508 cognitively normal and 53 with mild cognitive impairment), deriving from the HELIAD, an ongoing longitudinal study with follow-up evaluations every 3 years. Physical condition was assessed at baseline through walking time (WT), while a Polygenic Risk Score for late onset AD (PRS-AD) was used to estimate genetic predisposition. The association between WT and PRS-AD with AD incidence was evaluated with Cox proportional hazard models adjusted for age, sex, education years, global cognition score and APOE ε-4 genotype. Then, the association between WT and AD incidence was investigated after stratifying participants by low and high PRS-AD. Finally, we examined the association between PRS-AD and AD incidence after stratifying participants by WT. Results: Both WT and PRS-AD were connected with increased AD incidence (p < 0.05), after adjustments. In stratified analyses, in the slow WT group participants with a greater genetic risk had a 2.5-fold higher risk of developing AD compared to participants with lower genetic risk (p = 0.047). No association was observed in the fast WT group or when participants were stratified based on PRS-AD. Conclusions: Genetic predisposition for AD is more closely related to AD incidence in the group of older adults with slow WT. Hence, physical condition might be a modifier in the relationship of genetic predisposition with AD incidence.

Genetic risk for Alzheimer's disease and adherence to the Mediterranean diet: results from the HELIAD study

Obejctives: The aim of the current study was to investigate whether genetic risk factors may moderate the association between adherence to the Mediterranean diet and AD incidence.Mehtods: The sample was drawn from the HELIAD study, a longitudinal study with a follow-up interval of 3 years. In total 537 older adults without dementia or AD at baseline were included. Adherence to the Mediterranean diet was assessed at baseline and AD diagnosis was determined at both visits. A Polygenic Index for late onset AD (PGI-AD) was constructed. Cox proportional hazard models adjusted for age, sex, education, baseline Global cognition score and APOE e-4 genotype were employed to evaluate the association between PGI-AD and Mediterranean diet with AD incidence. Next, we examined the association between adherence to the Mediterranean diet and AD risk over time across participants stratified by low and high PGI-AD.Results: Twenty-eight participants developed AD at follow-up. In fully adjusted models both the PGI-AD and the adherence to the Mediterranean diet were associated with AD risk (p < 0.05 for both). In the low PGI-AD group, those with a low adherence had a 10-fold higher risk of developing AD per year of follow-up, than did the participants with a high adherence to the Mediterranean diet (p = 0.011), whereas no such association was found for participants in the high PGI-AD group.Discussion: The association of Mediterranean diet with AD risk is more prominent in the group of older adults with a low polygenic risk for developing AD. Our findings suggest that genetic risk factors should be taken into account when planning interventions aiming to improve cognitive health.

Deciphering the tissue-specific functional effect of Alzheimer risk SNPs with deep genome annotation

Alzheimer's disease (AD) is a highly heritable brain dementia, along with substantial failure of cognitive function. Large-scale genome-wide association studies (GWASs) have led to a significant set of SNPs associated with AD and related traits. GWAS hits usually emerge as clusters where a lead SNP with the highest significance is surrounded by other less significant neighboring SNPs. Although functionality is not guaranteed even with the strongest associations in GWASs, lead SNPs have historically been the focus of the field, with the remaining associations inferred to be redundant. Recent deep genome annotation tools enable the prediction of function from a segment of a DNA sequence with significantly improved precision, which allows in-silico mutagenesis to interrogate the functional effect of SNP alleles. In this project, we explored the impact of top AD GWAS hits on chromatin functions and whether it will be altered by the genetic context (i.e., alleles of neighboring SNPs). Our results showed that highly correlated SNPs in the same LD block could have distinct impacts on downstream functions. Although some GWAS lead SNPs showed dominant functional effects regardless of the neighborhood SNP alleles, several other SNPs did exhibit enhanced loss or gain of function under certain genetic contexts, suggesting potential additional information hidden in the LD blocks.

Machine Learning Models of Polygenic Risk for Enhanced Prediction of Alzheimer Disease Endophenotypes

Background and Objectives

Alzheimer disease (AD) has a polygenic architecture, for which genome-wide association studies (GWAS) have helped elucidate sequence variants (SVs) influencing susceptibility. Polygenic risk score (PRS) approaches show promise for generating summary measures of inherited risk for clinical AD based on the effects of APOE and other GWAS hits. However, existing PRS approaches, based on traditional regression models, explain only modest variation in AD dementia risk and AD-related endophenotypes. We hypothesized that machine learning (ML) models of polygenic risk (ML-PRS) could outperform standard regression-based PRS methods and therefore have the potential for greater clinical utility.

Methods

We analyzed combined data from the Mayo Clinic Study of Aging (n = 1,791) and the Alzheimer's Disease Neuroimaging Initiative (n = 864). An AD PRS was computed for each participant using the top common SVs obtained from a large AD dementia GWAS. In parallel, ML models were trained using those SV genotypes, with amyloid PET burden as the primary outcome. Secondary outcomes included amyloid PET positivity and clinical diagnosis (cognitively unimpaired vs impaired). We compared performance between ML-PRS and standard PRS across 100 training sessions with different data splits. In each session, data were split into 80% training and 20% testing, and then five-fold cross-validation was used within the training set to ensure the best model was produced for testing. We also applied permutation importance techniques to assess which genetic factors contributed most to outcome prediction.

Results

ML-PRS models outperformed the AD PRS (r2 = 0.28 vs r2 = 0.24 in test set) in explaining variation in amyloid PET burden. Among ML approaches, methods accounting for nonlinear genetic influences were superior to linear methods. ML-PRS models were also more accurate when predicting amyloid PET positivity (area under the curve [AUC] = 0.80 vs AUC = 0.63) and the presence of cognitive impairment (AUC = 0.75 vs AUC = 0.54) compared with the standard PRS.

Discussion

We found that ML-PRS approaches improved upon standard PRS for prediction of AD endophenotypes, partly related to improved accounting for nonlinear effects of genetic susceptibility alleles. Further adaptations of the ML-PRS framework could help to close the gap of remaining unexplained heritability for AD and therefore facilitate more accurate presymptomatic and early-stage risk stratification for clinical decision-making.

Polygenic risk scores for Alzheimer's disease in relation to cognitive change: A representative sample from the general population followed over 16 years

BACKGROUND

Polygenic risk scores for Alzheimer's disease (AD-PRSs) have been associated with cognition. However, few studies have examined the effect of AD-PRS beyond the APOE gene, and the influence of genetic variants related to level of cognitive ability (COG-PRS) on cognitive performance over time in the general older population.

METHOD

A population-based sample of 965 individuals born in 1930, with genetic and standardized cognitive data on six psychometric tests (Thurstone's picture memory, immediate recall of 10 words, Block design, word fluency, figure identification, delayed recall of 12 items), were examined at age 70, 75, 79, and 85 years. Non-APOE AD-PRSs and COG-PRSs (P < 5e-8, P < 1e-5, P < 1e-3, P < 1e-1) were generated from recent genome-wide association studies. Linear mixed effect models with random intercepts and slope were used to analyze the effect of APOE ε4 allele, AD-PRSs, and COG-PRSs, on cognitive performance and rate of change. Analyses were repeated in samples excluding dementia.

RESULTS

APOE ε4 and AD-PRS predicted change in cognitive performance (APOE ε4*age: β = -0.03, P < 0.0001 and AD-PRS *age: β = -0.01, P = 0.02). The results remained similar in the sample excluding those with dementia. COG-PRS predicted level of cognitive performance, while APOE ε4 and AD-PRS did not. COG-PRSs did not predict change in cognitive performance.

CONCLUSION

We found that genetic predisposition of AD predicted cognitive decline among 70-year-olds followed over 16 years, regardless of dementia status, while polygenic risk for general cognitive performance did not.

Genetic risk scores enhance the diagnostic value of plasma biomarkers of brain amyloidosis

Blood-based biomarkers offer strong potential to revolutionize diagnosis, trial enrolment and treatment monitoring in Alzheimer's disease (AD). However, further advances are needed before these biomarkers can achieve wider deployment beyond selective research studies and specialty memory clinics, including the development of frameworks for optimal interpretation of biomarker profiles. We hypothesized that integrating Alzheimer's disease genetic risk score (AD-GRS) data would enhance the diagnostic value of plasma AD biomarkers by better capturing extant disease heterogeneity. Analysing 962 individuals from a population-based sample, we observed that an AD-GRS was independently associated with amyloid PET levels (an early marker of AD pathophysiology) over and above APOE ε4 or plasma p-tau181, amyloid-β42/40, glial fibrillary acidic protein or neurofilament light chain. Among individuals with a high or moderately high plasma p-tau181, integrating AD-GRS data significantly improved classification accuracy of amyloid PET positivity, including the finding that the combination of a high AD-GRS and high plasma p-tau181 outperformed p-tau181 alone in classifying amyloid PET positivity (88% versus 68%; P = 0.001). A machine learning approach incorporating plasma biomarkers, demographics and the AD-GRS was highly accurate in predicting amyloid PET levels (90% training set; 89% test set) and Shapley value analyses (an explainer method based in cooperative game theory) indicated that the AD-GRS and plasma biomarkers had differential importance in explaining amyloid deposition across individuals. Polygenic risk for AD dementia appears to account for a unique portion of disease heterogeneity, which could non-invasively enhance the interpretation of blood-based AD biomarker profiles in the population.

Deciphering the tissue-specific functional effect of Alzheimer risk SNPs with deep genome annotation

Alzheimer's disease (AD) is a highly heritable brain dementia, along with substantial failure of cognitive function. Large-scale genome-wide association studies (GWAS) have led to a significant set of SNPs associated with AD and related traits. GWAS hits usually emerge as clusters where a lead SNP with the highest significance is surrounded by other less significant neighboring SNPs. Although functionality is not guaranteed with even the strongest associations in the GWAS, the lead SNPs have been historically the focus of the field, with the remaining associations inferred as redundant. Recent deep genome annotation tools enable the prediction of function from a segment of DNA sequence with significantly improved precision, which allows in-silico mutagenesis to interrogate the functional effect of SNP alleles. In this project, we explored the impact of top AD GWAS hits on the chromatin functions, and whether it will be altered by the genomic context (i.e., alleles of neighborhood SNPs). Our results showed that highly correlated SNPs in the same LD block could have distinct impact on the downstream functions. Although some GWAS lead SNPs showed dominating functional effect regardless of the neighborhood SNP alleles, several other ones do get enhanced loss or gain of function under certain genomic context, suggesting potential extra information hidden in the LD blocks.

The foundation and architecture of precision medicine in neurology and psychiatry

Neurological and psychiatric diseases have high degrees of genetic and pathophysiological heterogeneity, irrespective of clinical manifestations. Traditional medical paradigms have focused on late-stage syndromic aspects of these diseases, with little consideration of the underlying biology. Advances in disease modeling and methodological design have paved the way for the development of precision medicine (PM), an established concept in oncology with growing attention from other medical specialties. We propose a PM architecture for central nervous system diseases built on four converging pillars: multimodal biomarkers, systems medicine, digital health technologies, and data science. We discuss Alzheimer's disease (AD), an area of significant unmet medical need, as a case-in-point for the proposed framework. AD can be seen as one of the most advanced PM-oriented disease models and as a compelling catalyzer towards PM-oriented neuroscience drug development and advanced healthcare practice.

Polygenic risk scores for schizophrenia are associated with oculomotor endophenotypes

BACKGROUND

Schizophrenia is a heterogeneous disorder with substantial heritability. The use of endophenotypes may help clarify its aetiology. Measures from the smooth pursuit and antisaccade eye movement tasks have been identified as endophenotypes for schizophrenia in twin and family studies. However, the genetic basis of the overlap between schizophrenia and these oculomotor markers is largely unknown. Here, we tested whether schizophrenia polygenic risk scores (PRS) were associated with oculomotor performance in the general population.

METHODS

Analyses were based on the data of 2956 participants (aged 30-95) of the Rhineland Study, a community-based cohort study in Bonn, Germany. Genotyping was performed on Omni-2.5 exome arrays. Using summary statistics from a recent meta-analysis based on the two largest schizophrenia genome-wide association studies to date, we quantified genetic risk for schizophrenia by creating PRS at different p value thresholds for genetic markers. We examined associations between PRS and oculomotor performance using multivariable regression models.

RESULTS

Higher PRS were associated with higher antisaccade error rate and latency, and lower antisaccade amplitude gain. PRS showed inconsistent patterns of association with smooth pursuit velocity gain and were not associated with saccade rate during smooth pursuit or performance on a prosaccade control task.

CONCLUSIONS

There is an overlap between genetic determinants of schizophrenia and oculomotor endophenotypes. Our findings suggest that the mechanisms that underlie schizophrenia also affect oculomotor function in the general population.

The emergence of genotypic divergence and future precision medicine applications

Genotypic divergence is a term adapted from population genetics and intimately linked to evolution. We use divergence here to emphasize the differences that set individuals apart in any cohort. The history of genetics is filled with descriptions of genotypic differences, but causal inference of interindividual biological variation has been scarce. We suggest that the practice of precision medicine requires a divergent approach, an approach dependent on the causal interpretation of previous convergent (and preliminary) knowledge in the field. This knowledge has relied on convergent descriptive syndromology (lumping), which has overemphasized a reductionistic gene determinism on the quest of seeking associations without causal understanding. Regulatory variants with small effect and somatic mutations are some of the modifying factors that lead to incomplete penetrance and intrafamilial variable expressivity often observed in apparently monogenic clinical disorders. A truly divergent approach to precision medicine requires splitting, that is, the consideration of different layers of genetic phenomena that interact causally in a nonlinear fashion. This chapter reviews convergences and divergences in genetics and genomics, aiming to discuss what can be causally understood to approximate the as-yet utopian lands of Precision Medicine for patients with neurodegenerative disorders.

Learning high-order interactions for polygenic risk prediction

Within the framework of precision medicine, the stratification of individual genetic susceptibility based on inherited DNA variation has paramount relevance. However, one of the most relevant pitfalls of traditional Polygenic Risk Scores (PRS) approaches is their inability to model complex high-order non-linear SNP-SNP interactions and their effect on the phenotype (e.g. epistasis). Indeed, they incur in a computational challenge as the number of possible interactions grows exponentially with the number of SNPs considered, affecting the statistical reliability of the model parameters as well. In this work, we address this issue by proposing a novel PRS approach, called High-order Interactions-aware Polygenic Risk Score (hiPRS), that incorporates high-order interactions in modeling polygenic risk. The latter combines an interaction search routine based on frequent itemsets mining and a novel interaction selection algorithm based on Mutual Information, to construct a simple and interpretable weighted model of user-specified dimensionality that can predict a given binary phenotype. Compared to traditional PRSs methods, hiPRS does not rely on GWAS summary statistics nor any external information. Moreover, hiPRS differs from Machine Learning-based approaches that can include complex interactions in that it provides a readable and interpretable model and it is able to control overfitting, even on small samples. In the present work we demonstrate through a comprehensive simulation study the superior performance of hiPRS w.r.t. state of the art methods, both in terms of scoring performance and interpretability of the resulting model. We also test hiPRS against small sample size, class imbalance and the presence of noise, showcasing its robustness to extreme experimental settings. Finally, we apply hiPRS to a case study on real data from DACHS cohort, defining an interaction-aware scoring model to predict mortality of stage II-III Colon-Rectal Cancer patients treated with oxaliplatin.

The Allosteric Antagonist of the Sigma-2 Receptors-Elayta (CT1812) as a Therapeutic Candidate for Mild to Moderate Alzheimer's Disease: A Scoping Systematic Review

Nearly 35 million people worldwide live with Alzheimer's disease (AD). The prevalence of the disease is expected to rise two-fold by 2050. With only symptomatic treatment options available, it is essential to understand the developments and existing evidence that aims to target brain pathology and dementia outcomes. This scoping systematic review aimed to collate existing evidence of CT1812 for use in patients with AD and summarize the methodologies of ongoing trials. Adhering to PRISMA Statement 2020 guidelines, PubMed/MEDLINE, Embase, Cochrane, and ClinicalTrials.gov were systematically searched through up to 15 November 2022 by applying the following keywords: CT1812, Alzheimer's disease, dementia, and/or sigma-2 receptor. Three completed clinical trials were included along with three ongoing records of clinical trials. The three completed trials were in Phases I and II of testing. The sample size across all three trials was 135. CT1812 reached endpoints across the trials and obtained a maximum concentration in the cerebrospinal fluid with 97-98% receptor occupancy. The findings of this systematic review must be used with caution as the results, while mostly favorable so far, must be replicated in higher-powered, placebo-controlled Phase II-III trials.

Alzheimer’s Disease Polygenic Risk Score Is Not Associated With Cognitive Decline Among Older Adults With Type 2 Diabetes

Objectives

Multiple risk loci for late-onset Alzheimer’s disease (LOAD) have been identified. Type 2 diabetes (T2D) is a risk factor for cognitive decline, dementia and Alzheimer’s disease (AD). We investigated the association of polygenic risk score (PRS) for LOAD with overall cognitive functioning and longitudinal decline, among older adults with T2D.

Methods

The study included 1046 Jewish participants from the Israel Diabetes and Cognitive Decline (IDCD) study, aged ≥ 65 years, diagnosed with T2D, and cognitively normal at baseline. The PRS included variants from 26 LOAD associated loci (at genome-wide significance level), and was calculated with and without APOE. Outcome measures, assessed in 18 months intervals, were global cognition and the specific domains of episodic memory, attention/working memory, executive functions, and language/semantic categorization. Random coefficient models were used for analysis, adjusting for demographic variables, T2D-related characteristics, and cardiovascular factors. Additionally, in a subsample of 202 individuals, we analyzed the association of PRS with the volumes of total gray matter, frontal lobe, hippocampus, amygdala, and white matter hyperintensities. Last, the association of PRS with amyloid beta (Aβ) burden was examined in 44 participants who underwent an 18F-flutemetamol PET scan.

Results

The PRS was not significantly associated with overall functioning or decline in global cognition or any of the specific cognitive domains. Similarly, following correction for multiple testing, there was no association with Aβ burden and other brain imaging phenotypes.

Conclusion

Our results suggest that the cumulative effect of LOAD susceptibility loci is not associated with a greater rate of cognitive decline in older adults with T2D, and other pathways may underlie this link.

Polygenic Scores of Alzheimer’s Disease Risk Genes Add Only Modestly to APOE in Explaining Variation in Amyloid PET Burden

Background: Brain accumulation of amyloid-β is a hallmark event in Alzheimer’s disease (AD) whose underlying mechanisms are incompletely understood. Case-control genome-wide association studies have implicated numerous genetic variants in risk of clinically diagnosed AD dementia. Objective: To test for associations between case-control AD risk variants and amyloid PET burden in older adults, and to assess whether a polygenic measure encompassing these factors would account for a large proportion of the unexplained variance in amyloid PET levels in the wider population. Methods: We analyzed data from the Mayo Clinic Study of Aging (MCSA) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Global cortical amyloid PET burden was the primary outcome. The 38 gene variants from Wightman et al. (2021) were analyzed as predictors, with PRSice-2 used to assess the collective phenotypic variance explained. Results: Known AD risk variants in APOE, PICALM, CR1, and CLU were associated with amyloid PET levels. In aggregate, the AD risk variants were strongly associated with amyloid PET levels in the MCSA (p = 1.51×10–50) and ADNI (p = 3.21×10–64). However, in both cohorts the non-APOE variants uniquely contributed only modestly (MCSA = 2.1%, ADNI = 4.4%) to explaining variation in amyloid PET levels. Conclusion: Additional case-control AD risk variants added only modestly to APOE in accounting for individual variation in amyloid PET burden, results which were consistent across independent cohorts with distinct recruitment strategies and subject characteristics. Our findings suggest that advancing precision medicine for dementia may require integration of strategies complementing case-control approaches, including biomarker-specific genetic associations, gene-by-environment interactions, and markers of disease progression and heterogeneity.

Tau polygenic risk scoring: a cost-effective aid for prognostic counseling in Alzheimer's disease

Tau deposition is one of two hallmark features of biologically defined Alzheimer's disease (AD) and is more closely related to cognitive decline than amyloidosis. Further, not all amyloid-positive individuals develop tauopathy, resulting in wide heterogeneity in clinical outcomes across the population with AD. We hypothesized that a polygenic risk score (PRS) based on tau PET (tau PRS) would capture the aggregate inherited susceptibility/resistance architecture influencing tau accumulation, beyond solely the measurement of amyloid-β burden. Leveraging rich multimodal data from a population-based sample of older adults, we found that this novel tau PRS was a strong surrogate of tau PET deposition and captured a significant proportion of the variance in tau PET levels as compared with amyloid PET burden, APOE (apolipoprotein E) ε4 (the most common risk allele for AD), and a non-APOE PRS of clinical case-control AD risk variants. In independent validation samples, the tau PRS was associated with cerebrospinal fluid phosphorylated tau levels in one cohort and with postmortem Braak neurofibrillary tangle stage in another. We also observed an association of the tau PRS with longitudinal cognitive trajectories, including a statistical interaction of the tau PRS with amyloid burden on cognitive decline. Although additional study is warranted, these findings demonstrate the potential utility of a tau PRS for capturing the collective genetic background influencing tau deposition in the general population. In the future, a tau PRS could be leveraged for cost-effective screening and risk stratification to guide trial enrollment and clinical interventions in AD.

A Pathway-Based Genetic Score for Oxidative Stress: An Indicator of Host Vulnerability to Phthalate-Associated Adverse Neurodevelopment

The developing brain is highly sensitive to environmental disturbances, and adverse exposures can act through oxidative stress. Given that oxidative stress susceptibility is determined partly by genetics, multiple studies have employed genetic scores to explore the role of oxidative stress in human disease. However, traditional approaches to genetic score construction face a range of challenges, including a lack of interpretability, bias towards the disease outcome, and often overfitting to the study they were derived on. Here, we develop an alternative strategy by first generating a genetic pathway function score for oxidative stress (gPFSox) based on the transcriptional activity levels of the oxidative stress response pathway in brain and other tissue types. Then, in the Barwon Infant Study (BIS), a population-based birth cohort (n = 1074), we show that a high gPFSox, indicating reduced ability to counter oxidative stress, is linked to higher autism spectrum disorder risk and higher parent-reported autistic traits at age 4 years, with AOR values (per 2 additional pro-oxidant alleles) of 2.10 (95% CI (1.12, 4.11); p = 0.024) and 1.42 (95% CI (1.02, 2.01); p = 0.041), respectively. Past work in BIS has reported higher prenatal phthalate exposure at 36 weeks of gestation associated with offspring autism spectrum disorder. In this study, we examine combined effects and show a consistent pattern of increased neurodevelopmental problems for individuals with both a high gPFSox and high prenatal phthalate exposure across a range of outcomes, including high gPFSox and high DEHP levels against autism spectrum disorder (attributable proportion due to interaction 0.89; 95% CI (0.62, 1.16); p < 0.0001). The results highlight the utility of this novel functional genetic score and add to the growing evidence implicating gestational phthalate exposure in adverse neurodevelopment.

Polygenic coronary artery disease association with brain atrophy in the cognitively impaired

While a number of low-frequency genetic variants of large effect size have been shown to underlie both cardiovascular disease and dementia, recent studies have highlighted the importance of common genetic variants of small effect size, which, in aggregate, are embodied by a polygenic risk score. We investigate the effect of polygenic risk for coronary artery disease on brain atrophy in Alzheimer's disease using whole-brain volume and put our findings in context with the polygenic risk for Alzheimer's disease and presumed small vessel disease as quantified by white-matter hyperintensities. We use 730 subjects from the Alzheimer's disease neuroimaging initiative database to investigate polygenic risk score effects (beyond APOE) on whole-brain volumes, total and regional white-matter hyperintensities and amyloid beta across diagnostic groups. In a subset of these subjects (N = 602), we utilized longitudinal changes in whole-brain volume over 24 months using the boundary shift integral approach. Linear regression and linear mixed-effects models were used to investigate the effect of white-matter hyperintensities at baseline as well as Alzheimer's disease-polygenic risk score and coronary artery disease-polygenic risk score on whole-brain atrophy and whole-brain atrophy acceleration, respectively. All genetic associations were examined under the oligogenic (P = 1e-5) and the more variant-inclusive polygenic (P = 0.5) scenarios. Results suggest no evidence for a link between the polygenic risk score and markers of Alzheimer's disease pathology at baseline (when stratified by diagnostic group). However, both Alzheimer's disease-polygenic risk score and coronary artery disease-polygenic risk score were associated with longitudinal decline in whole-brain volume (Alzheimer's disease-polygenic risk score t = 3.3, P _FDR = 0.007 over 24 months in healthy controls) and surprisingly, under certain conditions, whole-brain volume atrophy is statistically more correlated with cardiac polygenic risk score than Alzheimer's disease-polygenic risk score (coronary artery disease-polygenic risk score t = 2.1, P _FDR = 0.04 over 24 months in the mild cognitive impairment group). Further, in our regional analysis of white-matter hyperintensities, Alzheimer's disease-polygenic risk score beyond APOE is predictive of white-matter volume in the occipital lobe in Alzheimer's disease subjects in the polygenic regime. Finally, the rate of change of brain volume (or atrophy acceleration) may be sensitive to Alzheimer's disease-polygenic risk beyond APOE in healthy individuals (t = 2, P = 0.04). For subjects with mild cognitive impairment, beyond APOE, a more inclusive polygenic risk score including more variants, shows coronary artery disease-polygenic risk score to be more predictive of whole-brain volume atrophy, than an oligogenic approach including fewer larger effect size variants.

Polygenic risk scores: the future of cancer risk prediction, screening, and precision prevention

Genome-wide association studies (GWASs) have shown that the genetic architecture of cancers are highly polygenic and enabled researchers to identify genetic risk loci for cancers. The genetic variants associated with a cancer can be combined into a polygenic risk score (PRS), which captures part of an individual's genetic susceptibility to cancer. Recently, PRSs have been widely used in cancer risk prediction and are shown to be capable of identifying groups of individuals who could benefit from the knowledge of their probabilistic susceptibility to cancer, which leads to an increased interest in understanding the potential utility of PRSs that might further refine the assessment and management of cancer risk. In this context, we provide an overview of the major discoveries from cancer GWASs. We then review the methodologies used for PRS construction, and describe steps for the development and evaluation of risk prediction models that include PRS and/or conventional risk factors. Potential utility of PRSs in cancer risk prediction, screening, and precision prevention are illustrated. Challenges and practical considerations relevant to the implementation of PRSs in health care settings are discussed.

Genetic prediction of complex traits with polygenic scores: a statistical review

Accurate genetic prediction of complex traits can facilitate disease screening, improve early intervention, and aid in the development of personalized medicine. Genetic prediction of complex traits requires the development of statistical methods that can properly model polygenic architecture and construct a polygenic score (PGS). We present a comprehensive review of 46 methods for PGS construction. We connect the majority of these methods through a multiple linear regression framework which can be instrumental for understanding their prediction performance for traits with distinct genetic architectures. We discuss the practical considerations of PGS analysis as well as challenges and future directions of PGS method development. We hope our review serves as a useful reference both for statistical geneticists who develop PGS methods and for data analysts who perform PGS analysis.

Interactions between dietary patterns and genetic factors in relation to incident dementia among 70-year-olds

PURPOSE

To investigate potential interactions between dietary patterns and genetic factors modulating risk for Alzheimer's disease (AD) in relation to incident dementia.

METHODS

Data were derived from the population-based Gothenburg H70 Birth Cohort Studies in Sweden, including 602 dementia-free 70-year-olds (examined 1992-93, or 2000-02; 64% women) followed for incident dementia until 2016. Two factors from a reduced rank regression analysis were translated into dietary patterns, one healthy (e.g., vegetables, fruit, and fish) and one western (e.g., red meat, refined cereals, and full-fat dairy products). Genetic risk was determined by APOE ε4 status and non-APOE AD-polygenic risk scores (AD-PRSs). Gene-diet interactions in relation to incident dementia were analysed with Cox regression models. The interaction p value threshold was < 0.1.

RESULTS

There were interactions between the dietary patterns and APOE ε4 status in relation to incident dementia (interaction p value threshold of < 0.1), while no evidence of interactions were found between the dietary patterns and the AD-PRSs. Those with higher adherence to a healthy dietary pattern had a reduced risk of dementia among ε4 non-carriers (HR: 0.77; 95% CI: 0.61; 0.98), but not among ε4 carriers (HR: 0.86; CI: 0.63; 1.18). Those with a higher adherence to the western dietary pattern had an increased risk of dementia among ε4 carriers (HR: 1.37; 95% CI: 1.05; 1.78), while no association was observed among ε4 non-carriers (HR: 0.99; CI: 0.81; 1.21).

CONCLUSIONS

The results of this study suggest that there is an interplay between dietary patterns and APOE ε4 status in relation to incident dementia.

Genetic effects on longitudinal cognitive decline during the early stages of Alzheimer's disease

Cognitive decline in early-stage Alzheimer's disease (AD) may depend on genetic variability. In the Swedish BioFINDER study, we used polygenic scores (PGS) (for AD, intelligence, and educational attainment) to predict longitudinal cognitive change (measured by mini-mental state examination (MMSE) [primary outcome] and other cognitive tests) over a mean of 4.2 years. We included 260 β-amyloid (Aβ) negative cognitively unimpaired (CU) individuals, 121 Aβ-positive CU (preclinical AD), 50 Aβ-negative mild cognitive impairment (MCI) patients, and 127 Aβ-positive MCI patients (prodromal AD). Statistical significance was determined at Bonferroni corrected p value < 0.05. The PGS for intelligence (beta = 0.1, p = 2.9e-02) was protective against decline in MMSE in CU and MCI participants regardless of Aβ status. The polygenic risk score for AD (beta =  - 0.12, p = 9.4e-03) was correlated with the rate of change in MMSE and was partially mediated by Aβ-pathology (mediation effect 20%). There was no effect of education PGS on cognitive measures. Genetic variants associated with intelligence mitigate cognitive decline independent of Aβ-pathology, while effects of genetic variants associated with AD are partly mediated by Aβ-pathology.

Careful feature selection is key in classification of Alzheimer's disease patients based on whole-genome sequencing data

Despite great increase of the amount of data from genome-wide association studies (GWAS) and whole-genome sequencing (WGS), the genetic background of a partially heritable Alzheimer's disease (AD) is not fully understood yet. Machine learning methods are expected to help researchers in the analysis of the large number of SNPs possibly associated with the disease onset. To date, a number of such approaches were applied to genotype-based classification of AD patients and healthy controls using GWAS data and reported accuracy of 0.65-0.975. However, since the estimated influence of genotype on sporadic AD occurrence is lower than that, these very high classification accuracies may potentially be a result of overfitting. We have explored the possibilities of applying feature selection and classification using random forests to WGS and GWAS data from two datasets. Our results suggest that this approach is prone to overfitting if feature selection is performed before division of data into the training and testing set. Therefore, we recommend avoiding selection of features used to build the model based on data included in the testing set. We suggest that for currently available dataset sizes the expected classifier performance is between 0.55 and 0.7 (AUC) and higher accuracies reported in literature are likely a result of overfitting.

Functional co-activation of the default mode network in APOE ε4-carriers: A replication study

Structural and functional alterations of the brain in persons genetically at-risk for Alzheimer's disease (AD) are crucial in unravelling AD development. Filippini et al. found that the default mode network (DMN) is already affected in young APOE ε4-carriers, with increased co-activation of the DMN during rest and increased hippocampal task activation. We aimed to replicate the early findings of Filippini et al, using the APOE gene, still the principal AD risk gene, and extended this with a polygenic risk score (PRS) analysis for AD, using the Human Connectome Project dataset (HCP). We included participants from the HCP S1200 dataset (age range: 22-36 years). We studied morphometric features, functional DMN co-activation and functional task activation of recollection performance. Permutation Analysis of Linear Models (PALM) was used to test for group differences between APOE ε4-carriers and non-carriers, and to test the association with PRS. PALM controls for biases induced by the family structure of the HCP sample. Results were family-wise error rate corrected at p < 0.05. Our primary analysis did not replicate the early findings of Filippini et al. (2009). However, compared with non-carriers, APOE ε4-carriers showed increased functional activation during the encoding of subsequently recollected items in areas related to facial recognition (p<0.05, t>756.11). This increased functional activation was also positively associated with PRS (APOE variants included) (p<0.05, t>647.55). Our results are supportive for none to limited genetic effects on brain structure and function in young adults. Taking the methodological considerations of replication studies into account, the true effect of APOE ε4-carriership is likely smaller than indicated in the Filippini paper. However, it still holds that we may not yet be able to detect already present measurable effects decades before a clinical expression of AD. Since the mechanistic pathway of AD is likely to encompass many different factors, further research should be focused on the interactions of genetic risk, biomarkers, aging and lifestyle factors over the life course. Sensitive functional neuroimaging as used here may help disentangling these complex interactions.

Interplay between stress-related genes may influence Alzheimer's disease development: The results of genetic interaction analyses of human data

Emerging evidence from experimental and clinical research suggests that stress-related genes may play key roles in AD development. The fact that genome-wide association studies were not able to detect a contribution of such genes to AD indicates the possibility that these genes may influence AD non-linearly, through interactions of their products. In this paper, we selected two stress-related genes (GCN2/EIF2AK4 and APP) based on recent findings from experimental studies which suggest that the interplay between these genes might influence AD in humans. To test this hypothesis, we evaluated the effects of interactions between SNPs in these two genes on AD occurrence, using the Health and Retirement Study data on white indidividuals. We found several interacting SNP-pairs whose associations with AD remained statistically significant after correction for multiple testing. These findings emphasize the importance of nonlinear mechanisms of polygenic AD regulation that cannot be detected in traditional association studies. To estimate collective effects of multiple interacting SNP-pairs on AD, we constructed a new composite index, called Interaction Polygenic Risk Score, and showed that its association with AD is highly statistically significant. These results open a new avenue in the analyses of mechanisms of complex multigenic AD regulation.

Variable Effects of PD-Risk Associated SNPs and Variants in Parkinsonism-Associated Genes on Disease Phenotype in a Community-Based Cohort

Genetic risk factors for Parkinson's disease (PD) risk and progression have been identified from genome-wide association studies (GWAS), as well as studies of familial forms of PD, implicating common variants at more than 90 loci and pathogenic or likely pathogenic variants at 16 loci. With the goal of understanding whether genetic variants at these PD-risk loci/genes differentially contribute to individual clinical phenotypic characteristics of PD, we used structured clinical documentation tools within the electronic medical record in an effort to provide a standardized and detailed clinical phenotypic characterization at the point of care in a cohort of 856 PD patients. We analyzed common SNPs identified in previous GWAS studies, as well as low-frequency and rare variants at parkinsonism-associated genes in the MDSgene database for their association with individual clinical characteristics and test scores at baseline assessment in our community-based PD patient cohort: age at onset, disease duration, Unified Parkinson's Disease Rating Scale I-VI, cognitive status, initial and baseline motor and non-motor symptoms, complications of levodopa therapy, comorbidities and family history of neurological disease with one or more than one affected family members. We find that in most cases an individual common PD-risk SNP identified in GWAS is associated with only a single clinical feature or test score, while gene-level tests assessing low-frequency and rare variants reveal genes associated in either a unique or partially overlapping manner with the different clinical features and test scores. Protein-protein interaction network analysis of the identified genes reveals that while some of these genes are members of already identified protein networks others are not. These findings indicate that genetic risk factors for PD differentially affect the phenotypic presentation and that genes associated with PD risk are also differentially associated with individual disease phenotypic characteristics at baseline. These findings raise the intriguing possibility that different SNPs/gene effects impact discrete phenotypic characteristics. Furthermore, they support the hypothesis that different gene and protein-protein interaction networks that underlie PD risk, the PD phenotype, and the neurodegenerative process leading to the disease phenotype, and point to the significance of the genetic background on disease phenotype.

Cumulative Genetic Risk and APOE ε4 Are Independently Associated With Dementia Status in a Multiethnic, Population-Based Cohort

Objective

Alzheimer disease (AD) is a common and costly neurodegenerative disorder. A large proportion of AD risk is heritable, and many genetic risk factors have been identified. The objective of this study was to test the hypothesis that cumulative genetic risk of known AD markers contributed to odds of dementia in a population-based sample.

Methods

In the US population-based Health and Retirement Study (waves 1995–2014), we evaluated the role of cumulative genetic risk of AD, with and without the APOE ε4 alleles, on dementia status (dementia, cognitive impairment without dementia, borderline cognitive impairment without dementia, and cognitively normal). We used logistic regression, accounting for demographic covariates and genetic principal components, and analyses were stratified by European and African genetic ancestry.

Results

In the European ancestry sample (n = 8,399), both AD polygenic score excluding the APOE genetic region (odds ratio [OR] = 1.10; 95% confidence interval [CI]: 1.00–1.20) and the presence of any APOE ε4 alleles (OR = 2.42; 95% CI: 1.99–2.95) were associated with the odds of dementia relative to normal cognition in a mutually adjusted model. In the African ancestry sample (n = 1,605), the presence of any APOE ε4 alleles was associated with 1.77 (95% CI: 1.20–2.61) times higher odds of dementia, whereas the AD polygenic score excluding the APOE genetic region was not significantly associated with the odds of dementia relative to normal cognition 1.06 (95% CI: 0.97–1.30).

Conclusions

Cumulative genetic risk of AD and APOE ε4 are both independent predictors of dementia in European ancestry. This study provides important insight into the polygenic nature of dementia and demonstrates the utility of polygenic scores in dementia research.

Polygenic mediation analysis of Alzheimer's disease implicated intermediate amyloid imaging phenotypes

Mediation models have been employed in the study of brain disorders to detect the underlying mechanisms between genetic variants and diagnostic outcomes implicitly mediated by intermediate imaging biomarkers. However, the statistical power is influenced by the modest effects of individual genetic variants on both diagnostic and imaging phenotypes and the limited sample sizes ofimaging genetic cohorts. In this study, we propose a polygenic mediation analysis that comprises a polygenic risk score (PRS) to aggregate genetic effects ofa set ofcandidate variants and then explore the implicit effect ofimaging phenotypes between the PRS and disease status. We applied our proposed method to an amyloid imaging genetic study of Alzheimer's disease (AD), identified multiple imaging mediators linking PRS with AD, and further demonstrated the promise of the PRS on mediator detection over individual variants alone.

Polygenic risk scores for Alzheimer's disease are related to dementia risk in APOE ɛ4 negatives

Introduction

Studies examining the effect of polygenic risk scores (PRS) for Alzheimer's disease (AD) and apolipoprotein E (APOE) genotype on incident dementia in very old individuals are lacking.

Methods

A population‐based sample of 2052 individuals ages 70 to 111, from Sweden, was followed in relation to dementia. AD‐PRSs including 39, 57, 1333, and 13,942 single nucleotide polymorphisms (SNPs) were used.

Results

AD‐PRSs (including 39 or 57 SNPs) were associated with dementia (57‐SNPs AD‐PRS: hazard ratio 1.09, confidence interval 1.01–1.19, P = .03), particularly in APOE ɛ4 non‐carriers (57‐SNPs AD‐PRS: 1.15, 1.05–1.27, P = 4 × 10^–3, 39‐SNPs AD‐PRS: 1.22, 1.10–1.35, P = 2 × 10^–4). No association was found with the other AD‐PRSs. Further, APOE ɛ4 was associated with increased risk of dementia (1.60, 1.35–1.92, P = 1 × 10^–7). In those aged ≥95 years, the results were similar for the AD‐PRSs, while APOE ɛ4 only predicted dementia in the low‐risk tertile of AD‐PRSs.

Discussion

These results provide information to identify individuals at increased risk of dementia.

Roles of interacting stress-related genes in lifespan regulation: insights for translating experimental findings to humans

AIM

Experimental studies provided numerous evidence that caloric/dietary restriction may improve health and increase the lifespan of laboratory animals, and that the interplay among molecules that sense cellular stress signals and those regulating cell survival can play a crucial role in cell response to nutritional stressors. However, it is unclear whether the interplay among corresponding genes also plays a role in human health and lifespan.

METHODS

Literature about roles of cellular stressors have been reviewed, such as amino acid deprivation, and the integrated stress response (ISR) pathway in health and aging. Single nucleotide polymorphisms (SNPs) in two candidate genes (GCN2/EIF2AK4 and CHOP/DDIT3) that are closely involved in the cellular stress response to amino acid starvation, have been selected using information from experimental studies. Associations of these SNPs and their interactions with human survival in the Health and Retirement Study data have been estimated. The impact of collective associations of multiple interacting SNP pairs on survival has been evaluated, using a recently developed composite index: the SNP-specific Interaction Polygenic Risk Score (SIPRS).

RESULTS

Significant interactions have been found between SNPs from GCN2/EIF2AK4 and CHOP/DDI3T genes that were associated with survival 85+ compared to survival between ages 75 and 85 in the total sample (males and females combined) and in females only. This may reflect sex differences in genetic regulation of the human lifespan. Highly statistically significant associations of SIPRS [constructed for the rs16970024 (GCN2/EIF2AK4) and rs697221 (CHOP/DDIT3)] with survival in both sexes also been found in this study.

CONCLUSION

Identifying associations of the genetic interactions with human survival is an important step in translating the knowledge from experimental to human aging research. Significant associations of multiple SNPxSNP interactions in ISR genes with survival to the oldest old age that have been found in this study, can help uncover mechanisms of multifactorial regulation of human lifespan and its heterogeneity.

Using machine intelligence to uncover Alzheimer’s disease progression heterogeneity

Aim: Research suggests that Alzheimer’s disease (AD) is heterogeneous with numerous subtypes. Through a proprietary interactive ML system, several underlying biological mechanisms associated with AD pathology were uncovered. This paper is an introduction to emerging analytic efforts that can more precisely elucidate the heterogeneity of AD. Methods: A public AD data set (GSE84422) consisting of transcriptomic data of postmortem brain samples from healthy controls (n = 121) and AD (n = 380) subjects was analyzed. Data were processed by an artificial intelligence platform designed to discover potential drug repurposing candidates, followed by an interactive augmented intelligence program. Results: Using perspective analytics, six perspective classes were identified: Class I is defined by TUBB1, ASB4, and PDE5A; Class II by NRG2 and ZNF3; Class III by IGF1, ASB4, and GTSE1; Class IV is defined by cDNA FLJ39269, ITGA1, and CPM; Class V is defined by PDE5A, PSEN1, and NDUFS8; and Class VI is defined by DCAF17, cDNA FLJ75819, and SLC33A1. It is hypothesized that these classes represent biological mechanisms that may act alone or in any combination to manifest an Alzheimer’s pathology. Conclusions: Using a limited transcriptomic public database, six different classes that drive AD were uncovered, supporting the premise that AD is a heterogeneously complex disorder. The perspective classes highlighted genetic pathways associated with vasculogenesis, cellular signaling and differentiation, metabolic function, mitochondrial function, nitric oxide, and metal ion metabolism. The interplay among these genetic factors reveals a more profound underlying complexity of AD that may be responsible for the confluence of several biological factors. These results are not exhaustive; instead, they demonstrate that even within a relatively small study sample, next-generation machine intelligence can uncover multiple genetically driven subtypes. The models and the underlying hypotheses generated using novel analytic methods may translate into potential treatment pathways.

Milestones in Personalized Medicine: From the Ancient Time to Nowadays-the Provocation of COVID-19

The first evidence of individual targeting medicine appeared in ancient times thousands of years ago. Various therapeutic approaches have been established since then. However, even nowadays, conventional therapies do not take into consideration individuals' idiosyncrasy and genetic make-up, failing thus to be effective in some cases. Over time, the necessity of a more precise and effective treatment resulted in the development of a scientific field currently known as “personalized medicine.” The numerous technological breakthroughs in this field have acknowledged personalized medicine as the next generation of diagnosis and treatment. Although personalized medicine has attracted a lot of attention the last years, there are still several obstacles hindering its application in clinical practice. These limitations have come to light recently, due to the COVID-19 pandemic. This review describes the “journey” of personalized medicine over time, emphasizing on important milestones achieved through time. Starting from the treatment of malaria, as a first more personalized therapeutic approach, it highlights the need of new diagnostic tools and therapeutic regimens based on individuals' genetic background. Furthermore, it aims at raising global awareness regarding the current limitations and the necessity of a personalized strategy to overpass healthcare problems and hence, the current crisis.

The Association between Polygenic Hazard and Markers of Alzheimer's Disease Following Stratification for APOE Genotype

BACKGROUND

Research indicates that polygenic indices of risk of Alzheimer's disease are linked to clinical profiles.

OBJECTIVE

Given the "genetic centrality" of the APOE gene, we tested whether this held true for both APOE-ε4 carriers and non-carriers.

METHODS

A polygenic hazard score (PHS) was extracted from 784 non-demented participants recruited in the Alzheimer's Disease Neuroimaging Initiative and stratified by APOE ε4 status. Datasets were split into sub-cohorts defined by clinical (unimpaired/MCI) and amyloid status (Aβ+/Aβ-). Linear models were devised in each sub-cohort and for each APOE-ε4 status to test the association between PHS and memory, executive functioning and grey-matter volumetric maps.

RESULTS

PHS predicted memory and executive functioning in ε4ε3 MCI patients, memory in ε3ε3 MCI patients, and memory in ε4ε3 Aβ+ participants. PHS also predicted volume in sensorimotor regions in ε3ε3 Aβ+ participants.

CONCLUSION

The link between polygenic hazard and neurocognitive variables varies depending on APOE-ε4 allele status. This suggests that clinical phenotypes might be influenced by complex genetic interactions.

Understanding polygenic models, their development and the potential application of polygenic scores in healthcare

The use of genomic information to better understand and prevent common complex diseases has been an ongoing goal of genetic research. Over the past few years, research in this area has proliferated with several proposed methods of generating polygenic scores. This has been driven by the availability of larger data sets, primarily from genome-wide association studies and concomitant developments in statistical methodologies. Here we provide an overview of the methodological aspects of polygenic model construction. In addition, we consider the state of the field and implications for potential applications of polygenic scores for risk estimation within healthcare.

Genetic and polygenic risk score analysis for Alzheimer's disease in the Chinese population

INTRODUCTION

Dozens of Alzheimer's disease (AD)-associated loci have been identified in European-descent populations, but their effects have not been thoroughly investigated in the Hong Kong Chinese population.

METHODS

TaqMan array genotyping was performed for known AD-associated variants in a Hong Kong Chinese cohort. Regression analysis was conducted to study the associations of variants with AD-associated traits and biomarkers. Lasso regression was applied to establish a polygenic risk score (PRS) model for AD risk prediction.

RESULTS

SORL1 is associated with AD in the Hong Kong Chinese population. Meta-analysis corroborates the AD-protective effect of the SORL1 rs11218343 C allele. The PRS is developed and associated with AD risk, cognitive status, and AD-related endophenotypes. TREM2 H157Y might influence the amyloid beta 42/40 ratio and levels of immune-associated proteins in plasma.

DISCUSSION

SORL1 is associated with AD in the Hong Kong Chinese population. The PRS model can predict AD risk and cognitive status in this population.

Effects of polygenic risk for Alzheimer's disease on rate of cognitive decline in normal aging

Most people's cognitive abilities decline with age, with significant and partly genetically driven, individual differences in rate of change. Although APOE ɛ4 and genetic scores for late-onset Alzheimer's disease (LOAD) have been related to cognitive decline during preclinical stages of dementia, there is limited knowledge concerning genetic factors implied in normal cognitive aging. In the present study, we examined three potential genetic predictors of age-related cognitive decline as follows: (1) the APOE ɛ4 allele, (2) a polygenic score for general cognitive ability (PGS-cog), and (3) a polygenic risk score for late-onset AD (PRS-LOAD). We examined up to six time points of cognitive measurements in the longitudinal population-based Betula study, covering a 25-year follow-up period. Only participants that remained alive and non-demented until the most recent dementia screening (1-3 years after the last test occasion) were included (n = 1087). Individual differences in rate of cognitive change (composite score) were predicted by the PRS-LOAD and APOE ɛ4, but not by PGS-cog. To control for the possibility that the results reflected a preclinical state of Alzheimer's disease in some participants, we re-ran the analyses excluding cognitive data from the last test occasion to model cognitive change up-until a minimum of 6 years before potential onset of clinical Alzheimers. Strikingly, the association of PRS-LOAD, but not APOE ɛ4, with cognitive change remained. The results indicate that PRS-LOAD predicts individual difference in rate of cognitive decline in normal aging, but it remains to be determined to what extent this reflects preclinical Alzheimer's disease brain pathophysiology and subsequent risk to develop the disease.

Dual-acting agents for improving cognition and real-world function in Alzheimer's disease: Focus on 5-HT6 and D3 receptors as hubs

To date, there are no interventions that impede the inexorable progression of Alzheimer's disease (AD), and currently-available drugs cholinesterase (AChE) inhibitors and the N-Methyl-d-Aspartate receptor antagonist, memantine, offer only modest symptomatic benefit. Moreover, a range of mechanistically-diverse agents (glutamatergic, histaminergic, monoaminergic, cholinergic) have disappointed in clinical trials, alone and/or in association with AChE inhibitors. This includes serotonin (5-HT) receptor-6 antagonists, despite compelling preclinical observations in rodents and primates suggesting a positive influence on cognition. The emphasis has so far been on high selectivity. However, for a multi-factorial disorder like idiopathic AD, 5-HT6 antagonists possessing additional pharmacological actions might be more effective, by analogy to "multi-target" antipsychotics. Based on this notion, drug discovery programmes have coupled 5-HT6 blockade to 5-HT4 agonism and inhibition of AchE. Further, combined 5-HT6/dopamine D3 receptor (D3) antagonists are of especial interest since D3 blockade mirrors 5-HT6 antagonism in exerting broad-based pro-cognitive properties in animals. Moreover, 5-HT6 and dopamine D3 antagonists promote neurocognition and social cognition via both distinctive and convergent actions expressed mainly in frontal cortex, including suppression of mTOR over-activation and reinforcement of cholinergic and glutamatergic transmission. In addition, 5-HT6 blockade affords potential anti-anxiety, anti-depressive and anti-epileptic properties, and antagonising 5-HT6 receptors may be associated with neuroprotective ("disease-modifying") properties. Finally D3 antagonism may counter psychotic episodes and D3 receptors themselves offer a promising hub for multi-target agents. The present article reviews the status of "R and D" into multi-target 5-HT6 and D3 ligands for improved treatment of AD and other neurodegenerative disorders of aging. This article is part of the special issue entitled 'Serotonin Research: Crossing Scales and Boundaries'.

The MUC6/AP2A2 Locus and Its Relevance to Alzheimer's Disease: A Review

We recently reported evidence of Alzheimer's disease (AD)-linked genetic variation within the mucin 6 (MUC6) gene on chromosome 11p, nearby the adaptor-related protein complex 2 subunit alpha 2 (AP2A2) gene. This locus has interesting features related to human genomics and clinical research. MUC6 gene variants have been reported to potentially influence viral-including herpesvirus-immunity and the gut microbiome. Within the MUC6 gene is a unique variable number of tandem repeat (VNTR) region. We discovered an association between MUC6 VNTR repeat expansion and AD pathologic severity, particularly tau proteinopathy. Here, we review the relevant literature. The AD-linked VNTR polymorphism may also influence AP2A2 gene expression. AP2A2 encodes a polypeptide component of the adaptor protein complex, AP-2, which is involved in clathrin-coated vesicle function and was previously implicated in AD pathogenesis. To provide background information, we describe some key knowledge gaps in AD genetics research. The "missing/hidden heritability problem" of AD is highlighted. Extensive portions of the human genome, including the MUC6 VNTR, have not been thoroughly evaluated due to limitations of existing high-throughput sequencing technology. We present and discuss additional data, along with cautionary considerations, relevant to the hypothesis that MUC6 repeat expansion influences AD pathogenesis.

Parkinson's disease polygenic risk score is not associated with impulse control disorders: A longitudinal study

OBJECTIVE

To examine the relationship between a Parkinson's disease (PD) polygenic risk score (PRS) and impulse control disorders (ICDs) in PD.

BACKGROUND

Genome wide association studies (GWAS) have brought forth a PRS associated with increased risk of PD and younger disease onset. ICDs are frequent adverse effects of dopaminergic drugs and are also more frequent in patients with younger disease onset. It is unknown whether ICDs and PD share genetic susceptibility.

METHODS

We used data from a multicenter longitudinal cohort of PD patients with annual visits up to 6 years (DIG-PD). At each visit ICDs, defined as compulsive gambling, buying, eating, or sexual behavior were evaluated by movement disorders specialists. We genotyped DNAs using the Megachip assay (Illumina) and calculated a weighted PRS based on 90 SNPs associated with PD. We estimated the association between PRS and prevalence of ICDs at each visit using Poisson generalized estimating equations, adjusted for dopaminergic treatment and other known risk factors for ICDs.

RESULTS

Of 403 patients, 185 developed ICDs. Patients with younger age at onset had a higher prevalence of ICDs (p < 0.001) as well as higher PRS values (p = 0.06). At baseline, there was no association between the PRS and ICDs (overall, p = 0.84). The prevalence of ICDs increased over time similarly across the quartiles of the PRS (overall, p = 0.88; DA users, p = 0.99).

CONCLUSION

Despite younger disease onset being associated with both higher PRS and ICD prevalence, our findings are not in favor of common susceptibility genes for PD and ICDs.

A polygenic biomarker to identify patients with severe hypercholesterolemia of polygenic origin

Background

Severe hypercholesterolemia (HC, LDL‐C > 4.9 mmol/L) affects over 30 million people worldwide. In this study, we validated a new polygenic risk score (PRS) for LDL‐C.

Methods

Summary statistics from the Global Lipid Genome Consortium and genotype data from two large populations were used.

Results

A 36‐SNP PRS was generated using data for 2,197 white Americans. In a replication cohort of 4,787 Finns, the PRS was strongly associated with the LDL‐C trait and explained 8% of its variability (p = 10^–41). After risk categorization, the risk of having HC was higher in the high‐ versus low‐risk group (RR = 4.17, p < 1 × 10⁻⁷). Compared to a 12‐SNP LDL‐C raising score (currently used in the United Kingdom), the PRS explained more LDL‐C variability (8% vs. 6%). Among Finns with severe HC, 53% (66/124) versus 44% (55/124) were classified as high risk by the PRS and LDL‐C raising score, respectively. Moreover, 54% of individuals with severe HC defined as low risk by the LDL‐C raising score were reclassified to intermediate or high risk by the new PRS.

Conclusion

The new PRS has a better predictive role in identifying HC of polygenic origin compared to the currently available method and can better stratify patients into diagnostic and therapeutic algorithms.

Mitonuclear interactions influence Alzheimer's disease risk

We examined the associations between mitochondrial DNA haplogroups (MT-hgs; mitochondrial haplotype groups defined by a specific combination of single nucleotide polymorphisms labeled as letters running from A to Z) and their interactions with a polygenic risk score composed of nuclear-encoded mitochondrial genes (nMT-PRS) with risk of dementia and age of onset (AOO) of dementia. MT-hg K (Odds ratio [OR]: 2.03 [95% CI: 1.04, 3.97]) and a 1 SD larger nMT-PRS (OR: 2.2 [95% CI: 1.68, 2.86]) were associated with elevated odds of dementia. Significant antagonistic interactions between the nMT-PRS and MT-hg K (OR: 0.45 [95% CI: 0.22, 0.9]) and MT-hg T (OR: 0.22 [95% CI: 0.1, 0.49]) were observed. Individual MT-hgs were not associated with AOO; however, a significant antagonistic interactions was observed between the nMT-PRS and MT-hg T (Hazard ratio: 0.62 [95% CI: 0.42, 0.91]) and a synergistic interaction between the nMT-PRS and MT-hg V (Hazard ratio: 2.28 [95% CI: 1.19, 4.35]). These results suggest that MT-hgs influence dementia risk and that variants in the nuclear and mitochondrial genome interact to influence the AOO of dementia.

Brain Imaging Genomics: Integrated Analysis and Machine Learning

Brain imaging genomics is an emerging data science field, where integrated analysis of brain imaging and genomics data, often combined with other biomarker, clinical and environmental data, is performed to gain new insights into the phenotypic, genetic and molecular characteristics of the brain as well as their impact on normal and disordered brain function and behavior. It has enormous potential to contribute significantly to biomedical discoveries in brain science. Given the increasingly important role of statistical and machine learning in biomedicine and rapidly growing literature in brain imaging genomics, we provide an up-to-date and comprehensive review of statistical and machine learning methods for brain imaging genomics, as well as a practical discussion on method selection for various biomedical applications.

Alzheimer's Prevention Initiative Generation Program: Development of an APOE genetic counseling and disclosure process in the context of clinical trials

Introduction

As the number of Alzheimer's disease (AD) prevention studies grows, many individuals will need to learn their genetic and/or biomarker risk for the disease to determine trial eligibility. An alternative to traditional models of genetic counseling and disclosure is needed to provide comprehensive standardized counseling and disclosure of apolipoprotein E (APOE) results efficiently, safely, and effectively in the context of AD prevention trials.

Methods

A multidisciplinary Genetic Testing, Counseling, and Disclosure Committee was established and charged with operationalizing the Alzheimer's Prevention Initiative (API) Genetic Counseling and Disclosure Process for use in the API Generation Program trials. The objective was to provide consistent information to research participants before and during the APOE counseling and disclosure session using standardized educational and session materials.

Results

The Genetic Testing, Counseling, and Disclosure Committee created a process consisting of eight components: requirements of APOE testing and reports, psychological readiness assessment, determination of AD risk estimates, guidance for identifying providers of disclosure, predisclosure education, APOE counseling and disclosure session materials, APOE counseling and disclosure session flow, and assessing APOE disclosure impact.

Discussion

The API Genetic Counseling and Disclosure Process provides a framework for large-scale disclosure of APOE genotype results to study participants and serves as a model for disclosure of biomarker results. The process provides education to participants about the meaning and implication(s) of their APOE results while also incorporating a comprehensive assessment of disclosure impact. Data assessing participant safety and psychological well-being before and after APOE disclosure are still being collected and will be presented in a future publication.

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Participants may need to learn their risk for Alzheimer's disease to enroll in studies.

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Alternatives to traditional models of apolipoprotein E counseling and disclosure are needed.

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An alternative process was developed by the Alzheimer's Prevention Initiative.

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This process has been implemented by the Alzheimer's Prevention Initiative Generation Program.

La responsabilidad: un principio para retomar en la reflexión bioética

La ciencia y su brazo, la tecnología, pueden centrarse en una lógica antropocéntrica en la medida en que se empecinen en satisfacer únicamente las necesidades humanas a expensas del dominio (o destrucción) de la naturaleza y las especies coexistentes. También es posible que se pongan al servicio de poderes económicos y políticos y se investigue únicamente bajo una lógica centrada en la ganancia. En medio de este panorama, se hace un llamado a un acto de conciencia para retomar la propuesta del filósofo alemán Hans Jonas sobre el principio de responsabilidad, según el cual, es necesaria una reflexión más allá de las relaciones inmediatas, que incluya a la naturaleza, los animales y las generaciones futuras en la formulación de una nueva ética que debe enseñarse y practicarse desde la academia.